### From: Wayland, Robertj Sent: Thursday, May 15, 2014 6:42 PM To: Higgins, Cortney Cc: Culligan, Kevin; Chappell, Linda; Weatherhead, Darryl Subject: 111(d) RIA Chapters

Cortney -
Attached please find the individual RIA chapters for the 111(d) RIA. Linda is working on creating a single file for you as requested; however, I want ed to go ahead and get these to you as quickly as possible. I will send the combined file as soon as possible - i.e., there were some formatting issues when combining the different chapters she's having to deal with.
Please let Kevin or me know if you have any concerns.

bob Robert J. Wayland, Ph.D. Leader, Energy Strategies Group U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Sector Policies and Programs Division Mail Code D243-01 Research Triangle Park, North Carolina 27709
Office: (919) 541-1045 Cell: (919) 306-2290 Fax: (919) 541-5450 Email: wayland.robertj@epa.gov

************************************** DRAFT/Deliberative Document - FOIA EXEMPT **************************************

<< EO 12866 RIA Cover pages 051514.docx >> << E.O. 12866 Exec_summary_05_15_14.docx >> << E.O. 12866 Chapter_1_Introduction_and_Background_5_15_14.docx >> << EO 12866 Chapter_2_Industry_Profile_05-15-14 w redline-strikeout & OMB-EPA comments.docx

>> << E.O. 12866 Chapter_3_Cost_Economic_Energy_Impacts_5_15_14.docx >> << E.O. 12866 Chapter_4_Existing_EGU_Benefits_5.15.14.docx >> << EO12866 CHAPTER 5 _051514 redline.docx >> << E O 12866 CHAPTER 6 Employment Impact Analysis 5-15-14 redline-strikeout.docx >> << E. O. 12866 Chapter_7_Statutory_and_Executive_Order_Analysis_051514.docx >> << E.O. 12866 Chapter_8_ Net Benefits 051514.docx >> << EO 12866 Chapter_9_Modifications_and_Reconstructions_051514.docx >> << Revised Appendix 3A Upstream Emissions 051514 RLSO.docx >> << E.O. 12866 Appendix_4A_Generating_BPT_estimates_5.15.14.docx >> << EO 12866 Response to Interagency RIA Comments 051514 final.docx >>

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1 

# Regulatory Impact Analysis for Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Pl anning and Standards Research Triangle Park, NC 27711

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2 CONTACT INFORMATION This document has been prepared by staff from the Office of Air Quality Planning and Standards, the Office of Atmospheric Programs, a nd the Office of Policy of the U.S. Environmental Protection Agency. Questions related to this docume nt should be addressed to Linda M. Cahppell, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina 27711 (email: chappell.linda@epa.gov ).

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

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3 EPA-452/R-14-002

March/2014

Regulatory Impact Analysis for Emission Guide lines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Air Economics Group Research Triangle Park, North Carolina 27711

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4

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

5 ACRONYMS
ACS American Cancer Society AEO Annual Energy Outlook AQ Air quality ASM Annual Survey of Manufactures ATSDR Agency for Toxic Substances and Disease Registry

BACT Best Availa ble Control Technology BenMAP Benefits Mappi ng and Analysis Program BPT Benefit-per-Ton BSER Best System of Emissions Reduction

Btu British Thermal Units

C Celsius

CAA Clean Air Act

CAIR Clean Air Interstate Rule

CCR Coal Combustion Residuals CCS Carbon Capture and Sequestra tion or Carbon Capture and Storage CCSP Climate Change Science Program CFR Code of Federal Regulations

CH4 Methane

CO Carbon Monoxide

CO2 Carbon Dioxide CRF Capital Recovery Factor

CSAPR Cross State Air Pollution Rule

CT Combustion Turbines CUA Climate Uncertainty Adder DICE Dynamic Integrated Climate and Economy Model

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6 ACRONYMS (CONTINUED)
DOE U.S. Department of Energy EAB Environmental Appeals Board

EC Elemental carbon ECS Energy Cost Share EG Emissions guidelines EGR Enhanced Gas Recovery EGU Electric Generating Unit EIA U.S. Energy Information Administration EMM Electricity Market Module

EO Executive Order EOR Enhanced Oil Recovery EPA U.S. Environmental Protection Agency ER Enhanced Recovery FERC Federal Energy Regulatory Commission

