# Regulatory Impact Analysis for the Proposed Cross-State Air Pollution Rule (CSAPR) for the 2008 Ozone National Ambient Air Quality Standards (NAAQS)ii 

### EPA-452/R-15-009 November 2015

Regulatory Impact Analysis for the Proposed Cross-S tate Air Pollution Rule (CSAPR) for the 2008 Ozone National Ambient Air Quality Standards (NAAQS)

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Research Triangle Park, NC 27711iii CONTACT INFORMATION This document has been prepared by staff from the O ffice of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Qu estions related to this document should be addressed to Kathy Kaufman, U.S. Environmental Prot ection Agency, Office of Air Quality Planning and Standards, C439-02, Research Triangle Park, North Carolina 27711 (email: kaufman.kathy@epa.gov ).

ACKNOWLEDGEMENTS In addition to EPA staff from the Office of Air Qua lity Planning and Standards, personnel from the Office of Atmospheric Programs, the Office of T ransportation and Air Quality, and the Office of Policy’s National Center for Environmenta l Economics contributed data and analysis to
this document.iv



### GLOSSARY OF TERMS AND ABBREVIATIONS

The following are abbreviations of terms used in th is Regulatory Impact Analysis.
CAA or Act Clean Air Act
CAIR Clean Air Interstate Rule CAMx Comprehensive Air Quality Model with Extensi ons CBI Confidential Business Information CEM Continuous Emissions Monitoring
CSAPR Cross-State Air Pollution Rule EGU Electric Generating Unit FIP Federal Implementation Plan FR Federal Register EPA U.S. Environmental Protection Agency
GHG Greenhouse Gas GW Gigawatts ICR Information Collection Request IPM Integrated Planning Model
km Kilometer lb/mmBtu Pounds per Million British Thermal Unit
LNB Low-NO x Burners NAAQS National Ambient Air Quality Standard
NBP NO X Budget Trading Program NEI National Emission Inventory
NO X Nitrogen Oxides
NSPS New Source Performance Standard
OFA Overfire Air
PM 2.5 Fine Particulate Matter ppb Parts Per Billion RIA Regulatory Impact Analysis SCR Selective Catalytic Reduction SIP State Implementation Planv

SMOKE Sparse Matrix Operator Kernel Emissions SNCR Selective Non-catalytic Reduction
SO 2 Sulfur Dioxide TIP Tribal Implementation Plan
TPY Tons Per Year TSD Technical Support Document11ii TABLE OF CONTENTS

page
LIST OF TABLES ....................................

................................................... ................................... ii

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

................................................... .................................. ii

EXECUTIVE SUMMARY .................................

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

Overview ..........................................

................................................... ......................................1 ES.1 Identifying Required Emissions Reductions ......... .................................................1 ES.2 Baseline and Analysis Year ........................

................................................... .........4 ES.3 Control Strategies and Emissions Reductions ....... .................................................6 ES.4 Costs ............................................

................................................... .......................7 ES.5 Benefits to Human Health and Welfare ..............

................................................... .8 ES.5.1 Human Health Benefits and Climate Co-benefits ..... .........................................9 ES.5.2 Combined Health Benefits and Climate Co-Benefits Es timates ......................12 ES.5.3 Unquantified Co-Benefits ..........................

................................................... ....13 ES.5 Results of Benefit-Cost Analysis ..................

................................................... .....16 E.S.6 References ........................................

................................................... ..................17

CHAPTER 1: INTRODUCTION AND BACKGROUND ........... ...............................................14

Introduction ......................................

................................................... ....................................14 1.1 Background ........................................

................................................... .......................14 1.2.1 Role of Executive Orders in the Regulatory Impact A nalysis ..........................14 1.2.2 Illustrative Nature of this Analysis ..............

................................................... ..14 1.2.3 The Need for Air Quality or Emissions Standards ... ........................................14 1.2 Overview and Design of the RIA ....................

................................................... ..........14 1.2.1 Methodology for Identifying Required Reductions ... .......................................14 1.2.2 States Covered by the Proposed Rule ............... ................................................14 1.2.3 Regulated Entities ................................

................................................... ..........14 1.2.4 Baseline and Analysis Year ........................

................................................... ...14 1.2.5 Emissions Controls and Cost Analysis Approach ..... .......................................14 1.2.6 Benefits Analysis Approach ........................

................................................... ..14 1.3 Organization of the Regulatory Impact Analysis.....

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

CHAPTER 2: ELECTRIC POWER SECTOR PROFILE..........

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

Overview ..........................................

................................................... ......................................1 2.1 Background ........................................

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

................................................... .................2 2.2.1 Generation ........................................

................................................... ...............2 2.2.2 Transmission ......................................

................................................... ............10 2.2.3 Distribution ......................................

................................................... ..............11 2.3 Sales, Expenses, and Prices .......................

................................................... ...............12 2.3.1 Electricity Prices ................................

................................................... ............12iii

2.3.2 Prices of Fossil Fuels Used for Generating Electric ity.....................................17 2.3.3 Changes in Electricity Intensity of the U.S. Econom y from 2000 to 2013 ......18 2.4 Deregulation and Restructuring ....................

................................................... ............19
CHAPTER 3: EMISSIONS AND AIR QUALITY MODELING IMPA CTS .............................14

Overview ..........................................

................................................... ....................................14 3.1 Air Quality Modeling Platform .....................

................................................... ............14 3.1.1 Simulation Periods ................................

................................................... .........14 3.1.2 Air Quality Modeling Domain ....................... ..................................................1 4 3.1.3 Air Quality Model Inputs ..........................

................................................... ....14 3.2 Development of Emissions Inventories ..............

................................................... ......14 3.2.1 2011 Base Year Emissions ..........................

................................................... ..14 3.2.2 2017 Baseline Emissions ...........................

................................................... ....14 3.2.3 2017 Illustrative Control Case Emissions .......... ..............................................14 3.3 Post-Processing of Air Quality Modeling for Benefit s Calculations ...........................14 3.3.1 Converting CAMx Ozone Outputs to Benefits Inputs .. ....................................14 3.3.2 Converting CAMx PM 2.5 Outputs to Benefits Inputs ........................ ...............14 3.4 References ........................................

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

CHAPTER 4: REGULATORY CONTROL Alternatives .......

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

Introduction ......................................

................................................... ....................................14 4.1 Background ........................................

................................................... .......................14 4.2 Regulatory Control Alternatives Considered ........

................................................... ....14 4.3 Rationale for Regulatory Control Alternatives Chose n ...............................................14
CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS ....................1

Overview ..........................................

................................................... ......................................1 5.1 Power Sector Modeling Framework ...................

................................................... ........1 5.2 EPA’s Power Sector Modeling Base Case for the Propo sal to Update CSAPR ............4 5.2.1 EPA’s IPM Base Case v.5.15 ........................

................................................... ..5 5.2.2 The IPM Base Case Used to Analyze the Proposal to U pdate CSAPR .............6 5.3 Evaluating the Regulatory Control Alternatives ....

................................................... .....7 5.3.1 Emission Reduction Assessment ..................... .................................................10 5.3.2 Compliance Cost Assessment ........................

................................................... 12 5.3.3 Impacts on Fuel Use, Prices and Generation Mix .... ........................................17 5.3. 4 Effect of Emission Reductions on Downwind Receptors ................................20 5.4 Employment Impacts ................................

................................................... ................22 5.4.1 Economic Theory and Employment .................... .............................................23 5.4.1.2 Current State of Knowledge Based on the Peer-Review ed Literature ......26 5.4.1.3 Regulated Sector ..................................

................................................... ...26 5.4.1.4 Economy-Wide ......................................

................................................... .27 5.4.1.5 Labor Supply Impacts .............................. ..................................................2 7 5.4.1.6 Conclusion ................................

................................................... .................28 5.4.2 Recent Employment Trends ..........................

................................................... 28iv 5.4.2.1 Electric Power Generation ......................... ................................................28 5.4.2.2 Fossil Fuel Extraction ............................

................................................... .29 5.4.3 Power and Fuels Sector Direct Employment Impacts .. ....................................31 5.5 Social Costs ..................................

................................................... .................................36 5.6 Secondary Economic Impacts.....................

................................................... ...................37 5.6.1 Methods ...........................................

................................................... ..............37 5.6.2 Summary of Secondary Market Impacts of Energy Price Changes .................39 5.6.3 Share of Total Production Costs ...................

................................................... .40 5.6.4 Ability to Substitute between Inputs to the Product ion Process .......................41 5.6.5 Availability of Substitute Goods and Services ..... ............................................41 5.6.6 Effect of Changes in Input Demand from Electricity Sector ............................41 5.6.7 Conclusions .......................................

................................................... ............42 5.7 References ........................................

................................................... .........................42 Chapter 6: Estimated Human Health Benefits and Clim ate Co-benefits ................................... ......1

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

................................................... ...........................1 6.2 Estimated Human Health Benefits ...................

................................................... ...........2 6.2.1 Health Impact Assessment for Ozone and PM 2.5 ................................................4 6.2.1.1 Mortality Effect Coefficients for Short-term Ozone Exposure ...................7 6.2.1.2 PM 2.5 Mortality Effect Coefficients for Adults and Infan ts ........................9 6.2.2 Economic Valuation for Health Benefits ............ ..............................................14 6.2.3 Benefit-per-ton Estimates for Ozone ............... .................................................16 6.2.4 Benefit-per-ton Estimates for PM 2.5
.................................................. ...............16 6.2.5 Estimated Health Benefits Results ................. ..................................................1 7 6.3 Estimated Climate Co-Benefits from CO 2
.................................................. .................27 6.3.1 Climate Change Impacts ............................

................................................... ....28 6.4 Combined Health Benefits and Climate Co-Benefits Es timates..................................36 6.5 Unquantified Co-benefits ..........................

................................................... ................38 6.5.1 HAP Impacts........................................

................................................... ..........40 6.5.2 Additional NO 2 Health Co-Benefits ............................... ..................................40 6.5.3 Additional SO 2 Health Co-Benefits ............................... ...................................41 6.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits .............................. ...................42 6.5.5 Ozone Welfare Co-Benefits .........................

................................................... ..43 6.5.6 Visibility Impairment Co-Benefits ................. ..................................................4 3 6.6 References ........................................

................................................... .........................44 6A.1 Overview of Benefit-per-Ton Estimates .............

................................................... .1 6A.2 Air Quality Modeling for the Proposed Transport Rul e .........................................1 6A.3 National PM 2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Transport Rule ...

................................................... .........4 6A.4 Regional Ozone Benefit-per-Ton Estimates .......... .................................................7 6A.5 References ........................................

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

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER REVIEWS . ....................................14

Overview ..........................................

................................................... ....................................14 7.1 Executive Order 12866: Regulatory Planning and Rev iew ........................................14v

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

................................................... ..............14 7.3 Regulatory Flexibility Act ........................

................................................... ................14 7.3.1 Identification of Small Entities ..................

................................................... ....14 7.3.2 Overview of Analysis and Results................... .................................................14 7.3.2.1 Methodology for Estimating Impacts of the Proposed CSAPR update on Small Entities .................................

................................................... ...............14 7.3.2.2 Results ...........................................

................................................... ........14 7.3.3 Summary of Small Entity Impacts.................... ................................................14 7.4 Unfunded Mandates Reform Act ......................

................................................... ........14 7.4.1 Identification of Government-Owned Entities ....... ..........................................14 7.4.2 Overview of Analysis and Results................... .................................................14 7.4.2.1 Methodology for Estimating Impacts of the proposed CSAPR update on Government Entities ............................

................................................... .........14 7.4.2.2 Results ...........................................

................................................... ........14 7.4.3 Summary of Government Entity Impacts .............. ...........................................14 7.5 Executive Order 13132: Federalism .................

................................................... ........14 7.6 Executive Order 13175: Consultation and Coordinatio n with Indian Tribal Governments .......................................

................................................... .............................14 7.7 Executive Order 13045: Protection of Children from Environmental Health & Safety Risks ......................................

................................................... ................................14 7.8 Executive Order 13211: Actions that Significantly A ffect Energy Supply, Distribution, or Use ..............................

................................................... ............................14 7.9 National Technology Transfer and Advancement Act .. ..............................................14 7.10 Executive Order 12898: Federal Actions to Address E nvironmental Justice in Minority Populations and Low-Income Populations

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

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS ....... ..............................................14

Overview ..........................................

................................................... ....................................14 8.1 Results ...........................................

................................................... ............................14 8.2 Net Present Value of a Stream of Costs and Benefits

.................................................. 14ii

LIST OF TABLES

Table ES-1. Projected 2017* EGU Emissions Reductions of NOx, SO 2 , and CO 2 with the Proposed NO X x Emissions Budgets and More and Less Stringent Alt ernatives (Tons) **

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

Table ES-2 Cost estimates (2011$) for proposal and more and le ss stringent alternatives .......8

Table ES-3. Summary of Avoided Health Incidences from Ozone-Rel ated and PM 2.5 - Related Benefits for the Proposal for 2017* .......

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

Table ES-4. Combined Health Benefits and Climate Co-Benefits fo r the Proposed CSAPR EGU NO X Ozone Season Emissions Budgets and for More and Le ss Stringent Alternatives for 2017 (millions of 2011$) *.................................................. ......13

Table ES-5. Unquantified Health and Welfare Co-benefits Categor ies ...................................14

Table ES-6. Total Costs, Total Monetized Benefits, and Net Bene fits of the Proposal in 2017 for U.S. (millions of 2011$) a,b,c

.................................................. ..............................16
Table 2-1. Existing National Electricity Ge nerating Capacity by Energy Source, 2000 and 2013 ..........................................

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

Table 2-2. Net Generation in 2000 and 2013 (Trillion kWh = TWh)............................... .......6
Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Thermal Efficiency in 2013 (Heat Rate) ............

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

Table 2-4. Total U.S. Electric Power Industry Retail Sales, 20 00 and 2013 (billion kWh) 12

Table 3-1 2011 Base Year and 2017 Baseline NOx and VOC Emissi ons by Sector (thousand tons) ...................................

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

Table 3-2 Illustrative Control Case NO x Emissions and Changes from the 2017 Baseline ..........................................

.................................... Error! Bookmark not defined.

Table 4-1 Ozone-Season NO X Emissions Budgets (Tons) for Proposed, More String ent and Less Stringent Regulatory Control Alternatives in 2017 and Later ..............................5

Table 5-1 NO X Mitigation Strategies Implemented for Compliance w ith the Regulatory Control Alternatives ..............................

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

Table 5-2 EGU Ozone Season NO X Emission Reductions (tons) for the Proposal and More and Less Stringent Alternatives...............

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

Table 5-3 EGU Annual Emission Reductions (tons) for SO 2 and CO 2 for the Proposal and More and Less Stringent Alternatives ..........

................................................... ............12iii

Table 5-4 Cost Estimates (millions of 2011$) for Proposal and More and Less Stringent Alternatives ............................

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

Table 5-5 Percent Changes* in Coal and Natural Gas Usage by E GUs for the Proposal and More and Less Stringent Alternatives for 2017 .

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

Table 5-6 Percent Changes* in Generation by Major Type for th e Proposal and More and Less Stringent Alternatives for 2017...........

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

Table 5-7 Changes* in Generating Capacity by Major Type for t he Proposal and More and Less Stringent Alternatives in 2017 ...........

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

Table 5-8 Percent Changes* in Retail Electricity Price for th e Proposal and More and Less Stringent Alternatives for 2017 & 2020 .......

................................................... ..........20

Table 5-9. Estimated Percentage Changes in Average Energy Pric es by Energy Type for the Proposed Alternative* .....................

................................................... ....................40

Table 6-1. Human Health Effects of Ambient Ozone and PM 2.5 .............................................5

Table 6-2. Summary of Ozone and PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from The Illustrative Control Case in 2017 (2011$)* ..........................18

Table 6-3. Emission Reductions of Criteria Pollutants for the Proposal and More and Less Stringent Alternatives in 2017 (thousands of s hort tons)* ....................................... .18

Table 6-4. Summary of Estimated Monetized Health Benefits for the Proposal and More and Less Stringent Alternatives Regulatory Con trol Alternatives for 2017 (millions of 2011$) * .............................

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

Table 6-5. Summary of Avoided Health Incidences from Ozone-Rel ated and PM 2.5 - Related Benefits for the Proposal and More and Less Stringent Alternatives for 2017* 19

Table 6-8. Climate Effects ...................................

................................................... ...............28

Table 6-9. Social Cost of CO 2 , 2015-2050 (in 2011$ per metric ton)* ............ .....................35

Table 6-10. Estimated Global Climate Co-benefits of CO 2 Reductions for the Proposal and More and Less Stringent Alternatives for 2017 ( millions of 2011$)* ........................35

Table 6-11. Combined Health Benefits and Climate Co-Benefits fo r the Proposal and More and Less Stringent Alternatives for 2017 (mill ions of 2011$)* ...............................38

Table 6-12. Unquantified Health and Welfare Co-benefits Categor ies ...................................39
Table 7-1. SBA Size Standards by NAICS Code .......

................................................... ..................8 Table 7-2. Projected Impact of the Proposed CSAPR Update on Small Entities in 2017 ............12iv

Table 7-3. Summary of Distribution of Economic Imp acts of the Proposed CSAPR Update on Small Entities in 2017 .........................

................................................... .......................13 Table 7-4. Incremental Annual Costs under the Prop osed CSAPR Update Summarized by Ownership Group and Cost Category in 2017 (2011$ mi llions) .......................................14 Table 7-5. Summary of Potential Impacts on Governm ent Entities under the Proposed CSAPR Update in 2017 ..............................

................................................... ....................20 Table 7-6. Incremental Annual Costs under the Prop osed CSAPR Update Summarized by Ownership Group and Cost Category (2011$ millions) in 2017 .......................................22
Table 8-1. Total Costs, Total Monetized Benefits, a nd Net Benefits of the Proposal and More or Less Stringent Alternatives for 2017 for U. S. (millions of 2011$) a,b,c ..................2

Table 8-2. Projected 2017* Reductions in Emissions of NOx, SO 2 , and CO 2 with the proposed NOx Emissions Budgets and More or Less Str ingent Alternatives (Tons)..........4ii

LIST OF FIGURES
Figure ES-1. Nonattainment and Maintenance Receptors Identified for this Proposal and Upwind States Linked to these Downwind Air Quality Problems with Respect to the 2008 Ozone NAAQS ..................................

................................................... ......................3 Figure ES-1. Nonattainment and Maintenance Receptors Identified for this Proposal and Upwind States Linked to these Downwind Air Quality Problems with Respect to the 2008 Ozone NAAQS ..................................

................................................... ......................4 Figure 2-1 National New Build and Retired Capacity (MW) by Fu el Type, 2000-2013 ........5
Figure 2-2 Regional Differences in Generating Capac ity (MW), 2013. ................................... .......6
Figure 2-3. Cumulative Distribution in 2013 of Coal and Natural Gas Electricity Capacity and Generation, by Age ...................

................................................... ..................9 Figure 2-4. Fossil Fuel-Fired Electricity Generating Facilities , by Size .................................10 Figure 2-5. Real National Average Electricity Prices for Three Major End-Use Categories ........................................

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

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

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

................................................... ...............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 ...............................................1 7 Figure 2-10. Relative Growth of Electricity Generation, Populati on and Real GDP Since 2000 18 Figure 2-11. Relative Change of Real GDP, Population and Electri city Generation Intensity Since 2000 ..............................

................................................... ..........................19 Figure 2-12. Status of State Electricity Industry Restructuring Activities ................................21
Figures 2-13 & 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 ...........22 Figure 3-1. National air quality modeling domain.

................................................... ......................2 Figure 5-1 Time series of annual costs for the Pro posal and More and Less Stringent Alternatives ......................................

................................................... ...............................15iii

Figure 5-2 Time series of annual costs and annualized costs for the Proposal and More and Less Stringent Alternatives ...................

................................................... ...................17 Figure 5-3 Electric Power Industry Employment ....

................................................... ..................29 Figure 5-4 Coal Production Employment ......................

................................................... ....30 Figure 5-5 Oil and Gas Extraction Employment ......

................................................... ..................31 Figure 6-1. Monetized Health Benefits of Proposal for 2017 * .. ............................................20 Figure 6-2. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Illustrative Modeling (used to generate the benefit-per-ton estimates) ...................26 Figure 6-3. Cumulative Distribution of Adult Population (age 30 +) by Annual Mean PM 2.5 Exposure in the Illustrative Modeling (used to gene rate the benefit-per-ton estimates) ........................................

................................................... ................................27ES-1

EXECUTIVE SUMMARY

Overview
The primary purpose of this the propos edal rule is to address interstate air quality impacts with respect to the 2008 ozone National Ambient Air Quality Standards (NAAQS). The EPA promulgated the Cross-State Air Pollution Rule (CSA PR) on July 6, 2011, 1 to address interstate transport of ozone pollution under the 1997 ozone N AAQS. 2 This action proposes toThe proposed rule would update CSAPR to address interstate emissions trans port of nitrogen oxides (NO X ) that contribute significantly to nonattainment or interfere with maintenance of the 2008 ozone NAAQS in downwind states. The propos edal rule also responds to the D.C. Circuit’s July 28, 2015 remand of certain CSAPR ozone season NO X emissions budgets to the EPA for reconsideration. This Regulatory Impact Analysis (R IA) presents the health and welfare benefits
and climate co-benefits of this the proposal to update CSAPR, and compares the benefits of the
rule to the estimated costs of implementing the proposed rule for the 2017 analysis year. This RIA also reports certain impacts from thisof the proposed rule such as its effect on employment and energy prices. This executive summary both explains the analytic a pproach taken in the RIA
and summarizes the RIA results.

ES.1 Identifying Needed Required Emissions Reductions
As described in the preamble for the proposal, CSA PR provides a 4-step process to address the requirements of CAA section 110(a)(2)(D )(i)(I) (sometimes called the “good neighbor provision”) for ozone or fine particulate matter ( PM 2.5 ) standards: (1) identifying downwind receptors that are expected to have proble ms attaining or maintaining clean air standards (i.e., NAAQS); (2) determining which upwi nd states contribute to these identified problems in amounts sufficient to “link” them to th e downwind air quality problems; (3) for states linked to downwind air quality problems, ide ntifying upwind emissions that significantly

1 See 76 FR 48208 (July 6, 2011)

2 CSAPR also addressed interstate transport of fine particulate matter (PM 2.5 ) under the 1997 and 2006 PM 2.5 NAAQS.ES-2

contribute to downwind nonattainment or interfere w ith downwind maintenance of a standard by quantifying available upwind emission reductions an d apportioning upwind responsibility among linked states; and (4) for states that are found to have emissions that significantly contribute to
nonattainment or interfere with maintenance of the NAAQS downwind, reducing the identified upwind emissions via regional emissions allowance t rading programs. This action proposes to apply this 4-step process to update CSAPR with resp ect to the 2008 ozone NAAQS. The reductions required by the proposed rule would be a chieved through a federal implementation plan (FIP) forin any state that does not have an approved state imp lementation plan (SIP) addressing its contribution by the date this rule i s finalized. Furthermore, under the FIPs, affected EGUs would participate in the CSAPR NO X ozone-season allowance trading program. More
Dd etails on the methods and results of applying this process can be found in the preamble for this proposal , and in Chapter 4 of this RIA

.
Application of the first two steps of this process with respect to the 2008 ozone NAAQS provides the analytic basis for proposing that ozon e season emissions in 23 eastern states 3 affect the ability of downwind states to attain and mainta in the 2008 ozone NAAQS. Figure ES-1
shows the affected states.

3 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas , Kentucky, Louisiana, Maryland, Michigan, Mississi ppi, Missouri, New Jersey, New York, North Carolina, Ohi o, Oklahoma, Pennsylvania, Tennessee, Texas, Virgin ia, West Virginia, and Wisconsin

.ES-3

Figure ES-1. Nonattainment and Maintenance Receptors Ident ified for this Proposal and

Upwind States Linked to these Downwind Air Quality Problems with Respect to the 2008 Ozone NAAQS
Applying Step 3 of this process, this action propo ses to quantify electric generating unit (EGU) NO X reductions in these 23 eastern states and to updat e CSAPR ozone season NO X emissions budgets. A state’s updated CSAPR ozone season NO X emissions budget is the quantity
of EGU NO X emissions that would remain after reducing signifi cant contribution to
nonattainment and interference with maintenance of the 2008 ozone NAAQS in an average

year. 4 These updated CSAPR NO X budgets were developed considering EGU NO X reductions
that are achievable for the 2017 ozone season. 5 The EPA applied a multi-factor test in step 3 to evaluate EGU NO X reduction potential for 2017 and proposes is proposing to quantify EGU

4 For example, assuming no abnormal variation in ele ctricity supply due to events such as abnormal mete orology.

5 Non-EGU NOx emission control measures and reductio ns are not included in this proposal. More informa tion on non-EGU NOx control measures and the potential for emission red uctions can be found in the Non-EGU TSD on Emissions and Potential Reductions in 2017, which c an be found in the docket for this proposal.ES-4

NO X ozone-season emissions budgets reflecting EGU NO X reduction strategies that are widely
available at a uniform annualized cost of $1,300 per ton (2011$). This assessment rev ealed that there is significant EGU NO X reduction potential that can be achieved by 2017, which would make meaningful and timely improvements in ozone ai r quality. Applying Step 4 of this process, the EPA proposes is proposing to implement these EGU NO X emissions budgets through the
CSAPR NO X ozone-season allowance trading program.

For the RIA, in order to implement the OMB Circular A-4 requirement to assess one less stringent and one more stringent alternative to the proposal, the EPA is also analyzing EGU NO X ozone season emissions budgets reflecting NO X reduction strategies that are widely available at a uniform cost of $500 per ton (2011$) and strategies that are widely available at a uniform cost of $3,400 per ton (2011$). That is, at step 3, applyingThe EPA applies these alternative uniform costs for quantifying EGU NO X ozone-season emissions budgets in Step 3 of the 4-step process

. Applying Step 4 of this process, the EPA proposes t o implement these EGU NO X emissions budgets through the CSAPR NO X ozone-season allowance trading program.

ES.2 Baseline and Analysis Year
The proposal sets forth the requirements for states to reduce their significant contribution
to downwind nonattainment or interference with main tenance of the 2008 ozone NAAQS. To develop and evaluate control strategies for address ing these obligationsevaluate the benefits and costs of this regulation , it is important to first establish a baseline pro jection of both emissions
and air quality in the analysis year of 2017, taking i nto account currently on-the-books Federal regulations, substantial Federal regulatory proposa ls, enforcement actions, state regulations, population, and where possible, economic growth. Es tablishing this baseline for the analysis then
allows us to estimate the incremental costs and ben efits of the additional emissions reductions that will be achieved by the proposal.

Below is a list of some of the national rules refle cted in the baseline. For a more complete
list of the rules reflected in the air quality modeling , please see the Technical Support Document: Preparation of Emissions Inventories for the Versio n 6.2, 2011 Emissions Modeling PlatformES-5

(U.S. EPA, 2015). 6 For a list of those regulations reflected in the co mpliance and cost modeling of the electricity sector, please see “EPA Base Case v.5.15 Using IPM Incremental Documentation” August, 2015

. 7

• Carbon Pollution Emission Guidelines for Existing S tationary Sources: Electric Utility Generating Units (Final Rule) (U.S. EPA, 2015)

• Standards of Performance for Greenhouse Gas Emissio ns from New, Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units (Final Rule) (U.S. EPA, 2015a)

• Tier 3 Motor Vehicle Emission and Fuel Standards (U .S. EPA, 2014)

• 2017 and Later Model Year Light-Duty Vehicle Greenh ouse Gas Emissions and Corporate Average Fuel Economy Standards (U.S. EPA, 2012)

• Cross State Air Pollution Rule (CSAPR) (U.S. EPA, 2 011) 8

• Mercury and Air Toxics Standards (MATS) (U.S. EPA, 2011a) 9

• Greenhouse Gas Emissions Standards and Fuel Efficie ncy Standards for Medium- and Heavy-Duty Engines and Vehicles (U.S. EPA, 2011b) 10

• C3 Oceangoing Vessels (U.S. EPA, 2010)

• Reciprocating Internal Combustion Engines (RICE) NE SHAPs (U.S. EPA, 2010a)

6 U.S. Environmental Protection Agency, 2015. Prepar ation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform, Research Triangle Park , NC, (http://www.epa.gov/ttn/chief/emch/2011v6/2011v6_2_ 2017_2025_EmisMod_TSD_aug2015.pdf).

7 http://www.epa.gov/powersectormodeling/

8 On July 28, 2015, the D.C. Circuit issued its opin ion regarding CSAPR on remand from the Supreme Cour t, EME Homer City Generation, L.P. , v. EPA , No. 795 F.3d 118, 129-30, 138 ( EME Homer City II )EME Homer City II, No. 11-1302, slip op . The court largely upheld CSAPR, but remanded to E PA without vacatur certain states’ phase 2 emissions budgets for reconsideration. Compliance w ith these remanded emissions budgets was included i n the baseline modeling for this proposal, which was alre ady underway when this opinion was issued.

9 On June 29, 2015, the United States Supreme Court reversed the D.C. Circuit opinion affirming the MAT S. The EPA is reviewing the decision and will determine an y appropriate next steps once the review is complet e. MATS is included in the baseline for this analysis, and the EPA does not believe including MATS substantially alters the results of this analysis , because MATS was remanded, not vacated

.

10 This rule is Phase 1 of the Heavy Duty Greenhouse Gas Standards for New Vehicles and Engines (76 FR 5 7106, September 15, 2011) and is included in the 2025 base case . Phase 2 of the Heavy Duty Greenhouse Gas Standar ds for New Vehicles and Engines (80 FR 40138, July 13, 2015) is not included in the 2025 base case because the rulemaking was not finalized in time to include in this analysis.ES-6

• Regulation of Fuels and Fuel Additives: Modificatio ns to Renewable Fuel Standard Program (RFS2) (U.S. EPA, 2010b)

• Light-Duty Vehicle Greenhouse Gas Emission Standard s and Corporate Average Fuel Economy Standards; Final Rule for Model-Year 2012-2 016 (U.S. EPA, 2010c)

• Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and Emission Guidelines: Final Rule Amendments (U.S. EP A, 2009)

• Emissions Standards for Locomotives and Marine Comp ression-Ignition Engines (U.S. EPA, 2008a)

• Control of Emissions for Nonroad Spark Ignition Eng ines and Equipment (U.S. EPA, 2008b)

• NO x Emission Standard for New Commercial Aircraft Engi nes (U.S. EPA, 2005)

• Regional Haze Regulations and Guidelines for Best A vailable Retrofit Technology Determinations (U.S. EPA, 2005a)
ES.3 Control Strategies and Emissions Reductions
The proposal requires EGUs in 23 eastern states to reduce interstate transport of NO X emissions that significantly contribute to nonattai nment or interfere with maintenance of the 2008 ozone NAAQS. The rule proposes toproposal set s EGU ozone season NO X emissions budgets (allowable emission levels) for 2017 and fu ture years. The EPA proposes toproposal
would implement these reductions through FIPs in any sta te that does not have an approved good neighbor SIP by the date this rule is finalized. Fu rthermore, under the FIPs, affected EGUs would participate in the CSAPR NO X ozone-season allowance trading program. The allowa nce trading program is the remedy in the FIP that achie ves the ozone season NO X emissions reductions required by the proposed rule. The allow ance trading program essentially converts the
EGU NO X emissions budget for each of the 23 states subject to the FIP into a limited number of
NO X ozone-season allowances that, on a tonnage basis, equal the state’s ozone season emissions budget.

The EGU ozone season NO X budgets for each state reflect EGU NO X reduction strategies that are widely available at a uniform cost of $1,3 00 per ton of NO X for affected EGUs. Specifically, this uniform cost reflects turning on idled selective catalytic reduction (SCR)
, selective non-catalytic reduction (SNCR) and upgrading combustion controls. Furthermore, thi sES-7

RIA analyzes regulatory control alternatives based on more and less stringent state emissions budgets based on uniform NO X control costs of $3,400 per ton and $500 per ton, respectively.

Table ES-1 show the emission reductions expected fr om the proposal and the more and less stringent alternatives analyzed. Included in the table are annual and seasonal NO X , sulfur dioxide (SO 2 ), and carbon dioxide (CO 2 ) reductions over the contiguous U.S.

Table ES-1. Projected 2017* EGU Emissions Reduction s of NO Xx , SO 2 , and CO 2 with the

Proposed NO X x Emissions Budgets and More and Less Stringent Alte rnatives (Tons) **

Proposal More Stringent Alternative Less Stringent Alternative NO x X (annual) 90,000 93,000 24,000 NO x X (ozone season) 85,000 84,6808 7 ,000 24,000 SO 2 (annual) 1,200 1,200 1,100 CO 2 (annual) 660,000 614 7 10 ,000 770,000 * The forecast of Aa nnual reductions of co-pollutants in 2017 areis based on 2018 IPM direct model outputs relied upon in this RIA to represent 2017 co-pollutant red uctions

. ** NO x and SO 2 emissions are reported in English (short) tons; CO 2 is reported in metric tons. All estimates rounded to two significant figures.

ES.4 Costs and Economic Impacts
The EPA analyzed ozone-season NO X emission s reductions
, and as well as the associated compliance costs from of the power sector of implementing the EGU NO X ozone-season emissions budgets in each of the 23 states
, using the Integrated Planning Model (IPM) and its underlying data and inputs. All EGU emissions reduc tion estimates are taken directly from IPM modeling results. We use the compliance cost estimate from IPM as a p roxy for social costs. Regarding costs, aA s currently configured, IPM directly estimates the costs for two of the NO X reduction strategies: turning on idled existing SCR s and SNCRs, and shifting generation to
lower-NO X emitting EGUs. The cost s of other mitigation measures, which are optimizing existing operating SCR or SNCR and installing or up grading NO X combustion controls, are
estimated outside of IPM . However, these cost estimates are based on inputs to IPM on the cost of reducing EGU NO X via these controls but are calculated using IPM in puts

. One additional aspect of the analysis conducted usi ng IPM is that the existing but idle
SCRs or SNCRs in the model baseline were are required to be turned on and fully utilized in the model for compliance with the emissions budgets. Fo r the proposal, the EPA estimates that,ES-8

given the characteristics of these units and the av erage cost per ton of NO X reduced to comply with the proposed emissions budgets, turning on the existing but idle SCRs is a reasonable and cost-effective compliance method to evaluate for th is RIA.

The estimate of the cost of this proposal, therefor e, is the combination of NO x costs estimated by IPM and additional costs estimated out side of IPM. The cost estimates for the proposal and more and less stringent alternatives a re presented in Table ES-2. 11 All costs are in
2011 dollars.

Table ES-2

. Cost Ee stimates (2011$) for Pp roposal and Mm ore and Ll ess Ss tringent

Aa lternatives

Policy Annualized* Proposal $ 92,900,00093,000,000 More Stringent Alternative $ 95,700,00096,000,000 Less Stringent Alternative $4,700,000 *Costs are annualized over the period 2016 through 2040 using the 4.77 percent discount rate used in IPM’s objective function for minimizing the net present v alue of the stream of total costs of electricity ge neration

.

ES.5 Benefits to Human Health and Welfare
Implementing this proposal to update CSAPR is expec ted to reduce emissions of ozone
season NO X . In the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Reducing NO X emissions also reduces human exposure to ozone and the incidence of ozone-related health effects, thou gh this also depends partly on local levels of volatile organic compounds (VOCs). In addition, imp lementing the proposal would reduce
emissions of NO X throughout the year. Because NO X is also a precursor to formation of ambient
PM 2.5 , reducing these emissions would also reduce human exposure to ambient PM 2.5 throughout the year and would reduce the incidence of PM 2.5 -related health effects. FurthermoreFinally
, these emissions reductions would reduce lower ozone and PM 2.5 concentrations in regions other than beyond those with nonattainment or maintenance receptors that ar e the subject of to this

11 It should be noted that the costs associated with implementation of compliance with monitoring, recordkeeping, and reports requirements are not included within th e estimates in this table. Such costs, which are e stimated separately from the approaches listed above and are found in Chapter 7, are actually negative on net but are very small compared to the magnitude of the costs in Tab le 5-3ES-2

.ES-9

proposal, and the RIA accounts for those additional benefits of reducing these pollutants in those regions are accounted for in this RIA

. Based on the IPM modeling results, the The EPA notes thatdoes not expect this proposal
, as demonstrated by the analysis using IPM, is not e xpected to significantly change annual power
sector emissions of SO 2 , which is also a PM 2.5 precursor. Accordingly, this RIA does not quantify SO 2 -related PM 2.5 health co-benefits. Additionally, although we do not have sufficient data to quantify these impacts in this analysis, re ducing emissions of NO X would also reduce ambient exposure to nitrogen dioxide ( NO 2 ) and its associated health effects.

In this section, we provide an overview of the mone tized ozone benefits and PM 2.5 - related co-benefits estimated from NO X reductions for compliance with the proposed CSAPR
EGU NO X ozone season emissions budgets and for the more an d less stringent alternatives. A full description of the underlying data, studies, a nd assumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA , 2015)

. .