FGD Flue Gas Desulfurization

FOAK First of a Kind FOM Fixed Operating and Maintenance FR Federal Register

FRCC Florida Reliabil ity Coordinating Council FUND Framework for Uncertainty, Negotiation, and Dist ribution Model v

GDP Gross Domestic Product

GHG Greenhouse Gas GS Geologic Sequestration Gt Gigaton H2S Hydrogen Sulfide

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7 ACRONYMS (CONTINUED)
HAP Hazardous air pollutant HCl Hydrogen chloride HFC Hydrofluorocarbons HIA Health impact assessment IARC International Agency for Research on Cancer IAM Integrated Assessment Model ICR Information Collection Request IGCC Integrated Gasification Combined Cycle IOU Investor Owned Utility IPCC Intergovernmental Panel on Climate Change IPM Integrated Planning Model IRIS Integrated Risk Information System IRP Integrated Resource Plan ISA Integrated Science Assessment

kWh Kilowatt-hour

lbs Pounds LCOE Levelized Cost of Electricity

LML Lowest measured level

LNB Low NOX Burners MATS Mercury and Ai r Toxics Standards

MEA Monoethanolamine MECSA Manufacturing Energy Consumption Survey MeHg Methylmercury MGD Millions of Gallons per Day mg/L Milligrams per Liter

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8 ACRONYMS (CONTINUED)

mmBtu Million British Thermal Units MW Megawatt MWh Megawatt-hour

N2O Nitrous Oxide NAAQS National Ambient Air Quality Standards

NAICS North American Indus try Classification System NaOH Sodium Hydroxide NATCARB National Carbon Seque stration Database and Geogra phic Information System NEEDS National Electric Energy Data System NEMS National Energy Modeling System

NERC North American Electr ic Reliability Corporation NETL National Energy Technology Laboratory NGCC Natural Gas Combined Cycle NMMAPS National Morbidity, Mo rtality Air Pollution Study

NOAK Next of a Kind or Nth of a Kind NOX Nitrogen Oxide

NRC National Research Council

NSPS New Source Performance Standard

NSR New Source Review NTTAA National Technology Transfer and Advancement Act OC Organic carbon

OFA Overfire Air OMB Office of Management and Budget PAGE Policy Analysis of the Greenhouse Gas Effect Model

PFC Perfluorocarbons

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9 PM2.5 Fine Particulate Matter ppm Parts per Million PRA Paperwork Reduction Act

PSD Prevention of Si gnificant Deterioration RCSP Regional Carbon Seque stration Partnerships RADS Relative Airway s Dysfunction Syndrome RES Renewable Electricity Standards RFA Regulatory Flexibility Act RGGI Regional Greenhouse Gas Initiative RIA Regulatory Impact Analysis

RPS Renewable Portfolio Standards SAB-CASAC Science Advisory Board Cl ean Air Scientific Advisory Committee SAB-HES Science Advisory Board Health E ffects Subcommittee of the Advisory Council on Clean Air Compliance SAB-EEAC Science Advisory Board Envi ronmental Economics Advisory Committee

SBA Small Business Administration SBREFA Small Business Regulatory Enforcement Fairness Act

SCC Social Cost of Carbon SCPC Super Critical Pulverized Coal SCR Selective Catalytic Reduction

SF6 Sulfur Hexafluoride SIP State Implementation Plan

SO2 Sulfur Dioxide

Tcf Trillion Cubic Feet

TDS Total Dissolved Solids TSD Technical Support Document TSM Transportation Storage and Monitoring

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10 UMRA Unfunded Mandates Reform Act

U.S.C. U.S. Code

USGCRP U.S. Global Cha nge Research Program USGS U.S. Geological Survey

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

U.S. NRC U.S. Nuclear Regulatory Commission VCS Voluntary Consensus Standards VOC Volatile Organic Compounds VOM Variable Operating and Maintenance

VSL Value of a statistical life WTP Willingness to pay***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-1 

### EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) discusses potential benefits, costs, and economic impacts of the proposed Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units (herein referred to EGU GHG Existing Source Guidelines). This RIA also discusses the potential benefits, costs and economic impacts of the proposed Standards of Performance for Greenhouse Gas Emissions from Reconstructed and Modified Stationary Sources (EGU GHG Reconstructed and Modified Source proposed
sS tandards).

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

Greenhouse gas pollution threatens Americans' health and welfare by leading to long lasting changes in our climate that can have a range of severely negative effects on human health
and the environment. Carbon Dioxide ( CO 2 ) is the primary greenhouse gas pollutant, accounting for nearly three-quarters of global greenhouse gas emissions and 84 percent of U.S. greenhouse gas emissions. Fossil fuel-fired electric generating units (EGUs) are, by far, the largest emitters of GHGs, primarily in the form of CO 2 , among stationary sources in the U.S.

In this action, the EPA is proposing emission guidelines for states to use in developing plans to address greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the EPA is proposing state-specific rate-based goal s for carbon dioxide emissions from the power sector, as well as emission guidelines for states to use in developing plans to attain the statespecific goals. This rule, as proposed, would set in motion actions to lower the carbon dioxide
emissions associated with existing power generation sources in the United States.

ES.2 Summary of Proposed EGU GHG Existing Source Guidelines

Under Clean Air Act (CAA) section 111(d), state plans must establish standards of performance that reflect the degree of emission limitation achievable through the application of the “best system of emission reduction” (BSER) that, taking into account the cost of achieving***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-2 such reduction and any non-air quality health and environmental impact and energy requirements, the Administrator determines has been adequately demonstrated. 1 Consistent with CAA section 111(d), this proposed rule contains state-specific goals that reflect the EPA’s calculation of the emission reductions that a state can achieve through the cost-effective application of BSER. The EPA is using the foll owing four building blocks to determine statespecific goals:

1. Lowering the carbon intensity of generation at individual affected EGUs (e.g.
, through heat-rate improvements). 2. Reducing emissions of the most carbon-inte nsive affected EGUs to the extent that this can be accomplished cost-effectively by shifting generation to less carbon-intensive existing fossil fuel-fired E GUs, including natural gas co mbined cycle (NGCC) units
that are under construction. 3. Reducing emissions of carbon-emitting EGUs to the extent that this can be accomplished cost-effectively by expanding the amount of new, lower (or no) carbonintensity generation. 4. Reducing emissions of carbon-emitting EGUs to the extent that this can be accomplished cost-effectively by increas ing demand-side energy efficiency.

The proposed rule also contains emission guidel ines for states to use in developing plans that set their performance standards. The EPA recognizes that each state has different state policy considerations, including varying emis sion reduction opportuniti es and existing state programs and measures, and characteristics of th e electricity system (e.g., utility regulatory structure, generation mix, electricity demand). The proposed emission guidelines provide states with options for establishing performance standards in a manner that accommodates a diverse

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

emission reduction achievable through applying the BSER; and the state is authorized to identify the emission standard that reflects that amount of emission reduction. In addition, the State is required to include in its state plan the standards of performance and measures to implem ent and enforce those standards. The State submits the plan to EPA, and EPA approves it if the standards of performance and implementing and enforcing measures are satisfactory.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-3 range of state approaches. The proposed guidelines would also allow states to collaborate and to demonstrate emission performance on a multi-sta te basis, in recognition of the fact that electricity is transmitted across state lines, and measures often impact regional EGU CO 2 emissions.

While the EPA must establish BSER and is proposing goals and guidelines that reflect BSER, CAA section 111(d) also provides the EP A with the flexibility to design goals and guidelines that recognize, and are tailored to, the uniqueness and complexity of the power generation sector and CO 2 emissions. And, importantly, CAA section 111(d) allows the states flexibility in designing the measures for their stat e plans in response to EPA’s guidelines. States are not required to use each of the measures th at the EPA determines constitute BSER, or use those measures to the same degree of stringency that the EPA determines is achievable and costeffective; rather, CAA section 111(d) allows each state to determine the appropriate combination of, and the extent of its reliance on, measures for its state plan, by way of meeting its statespecific goal. Given the flexibilities afforded st ates in complying with the emission guidelines, the benefits, cost and economic impacts reported in this RIA are illustrative of compliance actions states may take.

ES.3 Control Strategies for Existing EGUs

States will ultimately determine optimal approaches to comply with the goals established in this regulatory action. The EPA is proposing a BSER goal approach referred to as Option 1 and taking comment on a second approach referred to as Option 2. Each of these goal approaches use the four building blocks described above at different levels of stringency. Option 1 involves higher deployment of the four building blocks but allows a longer timeframe to comply (2030) whereas Option 2 has a lower deployment over a shorter timeframe (2025).