ES.5.1 Human Health Benefits and Climate Co-benefits
We follow a “damage-function” approach in calculati ng benefits, which estimates changes in individual health endpoints (specific ef fects that can be associated with changes in air quality) and assigns values to those changes assumi ng independence of the values for those individual endpoints. Because the EPA rarely has th e time or resources to perform new research to measure directly either health outcomes or their values for regulatory analyses, our estimates
are based on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under analysis. In addition to trans ferring information from other contexts to the context of this regulation, we also use a “benefit- per-ton” approach to estimate the ozone and
PM 2.5 benefits in this RIA. Benefit-per-ton approaches a pply an average benefit - per - ton derived from modeling of benefits of specific air quality s cenarios to estimates of emissions reductions for scenarios where no air quality modeling is avai lable. More information on these approaches is available in Chapter 6 of the RIA. Thus, to deve lop estimates of benefits for this RIA, we are transferring both the underlying health and economi c information from previous studies and information on air quality responses to emissions r eductions from other air quality modeling.ES-10

The benefit-per-ton approach we use in this RIA rel ies on estimates of human health responses to exposure to ozone and PM 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 Health Impact Assessment (HIA) for ozone and PM 2.5 , discussed further in Chapter 6 of this RIA, quantifies the changes in the incidenc e of adverse health impacts resulting from changes in human exposure to ozone and PM 2.5 . We use the environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE ) (version 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 ozone
and PM 2.5 concentrations. Table ES-3 provides national summar ies of the reductions in estimated health incidences associated with the proposed CSAP R EGU NO x ozone-season emissions budgets and for more and less stringent alternative s for 2017.ES-11

Table ES-3. Summary of Avoided Health Incidences fr om Ozone-Related and PM 2.5 -

Related Benefits from NO X reductions for the Proposal for 2017*

Ozone-related Health Effects Proposal More Stringent Alternative Less Stringent

Alternative Avoided Premature Mortality Smith et al. (2009) (all ages) 48 50 14 Zanobetti and Schwartz (2008) (all ages) 81 83 2423 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 79 81 2322 Emergency room visits for asthma (all ages) 320 330 9490 Asthma exacerbation (ages 6-18) 93,000 95,000 2726 ,000 Minor restricted-activity days (ages 18-65) 240,000 240,000 7067 ,000 School loss days (ages 5-17) 77,000 79,000 2322 ,000

PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 21 22 5.6 Lepeule et al. (2012) (adult) 48 50 13 Woodruff et al. (1997) (infant) 0<1 <10 <10 Avoided Morbidity Emergency department visits for asthma (all ages) 12 12 3.1 Acute bronchitis (age 8–12) 31 32 8.1 Lower respiratory symptoms (age 7–14) 390 400 100 Upper respiratory symptoms (asthmatics age 9–11) 560 570580 150 Minor restricted-activity days (age 18–65) 16,000 16,000 4,200 Lost work days (age 18–65) 2,700 2,700 700 Asthma exacerbation (age 6–18) 580 600 150 Hospital admissions—respiratory (all ages) 6.4 6.5 1.7 Hospital admissions—cardiovascular (age > 18) 7.8 8.0 2.1 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 2526 2625 6.6 Pooled estimate of 4 studies 2. 78 2. 87 0.7 1 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 ozone are based on ozone season NO X emissions. Confidence intervals are unavailable fo r 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). There may be other indirect health impacts associat ed with reducing emissions, such as occupational health exposures. Epidemiological stud ies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 ppb for ozone or μg/m 3 for PM 2.5 ). These relative risks can be used to develop risk coefficients that relate a unit reduction in ozone to changes in the incidence of a health effect. We refer the reader to Chapter 6 of this RIA, as well as to the Ozone N AAQS RIA (U.S. EPA, 2015) and PM NAAQS RIA (U.S. EPA, 2012a) for more information re garding the epidemiology studies and risk coefficients applied in this analysis.ES-12

Co-benefit s of the proposed rule is come from reducing emissions of CO 2 . Chapter 6 of this RIA provides a brief overview of the 2009 Enda ngerment Finding and climate science assessments released since then. Chapter 6 also pro vides information 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 i n CO 2 emissions in a given year.

ES.5.2 Combined Health Benefits and Climate Co-Bene fits Estimates
In this analysis, we were able to monetize the esti mated benefits associated with the reduced exposure to ozone and PM 2.5 and co-benefits of decreased emissions of CO 2. Specifically, we estimated combinations of health b enefits 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]) and climate co-benefits using four SC -CO 2 estimates (the average SC-CO 2 at each of three discount rates—5 percent, 3 perce nt, 2.5 percent—and the 95 th percentile SC-CO 2 at 3 percent ) ( as recommended in the current SC-CO 2 TSD 12 ; see Chapter 6 of this RIA for more details). In t his analysis we were unable to monetize the co-benefits associated with reducing exposure t o mercury, carbon monoxide (CO), SO 2 , and
NO 2 , as well as ecosystem effects and visibility impai rment. In addition, there are expected to be unquantified health and welfare impacts associated with changes in hydrogen chloride.

Table ES-4 provides the combined health and climate benefits for the proposal and for more and less stringent alternatives for the 2017 a nalysis year. In the table, ranges within the total benefits rows reflect multiple studies upon w hich the estimates of premature mortality were
derived.

12 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 nd Department of Treasury (May 2013, Revised July 2015 ). Available at: https://www.whitehouse.gov/omb/oira/socialcost-of-carbon

.ES-13

Table ES-4. Combined Health Benefits and Climate Co -Benefits for the Proposed CSAPR

EGU NO X Ozone Season Emissions Budgets and for More and Le ss Stringent Alternatives for 2017 (millions of 2011$)*

SC-CO 2 Discount Rate** Health and Climate Benefits (Discount Rate Applied to Health Co-Benefits) Climate CoBenefits Only 3% 7% Less Stringent Alternative 5% $ 200 190 to $ 350340 $ 200 190 to $ 340330 $7.6 3% $ 220 210 to $ 370360 $ 220 210 to $ 360350 $27 2.5% $230 to $ 390380 $ 230 220 to $ 380370 $41 3% (95 th percentile) $ 270 260 to $ 420410 $ 270 260 to $ 410400 $78 Proposal 5% $680 to $1200 $660 to $1200 $6.5 3% $700 to $1200 $680 to $1200 $23 2.5% $710 to $1300 $690 to $1200 $35 3% (95 th percentile) $740 to $1300 $720 to $1200 $66 More Stringent Alternative 5% $700 to $1300 $680 to $1200 $6.5 3% $720 to $1300 $700 to $1200 $23 2.5% $730 to $1300 $710 to $1200 $35 3% (95 th percentile) $760 to $1300 $740 to $1300 $66 *All estimates are rounded to two significant figur es. Co-benefits are based on benefit-per-ton estima tes. Benefits for ozone and PM 2.5 co-benefits are based on ozone season NO X emissions. Ozone benefits and PM 2.5 co- benefits occur in the analysis year, so they are the same for all discoun t rates. The health benefits range is based on adul t mortality functions (e.g., from Krewski et al. (200 9) with Smith et al. (2009) to Lepeule et al. (2012 ) with Zanobetti and Schwartz (2008)). Climate co-benefits are based on reductions in CO 2 emissions. The monetized health cobenefits do not include reduced changes in health effects from directly emitted PM 2.5 , direct exposure to NO 2 , SO 2 , and hazardous air pollutants; ecosystem effects; or visibility impairment. **The SC-CO 2 estimates are calculated with four different value s of a one ton reduction. See RIA Chapter 6 for a complete discussion.

ES.5.3 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 ozone and fine particles. Data, time, and resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with reduced exposure to several hazardous air pollutants (HAP) (including m ercury), SO 2 and NO 2 , as well as ecosystem effects, and visibility impairment from r educed NO X due to the absence of air quality modeling data for these pollutants in this analysis . This does not imply that there are no cobenefits associated with changes in emissions of HAP or reductions changes in exposures to SO 2 and NO 2 . These co-benefits are listed in Table ES-5 below, and discussed more fully in Chapter
6 of this RIA.ES-14

Table ES-5. 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) — — SO 2 ISA 1
Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 1 Premature mortality — — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2

Reduced incidence of morbidity from exposure to CO Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3 Central nervous system effects — — CO ISA 1,2,3 Premature mortality — — CO ISA 1,2,3

Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 1

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

Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologic, 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 1

Reduced effects on materials Household soiling — — PM ISA 1,2

Materials damage (e.g., corrosion, increased wear) — — PM ISA 2ES-15

Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information

Reduced effects from PM deposition (metals and organics) Effects on Individual

organisms and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to
ozone Visible foliar injury on

vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1
Damage to urban ornamental plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1
Recreational demand associated with forest aesthetics — — Ozone ISA 2

Other non-use effects Ozone ISA 2 Ecosystem functions (e.g., water cycling, biogeochemical cycles, netprimary productivity, leafgas 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 Effects on fish, birds, and

mammals (e.g., reproductive effects) — — Mercury Study RTC 2ES-16

Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information

exposure to methylmercury 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. ES.5 Results of Benefit-Cost Analysis Below in Table ES-6, we present the primary costs a nd benefits estimates for 2017. The net benefits of the proposal and more and less stri ngent alternatives reflect the benefits of implementing EGU NO X emissions reductions for the affected 23 states vi a the proposed FIPs
minus the costs of those emissions reductions.

The guidelines of OMB Circular A-4 require providin g comparisons of social costs and social benefits at discount rates of 3 and 7 percen t. Ideally, streams of social costs and social benefits over time would be estimated and the net p resent values of each would be compared to
determine net benefits of the illustrative control strategy. The three different uses of discounting in the RIA – (i) construction of annualized costs, (ii) adjusting the value of mortality risk for lags in mortality risk decreases, and (iii) adjusting th e cost of illness for non-fatal heart attacks to adjust for lags in follow up costs – are all approp riate. We explain our discounting of benefits in Chapter 6 of the RIA, specifically the application , in which we apply discount rates of 3 and 7 percent to air quality benefits and 2.5, 3, and 5 percent t o climate co-benefitsin Chapter 6 of the
RIA ; we explain our discounting of costs, in which we use a single discount rate of 4.77 %, percent, in Chapter 5. Our estimates of net benefits are th e approximations of the net value (in 2017) of benefits attributable to emissions reducti ons needed to implement the NO X emissions budgets for each state.

Table ES-6. Total Costs, Total Monetized Benefits, and Net Benefits of the Proposal in

2017 for U.S. (millions of 2011$) a,b,c

Proposal More Stringent AAA Less Stringent Climate Co-Benefits $23 $23 $27

Air Quality Health Benefits $ 70 700670 to $1200 $ 720 690 to $1300 $ 690190 to $ 1 3 0040

Total Benefits $7 20 0 to $1200 $7 2 0 to $1300 $210 to $360ES-17

Annualized Compliance Costs $93 10 $96 $4.7

Net Benefits $600 to $1100 $630 to $1100 $620 to $1200 $620 to $1200 $210 to $360 $210 to $360 Non-Monetized Benefits d Non-quantified climate benefits Reductions in exposure to ambient NO 2 and SO 2 Ecosystem benefits assoc. with reductions in emissi ons of NOx, SO 2 , and PM Visibility impairment

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitatio ns. As a result, we provide compliance costs and social benefits in 2017, using the best available informat ion to approximate social compliance costs and social benefits recognizing uncertainties and limitations in those estimates. b Benefits ranges represent discounting of health be nefits and climate co-benefits at a discount rate o f 3 % percent

. See Chapter 6 for additional detail and explanation . The costs presented in this table reflect engineeringcompliance costs annualized at a an approximately 54.77 percent discount rate possible and include monitoring, recordkeeping, and reporting costs. See Chapter 5 for additional d etail and explanation.

c All costs and benefits are rounded to two significa nt figures; rowscolumns may not appear to add correctly. d Non-monetized benefits descriptions are for all thr ee alternatives and are qualitative.
E.S.6 References
U.S. EPA, 2015. Carbon Pollution Emission Guidelines for Existing S tationary Sources: Electric Utility Generating Units (Final Rule) , http://www2.epa.gov/cleanpowerplan/clean-powerplan-existing-power-plants.
U.S. EPA, 2015a

. Standards of Performance for Greenhouse Gas Emissio ns from New, Modified, and Reconstructed Stationary Sources: Ele ctric Utility Generating Units (Final Rule) , http://www2.epa.gov/cleanpowerplan/carbon-polluti on-standards-new-modified-andreconstructed-power-plants.
U.S. EPA, 2014

. Tier 3 Motor Vehicle Emission and Fuel Standards
, http://www3.epa.gov/otaq/tier3.htm.
U.S. EPA, 2012

. 2017 and Later Model Year Light-Duty Vehicle Greenh ouse Gas Emissions and Corporate Average Fuel Economy Standards , http://www3.epa.gov/otaq/climate/regs-lightduty.htm#2017-2025.
U.S. EPA, 2011

. Cross State Air Pollution Rule (CSAPR) , http://www3.epa.gov/crossstaterule/.
U.S. EPA, 2011a

. Mercury and Air Toxics Standards (MATS) , http://www3.epa.gov/mats/.
U.S. EPA, 2011b
, Greenhouse Gas Emissions Standards and Fuel Efficie ncy Standards for Medium- and Heavy-Duty Engines and Vehicles , http://www3.epa.gov/otaq/climate/regsheavy-duty.htm.
U.S. EPA, 2010

. C3 Oceangoing Vessels , http://www3.epa.gov/otaq/oceanvessels.htm.ES-18

U.S. EPA, 2010a

. Reciprocating Internal Combustion Engines (RICE) NE SHAPs , http://www3.epa.gov/ttn/atw/icengines/.
U.S. EPA, 2010b

. Regulation of Fuels and Fuel Additives: Modificatio ns to Renewable Fuel Standard Program (RFS2) , http://www2.epa.gov/renewable-fuel-standard-progr am. U.S. EPA, 2010c

. Light-Duty Vehicle Greenhouse Gas Emission Standard s and Corporate Average Fuel Economy Standards; Final Rule for Mode l-Year 2012-2016
, http://www3.epa.gov/otaq/climate/regs-light-duty.ht m#2012-2016.
U.S. EPA, 2009

. Hospital/Medical/Infectious Waste Incinerators: New Source Performance Standards and Emission Guidelines: Final Rule Amend ments , http://www3.epa.gov/airtoxics/129/hmiwi/rihmiwi.htm l.
U.S. EPA, 2008a

. Emissions Standards for Locomotives and Marine Comp ression-Ignition Engines , http://www3.epa.gov/otaq/locomotives.htm.
U.S. EPA, 2008b

. Control of Emissions for Nonroad Spark Ignition Eng ines and Equipment
, http://www3.epa.gov/nonroad/.
U.S. EPA, 2005
, NO x Emission Standard for New Commercial Aircraft Engi nes , http://www3.epa.gov/otaq/aviation.htm.
U.S. EPA, 2005a

. Regional Haze Regulations and Guidelines for Best A vailable Retrofit Technology Determinations , http://www3.epa.gov/visibility/actions.html.

Climate Co-Benefits $23

Air Quality Health Benefits $700 to $1200

Total Benefits $720 to $1200

Annualized Compliance Costs $93 10ES-19

Net Benefits $630 to $1100

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits assoc. with reductions in emissi ons of NOx, SO 2 , and

PM Visibility impairment

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitatio ns. As a

result, we provide a snapshot of costs and benefits in 2017, using the best available information to approximate social costs and social benefits rec ognizing uncertainties and limitations in those estimates.

b Benefits ranges represent discounting of health be nefits and climate co-benefits at a discount rate o f 3%. See

Chapter 6 for additional detail and explanation. Th e costs presented in this table reflect compliance costs estimated using IPM annualized at a 4.77 percent discount rate and, to the extent possible, monitoring, recordkeeping, and rep orting costs. See Chapter 5 for additional detail and explanation.

c All costs and benefits are rounded to two significa nt figures; rows may not appear to add correctly.1-1

CHAPTER 1: INTRODUCTION AND BACKGROUND

Introduction
The EPA is proposing to update the Cross-State Air Pollution Rule (CSAPR) to reduce interstate transport of emissions of nitrogen oxide s (NO X ) that contribute significantly to
nonattainment or interfere with maintenance of the 2008 Ozone National Ambient Air Quality Standard (NAAQS) in downwind states. The primary pu rpose of the proposal (hereafter referred to as the “proposal to update CSAPR”, or simply “th e proposal”), is to address interstate air quality problems with respect to the 2008 ozone NAA QS. The proposal also responds to the D.C. Circuit’s July 28, 2015 remand of certain CSAPR ozo ne-season NO X emission budgets to the EPA for reconsideration. This Regulatory Impact Ana lysis (RIA) presents the health and welfare benefits of the proposal to update CSAPR, and compa res the benefits of the rule to the estimated costs of implementing the rule in 2017. This chapte r contains background information relative to the rule and an outline of the chapters of this RIA

.
1.1 Background
Clean Air Act (CAA or the Act) section 110(a)(2)(D) (i)(I), sometimes called the “good neighbor provision,” requires states to prohibit em issions that will contribute significantly to nonattainment in, or interfere with maintenance by, any other state with respect to any primary or secondary NAAQS. 13 The EPA promulgated CSAPR on July 6, 2011 14 to address interstate transport for the 1997 ozone NAAQS and the 1997 and 2006 fine particulate matter (PM 2.5 ) NAAQS. 15 (See section IV of the preamble to the proposal to update CSAPR for a discussion of CSAPR litigation and implementation.)

As described in the preamble for the proposal, CSA PR provides a 4-step process to address the requirements of the good neighbor provi sion for ozone or PM 2.5 standards: (1)

13 The EPA uses the term “states” to include the Dist rict of Columbia in this RIA.

14 See 76 FR 48208 (July 6, 2011)

15 CSAPR did not evaluate transport obligations for the 2008 ozone standard because the 2008 ozone NAAQ S was under reconsideration during the analytic work for the ruleCSAPR

.1-2

identifying downwind receptors that are expected to have problems attaining or maintaining clean air standards (i.e., NAAQS); (2) determining which upwind states contribute to these problems in amounts sufficient to “link” them to th e downwind air quality problems; (3) for states linked to downwind air quality problems, ide ntifying upwind emissions that significantly
contribute to nonattainment or interfere with maint enance by quantifying upwind NO X reductions and apportioning upwind responsibility ( this step establishes emissions budgets, which are remaining allowable emissions after reduc ing significant contribution to nonattainment and interference with maintenance); and (4) for sta tes that are found to have emissions that significantly contribute to nonattainment or interf ere with maintenance of the NAAQS downwind, reducing the identified upwind NO X emissions via regional emissions allowance trading programs . In this action, the EPA proposes to apply this 4 -step process to update CSAPR with respect to the 2008 ozone NAAQS. The reductions required by the proposed rule would be achieved through a FIP in any state that does not h ave an approved SIP addressing its contribution by the date this rule is finalized. Fu rthermore, under the FIPs, affected EGUs would participate in the CSAPR NO X ozone-season allowance trading program. DMore d etails on the methods and results of applying this process can be found in the preamble for this proposal and in Chapter 4 of this RIA . The 2008 ozone NAAQS is an 8-hour standard that w as set at 75 parts per billion (ppb). See 73 FR 16436 (March 27, 2008).

1.2.1 Role of Executive Orders in the Regulatory Im pact Analysis
Several statutes and executive orders apply to any public document. The analyses required by these statutes and executive orders are presented in detail in Chapter 7, and below we briefly discuss the requirements of Executive Order s 12866 and 13563 and the guidelines of the Office Of Management and Budget (OMB) Circular A-4 (U.S. OMB, 2003).

In accordance with Executive Orders 12866 and 13563 and the guidelines of OMB Circular A-4, the RIA analyzes the benefits and cos ts associated with emissions reductions for compliance with the proposal to update CSAPR. OMB C ircular A-4 requires analysis of one potential alternative standard level more stringent than the proposal and one less stringent than the proposal. This RIA evaluates the benefits, cost s, and certain impacts of a more and a less stringent alternative to the proposal.1-3

1.2.2 Illustrative Nature of this Analysis
The EPA proposes to implement the proposed EGU NO X emissions budgets via updating the CSAPR regional NO X ozone-season allowance trading program. This impl ementation approach provides utilities with the flexibility to determine their own compliance path. This RIA develops and analyzes one possible scenario for com pliance with the NOx budgets proposed by this action and possible scenarios for EGU compliance with more and less stringent alternative s.
1.2.3 The Need for Air Quality or Emissions Standar ds

OMB Circular A-4 indicates that one of the reasons a regulation may be issued is to address a market failure. The major types of market failure include: externalities, market power, and inadequate or asymmetric information. Correctin g market failures is one reason for regulation; it is not the only reason. Other possib le justifications include improving the function of government, correcting distributional unfairness , or securing privacy or personal freedom.

Environmental problems are classic examples of ext ernalities – uncompensated benefits or costs imposed on another party as a result of on e’s actions. For example, the smoke from a factory may adversely affect the health of local re sidents and soil the property in nearby neighborhoods. Pollution emitted in one state may b e transported across state lines and affect air quality in a neighboring state. If bargaining were costless and all property rights were well defined, people would eliminate externalities throu gh bargaining without the need for government regulation.

From an economics perspective, setting an emission s standard (i.e., EGU NO X ozoneseason emissions budgets in this proposal) is a str aightforward remedy to address an externality in which firms emit pollutants, resulting in health and environmental problems without compensation for those incurring the problems. Sett ing the emissions standard attempts to incentivize those who emit the pollutants to reduce their emissions, which lessens the impact on those who suffer the health and environmental probl ems from higher levels of pollution.1-4

1.2 Overview and Design of the RIA
1.2.1 Methodology for Identifying Needed Required Reductions
Application of the first two steps of the CSAPR pro cess (described above) with respect to the 2008 ozone NAAQS provides the analytic basis fo r proposing that ozone season emissions in
23 eastern states 16 affect the ability of downwind states to attain an d maintain the 2008 ozone NAAQS. Figure 1-1 shows the affected states.

Figure 1-1. Nonattainment and Maintenance Receptors Identi fied for this Proposal and

Upwind States Linked to these Downwind Air Quality Problems with Respect to the 2008 Ozone NAAQS

16 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas , Kentucky, Louisiana, Maryland, Michigan, Mississi ppi, Missouri, New Jersey, New York, North Carolina, Ohi o, Oklahoma, Pennsylvania, Tennessee, Texas, Virgin ia, West Virginia, and Wisconsin1-5

Applying Step 3 of this process, this action propo ses to quantify electric generating unit (EGU) NO X reductions in these 23 eastern states and to updat e CSAPR ozone season NO X emissions budgets. A state’s CSAPR ozone season NO X emissions budget is the quantity of EGU
NO X emissions that would remain after reducing signifi cant contribution to nonattainment and
interference with maintenance of the 2008 ozone NAA QS in an average year. 17 These NO X budgets were developed considering EGU NO X reductions that are achievable for the 2017 ozone

season. 18 The EPA applied a multi-factor test to evaluate EG U NO X reduction potential for 2017 and proposes to quantify EGU NO X ozone-season emissions budgets reflecting EGU NO X reduction strategies that are widely available at a uniform cost of $1,300 per ton (2011$). This assessment revealed that there is significant EGU N O X reduction potential that can be achieved by 2017, which would make meaningful and timely imp rovements in ozone air quality. Applying Step 4 of this process, the EPA is propos ing es to implement these EGU NO X emissions budgets through the CSAPR NO X ozone-season allowance trading program.

For the RIA, in order to implement the OMB Circular A-4 requirement to assess one less stringent and one more stringent alternative to the proposal, the EPA is also analyzing EGU NO X ozone season emissions budgets reflecting NO X reduction strategies that are widely available at a uniform cost of $500 per ton (2011$) and strategies that are widely available at a uniform cost of $3,400 per ton (2011$).

Applying Step 4 of this process, the EPA proposes t o implement these EGU NO X emissions budgets through the CSAPR NO X ozone-season allowance trading program.

17 For example, assuming no abnormal variation in ele ctricity supply due to events such as abnormal mete orology.

18 Non-EGU NOx emission control measures and reductio ns are not included in this proposal. More informa tion on non-EGU control measures and the potential for emis sion reductions can be found in the Non-EGU TSD on the Assessment of Non-EGU NOx Emission Controls, Cost of Controls, and Time for Complianc e s and Potential Reductions in 2017, which can be found in the docket for this proposal.1-6

1.2.2 States Covered by the Proposed Rule
For each state that would be affected by one of th e proposed federal implementation plans ( FIPs ) 19 (as shown in Figure ES-1) , and that is already included in the CSAPR NO X ozoneseason trading program to address interstate ozone transport for the 1997 NAAQS, the proposed
rule would lower EGU NO X ozone-season emissions budgets to reduce ozone tra nsport for the 2008 ozone NAAQS. One state, Kansas, would have a n ew CSAPR ozone-season requirement under this proposal. The remaining 22 states were i ncluded in the original CSAPR ozone-season program as to the 1997 ozone NAAQS.

1.2.3 Regulated Entities
The proposed rule affects fossil fuel-fired EGUs in these 23 eastern states , and regulates utilities (electric, natural gas, other systems) wh ich are classified as code 221112 by the North American Industry Classification System (NAICS) and have a nameplate capacity of greater than 25 megawatts (MWe).

1.2.4 Baseline and Analysis Year
As described in the preamble, the EPA proposes to a lign implementation of this rule with relevant attainment dates for the 2008 ozone NAAQS, as required by the D.C. Circuit’s decision
North Carolina v. EPA

. 20 The EPA’s final 2008 Ozone NAAQS SIP Requirements Rule revised
the attainment deadline for ozone nonattainment are as currently designated as moderate from December 2018 to July 2018 in accordance with the D .C. Circuit’s decision in NRDC v. EPA

. 21 Because July 2018 falls during the 2018 ozone seaso n, the 2017 ozone season will be the last full
season from which data can be used to determine att ainment of the NAAQS by the July 2018
attainment date. We believe that North Carolina compels the EPA to identify upwind reductions

19 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas , Kentucky, Louisiana, Maryland, Michigan, Mississi ppi, Missouri, New Jersey, New York, North Carolina, Ohi o, Oklahoma, Pennsylvania, Tennessee, Texas, Virgin ia, West Virginia, and Wisconsin.

20 531 F.3d 896, 911-12 (D.C. Cir. 2008) (holding tha t EPA must coordinate interstate transport complian ce deadlines with downwind attainment deadlines).

21 777 F.3d 456, 469 (D.C. Cir. 2014).1-7

and implementation programs to achieve these reduct ions, to the extent possible, for the 2017

ozone season.
The proposal to update CSAPR sets forth the require ments for states to reduce their significant contribution to downwind nonattainment and interference with maintenance of the 2008 ozone NAAQS. To develop and evaluate control s trategies for addressing these obligations, it is important to first establish a baseline proje ction of air quality in the analysis year of 2017, taking into account currently on-the-books Federal regulations, substantial Federal regulatory proposals, enforcement actions, state regulations, population, and where possible, economic growth. Establishing this baseline for the analysis then allows us to estimate the incremental
costs and benefits of the additional emissions redu ctions that will be achieved by the transport
rule.

Furthermore, the analysis in this RIA focuses on be nefits, costs and certain impacts in
2017. as well as cC ertain impacts in 2020, such as forecast emissions changes from the electricity sector, are also reported in this RIA. The results from the analysis in support of this proposal that are reported in this RIA areis limited to these two analysis years as because important regulatory actions, including the Clean P ower Plan (CPP) and potentially the 2015 ozone NAAQS, are expected to have an important and uncertain influence on the electricity sector in later years , as explained below

. For this reason, the EPA expects that most of the proposed CSAPR update’s influence on emissions redu ctions will occur between 2017 and 2020.

The first year that EGUs must comply with the CPP is 2022 and investments in renewable generation and energy efficiency in 2020 and 2021 are expected to influence generation patters in the electricity sector in tho se years, The CPP is not anticipated to have significant interactions with the CPP and the near- term (i.e., starting in 2017) ozone-season EGU
NO X emission reduction requirements under this proposa l. States must submit a state plan, or an initial submittal with an extension request, by Sep tember 6, 2016 describing how they will implement the emissions guidelines of the final CPP . See section VII.E of the preamble for
further discussion.

As discussed in the RIA for the final 2015 ozone NA AQS, it is assumed that potential nonattainment areas everywhere in the U.S., excludi ng California, will be designated such that1-8

they are required to attain the revised standard by 2025. Furthermore, as discussed in the memo to EPA Regional Administrators, Implementing the 2015 Ozone National Ambient Air Qu ality
Standards , implementation of the 2015 ozone NAAQS may use th e framework of the CSAPR. Doing so may influence compliance with the budgets that are the subject of this proposal.

In addition to other on-the-books Federal regulatio ns, the baseline for the analysis of the benefits, costs and certain impacts of this proposa l includes CSAPR phase II NOx ozone-season emissions budgets. As discussed in section III.C of the preamble, in EME Homer City II 22 , the D.C. Circuit declared invalid the CSAPR phase 2 ozo ne-season NOx emissions budgets of 11 states. Because the proposed rule modeling was perf ormed prior to the D.C. Circuit’s issuance of EME Homer City II , the modeling assumes that the baseline for all st ates includes the emission reductions associated with the CSAPR phase 2 ozone- season budgets. Furthermore, as these budgets were remanded, it is reasonable to treat th em, for the purposes of the analysis in this RIA, as on-the-books Federal regulations. 23 As discussed in the preamble, this proposal incorporates revised emissions budgets that would s upplant and replace the budgets promulgated in the original CSAPR rule including the remanded b udgets. Furthermore, these proposed budgets would be effective for the 2017 ozone seaso n, the same period in which the phase 2 budgets that were invalidated by the court are curr ently scheduled to become effective. Of the 11 states for which the CSAPR phase 2 ozone-season NOx emissions budgets were remanded, 9 are among the 23 states for which budgets are establish ed in this proposal. They are: Maryland, New Jersey, New York, North Carolina, Ohio, Pennsylvani a, Texas, Virginia, and West Virginia. The baseline and analysis year are discussed in more de tail in Chapter s 3 and 5 of this RIA.

In addition, on June 29, 2015, the United States Su preme Court reversed the D.C. Circuit opinion affirming the MATS. The EPA is reviewing th e decision and will determine any appropriate next steps once the review is complete. MATS is included in the baseline for this

22 EME Homer City Generation, L.P. , v. EPA , No. 795 F.3d 118, 129-30, 138 ( EME Homer City II ).

23 As discussed in the preamble, in the modeling for t he final rulemaking for this rule the implementatio n of the 4- step process to address the requirements of CAA sec tion 110(a)(2)(D)(i)(I) will the EPA may no longer assume that emissions from sources in states that are affected by the remand are constrained by the ozone-season N O X emissions budgets that the court declared invalid in EME Homer City II . If that decision is also applied to the baseline for the analysis of the benefits, costs and certain impacts of this rule, then this update to CSAPR is expecte d to lead to greater emission reductions than if the baseline in cluded the phase 2 CSAPR NO X ozone-season emissions budgets.1-9

analysis, and the EPA does not believe including MA TS substantially alters the results of this analysis, because MATS was remanded, not vacated.

1.2.5 Emissions Controls and Cost Analysis Approach
The EPA estimated the control strategies and compl iance costs of the proposed rule using the Integrated Planning Model (IPM) as well as cert ain costs that are estimated outside the model, but use IPM inputs for their estimation. The se cost estimates reflect costs incurred by the power sector, and include (but are not limited to) the costs of purchasing, installing, and operating NO X control technology, changes in fuel costs, and cha nges in the generation mix. A description of the methodologies used to estimate t he costs and economic impacts to the power sector is contained in chapter 5 of this RIA.

1.2.6 Benefits Analysis Approach
The EPA estimated human health benefits (i.e., mor tality and morbidity effects) considering an array of health impacts attributable to changes in exposure to ozone and fine particulate matter (PM 2.5 ). We estimated these benefits using benefit-per-to n estimates derived
from the BenMAP tool. The EPA also estimated the cl imate co-benefits of the proposal. A description of the methodologies used to estimate t he human health and climate benefits is contained in chapter 6 of this RIA. In addition, ch apter 6 contains a discussion of welfare cobenefits, such as ecosystem benefits from reduced n itrogen deposition.

1.3 Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining chapters:

• Chapter 2: Electric Power Sector Profile. This chapter describes the electric power sector in detail.

• Chapter 3 : Emissions and Air Quality Modeling Impacts . The data, tools, and methodology used for the air quality modeling are d escribed in this chapter, as well as the post-processing techniques used to produce a number of air quality metrics for input into the analysis of benefits and costs.

• Chapter 4: Regulatory Control Scenarios. The chapter summarizes the rationale for the three regulatory control alternatives analyzed and how the requirements of these alternatives are represented in IPM.1-10

• Chapter 5: Cost, Economic, Employment, and Energy I mpacts. The chapter summarizes the data sources and methodology used to estimate t he costs and other impacts incurred by the power sector.

• Chapter 6: Benefits Analysis Results . The chapter quantifies the health-related benefit s of the ozone-related air quality improvements associat ed with the three regulatory control alternatives analyzed.

• Chapter 7 : Statutory and Executive Order Impact Analyses . The chapter summarizes the Statutory and Executive Order impact analyses.

• Chapter 8: Comparison of Benefits and Costs . The chapter compares estimates of the total benefits with total costs and summarizes the net benefits of the three alternative regulatory control scenarios analyzed.2-1

CHAPTER 2: ELECTRIC POWER SECTOR PROFILE

Overview
This chapter discusses important aspects of the pow er sector that relate to today’s proposal to update CSAPR with respect to the interstate tran sport of emissions of nitrogen oxides (NO X ) that contribute significantly to nonattainment or i nterfere with maintenance of the 2008 ozone NAAQS in downwind states. This chapter describes ty pes of existing power-sector sources affected by the proposed regulation, and provides b ackground on the power sector and electricity generating units (EGUs). In addition, this chapter provides some historical background on trends in the past decade in the power sector, as well as about existing EPA regulation of the power

sector.

2.1 Background In the past decade there have been significant stru ctural changes in both the mix of generating capacity and in the share of electricity generation supplied by different types of generation. These changes are the result of 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 U.S. economy, growth and regional changes in the U. S. population, technological improvements in electricity generation from both existing and ne w units, changes in the prices and availability of different fuels, and substantial growth in elect ricity generation by renewable and unconventional methods. Many of these trends will c ontinue to contribute to the evolution of the power sector. The evolving economics of the power s ector, in particular the increased natural gas supply and subsequent relatively low natural gas pr ices, have resulted in more gas being utilized as base load energy in addition to supplying electr icity during peak load. This chapter presents data on the evolution of the power sector from 2000 through 2013. Projections of future power sector behavior and the impact of this rule are dis cussed in more detail in chapters 3 and 5 of this
RIA.2-2

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

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 an EGU is capable of producing in a typical hour, typically measured in megawatts (MW) for individual units, or gigawatts (1 GW = 1000 MW) for multiple EGUs. Electricity Generation refers to the amount of electricity actually produced by an EGU over some period of time, measur ed in kilowatt-hours (kWh) or gigawatthours (GWh = 1 million kWh). Net Generation is the amount of electricity that is available to the grid from the EGU (i.e., excluding the amount of el ectricity generated but used within the generating station for operations). Electricity gen eration is most often reported as the total annual generation (or some other period, such as seasonal) . In addition to producing electricity for sale to the grid, EGUs perform other services important to reliable electricity supply, such as providing backup generating capacity in the event o f unexpected changes in demand or unexpected changes in the availability of other gen erators. Other important services provided by generators include facilitating the regulation of t he 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 combined2-3

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 2000 and 2013.

In 2013 the power sector consisted of over 19,000 g enerating units with a total capacity 24 of 1,168 GW, an increase of 301 GW (or 35 percent) from the capacity in 2000 (867 GW). The 301 GW increase consisted primarily of natural gas fired EGUs (246 GW) and wind generators (58 GW), with substantially smaller net increases a nd decreases in other types of generating
units.

Table 2-1. Existing National Electricity Ge nerating Capacity by Energy Source,

2000 and 2013

2000 2013 Change Between '00 and '13

Energy Source Generator

Nameplate Capacity (MW) % Total

Capacity Generator

Nameplate Capacity (MW) % Total

Capacity %

Increase Nameplate

Capacity Change (MW) % of

Total Capacity Increase Coal 336,247 39% 329,815 29% -2% -6,433 -2% Natural Gas 242,602 28% 488,169 42% 101% 245,567 82 % Nuclear 104,734 12% 104,424 9% 0% -311 0% Hydro 95,879 11% 100,182 8% 4% 4,303 1%

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. Natural Gas information in this chapter (unless otherwise stated) reflects data for all generating units usin g natural gas as the primary fossil heat source. Th is includes Combined Cycle Combustion Turbine, Gas Turbine, and miscellaneous (< 1 percent)2-4

Petroleum 68,080 8% 49,794 5% -27% -18,287 -6% Wind 2,394 0.3% 60,712 5.1% 2436% 58,318 19% Other Renewable 14,047 1.6% 25,748 1.8% 83.3% 11,701 3.9% Misc 3,079 0.4% 5,180 0.4% 68.2% 2,101 0.7% Total 867,062 100% 1,167,995 100% 35% 300,933 100% 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 Capacity 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#gencapacity.> Accessed 8/7/15.

The 35 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 2000 to 2013, a to tal of 408,022 MW of new generating capacity was built and brought online, and 83,240 MW existin g units were retired. The net effect of the re-rating of existing units reduced the total capac ity by 23,848 MW. The overall net change in capacity was 300,933 MW, as shown in Table 2-1.

The newly built generating capacity was primarily n atural gas (307,764 MW), which was partially offset by gas retirements (36,876 MW). Wi nd capacity was the second largest type of new builds (58,477 MW), augmented by solar (6,273 M W). 25 The overall mix of newly built and retired capacity, along with the net effect, is sho wn on Figure 2-1.

25 Partially offset by 86 MW retired older wind or so lar capacity.2-5

Figure 2-1 National New Build and Retired Capacity (MW) by Fuel Type, 2000-2013 26

The information in Table 2-1 and Figure 2-1 present information about the generating capacity in the entire U.S. The proposal to update CSAPR, however, directly affects EGUs in 23 eastern states (i.e., the CSAPR 2008 Ozone Region), as discussed in Chapter 1. The share of generating capacity from each major type of generat ion differs between the CSAPR 2008 Ozone Region and the rest of the U.S. (non-region). Figur e 2-2 shows the mix of generating capacity for each region. The overall capacity and generation in the CSAPR 2008 Ozone Region is 60% of the national total, reflecting the larger total pop ulation in the region. The mix of capacity and generation are noticeably different in the two regi ons. In the CSAPR 2008 Ozone Region, coal makes up a significantly larger share of total capa city (34 percent) than it does in the rest of the country (19%). The shares of natural gas, however, are quite similar (41% in the region, and 43% in the rest of country). The difference in the shar e of coal’s capacity is balanced by relatively more hydro, wind, solar and other capacity in the r est of country compared to the CSAPR 2008 Ozone Region.