Table ES-1 shows the proposed state goals for Options 1 and 2. This RIA depicts
illustrative state’s rate-based compliance scenarios for the goals set for Options 1 and 2, as well as a regional and state compliance approaches for each option. With the state compliance approach, states are assumed to comply with the guidelines by implementing measures solely within the state and trading occurs between sources on an intrastate basis only. In contrast under***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-4 the regional approach, states may collaborativel y comply with the guidelines. States have the discretion of choosing between a regional or state compliance approach, and the economic consequences of both approaches are reported in this RIA. Th e guidance allows flexibility of compliance, including th e possibility of using a mass-based approach. For this reason, EPA recognizes that actual compliance may differ from the illustrative approaches shown in this RIA. The analysis in the illustrative scenarios does not assume that states use any specific policy mechanism to achieve the state goals. There may be various policy mechanisms that result in the same distribution of emissions and electricity gene ration as in the IPM analysis of the illustrative scenarios. While IPM finds a least cost way to achieve the state goals to achieve the rate-based constraints imposed in the illustrative scenarios, there may be less costly approaches that the states may adopt to achieve their state goals.

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

State 2 Option 1 Option 2
Interim Goal (2020-2029) Final Goal (2030 Forward) Interim Goal (2020-2024) Final Goal (2025 Forward) Alabama 1,147 1,059 1,270 1,237
Alaska 1,105 1,017 1,175 1,138
Arizona * 735 702 779 763
Arkansas 968 910 1,083 1,058
California 556 537 582 571
Colorado 1,159 1,108 1,265 1,227
Connecticut 597 540 651 627
Delaware 913 841 1,007 983
Florida 794 740 907 884 Georgia 891 834 997 964
Hawaii 1,378 1,306 1,446 1,417
Idaho 244 228 261 254
Illinois 1,366 1,271 1,501 1,457
Indiana 1,607 1,531 1,715 1,683

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

ES-5 Iowa 1,341 1,301 1,436 1,417
Kansas 1,578 1,499 1,678 1,625 Kentucky 1,844 1,763 1,951 1,918
Louisiana 948 883 1,052 1,025
Maine 393 378 418 410 Maryland 1,347 1,187 1,518 1,440
Massachusetts 655 576 715 683 Michigan 1,227 1,161 1,349 1,319
Minnesota 911 873 1,018 999 Mississippi 732 692 765 743
Missouri 1,621 1,544 1,726 1,694
Montana 1,882 1,771 2,007 1,960
Nebraska 1,596 1,479 1,721 1,671
Nevada 697 647 734 713 New Hampshire 546 486 598 557 New Jersey 647 531 722 676
New Mexico * 1,107 1,048 1,214 1,176
New York 635 549 736 697
North Carolina 1,077 992 1,199 1,156
North Dakota 1,817 1,783 1,882 1,870
Ohio 1,452 1,338 1,588 1,545
Oklahoma 931 895 1,019 986 Oregon 407 372 450 420 Pennsylvania 1,179 1,052 1,316 1,270
Rhode Island 822 782 855 840
South Carolina 840 772 930 897
South Dakota 800 741 888 861
Tennessee 1,254 1,163 1,363 1,326
Texas 853 791 957 924
Utah * 1,378 1,322 1,478 1,453 Virginia 884 810 1,016 962 Washington 264 215 312 284 West Virginia 1,748 1,620 1,858 1,817
Wisconsin 1,281 1,203 1,417 1,380 Wyoming 1,808 1,714 1,907 1,869

Table ES- 21 shows the emission reductions associat ed with the compliance scenarios for the proposed Options 1 regional and state compliance approaches and Table ES- 32 reports emission reductions associated with Option 2. In 2020, the EPA estimates that CO 2 emissions***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-6 will be reduced by 371 million metric tons under the regional compliance approach and by 383 million metric tons assuming a state specific co mpliance approach compared to base case levels.
CO 2 emission reductions for Option 1 increase to 545 and 555 million metric tons annually in 2030 when compared to the base case emissi ons for Option 1 regional and state compliance approaches, respectively. Tables ES- 21 and ES- 32 also show emission reductions for criteria air pollutants.

Table ES- 21 . Summary of Climate and Air Pollutant Emission Reductions Options 1 CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (thousands of tons) 2020 Regional Compliance Approach Base Case 2,161 1,476 1,559 N/A Proposed Guidelines 1,790 1,184 1,213 Emissions Change -371 -292 -345 2025 Regional Compliance Approach Base Case 2,231 1,515 1,587 Proposed Guidelines 1,730 1,120 1,166 N/A Emissions Change -501 -395 -421 2030 Regional Compliance Approach Base Case 2,256 1,530 1,537 N/A Proposed Guidelines 1,711 1,106 1,131 Emission Change -545 -424 -407 2020 State Compliance Approach Base Case 2,161 1,476 1,559 Proposed Guidelines 1,777 1,140 1,191 N/A Emissions Change -383 -335 -367 2025 State Compliance Approach Base Case 2,231 1,515 1,587 N/A Proposed Guidelines 1,724 1,090 1,151 Emission Change -506 -425 -436 2030 State Compli ance Approach Base Case 2,256 1,530 1,537 N/A Proposed Guidelines 1,701 1,059 1,109 Emissions Change -555 -471 -428 Source: Integrated Planning Model, 2014. N/A – not yet available Comment [A1]: OMB: Suggest separating CO2 goals from associated criteria pollutant reductions. Same suggestion for Table ES-2.

EPA: These tables reflect modeled emission reductions. Added new table (new ES-1 above) to show state goals.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-7 Table ES- 23 . Summary of Climate and Air Pollutant Emission Reductions Option 2 CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (thousands of tons) 2020 Regional Compliance Approach Base Case 2,161 1,476 1,559 N/A Proposed Guidelines 1,878 1,231 1,290 Emissions Change -283 -244 -268 2025 Regional Compliance Approach Base Case 2,231 1,515 1,587 Proposed Guidelines 1,862 1,218 1,279 N/A Emissions Change -368 -297 -309 2030 Regional Compliance Approach Base Case 2,256 1,530 1,537 Proposed Guidelines 1,903 1,243 1,286 N/A Emissions Change -353 -287 -252 2020 State Compliance Approach Base Case 2,161 1,476 1,559 Proposed Guidelines 1,866 1,208 1,277 Emissions Change -295 -267 -281 N/A 2025 State Complia nce Approach Base Case 2,231 1,515 1,587 Proposed Guidelines 1,855 1,188 1,271 Emissions Change -376 -327 -317 N/A 2030 State Complian ce Approach Base Case 2,256 1,530 1,537 Proposed Guidelines 1,882 1,209 1,264 Emissions Change -373 -321 -274 N/A Source: Integrated Planning Model, 2014. N/A– not yet available.

ES.4 Costs of Existing EGU Guidelines

The ‘‘compliance costs’’ of this proposed acti on are represented in this analysis as the change in electric power generation costs between the base case and policy case in which states pursue a distinct set of strategies not limited to the technologies and measures included in tbeyond t he best system of emission reductions (BSER) to meet the EGU GHG emission guidelines, and include cost estimates for demand side energy efficiency. The compliance assumptions, and therefore the projected “compliance costs” set forth in this analysis, are
illustrative in nature and do not represent the full suite of compliance flexibilities states may ultimately pursue.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-8 The EPA projects that the annual incremental compliance cost of the proposed Option 1 ranges from $5.4 to $7.4 billion in 2020 and from $7.3 to $8.8 billion in 2030 ($2011), excluding
the costs associated with monitori ng, reporting, and recordkeeping. The estimated cost of Option 2 is between $4.2 and $5.4 billion in 2020 and between $1.9 and $3.1 billion in 2030 (2011$). The estimated monitoring, reporting and recordkeeping costs for both options are $62 million in 2020, $8.2 million in 2025, and $8.2 million in 2030 (2011$).[MRR costs not yet available] The annual incremental cost is the pr ojected additional cost of comply ing with the proposed rule in the year analyzed and includes the annualized cost of capital investment in new generating sources and heat rate improvements at coal steam facilities, 3 the ongoing costs of operating additional pollution controls, shifts between or amongst various fuels, demand-side energy efficiency measures, and other actions associated with compliance. Costs for both options are
reflected oi n Table ES- 43 below and discussed more extensively in Chapter 3 of this RIA.

Table ES- 43 . Summary of Illustrative Compliance Costs Incremental Cost from Base Case (billions of 2011$) 2020 2025 2030 Option 1

State Compliance $7.4 $5.5 $8.8 Regional Compliance $5.4 $4.6 $7.3 Option 2

State Compliance $5.4 $5.5 $3.1 Regional Compliance $4.2 $4.5 $1.9

Source: Integrated Planning Model, 2014.