26 Source: EIA Form 860. Not visible: wind and solar retirements = 87 MW, net change in coal capacity = -4,186 MW

-50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000

New Build Retirement Net Change

Coal Nat Gas Wind & Solar Oil & Other2-6

Figure 2-2 Regional Differences in Generating Capac ity (MW), 2013.
Source: EIA Form 860 Note: “Other” includes petrole um, geothermal, other renewable, waste materials an d misc. In 2013, electric generating sources produced a net 4,058 trillion kWh to meet national electricity demand, a 7 percent increase from 2000. As presented in Table 2-2, almost 70 percent of electricity in 2013 was produced through the com bustion of fossil fuels, primarily coal and natural gas, with coal accounting for the largest s ingle share. Although the share of the total generation from fossil fuels in 2013 (67 percent) w as only modestly smaller than the total fossil share in 2000 (71 percent), the mix of fossil fuel generation changed substantially during that period. Coal generation declined by 18 percent and petroleum generation by 76 percent, while natural gas generation increased by 83 percent. Thi s reflects both the increase in natural gas capacity during that period as well as an increase in the utilization of new and existing gas EGUs during that period. Wind generation also grew from a very small portion of the overall total in 2000 to 4.1 percent of the 2013 total.

Table 2-2. Net Generation in 2000 and 2013 (Trillion kWh = TWh)

2000 2013 Change Between '00 and '13

Net Generation (TWh) Fuel

Source Share Net

Generation (TWh) Fuel

Source Share Net

Generation Change (TWh) % Change in

Net Generation Coal 1,966.3 52% 1,586.0 39% -380.3 -19.3% Natural Gas 615.0 16% 1,125.9 28% 510.9 83.1%

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

In Region Non-RegionGenerating Capacity (MW)

Coal Gas Nuclear Hydro Wind & Solar Other2-7

Nuclear 753.9 20% 789.0 19% 35.1 4.7% Hydro 270.0 7% 264.7 7% -5.3 -2.0% Petroleum 111.2 2.9% 26.9 0.7% -84.4 -75.8% Wind 5.6 0.1% 167.7 4.1% 162.1 - Other Renewable 75.3 2.0% 85.7 2.1% 10.4 13.7% Misc 4.8 0.1% 12.4 0.3% 7.6 157.7% Total 3,802 100% 4,058 100% 256 7% Source: U.S. EIA Monthly Energy Review, December 20 14. Table 7.2a Electricity Net Generation: Total (All Sectors). Available online a t: <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 size 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.

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

Thermal Efficiency in 2013 (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) COAL2-8

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 pe rcent 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 2-3 also in cludes the distribution of generation, which is similar to the distribution of capacity.2-9

Figure 2-3. Cumulative Distribution in 2013 of Coal and Natural Gas Electricity

Capacity and Generation, by Age Source: National Electric Energy Data System (NEEDS ) v.5.15 Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent of total)) over 70 years old for clarity. Figure is limited to coal-steam units in NEEDS v5.13 in operation in 2013 or earlier (excludes ~2,100 MW of coal-fired IGCC and fossil waste capacity), and excludes those units in NEEDS with planned reti rements in 2014 or 2015.

The locations of existing fossil units in EPA’s Nat ional Electric Energy Data System (NEEDS) v.5.15 are shown in Figure 2-4.2-10

Figure 2-4. Fossil Fuel-Fired Electricity Generatin g Facilities, by Size Source: National Electric Energy Data System (NEEDS ) v.5.15 Note: This map displays fossil capacity at faciliti es in the NEEDS v.5.15 IPM frame. NEEDS v.5.15 reflects generating capacity expected to be on-line at the end of 2016. This includes planned new builds already under construction and p lanned retirements. In areas with a dense concentration of facilities, some facilities may be obscured.

2.2.2 Transmission

Transmission is the term used to describe the bulk transfer of electricity over a network of high voltage lines, from electric generators to substations where power is stepped down for local distribution. In the U.S. and Canada, there a re three separate interconnected networks of high voltage transmission lines, 27 each operating synchronously. Within each of these transmission networks, there are multiple areas whe re the operation of power plants is monitored and controlled by regional organizations to ensure that electricity generation and load are kept in

27 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.jpg2-11

balance. In some areas, the operation of the transm ission system is under the control of a single regional operator; 28 in others, individual utilities 29 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, vertically integrated util ities established much of the existing transmission infrastructure. However, as parts of t he country have restructured the industry, transmission infrastructure has also been developed by transmission utilities, electric cooperatives, and merchant transmission companies, among others. Distribution, also historically developed by vertically integrated utilities, is no w often managed by a number of utilities that purchase and sell electricity, but do not generate it. As discussed below, electricity restructuring has focused primarily on efforts to reorganize the industry to encourage competition in the generation segment of the industry, including ensur ing open access of generation to the
transmission and distribution services needed to de liver power to consumers. In many states, such efforts have also included separating generati on assets from transmission and distribution
assets to form distinct economic entities. Transmis sion and distribution remain price-regulated throughout the country based on the cost of service

.

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

29 E.g., Los Angeles Department of Power and Water, F lorida Power and Light.2-12

2.3 Sales, Expenses, and Prices
These electric generating sources provide electrici ty for ultimate commercial, industrial and residential customers. Each of the three major ultimate categories consume roughly a quarter to a third of the total electricity produced 30 (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 2000 and 2013.

Table 2-4. Total U.S. Electric Power Industry Retai l Sales, 2000 and 2013 (billion kWh)

2000 2013

Sales/Direct Use (Billion kWh) Share of Total

End Use Sales/Direct

Use (Billion kWh) Share of Total End

Use

Sales Residential 1,192 33% 1,395 36%

Commercial 1,055 29% 1,344 35%
Industrial 1,064 30% 978 25% Transportation NA 8 0.2%
Other 109 3% NA

Total 3,421 95% 3,725 96%

Direct Use 171 5% 143 4%

Total End Use 3,592 100% 3,869 100%

Source: Table 2.2, EIA Electric Power Annual, 2013 and 2010 Notes: Retail sales are not equal to net generat ion (Table 2-2) because net generation includes net exported electricity and loss of electricity th at occurs through transmission and distribution. Direct Use represents commercial and industrial fac ility use of onsite net electricity generation; and electricity sales or transfers to a djacent or co-located facilities for which revenue information is not 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

30 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.2-13

distributing electricity to individual homes and co mmercial establishments. The higher 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 considerably 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 2013, the national average retail electricity price (all sectors) was 10.12 cents/KWh, with a range from 7.09 cents (Washington) to 33.26 (Hawaii).

Average national retail electricity prices increase d between 2000 and 2013 by 7.1 percent in real terms (2011$). The amount of increase diffe red for the three major end use categories (residential, commercial and industrial). National average residential prices increased the most (8.7 percent), and commercial prices increased the least (2.3 percent). The real year prices for 2000 through 2013 are shown in Figure 2-5.2-14

Figure 2-5. Real National Average Electricity Price s for Three Major End-Use

Categories
Source: EIA AEO 2013, Table 2.4 Most of these electricity price increases occurred between 2002 and 2008; since 2008 nominal electricity prices have been relatively sta ble while overall inflation continued to increase. The increase in nominal electricity price s for the major end use categories, as well as increases in the GDP price and CPI-U indices for co mparison, are shown in Figure 2-6.

.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

2000 2002 2004 2006 2008 2010 2012Electricity Price (Cents/kWh, 2011$)

Residential Commercial Industrial Total2-15

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

End-Use Categories, With Inflation Indices
For a longer term perspective, Figure 2-7 shows re al 31 (2011$) electricity prices for the three major customer categories since 1960, and Fi gure 2-8 shows the relative change in real electricity prices relative to the prices since 196 0. 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 2014 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. 0%

5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

2000 2002 2004 2006 2008 2010 2012% Change in Nominal Electricity Prices and InflationIndices since 2002

Residential Commercial Industrial CPI-U GDP Price2-16

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 32 for the three major fossil fuels used in electrici ty generation; coal, natural gas and oil. Relative to real prices in 2000, the national avera ge real price (in 2011$) of coal delivered to EGUs in 2013 had increased by 50.5 percent, while t he real price of natural gas decreased by 22.6 percent. The real price of oil increased by 11 2 percent, but with oil declining as an EGU fuel (in 2013 oil generated only 1 percent of elect ricity) the doubling of oil prices had little overall impact in the electricity market. The combi ned real delivered price of all fossil fuels in 2013 increased by 36.9 percent over 2000 prices. Fi gure 2-9 shows the relative changes in real price of all 3 fossil fuels between 2000 and 2013.

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 2015, Table 9.9

32 Fuel prices in this section are all presented in t erms of price per Btu to make the prices comparable

.

-50% -30% -10% 10% 30% 50% 70% 90% 110% 130% 150%

2000 2002 2004 2006 2008 2010 2012

Coal Oil Gas Average2-18

2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2000 to 2013
An important aspect of the changes in electricity g eneration (i.e., electricity demand) between 2000 and 2013 is that while total net gener ation increased by 6.7 percent over that period, the demand growth for generation was lower than both the population growth (12.2 percent) and real GDP growth (25.4 percent). Figure 2-10 shows the growth of electricity generation, population and real GDP during this per iod.

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

2000 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 mor e slowly than both the population and GDP, the relative electric intensity of the U.S . economy improved (i.e., less electricity used per person and per real dollar of output) during 20 00 to 2013. On a per capita basis, real GDP per capita grew by 11.8 percent, increasing from $44,50 0 (in 2011$) per person in 2000 to $49,800/person in 2013. At the same time electricit y generation per capita decreased by 4.8 percent, declining from 13.4 MWh/person in 2000 to 12.8 MWh/person in 2013. 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 14.9 percent, declining from 303 MWh per $1 million of GDP to 258 MWh/$1 million GDP. These relative changes are

-5% 0% 5% 10% 15% 20% 25% 30%

2000 2002 2004 2006 2008 2010 2012

Real GDP Population Generation2-19

shown in Figure 2-11. Figures 2-10 and 2-11 clearly show the effects of the 2007 – 2009 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 2000 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 electricity markets has changed the structure of the electric power ind ustry. 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 -15% -10% -5% 0% 5% 10% 15%

2000 2002 2004 2006 2008 2010 2012

Real GDP/Capita Generation/Capita Generation/ Real GDP2-20

(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 investment choices, the economic incentive to provide least-co st electric rates through market competition, reduced costs of combustion turbine technology that opened the door for more companies to sell power with smaller investments, and complexity of 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 2-13). Currently, the re are 15 states plus the District of Columbia where price deregulation of generation (restructuri ng) has occurred (“Active” in Figure 2-13). 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 deregula tion of the power sector was a significant change in type of ownership of electric ity generating units in the states that deregulated prices. Throughout most of the 20th cen tury electricity was supplied by vertically integrated regulated utilities. The traditional int egrated utilities provided generation, transmission and distribution in their designated areas, and pri ces were set by cost of service regulations set by state government agencies (e.g., Public Utility Com missions). Deregulation and restructuring resulted in unbundling of the vertical integration structure. Transmission and distribution continued to operate as monopolies with cost of ser vice regulation, while generation shifted to a mix of ownership affiliates of traditional utility ownership and some generation owned and operated by competitive companies known as Independ ent Power Producers (IPPs). The resulting generating sector differed by state or re gion, as the power sector adapted to the restructuring and deregulation requirements in each state.

By the year 2000, the major impacts of adapting to changes brought about by deregulation and restructuring during the 1990s wer e nearing completion. In 2000, traditional2-22

utilities owned 79 percent of U.S. generating capac ity (MW) while IPPs 33 and commercial and industrial producers owned 18 percent and 3 percent of U.S. generating capacity, respectively. The mix of electricity generated (MWh) was more hea vily weighted towards the utilities, with a distribution in 2000 of 81 percent, 15 percent and 4 percent for utilities, IPPs and commercial/industrial, respectively.

Since 2000, IPPs have expanded faster than traditi onal utilities, substantially increasing their share by 2013 of both capacity (59 percent ut ility, 38 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 and generation data for commercial and industrial owners are not shown on these figures du e to the small magnitude of those ownership

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

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

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000Capacity (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 3,000,000Generation (GWh) 

### Generation Mix, 2002 & 2012

Nuclear Coal Gas Hydro Wind All Other2-23

types. A portion of the shift of capacity and gener ation is due to sales and transfers of generation assets from traditional utilities to IPPs, rather t han strictly the result of newly built units.2A-13-1

CHAPTER 3: EMISSIONS AND AIR QUALITY MODELING IMPA CTS

Overview
This Chapter describes the methods for estimating emissions and air quality for the 2017 baseline and 2017 illustrative control case. In Sec tion 3.1, we describe the air quality modeling platform, in Section 3.2 we describe the developmen t of emissions inventories used in the air quality modeling, and in Section 3.3 we describe th e methods for processing the air quality modeling outputs to create inputs for estimating be nefits. The 2017 baseline and illustrative control case air quality model predictions were use d to calculate “benefit per ton” factors of reduced NO X on both ozone and fine particulate matter (PM 2.5 ) concentrations. 34,35 These factors were then used to estimate the benefits of the regu latory control alternatives, as described in Chapter 6. Details on the air quality modeling are provided in the Air Quality Modeling Technical Support Document, which can be found in t he docket for this proposed rule.
3.1 Air Quality Modeling Platform
We use the emissions inputs described in Section 3. 2 for national scale applications of the Comprehensive Air Quality Model with Extensions (CA Mx) modeling system to estimate ozone and PM 2.5 air quality in the contiguous U.S. CAMx is a thre e-dimensional grid-based Eulerian photochemical model designed to estimate ozone and PM 2.5 concentrations over seasonal and annual time periods. Because it accounts for spati al and temporal variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating the impacts of the rule on ozone and PM 2.5 concentrations.
For this analysis we used CAMx to simulate air qual ity for every hour of every day of the year. These model applications require a variety o f input files that contain information pertaining to the modeling domain and simulation pe riod. In addition to the CAMx model, our

34 The 2017 baseline air quality model predictions wer e also used to inform the EPA’s ozone transport pol icy analysis by identifying which states significantly contribute to nonattainment or interfere with maint enance of downwind receptors. See Ozone Transport Policy Analysis Proposed Rule Technical Support Document, which can be found in the docket for this proposed rule.

35 Note that the baseline underlying the air quality m odeling does not reflect the updated IPM emissions baseline used to develop costs and benefits in Chapters 5 an d 6. See the discussion in section 3.2.2 of this ch apter.3-2

modeling system includes (1) emissions for a 2011 b ase year and emissions for the 2017 baseline and illustrative control case, (2) meteorological d ata inputs for the year 2011, and (3) estimates of intercontinental transport (i.e., boundary conce ntrations) from a global photochemical model. Using these data, CAMx generates hourly predictions of ozone and PM 2.5 component species concentrations. The model predictions for the 2011 base year, the 2017 baseline, and the 2017 illustrative control case were combined with ambien t air quality observations to calculate seasonal mean ozone air quality metrics and annual mean PM 2.5 for the 2017 baseline and 2017 illustrative control case, which were then used as input for the benefits analysis.

3.1.1 Simulation Periods
For use in this benefits analysis, the simulation p eriod modeled by CAMx included separate full-year application for each of the thre e emissions scenarios (i.e., 2011 base year, 2017 baseline and 2017 illustrative control case).

3.1.2 Air Quality Modeling Domain
Figure 3-1 shows the geographic extent of the model ing domain that was used for air quality modeling in this analysis. The domain cover s the 48 contiguous states, along with the southern portions of Canada and the northern portio ns of Mexico. This modeling domain contains 25 vertical layers with a top at about 17, 550 meters, or 50 millibars (mb), and horizontal grid resolution of 12 km x 12 km. The model simulat ions produce hourly air quality concentrations for each 12 km 2 grid cell across the modeling domain.

Figure 3-1. National air quality modeling domain.3-3

3.1.3 Air Quality Model Inputs
CAMx requires a variety of input files that contai n information pertaining to the modeling domain and simulation period. These inclu de gridded, hourly emissions estimates and meteorological data, and initial and boundary condi tions. Separate emissions inventories were prepared for the 2011 base year, the 2017 baseline, and 2017 illustrative control case. All other inputs were specified for the 2011 base year model application and remained unchanged for each future-year modeling scenario.
CAMx requires detailed emissions inventories contai ning temporally allocated emissions for each grid-cell in the modeling domain for each species being simulated, as described in Section 3.2. The meteorological data model inputs for the 2011 base year were derived from running Version 3.4 of the Weather Research Forecas ting Model (WRF). The meteorological outputs from WRF include hourly-varying horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusi on rates, and rainfall rates for each grid cell in each vertical layer. The CAMx lateral boundary and initial species concentrations are provided by a three-dimensional global atmospheric chemistry and transport model (GEOS-Chem). The lateral boundary species concentrations varied with height and time (every 3 hours).
3.2 Development of Emissions Inventories

3.2.1 2011 Base Year Emissions
The 2011 emissions inventories are primarily based on the National Emissions Inventory, version 2 (2011NEIv2) for point sources, nonpoint s ources, commercial marine vessels (CMV), nonroad mobile sources and fires, although the inve ntories used for modeling often have temporal resolution additional to what is available in the NEI. The onroad mobile source emissions are similar to those in the 2011NEIv2, bu t were generated using the official release
2014 version of the Motor Vehicle Emissions Simulat or (MOVES2014) (http://www3.epa.gov/otaq/models/moves/), while the 2011NEIv2 emissions were generated using a slightly earlier version of MOVES2014. Biog enic emissions and emissions inventories
for Canada and Mexico are also included in the air quality modeling. The meteorological data used to develop and temporally allocate emissions w ere consistent with the 2011 data used for the air quality modeling.

The emissions inventories and modeling thereof inco rporate comments received on the Federal Register notices issued for the 2011 and 20 18 emissions modeling platforms: the Notice3-4

of Availability of the Environmental Protection Age ncy's 2011 Emissions Modeling Platform issued November 27, 2013 (78 FR 70935) and the Noti ce of Availability of the Environmental Protection Agency's 2018 Emissions Modeling Platfor m issued January 14, 2014 (79 FR 2437), respectively. The Sparse Matrix Operator Kernel Em issions (SMOKE) modeling system (Houyoux et al., 2000) version 3.6.5 was used to pr epare the emissions inventories for CAMx. Details regarding the development of the emission i nventories and emissions modeling for the 2011 base year and the 2017 baseline are documented in the Technical Support Document Preparation of Emissions Inventories for the Versio n 6.2, 2011 Emissions Modeling Platform (EPA, 2015) and can be found in the docket for this proposed rule.

3.2.2 2017 Baseline Emissions

The emission inventories for the 2017 future baseli ne have been developed using projection methods that are specific to emission so urce type. Future emissions are projected from the 2011 base year either by running models to esti mate future year emissions from specific types of emission sources (e.g., EGUs, and onroad a nd nonroad mobile sources), or for other types of sources by adjusting the base year emissio ns according to the best estimate of changes expected to occur in the intervening years (e.g., n on-EGU point and nonpoint sources). The biogenic, fire, offshore oil platforms, and Canadia n emissions use the same emissions in the base and future years. 36 For the remaining sectors, rules and specific lega l obligations that go into effect in the intervening years, along with changes in activity for the sector, are considered when possible. The modeled 2017 baseline emission invent ories represent predicted emissions that account for Federal and State measures promulgated or under reconsideration by December, 2014. With the exception of speciation profiles fo r mobile sources and temporal profiles for EGUs, the same ancillary data files are used to pre pare the future year emissions inventories for air quality modeling as were used to prepare the 20 11 base year inventories.

The 2017 baseline inventory for EGUs represents dem and growth, fuel resource availability, generating technology cost and perfor mance, and other economic factors affecting power sector behavior. The EGU emissions for the ai r quality modeling were developed using

36 The biogenic and fire emissions are normally held constant between base and future years. The offshor e and Canadian emissions were held constant due to the la ck of detailed information available to adequately project those emissions to future years.3-5

the IPM version 5.14 base case. 37 The IPM base case reflects the expected emissions accounting for the effects of environmental rules and regulati ons, consent decrees and settlements, plant closures, units built, control devices installed, a nd forecast unit construction through the calendar year 2017. The 2017 baseline EGU emissions include impacts from th e Final Mercury and Air Toxics Standards announced on December 21, 2011 38 and the CSAPR issued on July 6, 2011. 39 The EPA notes that because the modeling for the pro posal was performed prior to the D.C.
Circuit’s issuance of EME Homer City II , 40 that modeling assumed in its baseline for all stat es the emission reductions associated with the CSAPR N O X ozone-season phase 2 emissions budgets.

The Clean Power Plan (CPP) is not included in the modeled IPM 5.14 2017 baseli ne. The EPA notes that after the 2011 base year, the 2017 b aseline, and 2017 illustrative control case air quality modeling for this proposal were underway, t he EPA released an updated IPM base case, version 5.15, and the final CPP. In order to reflec t all on-the-books policies as well as the most current power sector modeling data, the EPA perform ed an assessment of benefits, costs, and impacts of this proposal using the IPM 5.15 base ca se and including the final CPP , as described further in Chapter 5 of this RIA.

The 2018 emissions output from IPM were adjusted to reflect 2017 emissions levels as described in “Calculating 2017 NOx Emissions” (see http://www2.epa.gov/airmarkets/calculating-2017-nox -emissions ). Temporal allocation was

37 IPM is a multiregional, dynamic, deterministic lin ear programming model of the U.S. electric power se ctor. This model is described in more detail in Chapter 5 of t his RIA. The documentation for version 5.14 can be found on EPA”s power sector modeling website: http://www.epa .gov/airmarkets/powersectormodeling.html

38 In Michigan v. EPA , the Supreme Court reversed on narrow grounds a po rtion of the D.C. Circuit decision upholding the Mercury and Air Toxics Standards, fin ding that EPA erred by not considering cost when de termining that regulation of EGUs was "appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case was remanded to the D.C. Circuit for further proceeding s, and the Mercury and Air Toxics Standards current ly remain in place.

39 We note that the NO x emissions changes that result from the Mercury and Air Toxics Standards and in the baseline do not substantially alter the results of the analysis. The NO x reductions based on EPA emissions modeling are only 7 percent of the nationwide total for EGUs in 2016, as found by subtraction of emissions in T able 5A-11 from Table 5A-9 of the Mercury and Air Toxics Stand ards RIA.

40 EME Homer City Generation, L.P. , v. EPA , No. 795 F.3d 118, 129-30, 138 ( EME Homer City II ) EME Homer City Generation, L.P. , v. EPA , No. 11-1302, slip op. at 19, 36 (July 28, 2015)3-6

used to process the seasonal emissions outputs from IPM to hourly emissions. To the extent possible, this temporal allocation process preserve d the emissions patterns from the base year (2011), while keeping the maximum emissions below t hose that occurred in the period 2011-
2014.

Projections for most stationary emissions sources other than EGUs (i.e., non-EGUs) were developed by using the EPA Control Strategy To ol (CoST) to create post-controls future year inventories. CoST is described at http://www3. epa.gov/ttnecas1/cost.htm. The 2017 baseline non-EGU stationary source emissions inventory inclu des all enforceable national rules and programs, including the Reciprocating Internal Comb ustion Engines (RICE) and cement manufacturing National Emissions Standards for Haza rdous Air Pollutants (NESHAPs) and Boiler Maximum Achievable Control Technology (MACT) reconsideration reductions. Projection factors and percent reductions for non-E GU point sources reflect comments received by EPA in response to 79 FR 2437, along with emissi ons reductions due to national and local rules, control programs, plant closures, consent de crees and settlements. Ancillary reductions to criteria air pollutant (CAP) emissions from station ary engines as a result of the Reciprocating Internal Combustion Engines (RICE) National Emissio n Standard for Hazardous Air Pollutants (NESHAP) are included. Reductions due to the New S ource Performance Standards (NSPS) volatile organic compound (VOC) controls for oil an d gas sources, and the NSPS for process heaters, internal combustion engines, and natural g as turbines are also included.

Regional projection factors for point and nonpoint oil and gas emissions were developed using Annual Energy Outlook (AEO) 2014 (U.S. EIA, 2 014) projections from year 2011 to year 2018.Projected emissions for corn ethanol, cellulos ic ethanol and biodiesel plants, refineries and upstream impacts represent the Energy Independence and Security Act (EISA) renewable fuel
standards mandate in the Renewable Fuel Standards P rogram (RFS2). Airport-specific terminal area forecast (TAF) data were used for aircraft to account for projected changes in landing/takeoff activity.

Projection factors for livestock are based on expec ted changes in animal population from 2005 Department of Agriculture data, updated accord ing to EPA experts in July 2012; fertilizer application ammonia (NH 3 ) emissions projections include upstream impacts re presenting EISA.3-7

Area fugitive dust projection factors for categorie s related to livestock estimates are based on expected changes in animal population and upstream impacts from EISA. Fugitive dust for paved and unpaved roads take growth in VMT and population into account. Residential Wood Combustion (RWC) projection factors reflect assumed growth of wood burning appliances based on sales data, equipment replacement rates and chan ge outs. These changes include growth in lower-emitting stoves and a reduction in higher emi tting stoves. Impacts from the NSPS for wood burning devices are also included.

Projection factors for the remaining nonpoint sourc es such as stationary source fuel combustion, industrial processes, solvent utilizati on, and waste disposal, reflect comments received on the projection of these sources as a re sult of rulemakings and outreach to states on emission inventories, and they also include emissio n reductions due to control programs. Future year portable fuel container (PFC) inventories refl ect the impact of the final Mobile Source Air Toxics (MSAT2) rule along with state comments recei ved in response to 79 FR 2437.

The MOVES2014-based 2017 onroad emissions 41 account for changes in activity data and the impact of on-the-books national rules inclu ding: the Tier 3 Vehicle Emission and Fuel Standards Program, the Light-Duty Vehicle Tier 2 Ru le, the Heavy Duty Diesel Rule, the Mobile Source Air Toxics Rule, the Renewable Fuel Standard (RFS2), the Light Duty Green House Gas/Corporate Average Fuel Efficiency (CAFE) standa rds for 2012-2016, the Heavy-Duty Vehicle Greenhouse Gas Rule, the 2017 and the Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (LD GHG). The 2017 onroad emissions also include state rules related to the adoption of low emission vehicle (LEV) standards, inspection and ma intenance programs, Stage II refueling controls, and local fuel restrictions. For Californ ia, the baseline emissions included most of this state’s on-the-books regulations, such as those for idling of heavy-duty vehicles, chip reflash, public fleets, track trucks, drayage trucks, and he avy duty trucks and buses. The California emissions do not reflect the impacts of the GHG/Sma rtway regulation, nor do they reflect state

41 The 2017 onroad mobile emissions were derived from a 2018 MOVES model run that was adjusted to accoun t for 2017. See the emissions modeling TSD (US EPA, 2015) for more details.3-8

GHG regulations for the projection of other emissio ns sectors because that information was not included in the provided inventories.

The nonroad mobile source emissions for 2017, inclu ding those for railroads and commercial marine vessel emissions, also include al l national control programs. These control programs include the Clean Air Nonroad Diesel Rule – Tier 4, the Nonroad Spark Ignition rules, and the Locomotive-Marine Engine rule. For ocean- going vessels (Class 3 marine), the emissions data reflect the 2005 voluntary Vessel Sp eed Reduction (VSR) within 20 nautical miles, the 2007 and 2008 auxiliary engine rules, th e 40 nautical mile VSR program, the 2009 Low Sulfur Fuel regulation, the 2009-2018 cold iron ing regulation, the use of 1% sulfur fuel in the Emissions Control Area (ECA) zone, the 2012-201 5 Tier 2 NO x controls, the 2016 0.1% sulfur fuel regulation in ECA zone, and the 2016 In ternational Marine Organization (IMO) Tier
3 NO x controls. Non-U.S. and U.S. category 3 commercial marine emissions were projected to 2017 using consistent methods that incorporated con trols based on ECA and IMO global NO x and SO 2 controls. For California, the 2017 emissions for t hese categories reflect the state’s OffRoad Construction Rule for “In-Use Diesel”, cargo h andling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and state rules through 2011 related to Transportation Refrigeration Units, the Spark-Ignit ion Marine Engine and Boat Regulations adopted on July 24, 2008 for pleasure craft, and th e 2007 and 2010 regulations to reduce
emissions from commercial harbor craft.

All modeled 2011 and 2017 emissions cases use 2010 Canada emissions data, which is the latest year for which Environment Canada had pr ovided data at the time the modeling was performed. No accompanying future-year projected ba seline inventories were provided in a form suitable for this analysis. For Mexico, emissions c ompiled from the Inventario Nacional de Emisiones de Mexico, 2008 were used for 2011, as th at was the latest complete inventory available. For the 2017 baseline, projected emissi ons for the year 2018 based on the 2008 inventory were used (ERG, 2014). Table 3-1 shows th e modeled 2011 and 2017 NO X and VOC emissions by sector. Additional details on the emis sions by state are given in the emissions modeling TSD (US EPA, 2015).3-9

Table 3-1 2011 Base Year and 2017 Baseline NOx and VOC Emissions by Sector (thousand

tons)

Sector 2011 NO x 2017 NO x

2011 VOC 2017 VOC EGU-point 2,000 1,500 36 40 NonEGU-point 1,200 1,200 800 810 Point oil and gas 500 410 160 170 Wildfires and Prescribed Fires 330 330 4,700 4,700 Nonpoint oil and gas 650 690 2,600 3,200 Residential wood combustion 34 35 440 440 Other nonpoint 760 730 3,700 3,500 Nonroad 1,600 1,100 2,000 1,400 Onroad 5,700 3,200 2,700 1,500 C3 Commercial marine vessel

(CMV) 130 130 5 6
Locomotive and C1/C2 CMV 1,100 910 48 35 Biogenics 1,000 1,000 41,000 41,000 TOTAL 15,000 11,200 58,000 57,000

3.2.3 2017 Illustrative Control Case Emissions The EPA’s approach to developing IPM v5.14-based em issions for the illustrative control case is methodologically consistent with the EPA’s approach to establishing the proposed EGU
NO X ozone-season emissions budgets to reduce interstat e ozone transport for the 2008 ozone NAAQS. Specifically, the EPA performed IPM modeling that applied a uniform cost of $1,300 per ton on EGU NO X emissions. Next, the EPA established illustrative EGU NO X ozone-season emissions budgets for the 23 eastern states include d in this proposal by multiplying the resulting state-specific emissions rate for affected EGUs by the corresponding (i.e., affected EGU) 2014 heat input. These illustrative EGU NO X ozone-season emissions budgets and their associate d assurance levels, along with corresponding emission changes for other pollutants as predicted by IPM, were modeled in IPM v5.14 to create the illust rative control case.

The emissions for the illustrative control case wer e processed for air quality modeling in the same way as the 2017 baseline. The only differ ence in the emissions inventories were the EGU emissions. The hourly temporal allocation for the illustrative control case inventories preserved the patterns from the 2017 baseline. This was accomplished by generating ratios of 2017 base hourly emissions to the total seasonal em issions by unit and then applying these ratios
to the total seasonal emissions from the 2017 illus trative control case. Thus, the same hourly temporal patterns in the baseline are reflected in this control case, including any adjustments3-10

made to constrain the hourly 2017 emissions below t he maximum levels during the 2011-2014 period.

3.3 Post-Processing of Air Quality Modeling for Ben efits Calculations
3.3.1 Converting CAMx Ozone Outputs to Benefits Inp uts The CAMx model generates predictions of hourly ozo ne concentrations for every grid cell. Future-year estimates of ozone for each of t hree health benefits metrics for ozone were calculated using model predictions. The modeled cha nge in ozone between the 2011 base year
and the 2017 future baseline and illustrative contr ol case were used to create relative reduction factors (RRFs) which were then applied to 2011 ambi ent ozone concentrations, as described
below. The health benefits metrics for ozone are May through September seasonal average 8- hour daily maximum ozone concentrations. The proce dures for determining the ozone RRFs for
these metrics are similar to those described in EPA guidance for modeling attainment of the ozone standard (EPA, 2014). This guidance recommen ds that model predictions be used in a relative sense to estimate changes expected to occu r in ozone concentrations for a future year emissions case. The RRFs and future year ozone con centrations were calculated using EPA’s software Modeled Attainment Test Software (MATS) (A bt, 2014). EPA used MATS to estimate the ozone impacts of the emissions reductions in th e 2017 illustrative control case.

For the purposes of projecting future ozone concen trations for input to the benefits calculations, we applied MATS using the base year 2 011 modeling results and the results from
the 2017 baseline and 2017 illustrative control cas e scenarios. In our application of MATS for ozone we used the ozone monitoring data centered ab out 2011 (2010-2012 ozone data) from the Aerometric Information Retrieval System (AIRS) as t he set of base-year measured
concentrations. The ambient ozone data and modeled ozone outputs were combined using the MATS “eVNA” spatial fusion technique to generate gr idded sets of spatial fields (interpolated ozone metrics for each modeled 12km grid cell in th e modeling domain) for each of the three ozone metrics for the 2011 base year period. The r atio of the seasonal average model-predicted future case ozone concentrations to the correspondi ng seasonal average model-predicted 2011 concentrations in each grid cell (RRF’s) was calcul ated and then multiplied by the gridded interpolated ozone concentrations for each metric t o produce gridded ozone concentrations for3-11

the 2017 baseline and 2017 illustrative control cas e. The resulting gridded files for the 2017
baseline and illustrative control cases were then i nput to the Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE) (version 1. 1) 42 BenMAP to calculate benefit per
ton factors for each metric. Information on the cal culation of the benefit per ton factors is provided in Chapter 6.

3.3.2 Converting CAMx PM 2.5 Outputs to Benefits Inputs

The CAMx model 43 generates predictions of hourly PM 2.5 species concentrations for every grid. The species include a primary fraction and s everal secondary PM 2.5 species (e.g., sulfates, nitrates, and organics). PM 2.5 is calculated as the sum of the primary and the se condary formed particles. Future-year estimates of PM 2.5 were calculated using RRFs applied to 2010-2012
ambient PM 2.5 and PM 2.5 species concentrations, as described below.

The procedures for determining the RRFs are simila r to those in EPA guidance for modeling the PM 2.5 NAAQS (EPA, 2014). This guidance recommends that model predictions be used in a relative sense to estimate changes expect ed to occur in each PM 2.5 species. The modeled attainment test procedure for calculating f uture year PM 2.5 values is described in the modeling guidance and is codified in EPA’s MATS. E PA used this procedure to estimate the ambient impacts of the emissions reductions in the 2017 illustrative control case. For the purposes of projecting future PM 2.5 concentrations for input to the benefits calculati ons, we applied the modeled attainment test procedure using the base year 2011 modeling results and the
results from the 2017 baseline and 2017 illustrativ e control case. In our application of MATS for
PM 2.5 we used the PM 2.5 monitoring data and speciated monitoring data cent ered about 2011 (2010-2012) from the state PM 2.5 Federal Reference Method (FRM) network, the Chemica l Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) network as the set of base-year measured concentrations. The ambient PM 2.5 and species data and modeled PM 2.5 and species outputs were combined using the MATS “e VNA”

42 Abt Associates, Inc. 2012. “BenMAP User’s Manual A ppendices,” prepared for U.S. Research Triangle Par k, NC: U. S. Environmental Protection Agency, Office of Ai r Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendi cesOct2012.pdf>. Accessed June 6, 2015

43 ENVIRON, 2014. User's Guide Comprehensive Air Qual ity Model with Extensions version 6.11, www.camx.com. ENVIRON International Corporation, No vato, CA.3-12

spatial fusion technique to generate gridded sets o f spatial fields (interpolated annual average
PM 2.5 and species concentrations for each modeled 12km g rid cell in the modeling domain) for the 2011 base year period. The ratio of the quarte rly average model-predicted future case PM 2.5 species concentrations to the corresponding quarter ly average model-predicted 2011 species concentrations in each grid cell (RRF’s) were calcu lated and then multiplied by the gridded interpolated PM 2.5 species concentrations to produce gridded PM 2.5 species concentrations for the
2017 baseline and 2017 illustrative control case. O utput files from this process include both quarterly and annual mean PM 2.5 mass concentrations and PM 2.5 species concentrations which are then processed to produce BenMAP input files contai ning annual mean PM 2.5 mass concentrations for the 2017 baseline and for the 20 17 illustrative control case. These data files were then input to BenMAP to calculate PM 2.5 benefit per ton factors. Information on the calculation of the benefit per ton factors is provi ded in Chapter 6.

3.4 References
Abt Associates, 2014. User’s Guide: Modeled Attainm ent Test Software. http://www3.epa.gov/scram001/modelingapps_mats.htm

. ERG, 2014. Develop Mexico Future Year Emissions Fin al Report. Available at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/2 011emissions/Mexico_Emissions_WA %204-09_final_report_121814.pdf

. Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler , N.J.M., Kasibhatla, P.S. (2000), Emissions inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project, Journal of Geophysical Res earch – Atmospheres, 105(D7), 9079- 9090. U.S. Energy Information Administration, 2014. Annua l Energy Outlook, 2014 ( http://www.eia.gov/forecasts/archive/aeo14/ ). U.S. Environmental Protection Agency, 2014. Modeli ng Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, a nd Regional Haze, Research Triangle Park, NC. ( http://www3.epa.gov/ttn/scram/guidance/guide/Draft_ O3-PMRH_Modeling_Guidance-2014.pdf ) U.S. Environmental Protection Agency, 2015. Prepara tion of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling Platform, Rese arch Triangle Park, NC. ( http://www3.epa.gov/ttn/chief/emch/2011v6/2011v6_2_ 2017_2025_EmisMod_TSD_aug20 15.pdf )3A-14-1

CHAPTER 4: REGULATORY CONTROL ALTERNATIVES

Introduction
The primary purpose of this proposal is to address interstate air quality impacts with respect to the 2008 ozone National Ambient Air Qual ity Standards (NAAQS). The EPA promulgated the Cross-State Air Pollution Rule (CSA PR) on July 6, 2011, 44 to address interstate transport of ozone pollution under the 1997 ozone N AAQS. 45 This action proposes to update CSAPR to reduce interstate transport of electricity generating unit (EGU) ozone-season (May 1 through September 30) emissions of nitrogen oxides (NO X ) that contribute significantly to
nonattainment or interfere with maintenance of the 2008 ozone NAAQS in downwind states. The EPA proposes to implement the proposed EGU NO X reductions by setting emissions budgets (limits on allowable emissions) that are implemente d through the CSAPR NO X ozone-season allowance trading program. As described in the pre amble, the EPA is proposing transport Federal Implementation Plans (FIPs) in this action. The EPA is proposing transport FIPs for 23

eastern states. The EPA would finalize a FIP for any state that doe s not have an approved State Implementation Plan (SIP) addressing its good neigh bor obligation by the date the rule is
finalized.