The costs reported in Table ES- 43 represent the estimated incremental electric utility generating costs changes from the base case, plus end-use energy efficiency program costs (paid by electric utilities) and end-use energy efficien cy participant costs (pai d by electric utility consumers). The compliance costs reported in Table ES- 43 are not social costs. These costs represent the illustrative real resources costs fo r states to comply with the BSER goals for Options 1 and 2. Electric sector Cc ompliance costs and monitoring, recordkeeping and reporting

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

http://www.epa.gov/powersector modeling/BaseCasev513.html Comment [A2]: OMB: Suggest presenting costs without netting out cost savings. Cost savings should be presented as benefits.
EPA: Comment addressed in written response document.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-9 costs are compared to social benefits in Tables ES- 87 , ES- 98 and ES- 109 to derive illustrative net benefits of the guidelines. For a more extensive discussion of social costs, see Chapter 3 of
this RIA.

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

Implementing the proposed guidelines is expected to reduce emissions of CO 2 and have ancillary human health co-benefits associated with reduced emissions of SO 2 , NO 2 , and directly
emitted PM 2.5 , which would lead to lower ambient concentrations of PM 2.5 and ozone. These estimates have been calculated using the marginal climate benefits value deemed central by the federal government – the model average at a 3% discount rate. Estimates calculated with additional marginal climate benefit values from the federal government are presented in Tables
ES- 65 and ES- 76 . Also, the range of combined benefits reflects different concentration-response functions for the health co-benefits, but it does not capture the full range of uncertainty inherent in the health co-benefits estimates. We were unable to quantify or monetize all of the climate
benefits and health and environmental co-ben efits associated with the proposed emission guidelines, including reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosyste m effects and visibility impairment. These unquantified benefits could be substantial , but it is difficult to approximate the potential magnitude of these unquantified benefits because previous quantification attempts have been incomplete

. The omission of these endpoints from th e monetized results should not imply that the impacts are small or unimportant. Table ES- 54 provides the list of the quantified and unquantified environmental and health benefits in this analysis.ES-10 Table ES- 54 . Quantified and Unquantified Benefits

Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information
Improved Environment

Reduced climate effects Global climate impacts from CO 2 —  SCC TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 1 Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC 1 Improved Human Health (co-benefits) Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality ba sed on cohort study estimates and expert elicitation estimates (age >25 or age >30)   PM ISA Infant mortality (age <1)   PM ISA

Reduced incidence of morbidity from exposure to PM 2.5 Non-fatal heart attacks (age > 18)   PM ISA Hospital admissions—respiratory (all ages)   PM ISA Hospital admissions—cardiovascular (age >20)   PM ISA Emergency room visits for asthma (all ages)   PM ISA Acute bronchitis (age 8-12)   PM ISA Lower respiratory symptoms (age 7-14)   PM ISA Upper respiratory symptoms (asthmatics age 9-11)   PM ISA Asthma exacerbation (asthmatics age 6-18)   PM ISA Lost work days (age 18-65)   PM ISA Minor restricted-activity days (age 18-65)   PM ISA Chronic Bronchitis (age >26) — — PM ISA Emergency room visits for cardiovasc ular effects (all ages) — — PM ISA 1 Strokes and cerebrovascular disease (age 50-79) — — PM ISA 1 Other cardiovascular effects (e .g., other ages) — — PM ISA 2Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

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

Reduced incidence of mortality from exposure to ozone Premature mortality based on short-term study estimates (all ages)   Ozone ISA Premature mortality based on long-term study estimates (age 30–99) — — Ozone ISA 1

Reduced incidence of morbidity from exposure to ozone Hospital admissions—respiratory causes (age > 65)   Ozone ISA Hospital admissions—respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted-activity days (age 18–65)   Ozone ISA School absence days (age 5–17)   Ozone ISA Decreased outdoor worker productivity (age 18–65) — — Ozone ISA 1 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA 2 Cardiovascular and nervous sy stem effects — — Ozone ISA 2 Reproductive and developmental effects — — Ozone ISA 2,3

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

Table ES-4. continuedES-11 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 methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 1 Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 2
Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2

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

Reduced vegetation and ecosystem effects from exposure to

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

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

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

1 We assess these co-benefits qualitatively due to data a nd resource limitations for this analysis, but we have quantified them in sensitivity analyses for other analyses. 2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.ES-12 3 We assess these co-benefits qualitatively because current evidence is only suggest ive of causality or there are other significant concerns over the strength of the association.