This Regulatory Impact Analysis (RIA) evaluates the benefits, costs and certain impacts of three different illustrative regulatory control alternatives. The alternatives differ in the level of
the NO X emissions budget. One of the alternatives represen ts the proposed budgets which are
based on a uniform NO X control cost of $1,300 per ton (2011$), whereas th e other two more and less stringent alternatives represent budgets based on uniform NO X control costs of $3,400 per ton and $500 per ton (2011$), respectively. The reg ulatory control alternatives also differ in the size of the assurance limit, which equals 21 percen t of the NO X ozone-season emissions budget, for 2017 and following years for each state. The as surance provision limits the total ozone season

44 See 76 FR 48208 (July 6, 2011)

45 CSAPR also addressed interstate transport of fine particulate matter (PM 2.5 ) under the 1997 and 2006 PM 2.5 NAAQS.4-2

NO X emissions from affected EGUs for each state as des cribed below. Other key regulatory features of the proposed allowance trading program, such as the ability to bank allowances for future use, are the same across all three alternati ves. This chapter describes the three alternatives analyzed in the RIA.

4.1 Background As described in detail in the preamble as well as in Chapter 1 , the proposed rule requires
23 states in the eastern U.S. to reduce interstate emission transport that significantly contributes to nonattainment, or interferes with maintenance, o f the 2008 ozone NAAQS by reducing their
ozone season EGU NO X emissions in 2017 and future years. The rule propo ses emissions budgets, which are allowable emissions after reduci ng significant contribution to nonattainment
and interference with maintenance of the 2008 ozone NAAQS for the emissions of affected EGUs in the 23 states. The reductions required by t he proposed rule may would be achieved through a FIP in any state that does not have an ap proved SIP addressing its contribution by the date this rule is finalized. Furthermore, under the FIPs, affected EGUs would participate in the
CSAPR NO X ozone-season allowance trading program. The allowa nce trading program is the remedy in the FIP that achieves the ozone season NO X emission reductions required by the proposed rule. The allowance trading program essent ially converts the EGU NO X emissions budget for each of the 23 states subject to the FIP into a limited number of NO X allowances that, on a tonnage basis, equal the state’s ozone season emissions budget. EGUs covered by the
seasonal NO X allowance trading program in the proposed FIPs are able to trade NO X ozoneseason emission allowances among EGUs within their state and across state boundaries, with emissions and the use of allowances subject to cert ain limits. This RIA analyzes the benefits, costs, and certain impacts of implementing the regu latory control alternative through the allowance trading program assuming that all 23 stat es are subject to the proposed FIPs.

In accordance with Executive Orders 12866 and 13563 , and the guidelines of OMB Circular A-4, this RIA analyzes the benefits and co sts associated with emissions controls to comply with the proposal to update CSAPR. OMB Circu lar A-4 requires analysis of one potential alternative standard level more stringent the proposed standard and one less stringent than the revised standard. In response to this requ irement, this RIA analyzes the proposed remedy under the FIP as well as a more and a less s tringent option (i.e., alternative) to the4-3

proposal under the FIP. The more and less stringent options differ from the proposed remedy in that they set different EGU NO X ozone-season emissions budgets for the affected EG Us.

4.2 Regulatory Control Alternatives Considered
This RIA analyzes the benefits, costs and certain i mpacts of the proposed rule. In the proposed rule, the EGU ozone-season NO X emissions budgets for each state are based on applying a uniform cost of $1,300 per ton (2011$) t o affected EGUs. This uniform cost of reducing a ton of NO X emissions reflects the cost of NO X reduction technologies that are both widely available and can be implemented in the near -term (i.e., by 2017). The budget setting process is described in the preamble and in detail in the proposal’s Ozone Transport Policy Analysis Technical Support Document (TSD). Furtherm ore, this RIA analyzes regulatory control alternatives with higher and with lower EGU emissio ns budgets. The EGU emissions budgets in these more and less stringent regulatory control al ternatives are based on uniform NO X costs of $3,400 per ton (2011$) and $500 per ton (2011$), respectively. The rationa le for choosing these regulatory control alternatives for analysis in the RIA is described in Section 4.3.

As described in Chapter 5 of this RIA, the benefits , costs, and impacts of the proposed rule are estimated, in part, by an economic model e stimating how affected EGUs may comply with the proposed rule. In addition to the limitati on on ozone-season NO X emissions required by the proposed EGU emissions budgets for the 23 state s, there are four important features of the allowance trading program that are represented in t he model that may influence the level and
location of NO X emissions from affected EGUs. They are: the abilit y of affected EGUs to buy
and sell ozone-season NO X emissions allowances from one another for complian ce purposes; the ability of affected EGUs to bank ozone-season NO X allowances for future use; the effect of
limits on the total ozone-season NO X emissions from affected EGUs in each state require d by the assurance provisions; and the treatment of banked 2 015 and 2016 vintage ozone-season NO X allowances issued under CSAPR to address interstate ozone transport for the 1997 ozone NAAQS. Each of these features of the ozone-season a llowance trading program is described
below.

Affected EGUs are expected to choose the least-cost method of complying with the requirements of the allowance trading program. As d escribed in Chapter 5, in the modeling of4-4

EGU compliance, the distribution of ozone season NO X emissions across affected EGUs is generally governed by this cost-minimizing behavior . In the analysis, the total ozone season NO X emissions from affected EGUs are limited to the amo unt allowed by the sum of the NO X budgets across the 23 states. Furthermore, as allowances ma y be banked for future use, if it is less costly
to abate ozone season NO X emissions in a current ozone season than to abate emissions in a later ozone season, affected EGUs are expected to bank NO X ozone-season allowances in the current ozone season for use in the later ozone season. Aga in, the ability of EGUs to bank allowances is represented in the compliance modeling discussed in Chapter 5, and the number of banked
allowances is based on an outcome that minimizes th e cost of complying with the NO X ozoneseason emissions budgets over time.

While there are no explicit limits on the exchange of allowances between affected EGUs and on the banking of 2017 and future vintage NO X ozone-season allowances in the proposal, the assurance provisions limit the amount of seasonal N O X emissions by affected EGUs in each of
the 23 states. The assurance level limits affected EGU emissions over an ozone season to the
state’s NO X ozone-season emissions budget plus an increment eq ual to 21 percent of each state’s emissions budget. The increment is called the varia bility limit. See section VII.B.4 of the preamble for a discussion of the purpose of the ass urance provision and further detail about how the variability and assurance limits are determined

. Following the approach in CSAPR, iI n the proposed rule, if a state exceeds its assurance lev el in a given year it is assessed a total of 32 -to-1
allowance surrender on the excess tons. Section VII .B.4 of the preamble also explains how EPA then determines which EGUs are subject to this surr ender requirement. In the analysis in this RIA the assurance provisions are represented by a l imit on the total amount of ozone season NO X emissions that can be emitted by affected EGUs in e ach state. That is, in the analysis, affected EGUs do not have the option of surrendering additio nal allowances at the 32 -to-1 rate in the compliance modeling.

As described in section VII.B.5 of the preamble the rule proposes to allow 2015 and 2016 vintage NO X ozone-season allowances that were issued under CSA PR to address interstate ozone transport for the 1997 ozone NAAQS to be used for c ompliance with this rule that reduces interstate ozone transport for the 2008 ozone NAAQS . Specifically, 2015 and 2016 vintage
CSAPR NO X ozone-season allowances may be used for compliance with this rule from 20174-5

forward. 46 If an EGU in one of the 23 states affected by this proposed rule chooses to do so, it must use four pre-2017 vintage allowances to cover one ton of NO X emitted. Based on EPA’s expectation of the size of the NO X allowance bank after the 2016 ozone season, the tr eatment of these banked allowances is represented in the model ing as an additional 71,982 tons of NO X allowances that may be used by affected EGUs during the 2017 ozone season or in later ozone

seasons.
Table 4.1 reports the seasonal budget for each of t he 23 affected states. It also shows the sum of all of the emissions budgets across all 23 s tates. As described above, in both the proposed rule and the analysis in this RIA, emissions from a ffected EGUs in the entire region cannot exceed this sum but for the ability to use banked a llowances from previous years. Furthermore, as described above, emissions from affected EGUs in a particular state may not exceed the state’s budget plus the assurance provision during the ozone season in any year (in 2017 and later).

Table 4-1 Ozone-Season NO X Emissions Budgets (Tons) for Proposed, More String ent and

Less Stringent Regulatory Control Alternatives in 2 017 and Later

Proposed Emissions Budgets Alternative More Stringent

Control Alternative Less Stringent Control Alternative Alabama 9,979 9,931 11,886 Arkansas 6,949 6,101 7,038 Illinois 12,078 11,992 12,144 Indiana 28,284 27,585 33,483 Iowa 8,351 8,118 8,614 Kansas 9,272 9,259 9,278 Kentucky 21,519 20,945 32,783 Louisiana 15,807 15,378 15,861 Maryland 4,026 4,026 4,026 Michigan 19,115 18,624 22,022 Mississippi 5,910 5,487 6,083 Missouri 15,323 15,240 15,380 New Jersey 2,015 2,011 2,016 New York 4,450 4,391 4,607 North Carolina 12,275 10,705 12,278 Ohio 16,660 16,637 20,194 Oklahoma 16,215 16,215 16,215 Pennsylvania 14,387 14,358 38,270 Tennessee 5,481 5,449 5,520

46 Allowances were only issued under CSAPR for the 20 15 and 2016 ozone seasons.4-6 Proposed Emissions Budgets Alternative More Stringent

Control Alternative Less Stringent Control Alternative Texas 58,002 55,864 58,492 Virginia 6,818 5,834 6,955 West Virginia 13,390 12,367 22,932 Wisconsin 5,561 5,511 5,588 TOTAL 311,867 302,028 371,665

These regulatory control alternatives are illustrat ive. For example, the EGUs have flexibility in determining how they will comply wit h the allowance trading program. The way that they comply may differ from the methods foreca st in the modeling for this RIA.

4.3 Rationale for Regulatory Control Alternatives C hosen As described in the preamble, the $1,300 per ton un iform cost used to set the proposed
EGU NO X ozone-season emissions budgets for the affected st ates is based on control technologies that are both widely available and can be implemented in the near-term (i.e., by 2017). The uniform cost of $500 per ton was used to establish NO X ozone-season emissions budgets for CSAPR and in this current rulemaking is the cost of fully optimizing postcombustion controls that are already running. The u niform cost of $3,400/ton was used to
establish NO X ozone-season emissions budgets for the NO X SIP Call and reflects the cost of turning on idled existing SNCRs. The proposal takes comment on budgets based on these two uniform cost thresholds. Therefore evaluating the b enefits and costs of complying with these higher and lower budgets both responds to the requi rement of Circular A

- 4 to evaluate a less and a more stringent option (i.e., alternatives) and is informative to evaluating alternative control stringencies on which the EPA is seeking comment. A chievable emission reductions from higher
uniform NO X cost levels were also evaluated for this proposal. Budgets are not being proposed for this rule based on these higher uniform cost le vels as they are based on the application of
NO X controls that could not be installed in the near t erm (i.e., by 2017). See section VI of the preamble and the Ozone Transport Policy Analysis TS D for further explanation.4A-15-1

CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS

Overview
This chapter reports the compliance cost, emissions , economic, energy and employment impact analyses performed for the proposal to updat e CSAPR. The EPA used the Integrated Planning Model (IPM), developed by ICF Internationa l, to conduct most of the analysis discussed in this chapter. The EPA conducted additi onal analysis using the IPM estimates of changes in the power and fuels sector as a basis fo r examining the potential direct labor impacts
in those sectors.

IPM is a dynamic linear programming model of the po wer sector that can be used to examine air pollution control policies affecting NO X , CO 2 , SO 2 , mercury ( Hg ) , hydrochloric acid ( HCl ) , and other air pollutants throughout the contiguou s United States for the entire power system. Future-year electricity demand levels are b ased on projections from the Energy Information Administration (EIA).

This chapter of the RIA presents analyses of the pr oposed rule that include assumptions about the possible actions that EGUs may pursue as they reduce their NO X emissions to comply with the proposed EGU NO X ozone-season emissions budgets in the 23-state reg ion. Over the past two decades, the EPA has used IPM and other an alytical methods described in this chapter to conduct extensive analyses of proposed and final federal environmental regulatory actions affecting the power sector. These previous analytic al efforts support the Agency’s understanding of key variables that influence the effects of a po licy and provide the framework for how the Agency estimates the costs and benefits associated with its actions.

5.1 Power Sector Modeling Framework
IPM is a state-of-the-art, peer-reviewed, dynamic l inear programming model that can be used to project power sector behavior under future business-as-usual conditions, and to examine prospective air pollution control policies througho ut the contiguous United States for the entire electric power system. EPA used IPM to project like ly future electricity market conditions with and without the proposed updates to CSAPR.5-2

IPM is a multi-regional, dynamic, deterministic lin ear programming model of the contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion, electricity dispatch, and emissions control strateg ies while meeting energy demand and environmental, transmission, dispatch, and reliabil ity constraints. The EPA has used IPM for over two decades to better understand power sector behavior under future business-as-usual
conditions and to evaluate the economic and emissio ns impacts of prospective environmental policies. The model is designed to reflect electric ity markets as accurately as possible. The EPA uses the best available information from utilities, industry 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. 47

The model incorporates a detailed representation of the fossil-fuel supply system that is used to estimate 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. 48 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

47 Detailed information andThe documentation of EPA’s Base Case using IPM (v5.15) contains detailed information , including all the underlying assumptions, data so urces, and architecture parameters . can be found on EPA’s website at: The documentation for EPA’s Base Case v.5.15 using IPM consists of a comprehensive document for IPM v. 5.13, and an incremental update document for both v.5.14 and v.5.15. All are avai lable at available at:
http://www.epa.gov/airmarkets/powersectormodeling.h tml

48 See Chapter 10 of EPA’s Base Case using IPM (v5.15 ) documentation, available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml5-3

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
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. 49

To estimate the annualized costs of additional capi tal investments in the power sector, the EPA uses a conventional and widely accepted approac h that applies a capital recovery factor (CRF) multiplier to capital investments and adds th at to the annual incremental operating expenses. The CRF is derived from estimates of the power sector’s cost of capital (i.e., private discount rate), the amount of insurance coverage re quired, local property taxes, and the life of capital. 50 It is important to note that there is no single CR F factor applied in the model; rather, the CRF varies across technologies, book life of th e capital investments, and regions in the model in order to better simulate power sector deci sion-making.

The EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the mo del has been used to estimate the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule (U.S. EPA, 2005)
, the 2011 Cross-State Air Pollution Rule (CSAPR) (U.S. EPA, 2005) , the Mercury and Air Toxics Standards (MATS) (U.S. EPA, 2011a) , the Clean Power Plan (CPP) for Existing Power
Plants (U.S. EPA, 2015) , and the Carbon Pollution Standards for New Power Plants (U.S. EPA, 2015a). EPA has also used IPM to estimate the air pollution reductions and power sector impacts of water and waste regulations affecting EGUs, incl uding Cooling Water Intakes (316(b)) Rule (U.S. EPA, 2014) , Disposal of Coal Combustion Residuals from Electr ic Utilities (CCR) (U.S. EPA, 2015b) and Steam Electric Effluent Limitation Guidelines (ELG) (U.S. EPA, 2015c)

.

49 See Chapter 9 of EPA’s Base Case using the IPM ( v.5.15 ) documentation . The documentation for EPA’s Base Case v.5.15 using IPM consists of a comprehensive d ocument for IPM v. 5.13, and an incremental update document for both v.5.14 and v.5.15. All are available at , available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml

50 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation , available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml.5-4

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 51 that are periodically conducted. The model has als o 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.

5.2 EPA’s Power Sector Modeling Base Case for the P roposal to Update CSAPR

The IPM “ bB ase cC ase” for any regulatory impact analysis is a busine ss-as-usual scenario that would be expected under market and re gulatory conditions in the absence of the rule. As such, an IPM base case represents an element of the baseline for this RIA. 52 The EPA frequently updates the IPM base case to reflect the latest available electricity demand forecasts from the U.S. Energy Information Agency (EIA) as we ll as expected costs and availability of new and existing generating resources, fuels, emiss ion control technologies, and regulatory requirements.

51 http://www2.epa.gov/clean-air-act-overview/benefit s-and-costs-clean-air-act

52 As described in Chapter 5 of EPA’s Guidelines for Preparing Economic Analyses , the baseline “should incorporate assumptions about exogenous changes in the economy that may affect relevant benefits and c osts (e.g., changes in demographics, economic activity, consume r preferences, and technology), industry compliance rates, other regulations promulgated by EPA or other gover nment entities, and behavioral responses to the pro posed rule by firms and the public.“ (USEPA, 2010).5-5

Our analysis of the proposal to update CSAPR involv ed two different IPM base cases. The EPA used IPM version 5.14 (IPM v.5.14) to provide p ower sector emissions data for air quality modeling. Specifically, IPM v.5.14 was used for the air quality modeling of the a 2017 baseline and 2017 illustrative control case. This air qualit y modeling and IPM v.5.14 are detailed in Chapter 3. 53 IPM v.5.14 was also used for the air quality model ing to quantify upwind state contributions, described in Section V of the preamb le for this proposal.

After these air quality modeling scenarios were und erway, the EPA released an updated IPM base case, version 5.15 (IPM v.5.15), and the f inal Clean Power Plan (CPP). The EPA used IPM v.5.15 for developing the proposed state NO X emissions budgets discussed in Chapter 4 of this RIA, and for analyzing the proposed rule’s cos t, benefits and impacts. The EPA relied on IPM v.5.15 for these analyses so that the base casebaseline for this proposal RIA would reflect all on-the-books policies, including the Clean Power PlanCPP , as well as the most current power sector modeling data. Using IPM v.5.15 for these an alyses provides EPA with the best information available to develop the proposed rule and to provide the public with the most current information possible.

5.2.1 EPA’s IPM Base Case v.5.15
When the EPA began using the current version of IPM (version 5), the EPA developed a comprehensive updated base case (v.5.13), as well a s a companion updated database of EGU units (the National Electricity Energy Data System, or NEEDS v.5.13) that is used in EPA’s modeling applications of IPM. The EPA base case is updated periodically to reflect the annual electricity demand forecasts from the EIA, among ot her data updates. EPA’s IPM modeling platform used to analyze this proposed rule (v.5.15 ) incorporates the version of the model used to analyze the potential impacts of the CPP, which was finalized in August, 2015. As discussed below in Section 5.2.2, the base case for the propo sal to update CSAPR also includes certain
revisions to the v.5.15 base case used in the CPP a nalysis.

53 As described in Chapter 3, the baseline used for t he air quality modeling used to generate the benefi t-per-ton estimates used in Chapter 6, differs from the basel ine used to estimate the benefits, costs and impact s of the proposed rule and more and less stringent alternati ves.5-6

The updates to the base case between v.5.14 and v.5 .15 include an update to the natural gas supply as well as routine calibrations with the EIA’s Annual Energy Outlook (AEO), such as updating the electric demand forecast consistent wi th the AEO 2015. 54 Additional updates, based on the most up-to-date information and/or public co mments received by the EPA, include unitlevel specifications (e.g., pollution control confi gurations), planned power plant construction and closures, and updated cost and performance for onsh ore wind and utility-scale solar technologies. The IPM v.5.15 modeling platform incorporates feder al and most state laws and regulations whose provisions were either in effect or enacted a nd clearly delineated by March 2015. This update also includes two non-air federal rules affe cting EGUs: Cooling Water Intakes (316(b))
Rule (U.S. EPA, 2014).

. and Combustion Residuals from Electric Utilities (CCR) (U.S. EPA, 2015b). . Additionally, all new capacity projected by the m odel is compliant with Clean Air Act 111(b) standards, including the final standards of performance for GHG emissions from new

sources 55 . For a detailed account of all updates made to the v.5.15 modeling platform, see the
Incremental Documentation for EPA Base Case v.5.15 Using IPM. 56

EPA also updated the National Electric Energy Data System (NEEDS) 57 . 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 unit in the contiguous U.S.

5.2.2 The IPM Base Case Used to Analyze the Proposa l to Update CSAPR
As discussed above, IPM Base Case v.5.15 was used a s the base case in EPA’s analysis
of the final Clean Power PlanCPP , which was finalized in August, 2015. We are using a different IPM base case for this analysis of the proposal to update CSAPR to reflect the fact that the Clean

54 The naming conventions used for EPA’s IPM modeling (such as v.5.15) reflect both the major overall ve rsion of IPM (v.5) and which iteration of the AEO (currently AEO 2015) that supplies the electricity demand for ecast used in IPM.

55 http://www2.epa.gov/cleanpowerplan/carbon-pollutio n-standards-new-modified-and-reconstructed-power-pl ants

56 Available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml/

57 http://www2.epa.gov/airmarkets/power-sector-modeli ng-platform-v5155-7

Power Plan is now a finalized and promulgated feder al regulation. The Clean Power PlanCPP is now a part of the business-as-usual scenario that w ould be expected under market and existing regulatory conditions in the absence of this propos al.

The base case used to analyze the proposal to upda te CSAPR is the IPM configuration EPA used to analyze one of the final CPP illustrati ve policy options. Specifically, the base case used in the current analysis is the IPM configurati on for the CPP’s “rate-based” illustrative plan approach. Using the CPP rate-based IPM configuratio n for the proposed CSAPR analysis assures that the IPM results are the incremental impacts on the power sector of the proposal to update CSAPR alone, and avoids “double counting” the impac ts of the CPP as well as all other federal and state regulations included the v.5.15 base case . Extensive information on the IPM configuration and detailed results of the CPP rate- based analysis is available in the Clean Power Plan Final Rule – Regulatory Impact Analysis 58 for the CPP as well as at EPA’s Power Sector Modeling website. 59

The 2017 baseline EGU emissions include impacts fro m the Final Mercury and Air Toxics
Standards MATS announced on December 21, 2011 60 and CSAPR issued on July 6, 2011. The EPA notes that because the modeling for the proposa l was performed prior to the D.C. Circuit’s issuance of EME Homer City II, 61 that modeling assumed in its baseline for all stat es the
emission reductions associated with the CSAPR NO X ozone-season phase 2 emissions budgets.

5.3 Evaluating the Regulatory Control Alternatives
To estimate the costs, benefits, and economic and e nergy market impacts of the proposal to update CSAPR, the EPA conducted quantitative analys is of three regulatory control alternatives: the proposed EGU NO X ozone-season emissions budgets that reflect EGU NO X control strategies

58 Available at: http://www2.epa.gov/cleanpowerplan/clean-power-plan -final-rule-regulatory-impact-analysis

59 Available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml/

60 In Michigan v. EPA, the Supreme Court reversed on narrow grounds a portion of the D.C. Circuit decisi on upholding MATS, finding that EPA erred by not consi dering cost when determining that regulation of EGU s was "appropriate" pursuant to CAA section 112(n)(1). 13 5 S.Ct. 192 (2015). The case was remanded to the D. C. Circuit for further proceedings, and because MATS was remanded but not vacated, MATS currently remain in place.

61 EME Homer City Generation, L.P. , v. EPA , No. 795 F.3d 118, 129-30, 138 ( EME Homer City II )EME Homer City Generation, L.P., v. EPA, No. 11-1302, slip op . at 19, 36 (July 28, 2015)5-8

represented by a uniform NO X cost of $1,300 per ton (2011$) ; and more and less stringent
alternative EGU NO X ozone-season emissions budgets that reflect EGU NO X control strategies represented by uniform NO X costs of $3,400 per ton and $500 per ton (2011$) , respectively. Details about these regulatory control alternatives , including state-specific EGU NO X ozoneseason emissions budgets for each alternative, are provided in Chapter 4 of this RIA.

Before undertaking power sector analysis to evaluat e compliance with the regulatory control alternatives, the EPA first considered avai lable EGU NO X mitigation strategies that could be implemented for the first compliance period (i.e ., the 2017 ozone season) assuming that this
rule is finalized in the summer of 2016. The EPA co nsidered all widely used EGU NO X control strategies: fully operating existing sS elective cC atalytic rR eduction (SCR) and sS elective nN oncC atalytic rR eduction (SNCR) – including optimizing NO X removal by existing, operational SCRs and SNCRs as well as turning on and optimizing existing idled SCRs and SNCRs; installation of (or upgrading to) state-of-the-art NO X combustion controls; shifting generation to
units with lower NO X emission rates within the same state; and installi ng new SCRs and SNCRs. EPA determined that the power sector could implemen t all of these NO X mitigation strategies, except installation of new SCRs or SNCRs, for the 2 017 ozone-season, assuming this proposed
rule is finalized in the summer of 2016. As to tT he installation of new SCRs or SNCRs (, the amount of time from contract award through commissi oning for retrofit ) with new SCR or
SNCR exceeds 18 and 12 months, respectively. It would th erefore not be feasible to retrofit new
SCR or SNCR to achieve EGU NO X reductions in the 2017 ozone season. For more deta ils on these assessments, including the assessment of EGU NO X mitigation costs and feasibility, please
refer to the EGU NO X Mitigation Strategies TSD, in the docket for this rule.

The EPA notes that, due to limitations on model siz e, IPM v.5.15 does not have the capacity to determine, within the model, whether ornot to operate existing EGU postcombustion NO X controls (i.e., SCR or SNCR) that are idle in the base case. 62 In order to evaluate compliance with the regulatory control alt ernatives (including the proposal and more and less stringent alternatives), the EPA determine d, outside the model, whether or not it would

62 The EPA notes that EGUs with idled SCR or SNCR in the base represent a small percentage (less than 10 percent) of the EGU fleet that is equipped with NO X post-combustion controls.5-9

be reasonably expected for these controls to operat e in order to comply with each of the evaluated regulatory control alternatives. After im posing the requirement to operate these control systems, IPM then estimated the associated NO X reductions and costs.

The EGU NO X mitigation strategies that are assumed to operate or be available to reduce
NO X in order to comply with each of the regulatory con trol alternatives are shown in Table 5-1;
more information about the estimated costs of these controls can be found in the EGU NO X Mitigation Strategies TSD.5-10

Table 5-1

. NO X Mitigation Strategies Implemented for Compliance w ith the Regulatory

Control Alternatives Regulatory Control Alternative NO X Controls Implemented

Less Stringent Alternative (1) Fully operating existing SCRs to achieve 0.075 lb/M MBtu NO X emission rate (outside IPM) (2) Shift generation to minimize costs (within IPM)
Proposal (All controls above) (3) Turn on idled SCRs (within IPM) (4) Install or upgrade combustion controls (outside IPM ) More Stringent Alternative (All controls above) (5) Turn on idled SNCRs (outside IPM)

5.3.1 Emission Reduction Assessment
As discussed in Chapter 3 the EPA determined that N O X emissions in 23 eastern states affect the ability of downwind states to attain and maintain the 2008 ozone NAAQS. For these 23 eastern states, the EPA is proposing to issue Fe deral Implementation Plans (FIPs) that generally update the existing CSAPR NO X ozone-season emission budgets for EGUs and implement these budgets via the CSAPR NO X ozone-season allowance trading program.

The EPA analyzed ozone-season NO X emission reductions from implementing each of the three regulatory control alternatives. Specifically , the EPA conducted IPM modeling of each of the regulatory control alternatives to evaluate the corresponding power sector emissions reductions from complying with the EGU NO X ozone-season emissions budgets that are provided in Chapter 4 of this RIA.

The NO X emissions reductions are presented for two time pe riods – 2017 (the principal year of interest for the proposal to update CSAPR) and 2020. However, this version of IPM was not structured to simulate 2017 directly. To evalua te the 2017 ozone season EGU NO X reductions from compliance with the regulatory cont rol alternatives, EPA developed estimates of emissions for 2017 by adjusting IPM’s direct estima tes for 2018 to account for a number of known differences. For example, these adjustments a ccount for emissions from EGUs that are expected to operate in 2017, but that have announce d plans to retire by 2018 (and whose futureyear emissions are thus not otherwise represented i n IPM’s direct 2018 estimates). The EPA has only made such adjustments for ozone season NO X emissions given the air quality objective of5-11

the proposal to address transported ozone pollution by the 2017 ozone season. 63 For co-pollutant EGU emissions (i.e., annual NO X , SO 2 , and CO 2 ), the EPA assumes that the 2018 IPM results are representative of 2017 with no further adjustme nt. The EPA believes that this is reasonable given that the overall magnitude of the adjustments to ozone season NO X emissions for 2017 is relatively small and that the adjustments generally affect both the base case and the regulatory
control scenarios and therefore have an even smalle r influence on the emissions difference between the base case and the regulatory control sc enarios.

Table 5-2 presents the reduction in EGU NO X emissions resulting from compliance with the regulatory control alternatives (i.e., emission s budgets) in the 23-state region, as well as the impact on states not in the region. The emission re ductions follow an expected pattern: the less stringent alternative produces substantially smalle r emission reductions than EPA’s proposed emissions budgets, and the more stringent alternati ve results in modestly more NO X reductions.

Table 5-2

. EGU Ozone Season NO X Emission Reductions (tons) for the Proposal and Mo re

and Less Stringent Alternatives

Ozone Season NO X Proposal More Stringent Alternative Less Stringent Alternative

2017 23-State Region 85,000 88,000 24,000

Non-Region -400 -500 -300 Total 85,000 87,000 24,000

2020 23-State Region 83,000 84,000 24,000

Non-Region -300 -460 -260 Total 83,000 84,000 24,000

In addition to the ozone season NO X reductions, there will also be reductions of other air emissions emitted by EGUs burning fossil fuels (i.e ., co-pollutants). These other emissions include the annual total changes in emissions of NO x, SO 2 and CO 2 . The co-pollutant emission reductions are presented in Table 5-3.

63 The 2018 emissions output from IPM were adjusted t o reflect 2017 emissions levels as described in http://www.epa.gov/airmarkets/documents/ipm/Adjuste d2017.pdf5-12

Table 5-3

. EGU Annual Emission Reductions (tons) for SO 2 and CO 2 for the Proposal and

More and Less Stringent Alternatives

Annual NOx Proposal More Stringent Alternative Less Stringent Alternative

2017 23-State Region 91,000 93,000 24,000

Non-Region -560 -700 -500 Total 90,000 93,000 24,000

2020 23-State Region 92,000 94,000 24,000

Non-Region -1,000 -1,700 -700 Total 91,000 92,000 24,000 Annual SO 2

2017 23-State Region 2,000 2,160 1,800

Non-Region -820 -920 -710 Total 1,200 1,200 1,100

2020 23-State Region 1,700 1,600 1,300

Non-Region -1,100 -1,500 -910 Total 610 100 360 Annual CO 2

2017 23-State Region 1,300,000 1,600,000 1,300,000

Non-Region -670,000 -920,000 -520,000 Total 660,000 710,000 770,000

2020 23-State Region 1,700,000 2,200,000 1,400,000

Non-Region -1,400,000 -2,400,000 -1,100,000 Total 270,000 -250,000 360,000,000

5.3.2 Compliance Cost Assessment
This section describes EPA’s approach to quantify t he costs for compliance with the regulatory control alternatives. IPM directly estim ated the costs for three of the NO X mitigation strategies: turning on idled SCRs, turning on idled SNCRs, and shifting generation to lower-NO X emitting EGUs. However, the costs of increasing the use and optimizing the performance of existing and operating SCRs and SNCRs, and for inst alling or upgrading NO X combustion controls, were estimated using an incremental analy sis step that EPA developed to quantify the
additional costs of these controls. These methods a nd analysis relies on data and methods used within IPM, including NO X control cost equations used in IPM. Therefore, thi s analysis is consistent with IPM and provides the best available quantification of the costs of these NO X mitigation strategies , consistent with IPM

. The following steps illustrate this analytical meth od and demonstrate the application of this method to the EPA’s cost estimate of compliance wit h the proposal:5-13 Analytical Method Application to Estimate the Costs of Compliance with the Proposal

• Identify the IPM modeled EGU NO X reductions that are attributable to NO X mitigation strategies with costs that are not included in the model. • Maximizing the use of existing SCRs accounts for approximately 33,000 tons of ozone season EGU NO X reductions.

• Installing or upgrading NO X combustion controls accounts for approximately 7,800 tons of ozone season EGU NO X reductions.

• Estimate the costs associated with these EGU NO X mitigation strategies. • Maximizing the use of existing SCRs costs approximately $500 per ton.

• Installing or upgrading NO X combustion controls costs approximately $1,200 per ton.

• Calculate the cost of each EGU NO X mitigation strategies as applied to reduce emissions within the model. • The cost of reductions from maximizing the use of existing SCRs is approximately $17,000,000 annually for 2017.

• The cost of reduction from installing or upgrading NO X combustion controls is approximately $9,400,000 annually for 2017.

The total costs of compliance with the regulatory c ontrol alternatives are estimated as the
sum of the costs that are modeled within IPM and th e costs that are calculated outside the model.

The results of EPA’s IPM analysis show that, with r espect to compliance with the proposed EGU NO X emissions budgets, turning on idle existing SCRs p rovides the largest
amount of ozone season NO X emission reductions (51 percent), and maximizing t he use of existing SCRs produces an additional 39 percent of the total ozone season NO X reductions. Combustion controls (9 percent) and generation shif ting (1 percent) make up the remainder of
the ozone season NO X reductions. In the more stringent alternative, com pliance by turning on idle existing SNCRs makes up 3 percent of the total reductions, while the shares attributed to the other four mitigation measures are similar to the s hares for compliance with the proposed EGU
NO X emissions budgets.

The estimates of the changes in the cost of supplyi ng electricity for the regulatory control alternatives are presented in Table 5-4. The costs associated with compliance with monitoring, recordkeeping, and reports requirements are not inc luded within the estimates in this table and can be found in Chapter 7.5-14

Table 5-4

. Cost Estimates (millions of 2011$) for Proposal an d More and Less Stringent

Alternatives

Proposal More Stringent Alternative Less Stringent Alternative Annualized* $92.9 $95.7 $4.7 Annual 2017 $71.6 $63.4 -$47.6 Annual 2020 $48.6 $20.3 -$27.6 *Levelized annual ized costs over the period 2016 through 2040, discounte d using the 4.77 discount rate used in IPM’s objective function of minimizing the net present value ( NPV ) of the stream of total costs of electricity generation

.

There are several notable aspects of the results pr esented on Table 5-4. The most notable
result in Table 5-4 is that the estimated annual co mpliance costs for the less stringent alternative are negative (i.e., a cost reduction) in 2017 and 2 020, although this regulatory control alternative
reduces NO X emissions by over 25,000 tons as shown in Table 5- 3. The estimate of the cost of controls determined outside the model are positive. Therefore, the finding of net negative annual compliance costs derives from the portion of the an nual compliance cost estimate that comes from IPM. While seemingly counter-intuitive, such a n estimated cost reduction is quite possible given the dynamic, perfect foresight linear program ing structure of IPM. IPM’s objective
function is to minimize the discounted net present value ( NPV ) of a stream of annual total cost of generation over a multi-decadal time period. For ex ample, IPM’s perfect foresight structure makes it possible that the least cost way of comply ing may be to delay a new investment otherwise expected to occur in the base case. Such a delay could result in a lowering of annual cost in an early time period and increases in later time periods.

A better understanding of the cause of the negative costs for the less stringent alternative can come from considering the entire time path of c osts, rather than focusing on the 2017 and 2020 estimates in isolation. Because IPM only repor ts estimates for certain years, it is necessary to estimate the cost values for the years between t he years directly simulated. A simple but straightforward method is to use a linear interpola tion between the annual cost estimates for directly simulated years to estimate a potential an nual compliance cost time series across all years in a given period. Figure 5-1 shows the annua l cost estimates, which are the sum of the annual compliance cost estimated by IPM and the ann ual compliance costs estimated outside the5-15

model, for each directly simulated year from 2016 t hrough 2040. It also shows the linearly interpolated annual cost between those years for al l three of the regulatory control alternatives. 64

Figure 5-1

. Time Ss eries of Aa nnual Cc osts for the Proposal and More and Less Stringent

Alternatives

Figure 5-1 shows that while two of the IPM year cos t estimates (2017 and 2020) in the less stringent alternative are negative, the rest of the annual costs are positive, resulting in the majority of the years between 2016 and 2040 having positive costs. All years have positive costs under compliance with the proposed EGU NO X ozone-season emissions budgets and the more stringent alternative.

Using the estimated annual cost time series, it is possible to use a standard NPV calculation to estimate an annualized cost (i.e., l evelized annual cost) of the annual cost stream associated with compliance with each regulatory con trol alternative. 65 For this analysis we first

64 EPA estimated year-specific outside-the-model cost adjustments only for 2017 and 2020. For this analy sis of the time series of costs, the outside-the-model complia nce cost adjustments estimated for 2020 are added t o all years between 2021 and 2040. While this approach is a rel atively rougher estimate of the likely cost of the exogenouslyimposed NOx controls for those years, it is consist ent with an assumption that once the CSAPR update i s implemented it will continue to have similar costs and emission reductions throughout the rest of the model’s forecast horizon.

65 The XNPV() function in Microsoft Excel 2013 was us ed to calculate the NPV of the variable stream of c osts.

-$50 $0 $50 $100 $150 $200

2016 2020 2024 2028 2032 2036 2040Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent5-16

calculated the NPV of the stream of costs from 2016 through 2040. We calculate the NPV in 2016 of each cost stream using a 4.77 percent disco unt rate. 66 While EPA typically uses a 3 and a 7 percent discount rate to discount future year social benefits and social costs in regulatory impact analyses (USEPA
, 2010) . , iI n this cost annualization approach we use a 4.77 percent rate to be consistent with the optimization approac h used within IPM. IPM’s cost minimization objective function estimates the lowest possible NP V, and uses a 4.77 percent discount rate to calculate the NPV. IPM v.5.15 uses a 4.77 percent d iscount rate as an estimate of the real opportunity cost of capital in the power sector. 67

After calculating the NPV of the cost streams, the same 4.77 percent discount rate and 2016-2040 time period is used to calculate the leve lized annualized annual (i.e., annualized) cost estimates shown in Table 5-3. 68

Figure 5-2 depicts the annualized cost estimates on top of the time stream of annual costs previously presented on Figure 5-1.