ES.5.1 Estimating Climate Benefits

We estimate the global social benefits of carbon dioxide (CO 2 ) emission reductions expected from this rulemaking using the federal government’s social cost of carbon (SCC)
estimates. The SCC is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given year. It includes a wide range of anticipated climate impacts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is typically used to assess the avoided damages as a result of regulatory actions (i.e.
, benefits
, of rulemakings that have an incremental achieve marginal impact on cumulative global reductions in CO 2 emissions).

The federal estimates of SCC used in this analysis have been developed over many years, using the best science available, and with input from the public. An interagency process that included the EPA and other executive branch entities used three integrated assessment models (IAMs) to develop SCC estimates and selected four global values for use in regulatory analyses. The federal government first released the estimates in February 2010 and updated them in 2013 using new versions of each IAM. The 2010 SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods used to develop the estimates and the 2013 SCC TSD presents and discusses the updated estimates. The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per metric ton of CO 2 emissions in the year 2020 (2011$),
and each estimate increases over time. These SC C estimates are associated with different discount rates. The first three estimates are the model average at 5 percent discount rate, 3 percent, and 2.5 percent, respectively, and the fourth estimate is the 95 th percentile at 3 percent.

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including theincomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Current integrated assessment models do not assign value to all of the importantES-13 physical, ecological, and economic impacts of clim ate change recognized in the climate change literature because of a lack of precise information on the nature of damages and because the science incorporated into these models understa ndably lags behind the most recent research. In particular, the IPCC Fourth Assessment Report concluded that “It is very likely that [SCC estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts.” Nonetheless, these estimates and the di scussion of their limitations represent the best
available information about the social benefits of CO 2 emission reductions to inform the benefitcost analysis.

ES 5.2 Estimating Air Po llution Health Co-Benefits

The health co-benefits estimates represent the total monetized human health benefits for populations exposed to less PM 2.5 and ozone from emission reductions of precursor pollutants (e.g., SO 2 , NOx, and directly emitted particles) anticipated by implementing the proposed guidelines. 4 Unlike the global SCC estimates, the air pollution health co-benefits are only estimated for the contiguous U.S. 5 The monetized PM 2.5 co-benefits include avoided premature deaths (derived from effect coefficients in two cohort studies [Krewski et al. ( 2009 ) and Lepeule
et al. ( 2012 ) ] for adults and one for infants [Woodruff et al. ( 1997 ) ] ), as well as avoided morbidity effects for ten non-fatal endpoints ra nging in severity from lower respiratory symptoms to heart attacks (U.S. EPA, 2012). The monetized ozone co-benefits include avoided premature deaths (derived from the range of eff ect coefficients represented by two short-term epidemiology studies [Bell et al. (2004) and Levy et al. (2005)]), as well as avoided morbidity effects for five non-fatal endpoints ranging in severity from school absence days to hospital admissions (U.S. EPA, 2008, 2011).

Because we were unable to conduct air quality modeling for this RIA, wW e instead used a “benefit-per-ton” approach to estimate the health co-benefits. To create the benefit-per-ton
estimates for PM 2.5 , this approach uses an air quality model to convert emissions of PM 2.5

4 We were unable to quantify the co-benefits associated with reducing direct exposure to SO 2 and NOx.

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

health co-benefits in Alaska and Hawaii anticipated from implementation of the proposed guidelines. Comment [A3]: OMB: Please explain briefly why this was not possible. Further, can EPA add a commitment to do air quality modeling for the final RIA.
EPA: Comment addressed in written response document.ES-14 precursors (e.g., SO 2 , NO x ,) and directly emitted particles ) into changes in ambient PM 2.5 concentrations and BenMAP to estimate the ch anges in human health associated with that change in air quality. We then divide these health impacts by the emissions in specific sectors at the regional level (i.e., East, West, and Californi a). We followed a similar process to estimate benefit-per-ton estimates for the ozone precursor NO x . To calculate the co-benefits for the proposed guidelines, we then multiplied the regional benefit-per-ton estimates for the EGU sector by the corresponding emission reduc tions. All benefit-per-ton estimates reflect the geographic distribution of the modeled emissions, which may not exactly match the emission reductions in this rulemaking, and thus they may not reflect the local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location.