66 While IPM also reports the costs for 2050, we do n ot include the 2050 estimates in this analysis. In this situation, extending the present value calculations to include 2050 does not materially change the results.

67 The IPM Base Case documentation (Section 8.2.1 Int roduction to Discount Rate Calculations) states “Th e real discount rate for expenditures (e.g., capital, fuel , variable operations and maintenance, and fixed op erations and maintenance costs) in the EPA Base Case v.5.13 is 4 .77%. This serves as the default discount rate for all expenditures.” Section 8.2 in that documentation de scribes the rationale for using a real opportunity cost of capital specific to the power sector, and describes the dat a used to derive the 4.77 percent rate.

68 The PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the es timated NPV.5-17

Figure 5-2

. Time sS eries of aA nnual Cc osts and Aa nnualized Cc osts for the Proposal

and More and Less Stringent Alternatives

As can be seen in Table 5-3 and Figure 5-2, the ann ualized costs are positive for compliance with each of the three regulatory contro l alternatives. Furthermore, the annualized costs have the expected relationship; the annualize d costs are lowest for the less stringent alternatives, and highest for the more stringent al ternative.

5.3.3 Impacts on Fuel Use, Prices and Generation Mi x The proposal to update CSAPR is estimated to have a variety of different impacts to the power sector. While all the impacts are relatively small in percentage terms, considering the potential impacts in addition to the cost and emiss ions estimates presented previously is an important component of assessing the overall impact of the proposal. In this section of the RIA we discuss the estimated changes in fuel use, price s of fuel and retail electricity, generation by fuel type, and capacity by fuel type.

Table 5-5 presents the percentage changes in nation al coal and natural gas usage by EGUs in 2017. Percent changes in fuel prices are also sh own. As will be seen for the other measures of impacts on the power sector, the fuel use estimates in Table 5-5 estimates reflect a modest shift to natural gas from coal.

-$50 $0 $50 $100 $150 $200

2016 2020 2024 2028 2032 2036 2040Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent

Annualized Annualized Annualized5-18

Table 5-5

. Percent Changes* in Coal and Natural Gas Usage by EGUs for the Proposal and

More and Less Stringent Alternatives for 2017

Proposal More Stringent Alternative Less Stringent Alternative Change in Fuel Quantity (TBtu) Coal -0.077% -0.072% -0.091% Natural Gas 0.105% 0.090% 0.134% Change in Fuel Prices Coal** -0.211% -0.279% -0.321% Natural Gas*** 0.002% -0.004% 0.004% *Fuel use changes measured in TBtu **Coal price changes are minemouth prices per MMBtu ***Natural gas price changes are delivered gas pric es per MMBtu

Table 5-6 presents the projected percentage changes in the amount of electricity generation nationally in 2017 by 3 major types of electricity generation: coal, gas, and conventional hydro. The amount generated by nuclear energy is unchanged in all three alternatives.

Table 5-6

. Percent Changes* in Generation by Major Fuel Type for the Proposal and More

and Less Stringent Alternatives for 2017

Proposal More Stringent Alternative Less Stringent Alternative Coal -0.08 74 % -0.079% -0.103% Natural Gas 0. 094108 % 0.101% 0.127% Hydro 0.062% 0.064% 0.068% Total Generation** 0.009% 0.008% 0.009% Detail by Region for Proposal In-Region Non-Region Total Coal -0.185% 0.174% -0.087% Natural Gas 0.167% -0.009% 0.094% Total Generation -0.009% 0.040% 0.009% *Changes in GWh generated in 2017. **Changes in total generation is from all types of generation nationally, including fossil, non-fossil and renewable EGUs dispatching electricity to the grid. Table 5-7 presents the projected percentage changes in the amount of generating capacity in 2017 for coal, natural gas and total capacity.

Table 5-7

. Changes* in Generating Capacity by Major Fuel Type for the Proposal and

More and Less Stringent Alternatives in 2017

Proposal More Stringent Alternative Less Stringent Alternative Percent Change5-19 Proposal More Stringent Alternative Less Stringent Alternative Coal -0.144% -0.136% -0.162% Natural Gas -0.006% -0.006% -0.006% Total Capacity -0.006% -0.004% -0.015% Capacity Change (MW) Coal -282 -318 -266 Natural Gas -24 -95 -22 Total Capacity -60 -146 -36 *Changes in generating capacity available in 2017. The EPA estimated the change in the retail price of electricity (2011$) using the Retail Price Model (RPM). 69 The RPM was developed by ICF International for the EPA, and uses the IPM estimates of changes in the cost of generating electricity to estimate the changes in average retail prices. The prices are average prices over c onsumer classes (i.e.

. , consumer, commercial and industrial) and regions, weighted by the amount of electricity used by each class and in each region. The RPM combines the IPM annual cost estimates in each of the 64 IPM regions with EIA electricity market data for each of the 22 elec tricity supply regions in the electricity market module of the National Energy Modeling System (NEMS ). 70

Table 5-8 presents the projected percentage changes in the retail price of electricity for the proposal and more and less stringent alternativ es. Estimates are presented for both 2017 and 2020. While all of the estimated changes in prices are quite small, Table 5-8 includes both positive and negative retail electricity price chan ges. This is consistent with the pattern of positive and negative annual cost estimates discuss ed above in Section 5.3.3, and also reflects some influence from aggregating results for the 22 NEMS regions into separate estimates for the
23 -sS tate region affected by the proposal to update CSAP R, and the rest of the country.

69 See documentation available at: http://www.epa.gov /powersectormodeling/

70 See documentation available at: http://www.eia.gov/forecasts/aeo/nems/documentation /electricity/pdf/m068(2014).pdf5-20

Table 5-8

. Percent Changes* in Retail Electricity Price for the Proposal and More and Less

Stringent Alternatives for 2017 & 2020

Base Case Percent Change in Retail Electricity Price Price (cents/kWh) Proposal More Stringent Alternative Less Stringent Alternative 2017 National Price 9.93 -0.069% -0.099% -0.025% 23 State Region 9.69 -0.161% -0.371% -0.047% Non-Region 10.75 -0.154% -0.371% -0.027% 2020 National Price 10.33 0.031% 0.034% 0.009% 23 State Region 10.26 0.027% 0.028% 0.022% Non-Region 11.11 0.018% 0.022% -0.001% *Changes in GW capacity available in 2017.

5.3. 4 Effect of Emission Reductions on Downwind Receptor s As described in Sections V and VI of the preamble, and in the Ozone Transport Policy Analysis Proposed Rule TSD, and summarized here, EPA evaluated the effect of the proposed rule on nonattainment and maintenance receptors wit h respect to interstate transport for the 2008 ozone NAAQS. The 2008 ozone standard is 0.075 parts per million (ppm), annual fourth-highest daily maximum 8 hour concentration, averaged over 3 years. As described in Section V of the preamble, the nonattainment and maintenance recepto rs with respect to interstate transport for the 2008 ozone NAAQS in 2017 were identified using the Comprehensive Air Quality Model with Extensions (CAMx) modeling system. From this C AMx analysis there are 37 receptors in 10 states evaluated for this proposed update to CSA PR. 71 All 37 have a maximum design value of 0.076 ppm or higher in 2017. Fourteen of these r eceptors have an average design value of 0.076 ppm or higher in 2017. 72 Furthermore, as described in the Ozone Transport P olicy

71 The CAMx air quality modeling scenario used to ide ntify these receptors was also used as the baseline in Chapter 3 to estimate benefit-per-ton values for NO X reductions.

72 As described in the preamble, section V.C, the app roach for projecting future ozone design values inv olved the projection of an average of up to 3 design value pe riods, which include the years 2009-2013 (design va lues for 2009-2011, 2010-2012, and 2011-2013). The average o f the 3 design values creates a “5-year weighted av erage” value. The 5-year weighted average values and the i ndividual design values for 2009-2011, 2010-2012, a nd 2011- 2013 were projected to 2017. The highest of the th ree individual values is the “maximum” design value . We are proposing to identify nonattainment receptors in th is rulemaking as those sites that are violating the NAAQS based on current measured air quality and also have proje cted average design values of 76 parts per billion (ppb) or greater. Maintenance-only receptors, i.e., those th at are at risk of not maintaining the NAAQS, theref ore include both (1) those sites with projected average design values above the NAAQS that are currently measuring clean data and (2) those sites with projected average design v alues below the level of the NAAQS, but with projec ted maximum design values of 76 ppb or greater. In addi tion to the maintenance-only receptors, the 2017 oz one5-21

Analysis Proposed Rule TSD, the forecast ambient co ncentration at these receptors was adjusted to reflect a new IPM base case (IPM v.5.15 with CPP ), different from the one that was represented in the emissions inventory for the CAMx modeling of the ambient concentrations (IPM v.5.14). The results described in this subsect ion, and in the rest of this RIA except where otherwise noted, are from a baseline that includes IPM version 5.15 with CPP. After this adjustment to reflect the updated IPM base case, 33 of the 37 receptors have a maximum design values of 0.076 ppm or higher in 2017, while 12 hav e average design values of 0.076 ppm or higher in 2017.

To evaluate the effect of the proposed rule on ambi ent ozone concentrations at the receptors in 2017 relative to the baseline, the EPA first evaluated the response of the electricity sector to the proposed rule using IPM. The IPM anal ysis used to estimate the compliance cost, benefits, and impacts of the proposed rule is the s ame analysis used to identify the effect of the proposed rule on the level and spatial pattern of o zone seasonal NO X emissions from EGUs.

The emissions outputs from IPM are then used to est imate the change in ambient ozone concentrations at the 37 receptors. The ozone Air Q uality Assessment Tool (AQAT) is used to estimate the impact of the upwind states’ EGU NO X reductions on downwind ozone pollution concentrations. AQAT is used to forecast both the a verage and maximum design values at all monitors, including the 37 receptors. The ozone AQA T was developed specifically for use in the proposed rule’s significant contribution analysis A QAT uses CAMx outputs to calibrate its predicted change in ozone concentrations to changes in NOx emissions. See the Ozone Transport Policy Analysis Proposed Rule TSD for the air quality estimates and for details on the construction of ozone AQAT.

In summary, the proposed rule is expected to reduce the average design values at the 37 receptors by 0.61 parts per billion (ppb) on averag e, and to reduce the maximum design values at these receptors by 0.63 ppb on average. At the 12 r eceptors with an average design values of 0.076 ppm or greater in the baseline, the proposed rule reduces the difference between the baseline average ambient concentration and 0.076 pp m by 14% on average (an average reduction

nonattainment receptors are also maintenance recept ors because the maximum design values for each of t hese sites is always greater than or equal to the average desi gn value.5-22

of 0.24 ppb). The proposed rule reduces the maximum design values below 0.076 ppm for 7 of the 33 receptors. Of the remaining 26 receptors wit h maximum design values of 0.076 ppm or greater, the proposed rule reduces the difference b etween the baseline maximum design value and 0.076 ppm by 21% on average (an average of redu ction of 0.37 ppb). The proposed rule is therefore expected to provide notable improvements in ambient air quality at these monitors by 2017. Results for each of the 37 receptors, as well as for the more and less stringent alternatives,
are described in the Ozone Transport Policy Analysis Proposed Rule TSD.

5.4 Employment Impacts 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, 73 we typically conduct employment analyses for economically significant ru les. This section discusses and projects potential employment impacts related to today’s pro posal , but the employment impacts have not been quantified

. 74

Section 5.5.1 describes the theoretical framework u sed to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 5.5.2 pre sents an overview of the peer-reviewed literature relevant to evaluating the effect of env ironmental regulation on employment. Section 5.5.3 provides background regarding recent employme nt trends in the electricity generation, coal and natural gas extraction sectors. Section 5.5.4 d iscusses the potential direct employment impacts in these sectors. The direct employment impacts are not quantified in this RIA.

73 Labor expenses do, however, contribute toward tota l costs in the EPA’s standard benefit-cost analyses

.

74 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).5-23

5.4.1 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 jobs, 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. 75 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. 76,77 Berman and Bui (2001) adapt this model to analyze how environmental regul ations affect labor demand. 78 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. 79 Regulation affects the profit-maximizing quantity of output by changing the marginal cost of production. If a regulation ca uses marginal production cost to increase, it will place upward pressure on output prices, leadin g to a decrease in demand, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a

75 See Layard and Walters (1978), a standard microeco nomic theory textbook, for a discussion, in Chapter 9.

76 See Hamermesh (1993), Ch. 2, for a derivation of t he firm’s labor demand function from cost-minimizat ion.

77 In this framework, labor demand is a function of q uantity of output and prices (of both outputs and i nputs).

78 Morgenstern, Pizer, and Shih (2002) develop a simi lar model.

79 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.5-24

decrease in production causes a decrease in labor d emand. As noted by Berman and Bui, although many assume that regulations must increase marginal cost, in some cases they may decrease it. A regulation could induce a firm to up grade to less polluting and more efficient equipment that lowers the marginal cost of producti on. In such a case, output could increase after firms comply with the regulation. An unregulated pr ofit-maximizing firm may not have chosen to install such an efficiency-improving technology if the return on investment were too low, but once the technology is in place it lowers marginal production costs.

The substitution effect describes how, holding outp ut constant, regulation affects the laborintensity of production. Although increased environ mental 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. In addition, the empirical literature illustrates diff iculties with estimation of net employment impacts. The most commonly used empirical methods, for example, Greenstone (2002), likely overstate employment impacts because they rely on r elative comparisons between more regulated and less regulated counties, which can le ad to “double counting” of impacts when production and employment shift from more regulated towards less regulated areas. Thus these empirical methods cannot be used to estimate net em ployment effects. 80

The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry, but employme nt impacts at an individual plant do not necessarily represent impacts for the sector as a w hole. At the industry-level, labor demand is

80 See Greenstone (2002) p. 1212.5-25

more responsive if: (1) the price elasticity of dem and for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total producti on costs. 81 For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may change slightly. 82

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 environmental protection sector. This proposal may increase demand for the nitrogeno us reagent (typically ammonia or urea) used in SCRs and SNCRs to reduce NOx, which may increase revenue and employment in the firms providing these chemicals.

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. 83 Instead, labor in affected sectors would primarily be reallocated from one pro ductive use to another (e.g., from producing electricity to manufacturing, installing, or operat ing and maintaining pollution-abatement equipment), and net national employment effects fro m environmental regulation would be small and transitory (e.g., as workers move from one job to another). 84 Some workers may retrain or relocate in anticipation of new requirements or req uire time to search for new jobs, while shortages in some sectors or regions could bid up w ages to attract workers. These adjustment costs can lead to local labor disruptions.

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

81 See Ehrenberg & Smith, p. 108.

82 This discussion draws from Berman and Bui (2001), pp. 293.

83 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 un employment rate at full employment is not zero.

84 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.5-26

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). Environmental regulation may also affect labor supp ly and productivity. In particular, reducing pollution and other environmental risks may improve labor productivity or employees’ ability to
work. 85 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
that uses detailed labor and environmental data to assess these impacts.

To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net emp loyment effect incorporates expected employment changes (both positive and negative) in the regulated sector and other related sectors including the environmental protection sect or. Labor demand impacts for regulated firms, and also for the regulated industry, can be decompo sed into output and substitution effects which may be either negative or positive. Estimation of n et 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 possibl e. In the next section, we discuss the empirical
literature.

5.4.1.2 Current State of Knowledge Based on the Pee r-Reviewed Literature
The peer-reviewed empirical literature specificall y estimating employment effects of environmental regulations is limited but growing. W e summarize it briefly in this section.

5.4.1.3 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

85 E.g. Graff Zivin and Neidell (2012).5-27

(Greenstone 2002, Walker 2011, 2013). However, sinc e these latter studies compare more regulated to less regulated counties, this methodol ogical approach likely overstates employment impacts to the extent that regulation causes plants to locate in one area of the country rather than 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.

5.4.1.4 Economy-Wide
Given the difficulty noted above with estimating na tional impacts of regulations, EPA has not generally estimated economy-wide employment imp acts of its regulations in its benefit-cost analyses. However, in its continuing effort to adva nce the evaluation of costs, benefits, and economic impacts associated with environmental regu lation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to ad vise EPA on the technical merits and challenges of using economy-wide economic models to evaluate the impacts of its regulations, including the impact on net national employment. 86 Once EPA receives guidance from this panel, it will carefully consider this input and th en decide if and how to proceed on economywide modeling of employment impacts of its regulati ons.
5.4.1.5 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. 87 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

86 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67C F77B54734285257BB0004F87ED?OpenDocument

87 For a recent review see Graff-Zivin and Neidell (2 013).5-28

billion (ppb) decreases in ozone concentrations inc reases worker productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 88

5.4.1.6 Conclusion
This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment estimates that would approp riately capture the way in which costs, compliance spending, and environmental improvements propagate through the macro-economy.

5.4.2 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.

5.4.2.1 Electric Power Generation
In 2014, the electric power generation, transmissio n and distribution sector (NAICS 2211) employed about 390,000 workers (U.S. BLS, 2015) in the U.S. Installation, maintenance, and repair occupations (U.S. BLS, 2014) accounted for the largest share of workers (25 perc ent). These categories include inspection, testing, repai ring and maintaining of electrical equipment and/or installation and repair of cables used in el ectrical power and distribution systems. Other major occupation categories include office and admi nistrative support (18 percent), production occupations (16 percent), architecture and engineer ing (10 percent), business and financial operations (7 percent) and management (7 percent). As shown in Figure 5-3, employment in the
eE lectric pP ower iI ndustry averaged about 420,000 workers 2000 to 2005 , declining to an average of about 400,000 workers for the rest of th e decade, and then declining to about 390,000
workers in 2014.

88 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.5-29

Figure 5-3

. Electric Power Industry Employment

5.4.2.2 Fossil Fuel Extraction
Coal Mining. The coal mining sector (NAICS 2121) is primarily en gaged in coal mining and coal mine site development, excluding metal ore min ing and nonmetallic mineral mining and quarrying. In 2014, BLS reported about 74,000 coal mining employees (Figure 5-4). During the 2000 to 2014 period, coal mining employment peaked in 2011 at about 87,000 employees.

0 50 100 150 200 250 300 350 400 450 500 

### Power Generation and Supply Employment

(NAICS = 2211, Annual Average, 1000s of Employees)5-30

Figure 5-4

. Coal Production Employment Source: BLS (2014a)

Oil and Gas Extraction. In 2014, there were close to 200,000 employees in t he oil and gas extraction sector (NAICS 211). This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sul fur recovery from natural gas, and recovery of hydrocarbon liquids. Activities include the develop ment of gas and oil fields, exploration activities for crude petroleum and natural gas, dri lling, completing, and equipping wells, and other production activities. In contrast with coal, Figure 5-5 shows there has been a sharp increase in employment in this sector over the past decade.5-31

Figure 5-5 Oil and Gas Extraction Employment Source: BLS (2014b)

5.4.3 Power and Fuels Sector Direct Employment Impa cts As described above, affected EGUs may respond to th e CSAPR update by upgrading or improving performance of existing combustion contro ls, or by upgrading, improving, or utilizing post-combustion NO X systems already in place. In addition, some net ge neration may shift from higher NO X -emitting EGUs to units with lower NO X emission rates. All of these changes will likely involve some amount of change in various typ es of amount of labor needed in different parts of the fuels and utility power sectors. There also may be other labor impacts in sectors that provide products and materials used in reducing NO X emissions at EGUs, such as catalysts used in SCR control systems. These direct labor impacts will likely include both increased demand for certain types of labor in some portions of the affe cted sectors, and reduced demand for labor in other portions of the affected sectors.

Installing and operating new equipment could change labor demand in the electricity generating sector itself, as well as associated equ ipment and services sectors. Specifically, the direct employment effects in the power sector that could occur because of actions taken by the
2017 ozone season include:

0 50 100 150 200 250 

### Oil and Gas Extraction Employment

(NAICS = 211, Annual Average, 1000s of Employees)5-32

• Optimizing NO X removal from existing and operational SCR and SNCR systems;

• Turning on and optimizing idled SCR and SNCR system s;

• Installing , optimizing or upgrading combustion-side improvements resultin g in reduced NO X emissions;

• Shifting generation from units with higher NO X emission rates to units with lower emission rates.
In addition, there could be directly induced employ ment impacts (both positive and negative) in the labor demand in the fossil fuels i ndustry supplying fuels to the power sector. Once implemented, both the potential increases in o perating efficiency and NO X reductions, as well as and shifting generation to lower NO X emitting assets, could impact the utility power sector’s demand for fossil fuels, and hence the dem and for labor needed in the coal mining and gas extraction sectors.

The direct net employment impacts of this proposed rule, in terms of the power sector and fuels sector, however, are anticipated to be relatively small. Th is is consistent with the relatively small estimated changes in the power sector’s overa ll cost of generation, as well as relatively small changes in generation, fuel use, capacity, an d the percent of total generation produced by each type of fuel.

For example, for the proposal in 2017, the estimate d impacts relevant to changes in labor
demand include:

• The overall total national cost of generation in 20 17 increases by 0.046 percent (the levelized annualized total cost of generation incre ases by 0.01 percent);

• Total net generation increases by 0.009 percent (co al generation decreases by .084 percent, and natural gas generation increases by 0.11 percen t);

• The power sector’s total tons of coal used for elec tricity generation decreases by 0.04 percent (or 0.09 percent increase in BTUs);

• Total natural gas increases by 0.11 percent.

While tT he results of the power sector modeling suggest that because of the very small changes in the power and fuels sector, the the direction and magnitude of the potential labor impacts are very small in all three regulatory alternative s analyzed. To illustrate this point, t,5-33

because of the very small changes in the power and fuels sector, t he direct labor impacts have
been are not quantified in this repor for the proposed regulation for 201 7 and 2020. The labor impacts for the more and less stringent alternative s have not been quantified.

Affected EGUs may respond the proposed requirement for EGUs in 23 eastern states to
reduce NO X emissions during the ozone season by improving and optimizing existing NO X emission control systems or to shift generation to generating sources with lower NO X emission rates. Meeting the new EGU ozone season NOx budget limits will involve changes in the amount of labor needed in different parts of the utility p ower sector. Installing and operating new equipment, optimizing combustion control operations to reduce NOx emissions, and shifting generation to other sources could affect labor dema nd in the electricity generating sector itself, as well as associated equipment and services sectors. Specifically, the direct employment effects of initiatives at existing fossil EGUs would include i ncreases in labor demand during the implementation phase for manufacturing, installing, and operating the NOx emission s controls at existing fossil units. Once implemented, increases in operating efficiency and shifting generation to existing generation resources will impact the ut ility power sector’s demand for fossil fuels and potentially plans for EGU retirement.

The generation-side employment analysis uses the co st projections from the engineeringbased Integrated Planning Model ( IPM ) to project labor demand impacts of the final emiss ion 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 improving the NOx control performance of combustion control systems, optimizi ng the operation of post-combustion NOx control systems, generation shifts, and changes in fuel use.

The following section presents the EPA’s quantitati ve projections of potential employment impacts in the electricity generation sector, as we ll as the impacts in the coal and natural gas fuel sectors. These projections are based in part on the IPM estimates of the impact of the proposed regulation previously discussed in this RIA.

5.4.3.1 Methods Used to Estimate Changes in Employm ent in Electricity Generation and Fuel Supply5-34

The analytical approach used in this analysis is a bottom-up engineering method combining the EPA’s cost analysis of the NOx emission limits with data on labor productivity, engineering estimates of the amount and types of la bor needed to manufacture, construct, and operate different types of NOx control systems, and prevailing wage rates for skilled and general labor categories. This approach is different from t he economy-wide types of economic analyses discussed in section 5.4.1. Lacking robust peer-rev iewed 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 approach projects labor changes measured as the change in ea ch analysis year in job-years employed in the utility power sector and directly related sectors ( e.g., equipment manufacturing and fuel supply). Some of the quantified employment impacts in this a nalysis are one-time impacts, such as changes associated with upgrading the combustion co ntrols. Other labor impacts will continue, such as changes associated with operating and maint aining generating units that will be retired, shifting generation to lower emitting generating un its, and changes in the demand for labor providing the fuels supplied to the affected fossil -fired EGUs.

The methods the EPA uses to estimate the labor impacts are based on the analytical methods used in many previous EPA regulatory analys es. The most relevant prior analysis was
the Regulatory Impact AnalysesRIAs for Clean Power PlanCPP (2015), and the Cross-State Air
Pollution RuleCSAPR (2011). The methods used in this analysis to estim ate the labor impacts (e.g., labor associated with changes in operating a nd maintaining generating units, as well as labor needed to mine coal and natural gas) are the same as we used in the CPP and CSAPR RIAs (with updated data where available).

The bottom-up engineering-based labor analysis in t he CPP RIA was primarily was concerned with the labor needs of improving CO 2 emission s from operating of existing EGUs nationwide, and changes in the expected retirements of existing coal-fired EGUs and avoiding the need for new gas-fired EGUs. A central feature of the labor analysis for this RIA, however, involves the labor needs of upgrading and optimizin g NOx control systems on existing EGUs in
the affected 23 - state region. In addition to the changes at EGUs wi thin the 23-state region, there are also estimated changes in the utilization of ex isting generating units in other states, as well as changes in the gas and coal supply sectors.5-35

The methods and data used to estimate the labor ass ociated with upgrading combustion control systems are assumed to be the same as those used to estimate heat rate improvements (HRI, another type of combustion control system upg rade) in the CPP RIA. There are two components of this assumption.

• The mix of labor categories needed to implement the combustion control upgrades in this NOx analysis (i.e., the share of the labor cost of the upgrades apportioned general construction, boilermaker, engineering and management labor) is the same as the HRI-related combustion control upgrades need ed in the CPP CO 2 analysis.

• The fully loaded labor cost of each labor category is the same for NOx control upgrades as for the HRI CO 2 upgrades

5.4.3.2 Estimates of the Changes in Employment in E lectricity Generation and Fuel Supply

The estimated labor impacts of the proposed revisio ns to the NOx budgets from EGUs in
the 23 - state region are presented in Table 5-9. Given the methods the EPA uses to estimate labor impacts, it is not possible to directly separate th e labor impacts that occur within the 23 - state region from the labor impacts in the states not in the region. However, all the labor changes associated with combustion control upgrades, and op timization of existing post-combustion NOx control systems, will occur within the 23 - state region. The fuel supply labor impacts, howev er, will occur both within the 23 - state region as well as in other states. This occur s for two reasons. First, coal and natural gas used at EGUs throughout the United States are both extracted within
the 23 - state region and in other states. Second, the shift s in fossil-fired generation will also
occur both with within the 23 - state region and in other states.5-36

Table 5-9

. Annual Net Employment Impacts for Power and Fuels Sectors of the Propos ed

Optional in 2017 & 2020

2017 2020 Upgrades and Optimization SCR 70 66 SNCR* 0 Combustion Control 67 66 Sub-Total 137 132 Plant Retirement Coal -160 -95 Fuel Use Change Coal -87 -42 Natural Gas 50 19 Subtotal -36 -22 Net Employment Impact -60 15 *SNCR optimization is only required in the more str ingent alternative. Job-year estimates are derived from IPM investment and upgrade estimates, as well as IPM fu el use estimates (tons coals or MMBtu gas). All job -year estimates are full-time equivalent (FTE) jobs.

5.5 Social Costs
As discussed in the EPA Guidelines for Preparing Ec onomic Analyses, social costs are the total economic burden of a regulatory action (USEPA , 2010). This burden is the sum of all opportunity costs incurred due to the regulatory ac tion, where an opportunity cost is the value lost to society of any goods and services that will not be produced and consumed as a result of reallocating some resources towards pollution mitig ation. Estimates of social costs may be compared to the social benefits expected as a resul t of a regulation to assess its net impact on society. The social costs of a regulatory action wi ll not necessarily be equivalent to the expenditures associated with compliance. Nonetheles s, here we use compliance costs as a proxy
for social costs.

The cost estimates for the three alternativeproposed and more or less stringen t NO X cost threshold scenariosalternatives presented in this chapter are the change in expend itures required by the electricity power sector for compliance under each alternative , with a focus on the proposed remedy scenario . The change in the expenditures required by the electricity power sector to maintain compliance reflect the changes i n electricity production costs due to changes in electricity demand resulting from application of the NO X control strategies described in Chapter 4 and changes in electricity production cos ts required to achieve the additional emission5-37

reductionsabove necessary to comply with the emissions budgets tha t are described earlier in this RIAin Chapter 4 . The changes in electricity production costs and changes in demand are estimated by IPM as described earlier in Chapter 5, and the change in expenditures required by
the electricity power sector to maintain compliance are also described ea rlier in Chapter 5.

5.6 Secondary Economic Impacts
The energy sector impacts presented earlier in Chap ter 5 of this RIA include potential changes in the prices for electricity, natural gas, and coal potentially resulting from the proposal. This chapter addresses the impact of these potentia l changes on other markets outside of the power sector and discusses some of the determinants of the magnitude of these impacts. We refer to these changes as secondary market impacts.

The analysis of costs for this proposal includes st rategies for affected states to reduce NO X emissions from EGUs to comply with the proposed EGU NO X ozone-season emissions budgets in 2017. Ultimately, given the flexibilities afford ed EGUs and states in complying with the proposed rule, the benefits, cost and economic impa cts reported in this RIA are illustrativemay differ given the compliance approaches affected EGU s adopt of actions that states may take . The implementation approaches adopted by the states, an d the abatement strategies adopted by affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from changes in the price of electricity, and the demand for inputs by the electricity sector, on other markets that use and produce these inputs.

To estimate the costs, benefits, and impacts of imp lementing the proposal, the EPA modeled a compliance compliance approach for the proposal and more and less stringe nt alternatives. Chapter 4 provides a description of t he proposal and the more and less stringent alternatives considered. This chapter provides a qu antitative assessment of the energy price impacts for these approaches and a qualitative asse ssment of the factors that will in part determine the timing and magnitude of effects in ot her markets.

5.6.1 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 aggregated5-38

representations of the whole economy in equilibrium in the baseline and potentially with regulation in place. As such, a CGE model may be ab le 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 F inal Ozone NAAQS (U.S. EPA
, 2008) and the 2010 Transport Rule proposal (U.S. EPA
, 2010a), the EPA used the Economic Model for Policy Analysis (EMPAX) CGE model to estimate the s econdary market effects based on the cost impacts projected by the Integrated Planning Model (IPM)IPM for the directly regulated

sector.

When considering the secondary market impacts of a regulation, the effects of the costs, the benefits of improved air quality, and their int eraction may be relevant. Therefore, in the Second Prospective Analysis under Section 812 of th e 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 ( CouncilCOUNCIL ) review of this study stated that inclusion of benefits in an econo my-wide model “represent[ed] a significant step forward in benefit-cost analysis” (Hammitt 201 0), the EPA recognizes that serious technical challenges remain when attempting to evaluate the b enefits 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 scheduled forwas held October 22 and 23,
2015. The EPA will use the recommendations and advi ce of this panel as an input into its process for improving benefit-cost and economic impact anal yses used to inform decision-making at the

agency.5-39

The advice from the Science Advisory Board ( SAB ) panel formed specifically to address the subject of economy-wide modeling was not availa ble in time for this final proposed action. Given the ongoing SAB panel on economy-wide modelin g, and the ongoing challenges of accurately representing costs, benefits, energy eff iciency improvements in economy-wide modeling, this chapter considers the energy impacts associated with the proposed alternatives analyzed and a qualitative assessment of the factor s that will, in part, determine the timing and magnitude of effects in other markets.

5.6.2 Summary of Secondary Market Impacts of Energy Price 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 compliance with the regulatory control alternatives for this rule. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector described in more detail earlier in Chapter 5. The Retail Price Model ( RPM ) uses estimated changes in wholesale prices to estimate changes in average ret ail prices. The prices are average prices over consumer classes and regions weighted by the amount used. Table 5- 910 shows these estimated price changes for the proposal in 2017 and, for ill ustrative purposes only, 2020. For other results generated by IPM and the RPM, please refer to earli er sections in Chapter 5.

There are many factors influencing the projected na tural gas prices. IPM (and its integrated gas resource and supply module) models natural gas natural gas supplies based on a multitude of factors. Since the model simulates perfect foresigh t, 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 drivers. The modeling frame work is simultaneously solving for all of5-40

these key market and policy parameters (both electr ic and natural gas), resulting in the impacts discussed in previous sections and shown in Table 5 -9.

Table 5- 109 . Estimated Percentage Changes in Average Energy Pr ices by Energy Type for

the Proposed Alternative*

Proposed Rule 2017 2020 Electricity Price Change -0.1% -0.2% Delivered Natural Gas Price Change 0.01% -0.1% Delivered Coal Price Change -0.3% -0.4%

*A minus sign in front of the number denotes a nega tive change, or a price decrease

.

For years when the price of electricity, natural ga s, or coal increased, one would expect decreases in production and increases in market prices in sec tors 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 pri ces within these sectors. Smaller changes in input price changes would lead to smaller impacts w ithin secondary markets. For compliance with the proposal and more and less stringent alter natives these price changes are quite small, or even negative, in 2017. Thus, one could readily sur mise that the impacts of this proposed rule upon secondary markets should be minimal. As seen i n Table 5-9, most of the price changes are negative, thus showing an estimated decrease in ene rgy prices, albeit very small. The variation for these prices at the regional level is not subst antially different from the national-level price changes, and the variation of energy prices related to the proposal and the regional level can be found among the IPM outputs in the public docket fo r this rulemaking. However, a number of factors, in addition to the magnitude and sign of t he energy price changes, influence the magnitude of the impact on production and market pr ices for sectors using electricity, natural gas, or coal as inputs to production. These factors are discussed below.

5.6.3 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, more5-41

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.

5.6.4 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.

5.6.5 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.

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, and vice versa.

5.6.6 Effect of Changes in Input Demand from Elect ricity Sector Section 5.7.2 focuses on the effects of changes in energy 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, EPA
estimates EGUs will increase use of ammonia and ure a to reduce NOx emissions, which could5-42

result in a price driven reduction in demand in oth er sectors (e.g., fertilizer manufacturing and agriculture) using these industrial chemicals. ther e could be changes in the demand for new generation sources such as natural gas combined cyc le units, and therefore sectors producing technologies such as these may expand.

5.6.7 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 direction and magnitude of these impacts are influenced by a number of factors. For example, the more producers in these sectors are able 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 will also affect secondary markets. If regulation results in changes in domestic markets that lead to an increase in imports, then domestic producers may experience less demand from their consumers, and vice

versa.
Modeling choices in IPM influence the estimated cha nges in electricity, natural gas, and coal prices in this RIA. Actual market conditions w ill ultimately influence the price changes of these energy inputs and consequent effects on secon dary markets, as will the plan approaches that states adopt.

5.7 References 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/rstavi ns/Papers/Benefit Cost Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015.
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.
Congressional Budget Office (2011), Statement of Do uglas W. Elmendorf, Director, before the Senate Budget Committee, “Policies for Increasing E conomic Growth and Employment in5-43

2012 and 2013” (November 15)<http://www.cbo.gov/sit es/default/files/11-15- Outlook_Stimulus_Testimony.pdf>
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.
Graff Zivin J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.” American Economic Review . 102(7):3652-73.
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. 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.
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. 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 Secto ral Reallocation: Evidence From the Clean Air Act and the Workforce." The Quarterly Journal of Economi cs 128 (4): 1787-1835

. . U.S. Bureau of Labor Statistics (BLS). 2014. “Occup ational Employment Statistics, May 2014 National Industry-Specific Occupational Employment and Wage Estimates, Electric Power5-44

Generation, Transmission, and Distribution (NAICS 2 211)”. Available at: <http://www.bls.gov/oes/current/naics4_221100.htm>. Accessed June 9, 2015.
U.S. Bureau of Labor Statistics (BLS). 2014a. “May 2014 National Industry-Specific Occupational Employment and Wage Estimates: NAICS 2 12100 - Coal Mining”. Available at: < http://www.bls.gov/oes/current/naics4_212100. htm#00-0000>. Accessed June 9, 2015.
U.S. Bureau of Labor Statistics (BLS). 2014b. “May 2014 National Industry-Specific Occupational Employment and Wage Estimates: NAICS 2 12100 – Oil and Gas Extraction”. Available at: < http://www.bls.gov/oes/current/naic s4_212100.htm>. Accessed June 9, 2015.
U.S. Bureau of Labor Statistics (BLS). 2015. “Curre nt Employment Survey Seasonally Adjusted Employment for Electric Power Generation (national) ” and “Current Employment Survey Seasonally Adjusted Employment for Transmission, an d Distribution (national).” Series ID: CES4422110001. Available at <http://www.bls.gov/dat a/>. Accessed June 9, 2015.
U.S. Energy Information Agency (EIA). 2014. The Electricity Market Module of the National Energy Modeling System: Model Documentation 2014 . Available at: <http://www.eia.gov/forecasts/aeo/nems/documentatio n/electricity/pdf/m068(2014).pdf>. Accessed 9/17/2015.
U.S. EPA, 2015. Carbon Pollution Emission Guidelines for Existing S tationary Sources: Electric Utility Generating Units (Final Rule) , http://www2.epa.gov/cleanpowerplan/clean-powerplan-existing-power-plants.
U.S. EPA, 2015a

. Standards of Performance for Greenhouse Gas Emissio ns from New, Modified, and Reconstructed Stationary Sources: Ele ctric Utility Generating Units (Final Rule) , http://www2.epa.gov/cleanpowerplan/carbon-polluti on-standards-new-modified-andreconstructed-power-plants.
U.S. EPA, 2015b. Disposal of Coal Combustion Residuals from Electric Utilities (Final Rule)
, http://www2.epa.gov/coalash/coal-ash-rule.
U.S. EPA, 2015c. Steam Electric Power Generating Effluent Guidelines (Final Rule)
, http://www2.epa.gov/eg/steam-electric-power-generat ing-effluent-guidelines-2015-final-rule.
U.S. EPA, 2014. Final Rule for Existing Power Plants and Factories, http://www2.epa.gov/cooling-water-intakes.
U.S. EPA, 2011. Cross-State Air Pollution Rule
, http://www3.epa.gov/airtransport/CSAPR/index.html
U.S. EPA, 2011a

. Mercury and Air Toxics Standards (MATS) , http://www3.epa.gov/mats/.

U.S. EPA. 2010. EPA Guidelines for Preparing Economic Analyses . Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/gu idelines.htm>. Accessed 9/21/2015.5-45

U.S. EPA. 2010a. Regulatory Impact Analysis for the Proposed Federal Transport Rule Analyses . Available at: < http://www3.epa.gov/ttnecas1/ria.html >. Accessed 9/21/2015.
U.S. EPA, 2005. Clean Air Interstate Rule
, http://archive.epa.gov/airmarkets/programs/cair/web /html/index.html.5-1

CHAPTER 6: ESTIMATED HUMAN HEALTH BENEFITS AND CLIM ATE COBENEFITS

6.1 Introduction
As discussed in Chapter 4, this action proposes to update CSAPR to reduce interstate transport of EGU ozone season NO X emissions that contribute significantly to nonatta inment or interfere with maintenance of the 2008 ozone NAAQS. The EPA proposes to implement the proposed EGU NO X reductions by setting emissions budgets that are i mplemented through the
CSAPR NO X ozone-season allowance trading program. Implementi ng this proposal to update CSAPR is expected to reduce emissions of NO X during the summer ozone season and provide ancillary annual NO X and carbon dioxide (CO 2 ) benefits (i.e., co-benefits). This chapter
describes the methods used to estimate the monetize d ozone-related air quality health benefits, fine particulate matter (PM 2.5 )-related air quality health co-benefits from reduc tions in NO X emissions and climate co-benefits from reductions o f CO 2 emissions. These health benefits are associated with reducing exposure to ambient ozone and PM 2.5 by reducing emissions of precursor pollutants (i.e., NO X ). Data, resource, and methodological limitations p revent the EPA from monetizing several important co-benefits from reducing emissions of several pollutants, including those from reducing direct exposure to NO 2 , ecosystem effects and visibility impairment. We qualitatively discuss these unquanti fied benefits in this chapter.

This chapter provides estimates of the monetized ai r quality health benefits and climate cobenefits associated with emission reductions for th e regulatory control alternatives across several
discount rates. The estimated benefits associated w ith these emission reductions are beyond those achieved by previous EPA rulemakings, includi ng the 2011 CSAPR 89 and the finalized Clean Power Plan (CPP).

89 As discussed in Chapters 1, 3 and 5, the 2017 base line EGU emissions for this proposal include impact s from CSAPR issued on July 6, 2011. As discussed and elab orated on in Chapter 1, because the modeling for th e proposal was performed prior to the D.C. Circuit’s issuance of EME Homer City II , 11 that modeling assumed in its baseline for all states the emission reductions associated w ith the CSAPR NO X ozone-season phase 2 emissions budgets.A.6-2

6.2 Estimated Human Health Benefits
The proposal to update CSAPR is expected to reduce emissions of ozone season NO X . In the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the at mosphere to form ozone. Reducing NO X emissions also reduces human exposure to ozone and the incidence of ozone-related health effects, though this depend s partly on local levels of volatile organic compounds (VOCs). The proposal would also reduce em issions of NO X throughout the year.
Because NO X is also a precursor to formation of ambient PM 2.5 , reducing these emissions would also reduce human exposure to ambient PM 2.5 throughout the year and would reduce the
incidence of PM 2.5 -related health effects. 90 This RIA does not quantify PM 2.5 -related benefits
associated with SO 2 emission reductions. As discussed in Chapter 3, th e EPA does not estimate significant SO 2 emission reductions as a result of this proposal. Furthermore, these reductions
would reduce ozone and PM 2.5 concentrations in regions other than those with no nattainment monitors that are the subject of this proposal, and the benefits of reducing these pollutants in those regions are assessed in this Chapter. Reducin g emissions of NO X would also reduce ambient exposure to NO 2 and its associated health effects, though we do no t quantify these effects because we have not developed a reduced-for m technique for estimating NO 2 impacts. In this section, we provide an overview of the monetiz ed ozone-related benefits and PM 2.5 -related co-benefits estimated for the proposed updated CSAP R EGU NO X ozone season emissions budgets and for the more and less stringent alterna tives. A full description of the epidemiological studies we use, the methods we apply and the tools we employ to quantify the incidence of these effects may be found in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2015). The estimated benefits associated with these emissions reductions are additional to those achieved by previous EPA rulemakings, includi ng the finalized CPP.

Implementing these updated CSAPR EGU NO X emissions budgets for the ozone season in 23 eastern states may reduce ambient ozone and PM 2.5 concentrations below the National Ambient Air Quality Standards (NAAQS) in some areas and assist other areas with attaining the
ozone and PM 2.5 NAAQS. The NAAQS RIAs (U.S. EPA, 2008, 2012a, 2015 ) also calculated the benefits of attaining alternate ozone and PM NAAQS, and so differences in the design and

90 Additionally, this RIA does not estimate changes i n emissions of directly emitted particles.A.6-3

analytical objectives of each RIA are worth noting here. The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide based on an array of emission reduction strategies for differen t sources reflecting the application of identified and unidentified controls, incremental to implement ation of existing regulations and controls needed to attain the NAAQS that currently is in eff ect. In short, NAAQS RIAs hypothesize, but do not predict, the strategies that States may choo se to enact when implementing a revised NAAQS. Setting a NAAQS does not directly result in costs or benefits, and as such, the EPA’s NAAQS RIAs are illustrative. The estimated costs an d benefits from NAAQS RIAs are not
intended to be added to the costs and benefits of o ther regulations that result in specific costs of control and prescribe specific emission reductions. For example, some of the emissions reductions estimated to result from implementing th e proposal to update CSAPR may achieve some of the air quality improvements that resulted from the hypothesized attainment strategies presented in the NAAQS RIAs. The emissions reductio ns from implementing the proposal to update CSAPR will decrease the remaining amount of emissions reductions needed in nonattainment areas to, and reduce the costs to tho se areas to, meet the 2008 ozone NAAQS. Specifically, the anticipated reductions in ozone c oncentrations from this rule would help areas attain and maintain the 2008 ozone NAAQS and achiev e some of the air quality improvements from the hypothesized attainment strategy from the 2008 ozone NAAQS RIA. These ozone improvements would similarly achieve some of the ai r quality improvements assumed in the baseline (i.e., 2008 ozone NAAQS attainment project ion) for the 2015 ozone NAAQS RIA.

As discussed in Chapter 4, the IPM modeling showing compliance with the regulatory
control alternatives for which emission reductions are estimated in this RIA is also illustrative in nature. However, unlike the illustrative control st rategies analyzed in NAAQS RIAs described above, all of the emission reductions for the illus trative compliance modeling for this proposal would occur in one well-characterized sector (i.e., the EGU sector). The EPA is more confident in the magnitude and location of the emission reduc tions for this proposal because it imposes a specific requirement that limits emissions from a s pecific sector. Emission reductions achieved under this rule, if finalized, will ultimately be r eflected in the baseline of future NAAQS analyses and would lower the emissions reductions n eeded to attain revised future NAAQS. For more information on the relationship between illust rative analyses, such as for the NAAQS and its associated implementation rules, please see the Ozone NAAQS RIA (U.S. EPA, 2015).A.6-4

6.2.1 Health Impact Assessment for Ozone and PM 2.5
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 also include premature mortal ity and a variety of morbidity effects associated with acute and chronic exposures. Simila rly, the Integrated Science Assessment for
Particulate Matter

(PM ISA) (U.S. EPA, 2009b) identified the human he alth effects associated
with ambient PM 2.5 exposure, which include premature mortality and a v ariety of morbidity effects associated with acute and chronic exposures . Table 6-1 identifies the quantified and unquantified benefit and co-benefit categories capt ured in the EPA’s health benefits estimates for reduced exposure to ambient ozone and PM 2.5 . Although the table below does not list unquantified health effects or welfare effects, suc h as acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is importa nt to emphasize that the list of unquantified benefits categories is not exhaustive, nor is quant ification of each effect complete.A.6-5

Table 6-1. Human Health Effects of Ambient Ozone an d PM 2.5
Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Improved Human Health 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 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

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. 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)A.6-6

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 for regulatory analyses, our estimates are based on the best available methods of benefits transfer, wh ich 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 information f rom other contexts to the context of this regulation, we also use a “benefit-per-ton” approac h to estimate the ozone and PM 2.5 benefits in this RIA. Benefit-per-ton approaches apply an avera ge benefit 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. Thus, to deve lop estimates of benefits for this RIA, we are transferring both the underlying health and economi c information from previous studies and information on air quality responses to emissions r eductions from other air quality modeling. This section describes the underlying basis for the health and economic valuation estimates that inform the benefit-per-ton estimates, and the subse quent section provides an overview of the benefit-per-ton estimates, which are described in d etail in the appendix to this chapter.

The benefit-per-ton approach we use in this RIA rel ies on estimates of human health responses to exposure to ozone and PM 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 ozone and PM 2.5 . We use the environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE ) (version 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 ozone
and PM 2.5 concentrations. There may be other indirect health impacts associated with reducing emissions, such as occupational health exposures. E pidemiological studies generally provideA.6-7

estimates of the relative risks of a particular hea lth effect for a given increment of air pollution (often per 10 ppb for ozone or μg/m 3 for PM 2.5 ). These relative risks can be used to develop risk coefficients that relate a unit reduction in pollut ion (e.g., ozone) to changes in the incidence of a
health effect. We refer the reader to the Ozone NAA QS RIA (U.S. EPA, 2015) and PM NAAQS RIA (U.S. EPA, 2012a) for more information regardin g the epidemiology studies and risk coefficients applied in this analysis, and we brief ly elaborate on adult premature mortality below. The size of the mortality effect estimates from epi demiological studies, the serious nature of the effect itself, and the high monetary value ascribed to reducing risks of premature death make mortality risk reduction the most significant healt h endpoint quantified in this analysis.

6.2.1.1 Mortality Effect Coefficients for Short-ter m Ozone Exposure The overall body of evidence indicates that there i s likely to be a causal relationship between short-term ozone exposure and premature dea th. The 2013 ozone ISA concludes that the evidence suggests that ozone effects are independen t of the relationship between PM and mortality. (U.S. EPA, 2013a). However, the ISA note s that the interpretation of the potential confounding effects of PM on ozone-mortality risk e stimates requires caution due to the PM sampling schedule (in most cities) which limits the overall sample size available for evaluating potential confounding of the ozone effect by PM (U. S. EPA 2013a).

In 2006, the EPA requested an NAS study to answer t he following four key questions regarding ozone-related mortality: (1) How did the epidemiological literature to that point improve our understanding of the size of the ozone- related mortality effect?; (2) How best can EPA quantify the level of ozone-related mortality i mpacts from short-term exposure?; (3) How might EPA estimate the change in life expectancy?; and (4) What methods should EPA use to estimate the monetary value of changes in ozone-rel ated mortality risk and life expectancy?

In 2008, the NAS (NRC, 2008) issued a series of rec ommendations to the EPA regarding the quantification and valuation of ozone-related s hort-term mortality. Chief among these was that “...short-term exposure to ambient ozone is like ly to contribute to premature deaths” and the committee recommended that “ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The N AS also recommended that “...the greatest emphasis be placed on the multi-city and NMMAPS stu dies without exclusion of the meta-A.6-8

analyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no weight to the assumption that there is no causal as sociation between estimated reductions in premature mortality and reduced ozone exposure” (NR C, 2008). In 2010, the Health Effects Subcommittee of the Advisory Council on Clean Air C ompliance Analysis, while reviewing
EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also
confirmed the NAS recommendation to include ozone m ortality benefits (U.S. EPA-SAB, 2010a).

In view of the findings of the ozone ISA, the NAS p anel, the SAB-HES panel, and the CASAC panel, we estimate ozone-related premature mo rtality for short-term exposure in the core health effects analysis using effect coefficie nts from the Smith et al. (2009) NMMAPS analysis and the Zanobetti and Schwartz (2008) mult i-city study with several additional studies as sensitivity analyses. This emphasis on newer mul ti-city studies is consistent with recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC supported using the Smith et al. (2009) and Zanobet ti and Schwartz (2008) studies for the ozone HREA (U.S. EPA-SAB, 2012, 2014), and these are mult i-city studies published more recently (as compared with other multi-city studies or meta- analyses included in the sensitivity analyses – see discussion below).

Smith et al. (2009) reanalyzed the NMMAPS dataset, evaluating the relationship between short-term ozone exposure and mortality. While this study reproduces the core national-scale estimates presented in Bell et al. (2004), it also explored the sensitivity of the mortality effect to different model specifications including (a) region al versus national Bayes-based adjustment, 91 (b) co-pollutant models considering PM 10 , (c) all-year versus ozone-season based estimates, and (d) consideration of a range of ozone metrics, incl uding the daily 8-hour max. In addition, the Smith et al. (2009) study did not use the trimmed m ean approach employed in the Bell et al.

91 In Bayesian modeling, effect estimates are “updated ” from an assumed prior value using observational d ata. In the Smith et al. (2009) approach, the prior values are either a regional or national mean of the indiv idual effect estimates obtained for each individual city. The Ba yesian adjusted city-specific effect estimates are then calculated by updating the selected prior value bas ed on the relative precision of each city-specific estimate and the variation observed across all city-specific ind ividual effect estimates. City-specific estimates a re pulled towards the prior value if they have low precision and/or if there is low overall variation across est imates. Cityspecific estimates are given less adjustment if the y are precisely estimated and/or there is greater o verall variation across estimates.A.6-9

(2004) study in preparing ozone monitor data. 92 In selecting effect estimates from Smith et al. (2009), we focused on an ozone-only estimate for no n-accidental mortality using the 8-hour max
metric for the warmer ozone season. For the sensit ivity analysis, we included a co-pollutant model (ozone and PM 10 ) from Smith et al. (2009) for all-cause mortality, using the 8-hour max ozone metric for the ozone season. Using a single p ollutant model for the core analysis and the co-pollutant model in the sensitivity analysis refl ects our concern that the reduced sampling frequency for days with co-pollutant measurements ( 1/3 and 1/6) could affect the ability of the study to characterize the ozone effect. This choice is consistent with the ozone ISA, which concludes that ozone effects are likely to be indep endent of the relationship between PM and mortality (U.S. EPA, 2013a).

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure (using an 8-hour mean metric for the warm season Ju ne-August) and all-cause mortality in 48 U.S. cities using data collected between 1989 and 2 000. The study presented single pollutant CR functions based on shorter (0-3 day) and longer ( 0-20 day) lag structures, with the comparison of effects based on these different lag structures being a central focus of the study. We used the shorter day lag based C-R function since this had t he strongest effect and tighter confidence
interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect
estimate based on an 8-hour max to account for the period of the day in which most individuals are exposed to ozone. To do this, we used the ozone metric approach wherein the original effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio.

6.2.1.2 PM

2.5 Mortality Effect Coefficients for Adults and Infan ts A substantial body of published scientific literatu re documents the association between
elevated PM 2.5 concentrations and increased premature mortality ( U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology, t oxicology, and clinical studies. The PM ISA completed as part of the most recent review of the PM NAAQS, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009a, 2009b), conclud ed that there is a causal relationship between mortality and both long-term and short-term exposure to PM 2.5 based on the entire body

92 There are a number of concerns regarding the trimm ed mean approach including (1) the potential loss o f temporal variation in the data when the approach is used (th is could impact the size of the effect estimate), a nd (2) a lack of complete documentation for the approach, which prev ents a full reviewing or replication of the techniq ue.A.6-10

of scientific evidence (U.S. EPA, 2009b). The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk red uction the most significant health endpoint quantified in this analysis.

Researchers have found statistically significant as sociations between PM 2.5 and premature mortality using different types of study designs. Time-series methods have been used to relate short-term (often day-to-day) changes in PM 2.5 concentrations and changes in daily mortality rates up to several days after a period o f exposure to elevated PM 2.5 concentrations. Cohort methods have been used to examine the potent ial relationship between community-level
PM 2.5 exposures over multiple years (i.e., long-term exp osures) and community-level annual mortality rates that have been adjusted for individ ual level risk factors. When choosing between using short-term studies or cohort studies for esti mating mortality benefits, cohort analyses are thought to capture more of the public health impact of exposure to air pollution over time because they account for the effects of long-term e xposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). Th e National Research Council (NRC) stated
that “it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). T he NRC further noted that “the overall effect estimates may be a combination of effects from long -term exposure plus some fraction from short-term exposure. The amount of overlap is unkno wn” (NRC, 2002, p. 108-9). To avoid double counting, we focus on applying the risk coef ficients from the long-term cohort studies in estimating the mortality impacts of reductions in P M 2.5
. Over the last two decades, several studies using “p rospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective cohort studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; Lepeule et al., 2012) and the “American Cancer Soci ety” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al ., 2009), provide the most extensive analyses of
ambient PM 2.5 concentrations and mortality. These studies have f ound consistent relationships between fine particle indicators and premature mort ality across multiple locations in the United States. The credibility of these two studies is fur ther enhanced by the fact that the initial published studies (Pope et al., 1995; Dockery et al ., 1993) were subject to extensiveA.6-11

reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effects Institute (HEI) and by a Special Pan el of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies conf irming and extending the findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional va lidation of the findings of these original studies (Pope et al., 2002, 2004; Laden et al., 200 6; Krewski et al., 2009; Lepeule et al., 2012). The SAB-HES also supported using these two cohorts for analyses of the benefits of PM reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM mortality benefit estimates [is] a good choice. The se are widely cited, well studied and extensively reviewed data sets” (U.S. EPA-SAB, 2010 a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present benefits estimates using relative risk estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using bot h ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six Cities cohorts provide the strongest evidence of th e association between long-term PM 2.5 exposure and premature mortality with support from a number of additional cohort studies (described below).

The extended analyses of the ACS cohort data (Krews ki et al., 2009) refined the earlier ACS studies by (a) extending the follow-up period b y 2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number o f urban areas; (c) addressing confounding by spatial autocorrelation by incorporating ecological , or community-level, co-variates; and (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well-s uited for the assessment of mortality risk from long-term PM 2.5 exposures for the EPA’s benefits analyses.

In 2009, the SAB-HES again reviewed the choice of m ortality risk coefficients for benefits analysis, concluding that “[t]he Krewski e t al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the
SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the support for including thisA.6-12

study in this RIA. For example, the PM ISA (U.S. EP A, 2009b) included this study among the key mortality studies. In addition, the risk assess ment supporting the PM NAAQS (U.S. EPA, 2010b) used risk coefficients drawn from the Krewsk i et al. (2009) study, the most recent reanalysis of the ACS cohort data. The PM risk asse ssment cited a number of advantages that informed the selection of the Krewski et al. (2009) study as the source of the core effect estimates, including the extended period of observa tion, the rigorous examination of model forms and effect estimates, the coverage for ecolog ical variables, and the large dataset with over 1.2 million individuals and 156 MSAs (U.S. EPA, 201 0b). The CASAC also provided extensive peer review of the PM risk assessment and supported the use of effect estimates from this study (U.S. EPA-SAB, 2009a, b, 2010b).

Consistent with the PM risk assessment (U.S. EPA, 2 010b), which was reviewed by the CASAC (U.S. EPA-SAB, 2009a, b), we use the all-caus e mortality risk estimate based on the random-effects Cox proportional hazard model that i ncorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04– 1.08 per 10 μg/m 3 increase in PM 2.5 ). The relative risk estimate (1.06 per 10 μg/m 3 increase in PM 2.5 ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though t he confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was publ ished after the last SAB-HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model specifications, lower exposures, and averaging time using eleven additional years of cohort follow-up that incorporated recent lower exposures. The authors found significant associations
between PM 2.5 exposure and increased risk of all-cause, cardiova scular and lung cancer mortality. The authors also concluded that the C-R relationship was linear down to PM 2.5 concentrations of 8 μ g/m 3 and that mortality rate ratios for PM 2.5 fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all-cause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07 –1.22 per 10 μg/m 3 increase in PM 2.5 ). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. (2006), with relatively smaller confidence intervals.A.6-13

Given that monetized benefits associated with PM 2.5 are driven largely by reductions in premature mortality, it is important to characteriz e the uncertainty in this endpoint. In order to do so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 (Roman et al., 2008; IEc, 2006). The results of tha t expert elicitation can be used as a characterization of uncertainty in the C-R function s.
In addition to the adult mortality studies describe d above, several studies show an association between PM exposure and premature morta lity in children under 5 years of age. 93
The PM ISA states that less evidence is available r egarding the potential impact of PM 2.5 exposure on infant mortality than on adult mortalit y and the results of studies in several countries include a range of findings with some finding signi ficant associations. Specifically, the PM ISA concluded that evidence exists for a stronger effec t at the post-neonatal period and for respiratory-related mortality, although this trend is not consistent across all studies. In addition, compared to avoided premature deaths estimated for adult mortality, avoided premature deaths for infants are significantly smaller because the n umber of infants in the population is much smaller than the number of adults and the epidemiol ogy studies on infant mortality provide smaller risk coefficients associated with exposure to PM 2.5
.
In 2004, the SAB-HES noted the release of the WHO G lobal Burden of Disease Study focusing on ambient air, which cites several recent ly published time-series studies relating daily PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study conducted by Woodruff et al. (1997), the SAB-HES no ted several strengths of the study, including the use of a larger cohort drawn from a l arge number of metropolitan areas and efforts to control for a variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and ma ternal smoking status). Based on these findings, the SAB-HES recommended that the EPA inco rporate infant mortality into the primary benefits estimate and that infant mortality be eval uated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA-SAB, 200 4).

In 2010, the SAB-HES again noted the increasing bod y of literature relating infant mortality and PM exposure and supported the inclusi on of infant mortality in the monetized

93 For the purposes of this analysis, we only calcula te benefits for infants age 0–1, not all children u nder 5 years old.A.6-14

benefits (U.S. EPA-SAB, 2010a). The SAB-HES general ly supported the approach of estimating infant mortality based on Woodruff et al. (1997) bu t also noted that a more recent study by Woodruff et al. (2006) continued to find associatio ns between PM 2.5 and infant mortality in California. The SAB-HES also noted, “when PM 10 results are scaled to estimate PM 2.5 impacts, the results yield similar risk estimates.” Consiste nt with The Benefits and Costs of the Clean Air
Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earl ier 1997 study in part due to the national–scale of the earlier study.

6.2.2 Economic Valuation for Health 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 analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

For this proposed rule avoided premature deaths acc ount for over 90 percent of monetized ozone-related benefits and 98 percent of monetized PM-related co-benefits. The economics literature concerning the appropriate method for va luing reductions in premature mortality risk is still developing. The adoption of a value for the p rojected reduction in the risk of premature mortality is the subject of continuing discussion w ithin the economics and public policy analysis community. Following the advice of the SAB’s Enviro nmental Economics Advisory Committee (SAB-EEAC), the EPA currently uses the value of sta tistical life (VSL) approach in calculating estimates of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual’s willingness to t rade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.A.6-15

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) 94 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 $6.3 million (2000$). 95 We then adjust this VSL to account for the currenc y year and to account for income growth from 1990 to the analysis year. Specifically, the VSL applied in this analysis in 2011$ after adjusting for income growth is $9.9 million for 2017.

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

94 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. 95 In 1990$, this base VSL is $4.8 million.A.6-16

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.

6.2.3 Benefit-per-ton Estimates for Ozone
We used a “benefit-per-ton” approach to estimate th e ozone benefits in this RIA. The EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 2014a; 2015). These benefit-per-ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of NO X (an ozone precursor). We generated benefit-per-ton estimates for ozone based on air quality modeling for the illustrative control case described in Chapter 3 of this RIA. The estimates correspond to NO X emissions from U.S. EGUs during the ozone-season (M ay to September). Because we estimate ozone health impacts from May to September only, th is approach underestimates ozone benefits in areas with a longer ozone season such as Texas. These estimates assume that EGU-attributable ozone formation at the regional-level is due to NO X 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. We provi de more detailed information regarding the generation of these estimates in the appendix to th is chapter.

As noted below in the characterization of uncertain ty, all benefit-per-ton estimates have inherent limitations. Specifically, the benefit-per -ton estimates reflect the geographic distribution of the modeled illustrative control case, which may not match the geographic distribution of emission reductions anticipated for compliance with the regulatory control alternatives, and they may not reflect local variability in population den sity, meteorology, exposure, baseline health incidence rates, or other local factors for any spe cific location.

6.2.4 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, which represent the total monetized human health co-benef its (the sum of premature mortality and premature morbidity), of reducing one ton of PM 2.5 (or PM 2.5 precursor such as NO X ) from a specified source. Specifically, in this analysis, w e multiplied the benefit-per-ton estimates by the corresponding emission reductions that were generat ed from air quality modeling of theA.6-17

illustrative control case. The appendix to this cha pter provides additional detail regarding these calculations. All benefit-per-ton estimates have i nherent limitations and should be interpreted with caution. Even though we assume that all fine p articles have equivalent health effects, the benefit-per-ton estimates vary between precursors d epending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure.

6.2.5 Estimated Health Benefits Results
Table 6-2 provides the benefit-per-ton estimates fo r the analysis year 2017; because the modeled change in SO 2 levels was very small, we did not quantify the SO 2 -related PM 2.5 benefits. Table 6-3 provides the emission reduction s estimated to occur in the analysis year.
Table 6-4 summarizes the national monetized ozone-r elated and PM-related health benefits estimated to occur for regulatory control alternati ves for the 2017 analysis year, by precursor pollutant using discount rates of 3 percent and 7 p ercent. Table 6-5 provides national summaries
of the reductions in estimated health incidences as sociated with the proposal and more and less stringent alternatives for the 2017 analysis year. 96 Figure 6-1 provides a visual representation of the range of estimated ozone and PM 2.5 -related benefits using benefit-per-ton estimates b ased on concentration-response functions from different stu dies and expert opinion for the proposal
evaluated for 2017.

96 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar b enefit per ton values. See Appendix 6-A for details.A.6-18

Table 6-2. Summary of Ozone and PM 2.5 Benefit-per-Ton Estimates Based on Air

Quality Modeling from the Illustrative Control Case in 2017 (2011$)*

Pollutant Discount Rate National

NO X (as Ozone) N/A $5,700 to $9,400

NO X (as PM 2.5 ) 3% $2,100 to $4,700 7% $1,900 to $4,300 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r ozone and PM 2.5 . All estimates are rounded to two significant figu res. The monetized co-benefits do not include reduc ed 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 the analysis year, so they are the same for all discount rates. Confidenc e intervals are unavailable for this analysis becau se 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 perce nt of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

Table 6-3. Emission Reductions of Criteria Polluta nts for the Proposal and More and

Less Stringent Alternatives in 2017 (thousands of s hort tons)*

Proposal More Stringent Alternative Less Stringent Alternative Ozone Season NO X 85,000 87,000 24,000 All Year NO X 90,000 93,000 24,000 All Year SO 2 380 430 300 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. Table 6-4. Summary of Estimated Monetized Health B enefits for the Proposal and

More and Less Stringent Alternatives Regulatory Con trol Alternatives for 2017 (millions of 2011$) *

Pollutant Proposal More Stringent Alternative Less Stringent

Alternative NOx (as Ozone) $490 to $790 $500 to $820 $140 to $220 NOx (as PM 2.5 ) 3% Discount Rate $190 to $430 $190 to $440 $49 to $110

7% Discount Rate $170 to $380 $170 to $390 $45 to $100

Total 3% Discount Rate $670 to $1,200 $690 to $1,300 $190 to $340

7% Discount Rate $650 to $1,200 $670 to $1,200 $180 to $330 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The health benefits range is based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The est imated monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 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 levels, which drive population exposure. The moneti zed co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for ozone are based on ozone season NO X emissions. Ozone co-benefits occur in analysis yea r, so they are the same for all discount rates. Confidence intervals are unavailable for this analy sis because of the benefit-per-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The confidence intervalsA.6-19

around the ozone mortality estimates are on the ord er of +/- 60 percent depending on the concentration -response function used. Table 6-5. Summary of Avoided Health Incidences fr om Ozone-Related and PM 2.5 -

Related Benefits for the Proposal and More and Less Stringent Alternatives for 2017*

Ozone-related Health Effects Proposal More Stringent Alternative Less Stringent

Alternative Avoided Premature Mortality Smith et al. (2009) (all ages) 48 50 14 Zanobetti and Schwartz (2008) (all ages) 81 83 23 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 79 81 22 Emergency room visits for asthma (all ages) 320 330 90 Asthma exacerbation (ages 6-18) 93,000 95,000 26,000 Minor restricted-activity days (ages 18-65) 240,000 240,000 67,000 School loss days (ages 5-17) 77,000 79,000 22,000 PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 21 22 5.6 Lepeule et al. (2012) (adult) 48 50 13 Woodruff et al. (1997) (infant) <1 <1 <1 Avoided Morbidity Emergency department visits for asthma (all ages) 12 12 3.1 Acute bronchitis (age 8–12) 31 32 8.1 Lower respiratory symptoms (age 7–14) 390 400 100 Upper respiratory symptoms (asthmatics age 9–11) 560 580 150 Minor restricted-activity days (age 18–65) 16,000 16,000 4,200 Lost work days (age 18–65) 2,700 2,700 700 Asthma exacerbation (age 6–18) 580 600 150 Hospital admissions—respiratory (all ages) 6.4 6.5 1.7 Hospital admissions—cardiovascular (age > 18) 7.8 8.0 2.1 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 26 25 6.6 Pooled estimate of 4 studies 2.8 2.7 0.71 * All estimates are rounded to whole numbers with t wo significant figures.. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals ar e unavailable for this analysis because of the inci dence-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). The confidence intervals around the ozone mortality estimates are on the order of +/- 60 perc ent depending on the concentration-response function us ed.A.6-20OzonePM2.5

Figure 6-1. Monetized Health Benefits of Proposal for 2017 *

*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. Ozone benefits occur in the analysis year, so they are the same f or all discount rates. These estimates do not include benefits from reductions in CO 2 . The monetized co-benefits do not include climate benefits from changes in NO 2 or reduced health effects from direct exposure to NO 2 , ecosystem effects, or visibility impairment. 6.2.6 Characterization of Uncertainty in the Estim ated Health 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 analysisA.6-21

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 benefits, and assumptions regarding the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain an d would affect the estimate of benefits. When the uncertainties from each stage of the analysis a re compounded, even small uncertainties can have large effects on the total quantified benefits . 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 benefits shou ld be viewed as representative of the general magnitude of benefits of the regulatory control alt ernatives for the 2017 analysis year, rather than the actual benefits anticipated from implement the proposal.

This RIA does not include the type of detailed unce rtainty assessment found in the Ozone NAAQS RIA (U.S. EPA, 2015) or the PM NAAQS RIA (U.S . EPA, 2012a) because we lack the necessary air quality modeling input and/or monitor ing data to run the benefits model. However, the results of the quantitative and qualitative unc ertainty analyses presented in the Ozone NAAQS RIA and PM NAAQS RIA can provide some informa tion regarding the uncertainty inherent in the estimated benefits results presente d 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 benefits by between 33 percent a nd −14 percent. Although we generally do not calculate confidence intervals for benefit-per-ton estimates as they can provide an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA provides an indication of the random sampling e rror in the health impact and economic valuation functions using Monte Carlo methods. In general, the 95 th percentile confidence interval
for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the
central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).A.6-22

We applied benefit-per-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and b enefits modeling assumptions. For example, these estimates may not reflect local variability i n population density, meteorology, exposure, baseline health incidence rates, or other local fac tors that might lead to an over-estimate or
under-estimate of the actual co-benefits of control ling PM and ozone precursors. As such, it is not feasible to estimate the proportion of co-benef its occurring in different locations. Use of these benefit-per-ton values to estimate benefits m ay lead to higher or lower benefit estimates
than if benefits were calculated based on direct ai r quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are
all based on a broad emission reduction scenario an d therefore represent average benefits-per-ton over the entire region. The benefit-per-ton for emi ssion reductions in specific locations may be very different than the estimates presented here. T o the extent that the geographic distribution of the emissions reductions achieved by implementing t he proposal is different than the emissions in the air quality modeling of the illustrative con trol case, the benefits may be underestimated or
overestimated.

Our estimate of the total monetized benefits is bas ed 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 fine particle standard and those areas that are in attai nment, down to the lowest modeled concentrations.A.6-23

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 benefits of air quality improvements. 97 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 r egulatory control alternatives. 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 the use of a no-threshold log-linear model to estim ate PM-related long-term mortality (U.S. EPA, 2009b).

We report also the key assumptions associated with our analysis of ozone-related effects:

97 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).A.6-24

• Key assumption and uncertainties related to modelin g of ozone-related premature mortality : Ozone-related short-term mortality represents a s ubstantial proportion of total monetized benefits (over 94% of the ozone-related-b enefits), and these estimates have the following key assumptions and uncertainties. We uti lize a log-linear impact function without a threshold in modeling short-term ozone-re lated mortality. However, we acknowledge reduced confidence in specifying the na ture of the C-R function in the range
of ≤ 20ppb and below (ozone ISA, section 2.5.4.4). Thus, the estimates include health benefits from reducing ozone in areas with varied c oncentrations of ozone, including both
areas that do not meet the ozone standard and those areas that are in attainment, down to the lowest modeled concentrationsThus, ozone-related pr emature deaths estimated at or below this level are subject to greater uncertainty, but we cannot judge whether (and in what direction) these impacts are biased. .

• Avoided premature mortality according to baseline p ollutant concentrations : We recognize that, in estimating short-term ozone-rela ted mortality, we are less confident in specifying the shape of the C-R function at lower a mbient ozone concentrations (at and below 20 ppb, ozone ISA, section 2.5.4.4). Quantita tive uncertainty analyses completed for the Ozone NAAQS RIA (U.S. EPA, 2015) found almost 1 00% of mortality reductions occurred above 20 ppb, where we are more confident in specifying the nature of the ozonemortality effect (ozone ISA, section 2.5.4.4). Howe ver, as discussed in section 6B.7 of that RIA, care must be taken in interpreting these resul ts since the ambient air metric used in modeling this endpoint is the mean 8-hour max valu e in each grid cell (and not the full distribution of 8-hour daily max values). Had the l atter been used, then the distribution would have likely been wider.

For this analysis, policy-specific air quality data is not available, and the control scenarios are illustrative of what states may choose to do. H owever, 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-tonA.6-25

estimates for this RIA using 12 km grid cells acros s the contiguous U.S. 98 It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population and change in air qualit y. In other words, the percentage of the population exposed to air pollution below the LML i s 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 proposal. Therefore, caution is warranted when interpreting the LML assessment in t his RIA because these results are not consistent with results from RIAs that had air qual ity modeling.

Figure 6-3 shows a bar chart of the percentage of t he population exposed to various air quality levels, including the LML concentration ben chmarks in the illustrative control case modeling, and Figure 6-4 shows a cumulative distrib ution function of the same data. Both figures identify the LML for each of the major cohort studi es.

98 As described in Chapter 3, the baseline for the ai r quality modeling used to calculate the benefit-pe r-ton values differs from the baseline used to estimate the bene fits, costs, and impacts of this rulemaking. See Ch apter 3 for more details about the differences between the two basel ines.A.6-26

Figure 6-2. Percentage of Adult Population (age 30 +) by Annual Mean PM 2.5 Exposure in

the Baseline used for the Air Quality Analysis in C hapter 3 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 47% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) studyA.6-27

Figure 6-3. Cumulative Distribution of Adult Popul ation (age 30+) by Annual Mean

PM 2.5 Exposure in the Baseline used for the Air Quality A nalysis in Chapter 3

6.3 Estimated Climate Co-Benefits from CO 2 A co-benefit of this proposal is reducing emissions of CO 2 . In this section, we provide a brief overview of the 2009 Endangerment Finding and climate science assessments released since then. We also provide information regarding t he economic valuation of CO 2 using the Social Cost of Carbon (SC-CO 2 ), a metric that estimates the monetary value of im pacts associated with marginal changes in CO 2 emissions in a given year. Table 6-6 summarizes th e quantified and unquantified climate benefits in thi s analysis. 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 47% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) studyA.6-28

Table 6-6. 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 analysis, are used in this RIA to estimate the welfare effect s 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. There are several important considerations in asses sing the climate-related benefits for an ozone air quality-focused rulemaking. First, the es timated health benefits do not account for any climate-related air quality changes (e.g., increase d ambient ozone associated with higher temperatures). Excluding climate-related air qualit y changes may underestimate ozone-related
health benefits. It is unclear how PM 2.5 -related health benefits would be affected by exclu ding 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 benefits als o do not consider temperature
modification of PM 2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 20 08a, 2008b). Third, the estimated climate co-benefits reported in this RIA reflect global benefits, while the estimated health benefits are calculated for the contiguous U .S. only. Excluding temperature modification of air pollution risks and international air qualit y-related health benefits likely leads to underestimation of quantified health benefits (Anen berg et al, 2009, Jhun et al, 2014). Fourth, as noted earlier, we do not estimate the climate co-be nefits associated with reductions in PM and

ozone precursors.

6.3.1 Climate Change Impacts
Through the implementation of CAA regulations, the EPA addresses the negative externalities caused by air pollution. In 2009, the EPA Administr ator found that elevated concentrations of greenhouse gases in the atmosphere may reasonably b e anticipated both to endanger public health and to endanger public welfare. For health, these include the increased likelihood of heatA.6-29

waves, negative impacts on air quality, more intens e hurricanes, more frequent and intense storms and heavy precipitation, and impacts on infe ctious and waterborne diseases. For welfare, these include reduced water supplies in some region s, increased water pollution, increased occurrences of floods and droughts, rising sea leve ls and damage to coastal infrastructure, increased peak electricity demand, changes in ecosy stems, and impacts on indigenous
communities. The preamble also summarizes new scientific assessm ents and recent climatic observations. Major scientific assessments released since the 200 9 Endangerment Finding have improved scientific understanding of the climate, and provid e even more evidence that GHG emissions endanger public health and welfare for current and future generations. The National Climate Assessment (NCA), in particular, assessed the impac ts of climate change on human health in the United States, finding that Americans will be affec ted by “increased extreme weather events, wildfire, decreased air quality, threats to mental health, and illnesses transmitted by food, water, and disease-carriers such as mosquitoes and ticks.” These assessments also detail the risks to vulnerable groups such as children, the elderly and low income households. Furthermore, the assessments present an improved understanding of th e impacts of climate change on public welfare, higher projections of future sea level ris e 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 millions of years of history, and new assessments of the impacts of climate change on permafrost and ocean a cidification. The impacts of GHG emissions will be realized worldwide, independent of their lo cation of origin, and impacts outside of the United States will produce consequences relevant to the United States.

6.3.2 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”). 99 We refer to these estimates,

99 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, EnvironmentalA.6-30

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).

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

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.A.6-31

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. 100

The SC-CO 2 estimates represent global measures because of the distinctive nature of 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 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. 101

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 non-

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

101 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.A.6-32

catastrophic 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. 102 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
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 rulemakings 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. In addition, OMB’s Office of Information and Regulatory Affairs issued a separate request for public comment on the approach used to develop the estimates. 103 After careful evaluation of the full range of comments submitted to OMB’s Office of Information and Regulatory Affairs, the IWG continues to recommend the use of these SC-CO 2 estimates in

102 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

.

103 See https://www.federalregister.gov/articles/2013/11/26 /2013-28242/technical-support-document-technicalupdate-of-the-social-cost-of-carbon-for-regulatory- impactA.6-33

regulatory impact analysis. With the release of the response to comments 104 , the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engineering, and Medicine (Academies) to ensure tha t the SC-CO 2 estimates continue to reflect
the best available scientific and economic informat ion on climate change. 105 The Academies’ process will be informed by the public comments rec eived and focuses on the technical merits and challenges of potential approaches to improving the SC-CO 2 estimates in future updates. 106

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, $41, $63, and $120 per metric ton of CO 2 emissions in the year 2017 (2011$). 107 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

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

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

.

106 See http://sites.nationalacademies.org/DBASSE/BECS/Curr entProjects/DBASSE_167526?utm_source=All%20DBASS E%20Newsletters&utm_campaign=e84c13e8c4- New_Project_the_Social_Cost_of_Carbon&utm_medium=em ail&utm_term=0_e16023964e-e84c13e8c4- 267347161 for more information about the National A cademies process and the status of the project.

107 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 201 1$ using GDP Implicit Price Deflator (1.061374), http://www.bea.gov/iTable/index_nipa.cfm . The estimates presented here have been rounded t o two significant digits.A.6-34

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).

Table 6-7 presents the global SC-CO 2 estimates in metric tons for the years 2015 to 205 0.
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
. 108, 109 The
SC-CO 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. Table 6-8 reports the incremental climate co-benefi ts from CO 2 emission impacts estimated for the proposal and more and less stringent alternativ es for the 2017 analysis year.

108 Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. T herefore, the conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in t he year 2017.

109 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.A.6-35

Table 6-7. Social Cost of CO 2 , 2015-2050 (in 2011$ per metric ton)*

Year Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percent ile) 2015 $12 $38 $59 $110 2017 $12 $41 $63 $120 2020 $13 $45 $66 $130 2025 $15 $49 $72 $150 2030 $17 $53 $77 $160 2035 $19 $58 $83 $180 2040 $22 $64 $89 $190 2045 $24 $68 $94 $210 2050 $28 $73 $100 $230 * These SC-CO 2 values are stated in $/metric ton and rounded to t wo significant figures. 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.

Table 6-8. Estimated Global Climate Co-benefits of CO 2 Reductions for the Proposal and More and Less Stringent Alternatives for 2017 ( millions of 2011$)*

Discount rate and statistic Proposal More Stringent Alternative Less Stringent

Alternative Thousands of short tons of CO 2 reduced** 610 610 720

5% (average) $6.5 $6.5 $7.6 3% (average) $23 $23 $27 2.5% (average) $35 $35 $41 3% (95 th percentile) $66 $66 $78 * The SC-CO 2 values are dollar-year and emissions-year specific . SC-CO 2 values represent only a partial accounting of climate impacts.
**Emission impacts for this rulemaking are shown fo r the year 2017 and are calculated in short tons. The conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in t he year 2017.

It is important to note that the climate co-benefit s presented above are associated with changes in CO 2 emissions only. Implementing this proposal, howeve r, will have an impact on the emissions of other pollutants that would affect the climate. Both predicting reductions in emissions and estimating the climate impacts of the se other pollutants, however, is complex. The climate impacts of these other pollutants have not been calculated for this proposal. 110

110 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.A.6-36

The other emissions potentially reduced as a result of the proposal to update CSAPR include other greenhouse gases (such as methane), a erosols 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 compoun ds. Changes in emissions of these pollutants (both increases and decreases) could directly resul t from changes in electricity generation, upstream fossil fuel extraction and transport, and/ or downstream secondary market impacts. Reductions in black carbon or ozone precursors are projected to lead to further cooling, but reductions in the other aerosol species and precurs ors are projected to lead to warming. Therefore, changes in non-CO 2 pollutants could potentially augment or offset the climate cobenefits calculated here. These pollutants can act in different ways and on different timescales than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. In addition, these aerosols are thought to affect climate indire ctly 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 c an increase concentrations in the atmosphere for hundreds or thousands of years, many of these o ther pollutants are short lived and remain in the atmosphere for short periods of time ranging fr om days to weeks and can therefore exhibit large spatial and temporal variability.

6.4 Combined Health Benefits and Climate Co-Benefi ts Estimates
In this analysis, we were able to monetize the esti mated benefits associated with the reduced exposure to ozone and PM 2.5 and co-benefits of decreased emissions of CO 2 , but we
were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon monoxide, and NO 2 , as well as ecosystem effects and visibility impai rment. In addition, there are expected to be unquantified health and welfare impa cts associated with changes in hydrogen chloride. Specifically, we estimated combinations o f health 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]) and climate co-benefits at estimates
of the SC-CO 2 (average SC-CO 2 at each of three discount rates—5 percent, 3 perce nt, 2.5 percent—and the 95 th percentile SC-CO 2 at 3 percent) (as recommended by the IWG).A.6-37

Different discount rates are applied to SC-CO 2 than to the health 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 consensus exists on th e 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 cons idering all four SC-CO 2 estimates. 111 The EPA has evaluated the range of potential impacts by combining all SC-CO 2 values with health benefits values at the 3 percent and 7 percent disc ount rates. Combining the 3 percent SC-CO 2 values with the 3 percent health benefit values ass umes that there is no difference in discount

rates

Table 6-9 provides the combined health and climate benefits for the proposal and more and less stringent alternatives for the 2017 analysis y ear.

111 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.A.6-38

Table 6-9. Combined Health Benefits and Climate Co -Benefits for the Proposal and

More and Less Stringent Alternatives for 2017 (mill ions of 2011$)*

SC-CO 2 Discount Rate** Health and Climate Benefits (Discount Rate Applied to Health Co-Benefits) Climate CoBenefits Only 3% 7% Proposal 5% $680 to $1200 $660 to $1200 $6.5 3% $700 to $1200 $680 to $1200 $23 2.5% $710 to $1300 $690 to $1200 $35 3% (95 th percentile) $740 to $1300 $720 to $1200 $66 More Stringent Alternative 5% $700 to $1300 $680 to $1200 $6.5 3% $720 to $1300 $700 to $1200 $23 2.5% $730 to $1300 $710 to $1200 $35 3% (95 th percentile) $760 to $1300 $740 to $1300 $66 Less Stringent Alternative 5% $190 to $340 $190 to $330 $7.6 3% $210 to $360 $210 to $350 $27 2.5% $230 to $380 $220 to $370 $41 3% (95 th percentile) $260 to $410 $260 to $400 $78 *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 benefits occur in analysis year, s o they are the same for all discount rates. The hea lth benefits 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 Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 and HAP; ecosystem effects; or visibility impairment. **As discussed in section 6.3, the SC-CO 2 estimates are calculated with four different value s of a one ton reduction.

6.5 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 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 and NO 2 , as well as ecosystem effects, and visibility impa irment due to the absence of air quality modeling data for these pollutants in this analysis. This does not imply
that there are no co-benefits associated reductions in exposures to NO 2 . In this section, we provide a qualitative description of these benefits , which are listed in Table 6-10.A.6-39

Table 6-10. Unquantified Health and Welfare Co-ben efits 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

Improved Environment Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 1 Visibility in residential areas — — PM ISA 1

Reduced effects on materials Household soiling — — PM ISA 1,2 Materials damage (e.g., corrosion, increased wear) — — PM ISA 2
Reduced 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 2A.6-40 Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information

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

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.
6.5.1 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 a
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).

6.5.2 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.A.6-41

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 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.

6.5.3 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 alsoA.6-42

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.

6.5.4 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 andA.6-43

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)

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).

6.5.5 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.

6.5.6 Visibility Impairment Co-Benefits
Reducing secondary formation of PM 2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visib ility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light- extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soi l (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activit ies and their overall sense of wellbeing. Good visibility increases the quality of life where indi viduals live and work, and where they engage in
recreational activities. Particulate sulfate is the dominant source of regional haze in the easternA.6-44

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.

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A PPENDIX 6A: E STIMATING THE B ENEFIT - PER -T ON OF R EDUCING PM 2.5 AND O ZONE P RECURSORS This appendix reports additional detail regarding o ur approach for estimating the benefit-per-ton (BPT) of reducing PM 2.5 and ozone precursor emissions. This BPT was applie d in Chapter 6 of this Regulatory Impact Analysis (RIA). Specifically , this appendix describes the methods for estimating these values for the contiguous U.S. for PM 2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector in the Prop osed Ozone Transport Rule. 6A.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 calcul ating average benefit-per-ton coefficients generally foll ows three steps. As an example, in order to calculate regional average benefit-per-ton estimate s 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). 112,113 Aggregate those impacts and economic values to the United States.

3. Divide the national health impacts attributable to each precursor, and the national monetary value of these impacts, by the amount of a ssociated national precursor emissions. That is, nitrate benefits are divided by NO X emissions, and ozone benefits are divided by ozone-season NO X emissions.

6A.2 Air Quality Modeling for the Proposed Transpor t Rule 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: concentrations. The model predictions f or the 2011 base year, the 2017 baseline, and
the 2017 illustrative control case were combined wi th ambient air quality observations to calculate seasonal mean ozone air quality metrics a nd annual mean PM2.5 for the 2017 baseline and 2017 illustrative control case, which were then used as input for the benefits analysis.

112 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.
113 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/.6A-2

Each of the CAMx model simulations was performed fo r a nationwide modeling domain 114 using a full year of meteorological conditions for 2011. The modeling for 2011 was used as the anchor point for projecting ozone and annual PM 2.5 concentration values for the 2017 base case
and for the 2017 illustrative control scenario usin g methodologies consistent with the EPA’s air quality modeling guidance (U.S. EPA, 2007). The air quality modeling results for the 2017 base case served as the baseline for gauging the future year impacts on ozone and annual PM 2.5 of the
illustrative control case scenario. The 2017 base c ase reflects emissions reductions between 2011 and 2017 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 through Tier-3, and various state emissions control programs and consent decrees. The methods for estimating the EGU emissions for the proposal a re described in Chapter 3 of the RIA for the Transport Rule proposal (U.S. EPA, 2015). State tot al annual EGU emissions for NO X and SO 2 for each of the scenarios modeled are provided in T able 6A-1. The data indicate that, overall nationwide, EGU SO 2 and NO X emissions with the illustrative control case scena rio would be
about 14% lower than the 2017 base case.

Table 6A-1. State Total Annual EGU Emissions for NO X for the 2011 Base Year, 2017

Base Case, and 2017 Transport Rule Proposal (in tho usands of tons)

State 2011 Base Year 2017 Base Case 2017 Transport Rule Proposal Alabama 63 13,592 10,486 Arizona 35 16,960 16,975 Arkansas 38 6,399 6,414 California 6 3,789 3,789 Colorado 51 13,467 13,467 Connecticut 1 1,607 1,607 Delaware 4 580 580 Florida 61 0 0 Georgia 54 30,046 30,433 Idaho - 7,498 7,510 Illinois 73 251 248 Indiana 121 11,002 10,761 Iowa 40 42,496 31,033 Kansas 44 8,307 7,935 Kentucky 92 11,820 11,393 Louisiana 47 27,201 15,976 Maine 2 11,162 11,077

114 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.6A-3

State 2011 Base Year 2017 Base Case 2017 Transport Rule Proposal Maryland 19 1,565 1,565 Massachusetts 5 7,324 6,138 Michigan 75 2,219 2,222 Minnesota 32 22,233 20,635 Mississippi 26 11,223 11,226 Missouri 66 8,299 7,579 Montana 20 18,663 17,793 Nebraska 37 7,759 7,759 Nevada 7 14,613 14,577 New Hampshire 4 4,547 4,546 New Jersey 6 289 289 New Mexico 23 3,950 3,090 New York 22 17,372 17,372 North Carolina 46 7,911 7,675 North Dakota 51 17,307 15,341 Ohio 104 16,423 16,246 Oklahoma 82 29,249 19,823 Oregon 5 19,620 18,103 Pennsylvania 149 800 800 Rhode Island 0 43,599 17,094 South Carolina 25 257 255 South Dakota 11 5,875 6,046 Tennessee 27 297 297 Texas 146 5,566 5,493 Tribal Data 65 59,199 57,146 Utah 51 24,489 24,489 Vermont 0 163 163 Virginia 38 9,201 8,882 Washington 7 747 747 West Virginia 58 25,664 14,755 Wisconsin 32 5,923 5,917 Wyoming 53 10,796 10,796 National Total 2,024 609,317 524,543

As indicated above, the air quality modeling was u sed to project gridded ozone and
annual PM 2.5 concentrations at the 12km by 12km resolution for the 2017 base case and the
illustrative control case scenario modeled for thi s analysis. The air quality modeling results were
combined with monitored ozone and PM 2.5 data to create projected spatial fields of annual PM 2.5 and seasonal mean (May through September) 8-hour da ily maximum ozone for the 2017 base case and for the proposal scenario. These spatial f ields were then used as inputs to estimate the health co-benefits of the proposed Transport Rule a s described below.6A-4

6A.3 National PM 2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Transport Rule After estimating the 12km by 12km resolution PM 2.5 benefits for each of the analysis years applied in this RIA (i.e., 2017), we aggregated the benefits results nationally. 115 In order to calculate the benefit-per-ton estimates, we divided the regional benefits estimates by the corresponding emissions, as shown in Table 4A-1. La stly, we adjusted the benefit-per-ton estimates for a currency year of 2011$. 116 This method provides estimates of the national 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 emissions and for mation 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 levels of emission s 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 would better appr oximate 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 on the level of emissions reductions and the baseline emissions levels. However, we do not have sufficien t air quality modeling data to calculate marginal benefit-per-ton estimates for the EGU sect or. Therefore, using an average benefit-perton estimate for NO X adds uncertainty to the co-benefits estimated in t his RIA. In this RIA, we estimate emission reductions from E GUs using IPM. 117 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 organic co mpounds, ammonia and total directly emitted PM 2.5 and PM 10 emissions are post-calculated. 118 In addition, directly emitted particle emissions calculated from IPM outputs do not includ e speciation, i.e. they are only the total emissions. In order to conduct air quality modeling , 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 reflects the fuel and the unit configuration. Model-predicted concentrations of ni trate and sulfate include 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. Because this proposed rule is not e xpected to greatly affect the levels of directly emitted PM 2.5 or SO 2 , we do not quantify a benefit per-ton value for either pollutant. 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

115 This aggregation is identified as the shapefile “R eport Regions” in BenMAP’s grid definitions. 116 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.
117 See Chapter 3 of this RIA for additional informatio n regarding the Integrated Planning Model ( IPM).

118 Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/docs/v513/Fl atFile_Methodology.pdf6A-5

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 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. Table 6A-3 reports the national benefit-per-ton est imates for the EGU sector at discount rates of 3% and 7% in 2017. Tables 6A-4 reports the incidenc e per ton estimates (which follows the same general methodology as for the benefit-per-ton calculations) for the EGU sector in 2017 for the set of health endpoints used to calculate the b enefit-per-ton estimates.6A-6

Table 6A-3. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality

Modeling from Proposed Transport Rule in 2017 (2011 $)*

Pollutant Discount

Rate Benefit per ton
NO X (as PM 2.5 ) 3% $2,100 to $4,700

7% $1,900 to $4,300 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality for PM 2.5 . All estimates are rounded to two significant figu res. All fine particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized benefits incorporate the conversion f rom precursor emissions to ambient fine particles. The estimates do not include reduced hea lth effects from direct exposure to ozone, NO 2 , SO 2 , ecosystem effects, or visibility impairment.
Table 6A-4. Summary of Regional PM 2.5 Incidence-per-Ton Estimates Based on Air

Quality Modeling from Proposed Transport Rule in 20 17*

Health Endpoint NOx Incidence per ton
Premature Mortality Krewski et al. (2009) – adult 0.00024 Lepeule et al. (2012) – adult 0.00054 Woodruff et al. (1997) – infants 0.00001 Morbidity Emergency department visits for asthma 0.00013 Acute bronchitis 0.00034 Lower respiratory symptoms 0.00436 Upper respiratory symptoms 0.00623 Minor restricted-activity days 0.17575 Lost work days 0.02947 Asthma exacerbation 0.00642 Hospital Admissions, Respiratory 0.00007 Hospital Admissions, Cardiovascular 0.00009 Non-fatal Heart Attacks (age>18) Peters et al (2001) 0.00028 Pooled estimate of 4 studies 0.00003 * All estimates are rounded to two significant figu res. All fine particles are assumed to have equivalent health effects, but the incidence-per-to n estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The incid ence benefit-per-ton estimates incorporate the conversio n from precursor emissions to ambient fine particles.6A-7

6A.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 benefit-pe r-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 insignifi cant 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 bene fitper-ton estimate for NO X adds uncertainty to the ozone co-benefits estimate d for the proposed guidelines. 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 Proposed Trans port Rule during the ozoneseason only (May to September). As shown in Table 6 A-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, this approach u nderestimates ozone co-benefits in areas with longer ozone seasons such as southern Californ ia and Texas. When the underestimated benefit-per-ton estimate is multiplied by ozone-sea son only NO X emission reductions, this results in an underestimate of the monetized ozone co-benefits. Table 6A-5 reports the ozone benefitper-ton estimates using ozone-season only NO X for 2017. Table 6A-7 reports the ozone season incidence-per-ton estimates for 2017.
Table 6A-5. Summary of National Ozone Benefit-per-T on Estimates Based on Air

Quality Modeling from Proposed Transport Rule in 20 17 (2011$)*
Ozone precursor Pollutant Benefit per ton

Ozone season NO X $4,300 to $20,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality for ozone. All estimates are rounded to t wo significant figures. The monetized benefits incorporate the conversion from NO X precursor emissions to ambient ozone.6A-8

Table 6A-6. Summary of National Ozone Incidence-per-Ton Estimates Based on Air

Quality Modeling from Proposed Transport Rule in 20 17* Health Endpoint Incidence per ton Premature Mortality – adult Smith et al. (2009) 0.000569 Zanobetti & Schwartz (2008) 0.000956 Morbidity Hospital Admissions, Respiratory (ages < 2 & > 65) 0.000927 Emergency Room Visits, Respiratory 0.003771 Acute Respiratory Symptoms 2.781551 School Loss Days 0.903847 * All estimates are rounded to two significant figu res. The incidence benefit-per-ton estimates incorporate the conversion from NO X precursor emissions to ambient ozone. These estima tes reflect ozone-season NO X emissions.

6A.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 . 16:547-562.6A-9

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. 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. December. Ava ilable at: < http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.p df>. Accessed June 4, 2015. 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 >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 15. Regulatory Impact Analysis for the Carbon Pollution Guidelines for Existing Power Plan ts 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. Availa ble at < http://www3.epa.gov/airquality/cpp/cpp-final-rule-r ia.pdf >.6B-17-1

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER REVIEWS

Overview
This section explains the statutory and executive o rders applicable to EPA rules, and discusses EPA’s actions taken pursuant to these ord ers.
7.1 Executive Order 12866: Regulatory Planning an d Review
This action is an economically significant regulato ry action that was submitted to the Office of Management and Budget (OMB) for review. A ny changes made in response to OMB
recommendations have been documented in the docket. This RIA estimates the costs and
monetized human health and welfare benefits of the proposal. Consistent with Executive Order (EO) 12866 and EO 13563, the EPA estimated the cost s and benefits for three regulatory control alternatives: the proposed EGU NO X ozone-season emissions budgets and more and less stringent alternatives. The proposed action would r educe ozone-season NO X emissions from EGUs in 23 eastern states. Actions taken to comply with the proposed EGU NO X ozone-season emissions budgets would also reduce emissions of ot her criteria air pollution and hazardous air pollution emissions, including annual NO X , and CO 2 . The benefits associated with these copollutant reductions are referred to as co-benefits , as these reductions are not the primary objective of the rule.

7.2 Paperwork Reduction Act
The information collection activities in this propo sed rule have been submitted for approval to the Office of Management and Budget (OM B) under the Paperwork Reduction Act (PRA), 44 U.S.C. 3501 et seq. The Information Collection Request (ICR) document t hat the EPA prepared has been assigned EPA ICR number 2527.01 . You can find a copy of the ICR in the
docket for th eis proposed rule, and it is briefly summarized here. The information generated by information collection activities under CSAPR is used by the EPA to ensure that affected facilities comply w ith the emission limits and other requirements. Records and reports are necessary to enable EPA or states to identify affected facilities that may not be in compliance with the requirements. The rec ordkeeping requirements require only the specific information needed to determine compliance . These recordkeeping and reporting requirements are established pursuant to CAA sectio ns 110(a)(2)(D) and (c) and 301(a) (427-2

U.S.C. 7410(a)(2)(D) and (c) and 7601(a)) and are s pecifically authorized by CAA section 114 (42 U.S.C. 7414). Reported data may also be used fo r other regulatory and programmatic purposes. All information submitted to the EPA for which a claim of confidentiality is made will be safeguarded according to EPA policies in 40 CFR part 2, subpart B, Confidentiality of
Business Information. All of the EGUs that would be subject to changed in formation collection requirements under this proposed rule are already subject to inf ormation collection requirements under CSAPR. Most of these EGUs also are already subject to information collection requirements under the Acid Rain Program (ARP) established under Title IV of the 1990 Clean Air Act Amendments. Both CSAPR and the ARP have existing ap proved ICRs or pending renewals:
EPA ICR Number 2391.03/OMB Control Number 2060-0667 (CSAPR) and EPA ICR Number 1633.16/OMB Control Number 2060-0258 (ARP). The bur den and costs of the information collection requirements covered under the CSAPR ICR are estimated as incremental to the information collection requirements covered under t he ARP ICR. Most of the information used
to estimate burden and costs in this ICR was develo ped for the existing CSAPR and ARP ICRs. This proposed rule would change the universe of sou rces subject to certain information collection requirements under CSAPR but would not c hange the substance of any CSAPR information collection requirements. The burden and costs associated with the proposed changes in the reporting universe are estimated as reductio ns from the burden and costs under the existing CSAPR ICR. (This proposed rule would not change any source’s information collection requirements with respect to the ARP.) The EPA inte nds to incorporate the burden and costs associated with the proposed changes in the reporti ng universe under this rule into the next
renewal of the CSAPR ICR. Respondents/affected entities : Entities potentially affected by this proposed ac tion are EGUs in the states of Florida, Kansas, and South Ca rolina that meet the applicability criteria for
the CSAPR NO X Ozone Season Trading Program in 40 CFR 97.404. Respondent’s obligation to respond : Mandatory (sections 110(a) and 301(a) of the Clea n Air Act). Estimated number of respondents : 116 sources in Florida, Kansas, and South Carolin a with one or more EGUs. Frequency of response : Quarterly, occasionally.7-3

Total estimated burden : reduction of 14,064 hours (per year). Burden is d efined at 5 CFR 1320.3(b).
Total estimated cost : reduction of $1,472,047 (per year), includes redu ction of $450,951 operation and maintenance costs.
The burden and cost estimates above reflect the red uction in burden and cost for Florida sources with EGUs that would no longer be required to report NO X mass emissions and heat input data for the ozone season to the EPA under th e proposed rule and that are not subject to similar information collection requirements under t he Acid Rain Program. Because these EGUs would no longer need to collect NO X emissions or heat input data under 40 CFR part 75, the
estimates above also reflect the reduction in burde n and cost to collect and quality assure these data and to maintain the associated monitoring equi pment. The EPA estimates that the proposed rule would caus e no change in information
collection burden or cost for EGUs in Kansas that w ould be required to report NO X mass emissions and heat input data for the ozone season to the EPA or for EGUs in South Carolina that would no longer be required to report NO X emissions and heat input data for the ozone
season to the EPA. The EGUs in both Kansas and Sout h Carolina already are and would remain subject to requirements to report NO X mass emissions and heat input data for the entire year to the EPA under the CSAPR NO X Annual Trading Program, and the requirements relat ed to ozone season reporting are a subset of the requirements r elated to annual reporting. Similarly, the EPA estimates that the proposed rule would cause no cha nge in information collection burden or cost for EGUs in Florida that are subject to the Acid Ra in Program because of the close similarity between the information collection requirements und er CSAPR and under the Acid Rain Program. More information on the ICR analysis is included in the docket for the proposed rule. 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.

.7-4

7.3 Regulatory Flexibility Act

The EPA certifies that this action will not have a significant economic impact on a
substantial number of small entities under the RFA. The small entities subject to the requirements of this action are small businesses, s mall organizations, and small governmental jurisdictions. The EPA has determined that 1 entity (of 7 small entities identified as potentially affected) may experience an impact of greater than 3 percent of annual revenues. Details of this analysis are presented below.

The Regulatory Flexibility Act (5 U.S.C. 601 et seq .), as amended by the Small Business Regulatory Enforcement Fairness Act (Public Law No. 104 121), provides that whenever an agency is required to publish a general notice of p roposed rulemaking, it must prepare and make available an initial regulatory flexibility analysi s, unless it certifies that the proposed rule, if promulgated, will not have a significant economic i mpact on a substantial number of small entities (5 U.S.C. 605[b]). Small entities include small businesses, small organizations, and small governmental jurisdictions.

The EPA conducted regulatory flexibility analysis a t the ultimate (i.e., highest) level of ownership, evaluating parent entities with the larg est share of ownership in at least one potentially-affected EGU included in EPA’s base cas e using the IPM v.5.15, used in this RIA. 1 This analysis draws on the “parsed” unit-level esti mates using IPM results for 2018, 2 as well as ownership, employment, and financial information fo r the potentially affected small entities
drawn from other resources described in more detail below.

The EPA identified the size of ultimate parent enti ties by using the Small Business Administration (SBA) size threshold guidelines. 3 The criteria for size determination vary by the organization/operation category of the ultimate par ent entity, as follows:

• Privately-owned (non-government) entities (see Tabl e 7-1)

1 Detailed documentation for IPM v.5.15 is available at: http://www.epa.gov/airmarkets/powersectormodeling.h tml

.

2 For this analysis, the 2018 parsed file is used to represent 2017 for the purposes of RIA analysis.

3 U.S. Small Business Administration (SBA). 2014. Sm all Business Size Standards. Effective as of July 1 4, 2014. See: http://www.sba.gov/sites/default/files/Size_Standar ds_Table.pdf

.7-5 o Privately-owned entities include investor-owned uti lities, non-utility entities, and entities with a primary business other than ele ctric power generation.

o For entities with electric power generation as a pr imary business, small entities
are those with less than the threshold number of em ployees specified by SBA for each of the relevant North American Industry Cl assification System (NAICS) sectors (NAICS 2211).

o For entities with a primary business other than ele ctric power generation, the relevant size criteria are based on revenue, assets , or number of employees by
NAICS sector. 4

• Publicly-owned entities

o Publicly-owned entities include federal, state, mun icipal, and other political
subdivision entities.

o The federal and state governments were considered t o be large. Municipalities and other political units with population fewer tha n 50,000 were considered to
be small.

• Rural Electric Cooperatives

o Small entities are those with fewer than the thresh old level of employees or revenue specified by SBA for each of the relevant N AICS sectors.
7.3.1 Identification of Small Entities
In this analysis, the EPA considered EGUs which mee t the following five criteria: 1) EGU is represented in NEEDS v5.15; 2) EGU is fossil fuel-fired; 3) EGU is located in a state covered by this proposed rule; 4) EGU is neither a cogeneration unit nor solid waste incineration unit; 5) EGU capacity is 25MW or larger. The EPA ne xt refined this list of EGUs, narrowing it to those that exhibit at least one of the following changes under the proposal, in comparison to
the baseline.

4 Certain affected EGUs are owned by ultimate parent entities whose primary business is not electric po wer generation.7-6

• Summer fuel use (BTUs) changes by +/- 1% or more

• Summer generation (GWh) changes by +/- 1% or more

• NOx summer emissions (tons) changes by +/- 1% or mo re
Based on these criteria, the EPA identified a tota l of 318 potentially affected EGUs warranting examination in this RFA analysis. Next, we determined power plant ownership information, including the name of associated ownin g entities, ownership shares, and each entity’s type of ownership. We primarily used data from SNL and Ventyx, supplemented by limited research using publicly available data. 5 Majority owners of power plants with affected EGUs were categorized as one of the seven ownership types. 6 These ownership types are:

1. Investor-Owned Utility (IOU) : Investor-owned assets (e.g., a marketer, independ ent power producer, financial entity) and electric comp anies owned by stockholders, etc.

2. Cooperative (Co-Op) : Non-profit, customer-owned electric companies tha t generate and/or distribute electric power.

3. Municipal : A municipal utility, responsible for power supply and distribution in a small region, such as a city.

4. Sub-division : Political subdivision utility is a county, munici pality, school district, hospital district, or any other political subdivisi on that is not classified as a municipality
under state law.

5. Private : Similar to an investor-owned utility, however, ow nership shares are not openly
traded on the stock markets.

6. State : Utility owned by the state.

7. Federal : Utility owned by the federal government.

5 SNL Financial data covers the energy market and ot her industries, and includes detailed immediate and ultimate ownership at the EGU level. For more information, s ee: www.snl.com . The Ventyx Energy Velocity Suite database consists of detailed ownership and corporate affili ation information at the EGU level. For more inform ation, see: www.ventyx.com

.

6 Throughout this analysis, the EPA refers to the ow ner with the largest ownership share as the “majori ty owner” even when the ownership share is less than 51 perce nt.7-7

Next, the EPA used both the Hoover’s online databas e and the SNL database to identify the ultimate owners of power plant owners identifie d in the SNL and Ventyx databases. This was necessary, as many majority owners of power plants (listed in SNL or Ventyx) are themselves owned by other ultimate parent entities (listed in Hoover’s or SNL). 7 In these cases, the ultimate parent entity was identified via Hoover’s or SNL, w hether domestically or internationally owned.

The EPA followed SBA size standards to determine wh ich non-government ultimate parent entities should be considered small entities in this analysis. These SBA size standards are specific to each industry, each having a threshold level of either employees, revenue, or assets below which an entity is considered small. SBA guid elines list all industries, along with their
associated NAICS code and SBA size standard. Theref ore, it was necessary to identify the specific NAICS code associated with each ultimate p arent entity in order to understand the appropriate size standard to apply. Data from Hoove r’s was used to identify the NAICS codes for most of the ultimate parent entities. In many cases , an entity that is a majority owner of a power plant is itself owned by an ultimate parent entity with a primary business other than electric power generation. Therefore, it was necessary to co nsider SBA entity size guidelines for the range of NAICS codes listed in Table 7-1. This tabl e represents the range of NAICS codes and areas of primary business of ultimate parent entiti es which are majority owners of potentially
affected EGUs in the EPA’s IPM base case.

7 The Hoover’s Inc. online platform includes company records that can contain NAICS codes, number of em ployees, revenues, and assets. For more information, see: http://www.hoovers.com7-8

Table 7-1. SBA Size Standards by NAICS Code

NAICS Code NAICS Description SBA Size Standard

221112 Fossil Fuel Electric Power Generation 750 em ployees 221118 Other Electric Power Generation 250 employee s 221122 Electric Power Distribution 1,000 employees 221210 Natural Gas Distribution 500 employees 238210 Electrical Contractors and Other Wiring Inst allation Contractors $15 million in revenue 324110 Petroleum Refineries 1,500 employees 325180 Other Basic Inorganic Chemical Manufacturing 1,000 employees 325320 Pesticide and Other Agricultural Chemical Ma nufacturing 500 employees 331313 Alumina Refining and Primary Aluminum Produc tion 1,000 employees 333613 Mechanical Power Transmission Equipment Manu facturing 500 employees 424720 Petroleum and Petroleum Products Merchant Wh olesalers

(except Bulk Stations and Terminals) 100 employees
486210 Pipeline Transportation of Natural Gas $27.5 million in revenue 522110 Commercial Banking $550 million in assets 522220 Sales Financing $38.5 million in revenue 523120 Securities Brokerage $38.5 million in revenue 523910 Miscellaneous Intermediation $38.5 million i n revenue 523930 Investment Advice $38.5 million in revenue 524126 Direct Property and Casualty Insurance Carri ers 1,500 employees 525120 Health and Welfare Funds $32.5 million in revenue 525990 Other Financial Vehicles $32.5 million in revenue 541611 Administrative Management and General Manage ment

Consulting Services $15 million in revenue
551112 Offices of Other Holding Companies $20.5 mil lion in revenue Note: Based on size standards effective at the time the EPA conducted this analysis (SBA size standard s, effective July 14, 2014) Source: SBA, 2014
The EPA compared the relevant entity size criterion for each ultimate parent entity to the
SBA threshold value noted in Table 7-1. We used the following data sources and methodology to
estimate the relevant size criterion values for eac h ultimate parent entity:

1. Employment, Revenue, and Assets : The EPA used the Hoover’s database as the primary source for information on ultimate parent e ntity employee numbers, revenue, and

assets. 8 In parallel, the EPA also considered estimated rev enues from affected EGUs based on analysis of parsed-file estimates for the proposal. The EPA assumed that the ultimate parent entity revenue was the larger of th e two revenue estimates. In limited

8 Estimates of sales were used in lieu of revenue es timates when revenue data was unavailable.7-9

instances, supplemental research was also conducted to estimate an ultimate parent entity’s number of employees, revenue, or assets.

2. Population : Municipal entities are defined as small if they s erve populations of less than 50,000. The EPA primarily relied on data from the V entyx database and the U.S. Census Bureau to inform this determination. Supplemental r esearch of individual municipalities
was also conducted in some instances.

Ultimate parent entities for which the relevant mea sure is less than the SBA size criterion
were identified as small entities and carried forwa rd in this analysis. In the case of one entity, data limitations prevented the comparison of the en tity against its appropriate SBA size standard. For the purposes of this analysis, the EPA assumed that this entity is a small entity. Overall, the EPA identified 16 potentially affected EGUs owned b y 7 small entities included in the EPA’s
Base Case.

7.3.2 Overview of Analysis and Results
This section presents the methodology and results for estimating the impact of the proposed CSAPR update to small entities in 2017 bas ed on the following endpoints:

• annual economic impacts of the proposed CSAPR updat e on small entities, and

• ratio of small entity impacts to revenues from elec tricity generation.

7.3.2.1 Methodology for Estimating Impacts of the Proposed CSAPR update on Small Entities
An entity can comply with the proposed CSAPR updat e through some combination of the following: optimizing existing SCR and SNCRs, turni ng on idled SCR or SNCR controls, upgrading to state of the art combustion controls, using allocated allowances, purchasing allowances, or reducing emissions through a reducti on in generation or improved efficiency. Additionally, units with more allowances than neede d can sell these allowances in the market. The chosen compliance strategy will be primarily a function of the unit’s marginal control costs and its position relative to the marginal control c osts of other units.
To attempt to account for each potential control st rategy, the EPA estimates compliance costs as follows:7-10 C Compliance = ∆ C Operating+Retrofit + ∆ C Fuel + ∆ C

Allowances + ∆ C Transaction + ∆ R where C represents a component of cost as labeled, and ∆ R represents the value of foregone electricity generation, calculated as the differenc e in revenues between the base case and the proposed CSAPR update in 2017.
In reality, compliance choices and market condition s can combine such that an entity may actually experience a savings in any of the individ ual components of cost. Under the proposed CSAPR update, some units will forgo some level of e lectricity generation (and thus revenues) to comply and this impact will be lessened on these en tities by the projected increase in electricity prices under the proposed CSAPR update. On the othe r hand, those increasing generation levels will see an increase in electricity revenues and as a result, lower net compliance costs. If entities are able to increase revenue more than an increase in fuel cost and other operating costs, ultimately they will have negative net compliance c osts (or savings). Overall, small entities are not projected to install relatively costly emission s control retrofits, but may choose to do so in some instances. Because this analysis evaluates the total costs along each of the compliance strategies laid out above for each entity, it inevi tably captures savings or gains such as those described. As a result, what we describe as cost is really more of a measure of the net economic impact of the rule on small entities.
For this analysis, the EPA used IPM-parsed output t o estimate costs based on the parameters above, at the unit level. These impacts were then summed for each small entity, adjusting for ownership share. Net impact estimates were based on the following: operating and retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity generation revenues under the proposed CSAPR update relative to the base case. These individual components of compliance cost were estim ated as follows:
(1) Operating and retrofit costs: Using the IPM-parsed output for the base case and the proposed CSAPR update, the EPA identified units that install control technology under the proposed CSAPR update, and wha t technology was installed. The equations for calculating retrofit c osts were adopted from the EPA’s version of IPM. The model calculates the capital co st (in $/MW); the fixed operation and maintenance (O&M) cost (in $/MW-year) ; the variable O&M cost (in $/MWh); and the total annual cost for units pro jected to optimize and/or turn on existing idled SCR or SNCR.
(2) Sale or purchase of allowances: To estimate the value of allowances holdings, allocated allowances were subtracted from projected emissions, and the difference7-11 was then multiplied by $1,300 per ton. $1,300 per t on is the marginal cost of NO X reductions used to set the proposed budgets in the proposed CSAPR update. While this is a reasonable approximation, it is pos sible that the actual allowance price could be lower. Units were assumed to purchas e or sell allowances to exactly cover their projected emissions under the p roposed CSAPR update.

(3) Fuel costs: The change in fuel expenditures under the proposed CSAPR update was estimated by taking the difference in projected fuel expenditures between the IPM estimates for the proposed CSAPR update and the base case.
(4) Value of electricity generated: To estimate the value of electricity generated, the projected level of electricity generation is multip lied by the regional-adjusted retail electricity price ($/MWh) estimate, for all entities except those categorized as Private in Ventyx. For private entities, the EPA used wholesale electricity price instead retail electricity price because most of th e private entities are independent power producers (IPP). IPPs sell their electricity to wholesale purchasers and do not own transmission facilities and thus their reve nue was estimated with wholesale electricity prices.

(5) Administrative costs: Because most affected units are already monitored as a result of other regulatory requirements, the EPA co nsidered the primary administrative cost to be transaction costs related to purchasing or selling allowances. The EPA assumed that transaction costs were equal to 1.5 percent of the total absolute value of the difference between a unit’s allocation and projected NOX emissions. This assumption is based on market r esearch by ICF International.

7.3.2.2 Results
The potential impacts of the proposed CSAPR update on small entities are summarized in Table 7-2. All costs are presented in $2011. The EP A estimated the annual net compliance cost to small entities to be approximately - $38.3 milli on in 2017 or savings of $38.3 million. The fact that the net compliance costs for all entities are actually net savings does not mean that each small entity would benefit from the proposal to upd ate CSAPR. The net savings are driven by
entities that are able to increase their revenues b y increasing generation.7-12

Table 7-2. Projected Impact of the Proposed CSAPR Update on Small Entities in 2017

EGU Ownership Type Number of

Potentially Affected Entities Total Net

Compliance Cost ($2011 millions) Number of Small

Entities with Compliance Costs >1% of Generation Revenues Number of Small

Entities with Compliance Costs >3% of Generation Revenues Cooperative 2 $0.2 0 0

Investor-Owned Utility 1 -$38 0 0

Municipal 2 -$0.1 1 1

Private 2 -$0.4 0 0

Total 7 -$38.3 1 1 Note: The total number of entities with costs grea ter than 1 percent or 3 percent of revenues include s only entities experiencing positive costs. A negative cost value implies that the group of entities experiences a ne t savings under the proposed CSAPR update. Source: IPM analysis The EPA assessed the economic and financial impacts of the rule using the ratio of compliance costs to the value of revenues from elec tricity generation, focusing in particular on entities for which this measure is greater than 1 p ercent. Although this metric is commonly used in the EPA impact analyses, it makes the most sense when as a general matter an analysis is looking at small businesses that operate in competi tive environments. However, small businesses in the electric power industry often operate in a p rice -regulated environment where they are able to recover expenses through rate increases. Given t his, the EPA considers the 1 percent measure in this case a crude measure of the price increases these small entities will be asking of rate commissions or making at publicly owned companies.
Of the 7 small entities considered in this analysis , 1 entity may experience compliance costs greater than 1 percent of generation revenues in 2017. Since this entity is not projected to operate in the base case, we are unable to compare the estimated compliance costs to generation revenues. However, we note that this entity is loca ted in a cost of service market, where typically we expect entities should be able to recover all of their costs of complying with the proposed CSAPR update. Entities that experience negative net costs under the proposed CSAPR update are excluded from these totals. The EPA has conclud ed that there is no significant economic impact on a substantial number of small entities (N o SISNOSE) for this rule. The number of7-13

entities with compliance costs exceeding 3 percent of generation revenues is also included in Table 7-2.
The distribution across entities of economic impact s as a share of base case revenue is summarized in Table 7-3. Since there are few potent ially-impacted small entities included in this analysis, the distributions of economic impacts on each ownership type are in general fairly tight. Note that one municipal entity is not projected to operate in the base case, and we are therefore unable to compare the estimated compliance costs to generation revenues. We estimate a positive compliance cost for this entity.
Table 7-3. Summary of Distribution of Economic Imp acts of the Proposed CSAPR Update

on Small Entities in 2017

EGU Ownership Type Capacity-Weighted

Average Economic Impacts as a % of Generation Revenues Min Max

Cooperative 0.3% 0.1% 0.5%
Investor-owned utility -60.1% -60.1% -60.1%
Municipal* -0.5% -0.5% N/A*

Private -2.3% -4.2% -1.5% *Note: One municipal entity is not projected to op erate in the base case, and we are therefore unable to compare the estimated compliance costs to generation revenu es. Source: IPM analysis7-14

The separate components of annual costs to small en tities under the proposed CSAPR update are summarized in Table 7-4. The most signif icant components of incremental cost to these entities under the proposed CSAPR update are due to lower electricity revenues and increased fuel costs. Fuel costs increase over all ownership groups except the ones under the ownership type “Private” because an entity with the second largest generation under “Private” is projected to cut its generation by 25 percent under the proposed CSAPR update, which translates to lower fuel costs for the whole group. Additional ly, increases in electricity generation revenue, shown as cost savings or negative costs are experie nced by cooperative, investor-owned utility, municipal and subdivision entities. This is due lar gely to the projected increase in electricity prices under the proposed CSAPR update. Among the p rivate category, however, reduced generation by the one entity with a large share of generation leads to higher net costs for the entire category. Our data suggests this entity owns a group of combined cycle units and which are presumably marginal units in their respective l oad segments under the base case.
Table 7-4. Incremental Annual Costs under the Prop osed CSAPR Update Summarized by

Ownership Group and Cost Category in 2017 (2011$ mi llions)

EGU Ownership Type Operating

Cost Net Purchase

of Allowances Fuel Cost Lost Electricity Revenue Administrative

Cost

Cooperative $0.1 $0.2 -$0.5 $0.4 $0.00

InvestorOwned Utility $1.9 $0.0 $26.8 -$66.7 $0.00
Municipal $0.1 $0.0 -$0.2 -$0.1 $0.00

Private -$0.5 -$0.3 -$3.9 $4.4 $0.00 Source: IPM analysis

.
7.3.3 Summary of Small Entity Impacts

The EPA examined the potential economic impacts to small entities associated with this rulemaking based on assumptions of how the affected states will implement control measures to meet their emissions. To summarize, of the 7 small entities potentially affected, 1 may experience compliance costs in excess of 1 percent of revenues in 2017, based on assumptions of how the affected states implement control measures to meet their emissions budgets as set forth in this rulemaking. Potentially affected small enti ties experiencing compliance costs in excess of7-15

1 percent of revenues have some potential for signi ficant impact resulting from implementation of the proposed CSAPR update. However, as noted abo ve, it is the EPA’s position that because this entity does not operate in a competitive marke t environment, they should generally be able to pass the costs of complying with the proposed CS APR update on to rate-payers.

The EPA has lessened the impacts for small entities by excluding all units smaller than 25 MW. This exclusion, in addition to the exemption s for cogeneration units and solid waste incineration units, eliminates the burden of higher costs for a substantial number of small entities located in the 23 states for which the EPA is propo sing FIPs.

7.4 Unfunded Mandates Reform Act
Title II of the UMRA of 1995 (Public Law 104-4)(UM RA) establishes requirements for federal agencies to assess the effects of their reg ulatory actions on state, local, and Tribal governments and the private sector. Under Section 2 02 of the UMRA, 2 U.S.C. 1532, the EPA generally must prepare a written statement, includi ng a cost-benefit analysis, for any proposed or final rule that includes any Federal mandate that m ay result in the expenditure by State, local, and Tribal governments, in the aggregate, or by the private sector, of $100,000,000 or more in any one year. A Federal mandate is defined under Se ction 421(6), 2 U.S.C. 658(6), to include a Federal intergovernmental mandate and a Federal pri vate sector mandate. A Federal intergovernmental mandate, in turn, is defined to i nclude a regulation that would impose an enforceable duty upon State, Local, or Tribal gover nments, Section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a dut y that is a condition of Federal assistance, Section 421(5)(A)(i)(I). A Federal private sector m andate includes a regulation that would impose an enforceable duty upon the private sector, with certain exceptions, Section 421(7)(A), 2 U.S.C. 658(7)(A).

Before promulgating an EPA rule for which a written statement is needed under Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires the EPA to identify and consider a reasonable number of regula tory alternatives and adopt the least costly, most cost-effective, or least burdensome alternativ e that achieves the objectives of the rule. Moreover, section 205 allows the EPA to adopt an al ternative other than the least costly, most7-16

cost-effective or least burdensome alternative if t he Administrator publishes with the final rule an explanation why that alternative was not adopted

.
Furthermore, as the EPA stated in the proposal, the EPA is not directly establishing any regulatory requirements that may significantly or u niquely affect small governments, including Tribal governments. Thus, under the proposed CSAPR update, the EPA is not obligated to develop under Section 203 of the UMRA a small gover nment agency plan.

The EPA did analyze the economic impacts of the pro posed CSAPR update on government entities, however. This analysis does no t examine potential indirect economic impacts associated with the proposed CSAPR update, such as employment effects in industries providing fuel and pollution control equipment, or the potential effects of electricity price
increases on industries and households.

7.4.1 Identification of Government-Owned Entities
In this analysis, the EPA considered EGUs which mee t the following five criteria: 1) EGU is represented in NEEDS v5.15; 2) EGU is fossil -fuel fired; 3) EGU is located in a state covered by this proposed rule; 4) EGU is neither a cogeneration unit nor solid waste incineration unit; and 5) EGU capacity is 25 MW or larger.

The EPA next refined this list of EGUs, narrowing i t to those that exhibit at least one of the following changes under the proposal, in compar ison to the base case.

• Summer fuel use (BTUs) changes by +/- 1% or more

• Summer generation (GWh) changes by +/- 1% or more

• NOx summer emissions (tons) changes by +/- 1% or mo re
From the inventory of units meeting the criteria a bove, the EPA used Ventyx data to identify state and municipality-owned utilities and subdivisions in the proposed CSAPR update region. The EPA then used IPM-parsed output to asso ciate these plants with individual generating units. The EPA identified nine municipal ity-owned utilities that are potentially affected by the proposed CSAPR update.7-17

7.4.2 Overview of Analysis and Results
After identifying potentially affected government entities, the EPA estimated the impact of the proposed CSAPR update in 2017 based on the f ollowing:

• total impacts of compliance on government entities; and

• ratio of government entity impacts to revenues from electricity generation.

The financial burden to owners of EGUs under the pr oposed CSAPR update is composed of compliance and administrative costs. This sectio n outlines the compliance and administrative costs for the nine potentially affected government- owned units in the proposed CSAPR update region.

7.4.2.1 Methodology for Estimating Impacts of the proposed CSAPR update on Government Entities An entity can comply with the proposed CSAPR updat e through any combination of the following: optimizing existing SCR and SNCRs, turni ng on idled SCR or SNCR controls, upgrading to state of the art combustion controls, using allocated allowances, purchasing allowances, or reducing emissions through a reducti on in generation or improved efficiency. Additionally, units with more allowances than neede d can sell these allowances on the market. The chosen compliance strategy will be prim arily a function of the unit’s marginal control costs and its position relative to the marg inal control costs of other units.

To attempt to account for each potential control st rategy, the EPA estimates compliance costs
as follows:

C Compliance = ∆ C Operating+Retrofit + ∆ C Fuel + ∆ C

Allowances + ∆ C Transaction + ∆ R where C represents a component of cost as labeled, and ∆ R represents the retail value of foregone electricity generation.

In reality, compliance choices and market conditio ns can combine such that an entity may actually experience a savings in any of the individ ual components of cost. Under the proposed CSAPR update, for example, some units will forgo some level of electricity generation (and thus revenues) to comply, this impa ct will be lessened on these entities by7-18

the projected increase in electricity prices under the proposed CSAPR update, while those not reducing generation levels will see an increase in electricity revenues. Because this analysis evaluates the total costs along each of the complia nce strategies laid out above for each entity, it inevitably captures savings or gains suc h as those described. As a result, what we describe as cost is really more of a measure of the net economic impact of the rule on small
entities.

In this analysis, the EPA used IPM-parsed output f or the base case and the proposed CSAPR update to estimate compliance cost at the uni t level. These costs were then summed for each small entity, adjusting for ownership shar e. Compliance cost estimates were based on the following: operating and retrofit costs, sal e or purchase of allowances, and the change in fuel costs or electricity generation revenues un der the proposed CSAPR update relative to the base case. These components of compliance cost were estimated as follows:

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case and the proposed CSAPR update, the EPA identified units that install control technology under the proposed CSAPR update and the technology installed. The equations for calculating retrofit costs were adopt ed from the EPA’s version of IPM. The model calculates the capital cost (in $/MW ); the fixed operation and maintenance (O&M) cost (in $/MW-year); the variable O&M cost (in $/MWh); and the total annual cost for units projected to op timize and/or turn on existing
idled SCR or SNCR.

(2) Sale or purchase of allowances: To estimate the value of allowances holdings, allocated allowances were subtracted from projected emissions, and the difference was then multiplied by $1,300 per ton. $1,300 per t on is the marginal annualized
cost of NO X reductions used to set the proposed budgets. While this is a reasonable approximation, it is possible that the a ctual allowance price could be lower. Units were assumed to purchase or sell allow ances to exactly cover their projected emissions under the proposed CSAPR update

.7-19

(3) Fuel costs: The change in fuel expenditures under the proposed CSAPR update was estimated by taking the difference in projected fuel expenditures between the proposed CSAPR update and the base case.

(4) Value of electricity generated: To estimate the value of electricity generated, th e projected level of electricity generation is multip lied by the regional-adjusted retail electricity price ($/MWh) estimate, for all entities except those categorized as Private in Ventyx. For private entities, the EPA used wholesale electricity price instead retail electricity price because most of th e private entities are independent power producers (IPP). IPPs sell their electricity to wholesale purchasers and do
not own transmission facilities and thus their reve nue was estimated with wholesale electricity prices.

(5) Administrative costs: Because most affected units are already monitored as a result of other regulatory requirements, the EPA co nsidered the primary
administrative cost to be transaction costs related to purchasing or selling
allowances. The EPA assumed that transaction costs were equal to 1.5 percent of
the total absolute value of the difference between a unit’s allocation and projected
NO X emissions. This assumption is based on market rese arch by ICF
International.

7.4.2.2 Results

A summary of economic impacts on government owned e ntities is presented in Table 7-5. According to the EPA’s analysis, the tot al net economic impact on government -owned entities (state- and municipality -owned utilities and subdivisions) is expected to be negative in 2014.9 Note that we expe ct the proposed CSAPR update to potentially have an impact on only one category of government-owned entities (municipality-owned entities).

9 All costs are reported in 2011 dollars.7-20

Table 7-5. Summary of Potential Impacts on Governm ent Entities under the Proposed

CSAPR Update in 2017

EGU Ownership Type Potentially

Affected Entities Projected

Annualized Costs ($2011 millions) Number of

Government Entities with Compliance Costs >1% of Generation Revenues Number of

Government Entities with Compliance Costs >3% of Generation Revenues

Municipal 9 -$4.6 4 2

Total 9 -$4.6 4 2 Note: The total number of entities with costs great er than 1 percent or 3 percent of revenues includes only entities experiencing positive costs, and includes one entit y for which we are unable to estimate base case gen eration revenues due to projected closure in the base case. A negative cost value implies that the group of en tities experiences a net savings under the proposed CSAPR update. As was done for the small entities analysis, the EP A further assessed the economic and financial impacts of the rule using the ratio of co mpliance costs to the value of revenues from electricity generation in the base case, also focus ing specifically on entities for which this measure is greater than 1 percent. 10 The EPA projects that four government entities wil l have compliance costs greater than 1 percent of revenues from electricity generation in 2017. One municipal entity is not projected to operate in the base case, and we are therefore unable to compare the estimated compliance costs to generatio n revenues. We include this entity in the >3% category. The majority of the units that have h igher costs are not expected to make operational changes as a result of this rule (e.g., turn on controls). Their increased costs are largely due to a change in generation level, which results in either a decrease in electricity revenue or increased fuel cost, coupled with a rela tively low base case revenue estimate. The EPA notes that increased fuel costs are often passe d through to rate-payers as common practice in many areas of the U.S. due to fuel adder arrange ments instituted by state public utility
commissions. Entities that are projected to experience negative compliance costs under the proposed CSAPR update are not included in those tot als. This approach is more indicative of a

10 Neither the costs nor the revenues of units that r etire under the proposed CSAPR update are included in this portion of the analysis. Because these units are be tter off retiring under the proposed CSAPR update t han continuing operation, the true cost of the rule on these units is not represented by our modeling. The true cost of the proposed CSAPR update for these units is the differential be tween their costs in the base case and the costs of meeting their customers’ demand under the rule.7-21

significant impact when an analysis is looking at e ntities operating in a competitive market environment. Government-owned entities do not opera te in a competitive market environment and therefore will be able to recover expenses unde r the proposed CSAPR update through rate increases. Given this, the EPA considers the 1 perc ent measure in this case a crude measure of
the extent to which rate increases will be made at publicly owned companies.

For municipality-owned entities, the capacity-weigh ted average economic impact as a share of base case revenue is 4.6 percent. This ave rage is heavily influenced by an outlier, for which the maximum economic impact as a share of bas e case revenues is 470 percent. This entity holds two small combustion turbines that are not projected to make any operational changes (e.g., turn on controls), but rather is pro jected to operate more under the proposed CSAPR update in 2017 than it was projected to opera te in the base case. The relatively large impact estimate on a percent basis results from the low level of generation and thus revenue in the base case. This is the only entity that experie nces economic impacts that are significantly higher than the capacity-weighted average for this group. The next highest economic impact in this group is 2.1 percent.

Additionally, four of the nine entities are project ed to experience a negative economic impact as a share of base case revenue, which impli es that this group of four entities experiences a net savings under the proposed CSAPR update.

The various components of annual incremental cost u nder the proposed CSAPR update to government entities are summarized in Table 7-6. In 2017, municipals are a net purchaser of allowances, and experience both an increase in fuel expenditures and an increase in electricity revenue under the proposed CSAPR update. Incrementa l fuel costs are positive because these entities are projected to increase generation and f ace higher fuel prices. Overall, increases in total electricity revenue by government entities under th e proposed CSAPR update exceed the increases in fuel and operating costs.7-22

Table 7-6. Incremental Annual Costs under the Prop osed CSAPR Update Summarized by

Ownership Group and Cost Category (2011$ millions) in 2017

EGU Ownership Type Retrofit +

Operating Cost Net Purchase

of Allowances Fuel Cost Lost Electricity Revenue Administrative

Cost

Municipal $2.7 $0.7 $1.4 -$9.4 $0.0 Source: IPM analysis 7.4.3 Summary of Government Entity Impacts The EPA examined the potential economic impacts on government-owned entities associated with this rulemaking based on assumption s of how the affected states will implement control measures to meet their emissions. According to the EPA’s analysis, the total net economic impact on government-owned entities is exp ected to be -$4.6 million in 2017 or a net savings of $4.6 million. This does not mean that ea ch government entity will experience net savings as the overall net savings is driven by som e entities garnering savings. Of the nine government entities considered in this analysis, fo ur may experience compliance costs in excess of 1 percent of revenues in 2017, based on our assu mptions of how the affected states implement control measures to meet their emissions budgets as set forth in this rulemaking.

Government entities projected to experience complia nce costs in excess of 1 percent of revenues have some potential for significant impact resulting from implementation of the proposed CSAPR update. However, as noted above, it is the EPA’s position that because these government entities can pass on their costs of comp liance to rate-payers, they will not be significantly affected.

7.5 Executive Order 13132: Federalism

This action does not have federalism implications. It will not have substantial direct effects on the states, on the relationship between the national government and the states, or on the distribution of power and responsibilities amon g the various levels of government.7-23

7.6 Executive Order 13175: Consultation and Coordi nation with Indian Tribal Governments
This action has tribal implications as specified in Executive Order 13175.

However, it will neither impose substantial direct compliance costs on federally recognized tribal governments, nor preempt tribal l aw.

This action proposes to implement EGU NO X ozone-season emissions reductions in 23 eastern states. However, at this t ime, none of the existing or planned EGUs affected by this rule are owned by tri bes or located in Indian country. This action may have tribal implications i f a new affected EGU is built in Indian country. Additionally, tribes have a vested interest in how this proposed rule would affect air quality.

In developing CSAPR, which was promulgated on July 6, 2011 to address interstate transport of ozone pollution under the 1 997 ozone NAAQS, 11 the EPA
consulted with tribal officials under the EPA Polic y on Consultation and Coordination with Indian Tribes early in the proces s of developing that regulation to permit them to have meaningful and timely input into its development. A summary of that consultation is provided in 76 FR 4 8346 (August 8, 2011).

The EPA received comments from several tribal comme nters regarding the availability of CSAPR allowance allocations to new units in Indian country. The EPA responded to these comments by instituting Indian country new unit setasides in the final CSAPR. In order to protect trib al sovereignty, these set-asides are managed and distributed by the federal governme nt regardless of whether CSAPR in the adjoining or surrounding state is impl emented through a FIP or SIP. While there are no existing affected EGUs in I ndian country covered by this proposal, the Indian country set-asides will ensure that any future new units built in Indian country will be able to obtain the necess ary allowances. This proposal

11 CSAPR also addressed interstate transport of fine particulate matter (PM2.5) under the 1997 and 2006 PM2.5 NAAQS.7-24 maintains the Indian country new unit set-aside and adjusts the amounts of allowances in each set-aside according to the same methodology of the original
CSAPR rule. The EPA has informed tribes of our development of t his proposal through a National Tribal Air Association – EPA air policy conference call (January 29, 2015). The EPA plans to further consult with tribal officials under the EPA Policy
on Consultation and Coordination with Indian Tribes early in the process of developing this regulation to permit them to have m eaningful and timely input into its development. The EPA will facilitate this consultation before finalizing this proposed rule.

As required by section 7(a), the EPA’s Tribal Consu ltation Official has certified that the requirements of the executive or der have been met in a meaningful and timely manner. A copy of the certifi cation is included in the docket for the proposed rule.

7.7 Executive Order 13045: Protection of Children from Environmental Health & Safety Risks The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those regulatory actions that concern health or safety ri sks, such that the analysis required under section 5–501 of the Order has the potential to inf luence the regulation. This action is not subject
to EO 13045 because it does not involve decisions o n environmental health or safety risks that may disproportionately affect children. The EPA bel ieves that the ozone-related benefits, PM 2.5 - related co-benefits, and CO 2 -related co-benefits would further improve children ’s health.

7.8 Executive Order 13211: Actions that Significan tly 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 notes that one aspect of this proposal that may affect energy supply, dispos ition or use is the EPA’s proposing and taking comment on a range of options with respect to use o f 2015 vintage and 2016 vintage CSAPR
NO X ozone-season allowances for compliance with 2017 a nd later ozone-season requirements.7-25

The EPA has prepared a Statement of Energy Effects for the proposed regulatory control
alternative as follows. We estimate a much less tha n 1 percent change in retail electricity prices on average across the contiguous U.S. in 2017, and a much less than 1 percent change in coalfired electricity generation in 2017 as a result of this rule. The EPA projects that utility power sector delivered natural gas prices will change by less than 1 percent in 2017. For more information on the estimated energy effects, please see chapter 5 of this RIA.

7.9 National Technology Transfer and Advancement A ct The proposed rulemaking does not involve technical standards.

7.10 Executive Order 12898: Federal Actions to Add ress Environmental Justice in Minority Populations and Low-Income Populations

The EPA believes the human health or environmental risk addressed by this proposal will not

have potential disproportionately high and adverse human health or environmental effects on minority, low-in come or indigenous populations. The EPA notes this action proposes to update CSAPR to reduce interstate ozone transport with respect to the 2008 ozone NAAQ S. The rule uses EPA’s authority in CAA section 110(a)(2)(d) to reduce (ni trogen oxides) NO X pollution that significantly contributes to downwind ozone no nattainment or maintenance areas. As a result, the rule will reduce exposures to ozone in the mostcontaminated areas (i.e., areas that are not meetin g the 2008 ozone National Ambient Air Quality Standards (NAAQS)). In addition , the rule separately
identifies both nonattainment areas and maintenance areas. This requirement
reduces the likelihood that areas close to the leve l of the standard will exceed the
current health-based standards in the future. The E PA proposes to implement these emission reductions using the CSAPR EGU NO X ozone-season emissions trading program with assurance provisions. The EPA recognizes that many environmental justice communities have voiced concerns in the past about emission trading and the potential for any emission increases in any location. The EPA believe s that CSAPR mitigated these concerns and that this proposal, which applies the CSAPR framework to reduce7-26 interstate ozone pollution and implement these redu ctions, will also minimize community concerns. The EPA seeks comment from comm unities on this proposal. Ozone pollution from power plants have both local a nd regional components: part of the pollution in a given locati on—even in locations near emission sources—is due to emissions from nearby so urces and part is due to
emissions that travel hundreds of miles and mix wit h emissions from other

sources. It is important to note that the section of the Cle an Air Act providing authority for this rule, section 110(a)(2)(D), unli ke some other provisions, does not dictate levels of control for particular facili ties. CSAPR allows sources to
trade allowances with other sources in the same or different states while firmly constraining any emissions shifting that may occur by requiring a strict emission ceiling in each state (the assurance level). In add ition, assurance provisions in the rule outline the allowance surrender penalties for failing to meet the assurance level; there are additional allowance penalties as well as financial penalties for failing to hold an adequate number of allowances to cover emissions. This approach reduces EGU emissions in each state t hat significantly
contribute to downwind nonattainment or maintenance areas, while allowing power companies to adjust generation as needed and ensure that the country’s electricity needs will continue to be met. The EPA maintains that the existence of these assurance provisions, including the penalties imposed when triggered, will ensure that state emissions will stay below the lev el of the budget plus variability
limit. In addition, all sources must hold enough allowance s to cover their emissions. Therefore, if a source emits more than i ts allocation in a given year,
either another source must have used less than its allocation and be willing to sell some of its excess allowances, or the source itself had emitted less than its allocation in one or more previous years (i.e., ban ked allowances for future use). In summary, the CSAPR minimizes community concerns about localized hot spots and reduces ambient concentrations of pol lution where they are most7-27 needed by sensitive and vulnerable populations by: considering the science of ozone transport to set strict state emissions budge ts to reduce significant
contributions to ozone nonattainment and maintenanc e (i.e., the most polluted) areas; implementing air quality-assured trading; re quiring any emissions above the level of the allocations to be offset by emissi on decreases; and imposing strict penalties for sources that contribute to a state’s exceedance of its budget plus variability limit. In addition, it is important to note that nothing in this final rule allows sources to violate their title V permit or a ny other federal, state, or local emissions or air quality requirements. Finally, it is also important to note that CAA sect ion 110(a)(2)(d), which addresses transport of criteria pollutants between states, is only one of many provisions of the CAA that provide EPA, states, and local governments with authorities to reduce exposure to ozone in communit ies. These legal authorities work together to reduce exposure to these pollutant s in communities, including for minority, low-income, and tribal populations, a nd provide substantial health benefits to both the general public and sensitive s ub-populations.
The EPA has informed communities of our development of this proposal through an Environmental Justice community call (January 28, 2 015) and a National Tribal Air Association – EPA air policy conference call (January 29, 2015) . The EPA plans to further consult with communities early in the process of developing this regulation to permit them to have meaningful and timely input into its development. T he EPA will facilitate this engagement before finalizing the proposed rule.7-18-18-1

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

Overview
The EPA performed an illustrative analysis to estim ate the costs, human health benefits, and climate co-benefits of compliance with the prop osed and more and less stringent alternatives. The EPA is proposing EGU NOx ozone season emissions budgets for 23 states. 1 The emissions reductions evaluated in the proposal reflect EGU NO X reduction strategies that are achievable for the 2017 ozone season. The EPA proposes to quantify EGU NO X ozone-season emissions budgets reflecting EGU NO X reduction strategies that are widely available at a uniform
annualized cost of $1,300 per ton (2011$). For the RIA, in ord er to implement the OMB Circular A-4 requirement to assess one less stringent and on e more stringent alternative to the proposal, the EPA is also analyzing EGU NO X ozone season emissions budgets reflecting NO X reduction strategies that are widely available at a uniform c ost of $500 per ton (2011$) and strategies that are widely available at a uniform cost of $3,400 pe r ton (2011$). This chapter summarizes these
results.

8.1 Results As shown in Chapter 5, the estimated social annualized costs to implement the proposal, as described in this document, are approximately $9 3 million (2011 dollars). As shown in Chapter 6, the total estimated combined benefits from implementation of the proposal are approximately $660 to $1,300 million in 2017 (2011 dollars, based on a discount rate of 3 percent and 7 percent for health benefits (ozone benefits and PM 2.5 co-benefits ) , a range of 2.5% to 5% for climate co-benefits, and rounded to three two significant figures). EPA can thus calculate the net benefits of the proposal by subtr acting the estimated social annualized costs from the estimated benefits in 2017. The annual net benefits of the proposal are approximately $ 700 6 3 70 0 to $1, 1 21 2 00 million (based on air quality benefits discounted at 3 perc ent, the
central estimate of CO 2 co-benefits, and annualized cost estimates) using a 3 percent discount

rate or $ 680 650580 to $1, 12 00 million (based on air quality benefits discounted at 7 perc ent, the
central estimate of CO 2 co-benefits, and annualized cost estimates)using a 7 percent discount

1 Alabama, Arkansas, Illinois, Indiana, Iowa, Kansas , Kentucky, Louisiana, Maryland, Michigan, Mississi ppi, Missouri, New Jersey, New York, North Carolina, Ohi o, Oklahoma, Pennsylvania, Tennessee, Texas, Virgin ia, West Virginia, and Wisconsin.8-2

rate . Therefore, the EPA expects that implementation o f this rule, based solely on economic efficiency criteria, will provide society with a si gnificant net gain in social welfare, notwithstanding the expansive set of health and env ironmental effects we were unable to quantify. Further quantification of directly emitt ed PM 2.5 -, mercury-, acidification-, and eutrophication-related impacts would increase the e stimated net benefits of the rule. Table 8-1 presents a summary of the benefits, costs, and net benefits of the proposal and also the more and less stringent alternatives.

Table 8-1. Total Costs, Total Monetized Benefits, a nd Net Benefits of the Proposal and

More or Less Stringent Alternatives for 2017 for U. S. (millions of 2011$) a,b,c

Proposal More Stringent AAA Less Stringent Climate Co-Benefits $23 $23 $27

Air Quality Health Benefits $670 to $1200 $690 to $1,300 $190 to $340

Total Benefits $700 to $1200 $720 to $1300 $210 to $360

Annualized Compliance Costs $93 10 $96 $4.7

Net Benefits $6 02 0 to $1 12 00 $620 to $1200 $210 to $360

Non-Monetized Benefits d Non-quantified climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in HAP, including mercury deposition Ecosystem benefits assoc. with reductions in emissi ons of NOx, SO 2 , and PM Visibility impairment

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitations. As a result, we provide compliance costs and social benefits in 2017, using the best available information to appro ximate compliance costs and social benefits recognizing un certainties and limitations in those estimates. b Benefits ranges represent discounting of health be nefits and climate co-benefits at a discount rate o f 3 percent. See Chapter 6 for additional detail and explanation. Th e costs presented in this table reflect compliance costs annualized at 4.77 percent discount rate possible and monitori ng, recordkeeping, and reporting costs. See Chapter 5 for additional detail and explanation.

c All costs and benefits are rounded to two significa nt figures; columns may not appear to add correctly

. d Non-monetized benefits descriptions are for all thr ee alternatives and are qualitative.

Proposal More Stringent Alternative Less Stringent Climate Co-Benefits $23 23 27

Air Quality Health Benefits $700 670 to $1200 $720 690 to $1300 $1300 $220 190 to 3708-3

Total Benefits $720 to $1200 $740 720 to $1300 $250 to $400

Annualized Compliance Costs $93 $96 $5

Net Benefits $630 to $1100 $640 to $1200 $250 to $400

Non-Monetized Benefits d Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits assoc. with reductions in emissions of NOx, SO , and PM Visibility impairment

a Estimating multiple years of costs and benefits is limited for this RIA by data and resource limitatio ns. As a result, we provide a snapshot of costs and benefits in 2017 , using the best available information to approxima te social costs and social benefits recognizing uncertainties and l imitations in those estimates.

b Benefits ranges represent discounting of health be nefits and climate co-benefits at a discount rate o f 3%. See chapter 6 for additional detail and explanation. Th e costs presented in this table reflect engineering costs annualized at an approximately 5 percent discount rate to the extent possible and monitoring, recordkeeping, and reporting costs. See chapter 5 for additional detail and expl anation.

c All costs and benefits are rounded to two significa nt figures; rows may not appear to add correctly.

d Non-monetized benefits descriptions are for all thr ee alternatives and are qualitative.

In accordance with Circular A-4 Guidance (OMB, 2003 ), the EPA also analyzed the costs and benefits of two regulatory control alternatives that impose relatively more stringent and relatively less stringent EGU NOx emissions budgets , compared to the proposed rule. These
alternatives are illustrative of the cost and benef it impacts of varying program stringency. They are designed to show the effects of more stringent and less stringent NOx reduction requirements in a regulatory structure that is otherwise the sam e as the proposed NOx emissions budgets. Air quality modeling was not conducted for the proposal or the more and less stringent alternatives. EPA applied a benefit-per-ton approach appropriate for deriving benefits for the evaluated regulatory control alternatives, as described in Ch apter 6. The proposal and alternatives’ social costs are estimated using the IPM model, as describ ed in Chapter 5. Table 8-2 presents the projected emissions reductions for ozone season NOx , as well as reductions in co-pollutant
annual NO X , annual SO 2 , and annual CO 2 , in 2017 under the proposed rule and the more and less stringent alternatives.8-4

Table 8-2. Projected 2017* Reductions in Emissions of NOx, SO 2 , and CO 2 with the

proposed NOx Emissions Budgets and More or Less Str ingent Alternatives (Tons)
Proposal More Stringent Alternative Less Stringent

Alternative NOx (annual) 90,000 93,000 24,000 NOx (ozone season) 85,000 84 8 7 ,000 25 2 4 ,000 SO 2 (annual) 390 1,20 3 8 0 1,20 43 0 1,1 3 00 CO 2 (annual short tons) 6 6 1 0,000 7 6 10,000 7 7 2 0,000 *Annual reductions are based on 2018 IPM direct mod el outputs relied upon in this RIA to represent 201 7 copollutant reductions In this RIA, we quantify an array of adverse healt h impacts attributable to ozone and
PM 2.5 . The Integrated Science Assessment for Ozone and R elated Photochemical Oxidants (“Ozone ISA”) (U.S. EPA, 2013a) identifies the huma n health effects associated with ozone exposure, which include premature death and a varie ty of illnesses associated with acute (dayslong) and chronic (months to years-long) exposures. Similarly, the Integrated Science Assessment for Particulate Matter (“PM ISA”) (U.S. EPA, 2009) identifies the human health effects associated with ambient particles, which in clude premature death and a variety of illnesses associated with acute and chronic exposur es. Air pollution can affect human health in a variety of ways .,\.

8.2 Net Present Value of a Stream of Costs and Ben efits
The EPA believes that providing comparisons of soci al costs and social benefits at discount rates of 3 and 7 percent is appropriate to the extent this is possible given available models and techniques. The three different uses of discounting in the RIA – (i) construction of annualized costs, (ii) adjusting the value of morta lity risk for lags in mortality risk decreases, and (iii) adjusting the cost of illness for non-fatal h eart attacks to adjust for lags in follow up costs -- are all appropriate. We explain our discounting of benefits in Chapter 6 of the RIA, specifically the application, in which we apply discount rates of 3 and 7 percent to air quality benefits and 2.5, 3, and 5 percent to climate co-benefits; in Ch apter 6 of the RIA; we explain our discounting of costs, in which we use a single discount rate of 4.77 percent% , in Chapter 5. Our estimates of net benefits are the approximations of the net valu e (in 2017) of benefits attributable to emissions reductions needed to attain just for the year 2017.8-5

The EPA presents annualized costs and benefits in a single year for comparison in this RIA because there are a number of methodological comple xities associated with calculating the net present value (NPV) of a stream of costs and benefi ts for a NAAQS. While NPV analysis allows evaluation of alternatives by summing the present v alue of all future costs and benefits, insights into how costs will occur over time are limited by underlying assumptions and data. Calculating a present value (PV) of the stream of future benefi ts also poses special challenges, which we describe below. In addition, the method requires de finition of the length of that future time period, which is not straightforward for this analy sis and subject to uncertainty. We provide annualized costs of compliance instead of using NPV or alternatives in this RIA, and our explanation for this is in Chapter 5.

Further, because we do not know when a facility wil l stop using a control measure or change to another measure based on economic or othe r reasons, the EPA assumes the control equipment and measures applied in the illustrative control strategies remain in service for their full useful life. As a result, the annualized cost of controls in a single future year is the same throughout the lifetimes of control measures analyz ed, allowing the EPA to compare the
annualized control costs with the benefits in a sin gle year for consistent comparison.

The EPA’s RIAs for air quality rules generally repo rt the estimated net benefits of improved air quality for a single year. The estimat ed NPV can better characterize the stream of benefits and costs over a multi-year period. Howeve r, calculating the PV of improved air quality is generally quite data-intensive and costly. Furt her, the results are sensitive to assumptions regarding the time period over which the stream of benefits is discounted.

The theoretically appropriate approach for characte rizing the PV of benefits is the life table approach. The life table, or dynamic population, ap proach explicitly models the year-to-year influence of air pollution on baseline mortality ri sk, population growth and the birth rate— typically for each year over the course of a 50-to- 100 year period (U.S. EPA SAB, 2010; Miller, 2003). In contrast to the pulse approach 2 , a life table models these variables endogenously by following a population cohort over time. For exampl e, a life table will “pass” the air pollution-

2 The pulse approach assumes changes in air pollutio n in a single year and affects mortality estimates over a 20-year period.8-6

modified baseline death rate and population from ye ar to year; impacts estimated in year 50 will account for the influence of air pollution on death rates and population growth in the preceding 49 years.

Calculating year-to-year changes in mortality risk in a life table requires some estimate of the annual change in air quality levels. It is both impractical to model air quality levels for each year and challenging to account for changes in fede ral, state and local policies that will affect the annual level and distribution of pollutants. For ea ch of these reasons, the EPA has not generally reported the PV of benefits for air rules but has i nstead pursued a pulse approach. While we agree that providing the NPV of a stream of costs a nd benefits could be informative, based on these reasons we are not able to provide the NPV of that stream in this RIA.8-19-1