Our estimate of the monetized co-benefits is based on EPA’s interpretation of the best available scientific literature (U.S. EPA, 2009) and methods and supported by EPA’s Science Advisory Board and the NAS (NRC, 2002). Below are key assumptions underlying the estimates
for PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 health co-benefits:

1. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differ entiation of effect es timates by particle type. The PM ISA concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates include health co-benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in aES-15 distributed fashion over the 20 years following exposure based on the advice of theSAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates.
Every benefits analysis examining the potential effects of a change in environmental protection requirements is limited, to some ex tent, by data gaps, model capabilities (such as geographic coverage) and uncertainties in the unde rlying scientific and economic studies used to configure the benefit and cost models. Despite these uncertainties, we believe this analysis provides a reasonable indication of the expected health co-benefits of the air pollution emission reductions for the illustrative compliance options for the proposed standards under a set of reasonable assumptions. This analysis does not include the type of detailed uncertainty
assessment found in the 2012 PM 2.5 National Ambient Air Quality Standard (NAAQS) RIA (U.S. EPA, 2012) because we lack the necessary air quality input and monitoring data to conduct a complete benefits assessment. In addition, using a benefit-per-ton approach adds another important source of uncertainty to the benefits estimates.

ES 5.3 Combined Benefits Estimates

The EPA has evaluated the range of potential impacts by combining all four SCC values with health co-benefits values at the 3 percent and 7 percent discount rates. Different discount rates are applied to SCC than to the health co-benefit estimates; because CO 2 emissions are longlived and subsequent damages occur over many years. Moreover, several rates are applied to
SCC because the literature shows that it is se nsitive to assumptions about discount rate and because no consensus exists on the appropriate rate to use in an intergenerational context. The U.S. government centered its attention on th e average SCC at a 3 percent discount rate but emphasized the importance of considering all four SCC estimates. Tables ES- 56 and ES- 76 provide the combined climate benefits and health co-benefits for each option evaluated for 2020, 2025 and 2030 for Options 1 and 2, respectively for each discount rate combination.ES-16 Table ES- 65 . Combined Climate Benefits and Health Co-Benefits for Proposed Existing EGU GHG Rule – Regional Complia nce Approach (billions of 2011$)*

Option SCC Discount Rate and Statistic** Climate Benefits Only Climate Benefits and Health Co-Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Option

1 In 2020 371 million metric tonnes CO 2 5% $4.7 $19 to $39 $18 to $36 3% $17 $32 to $51 $30 to $48 2.5% $25 $40 to $59 $38 to $56 3% (95 th percentile) $51 $65 to $85 $64 to $81 In 2025 501 million metric tonnes CO 2

5% $7.5 $29 to $57 $27 to $53 3% $25 $46 to $75 $44 to $70 2.5% $37 $58 to $86 $56 to $82 3% (95 th percentile) $76 $98 to $130 $96 to $120 In 2030 545 million metric tonnes CO 2 5% $9.3 $33 to $65 $31 to $60 3% $30 $54 to $86 $52 to $81 2.5% $44 $67 to $99 $65 to $94 3% (95 th percentile) $92 $120 to $150 $110 to $140 Option

2 In 2020 283 million metric tonnes CO 2 5% $3.6 $16 to $32 $15 to $29 3% $13 $25 to $41 $24 to $38 2.5% $19 $31 to $47 $30 to $45 3% (95 th percentile) $39 $51 to $67 $50 to $64 In 2025 368 million metric tonnes CO 2 5% $5.5 $21 to $43 $20 to $39 3% $18 $34 to $56 $33 to $52 2.5% $27 $43 to $64 $42 to $61 3% (95 th percentile) $56 $72 to $93 $71 to $90 In 2030 353 million metric tonnes CO 2

5% $6.0 $22 to $43 $20 to $40 3% $20 $35 to $56 $34 to $53 2.5% $28 $44 to $65 $43 to $62 3% (95 th percentile) $60 $76 to $97 $74 to $93 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The heal th co-benefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., fr om Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted pa rticles are not yet available. **Unless otherwise specified, it is the model average. Comment [A4]: The total benefits will be updated to reflect these emissions when they are available.ES-17 Table ES- 76 . Combined Climate Benefits and Health Co-Benefits for Proposed Existing EGU GHG Rule – State Compliance Approach (billions of 2011$)*

Option SCC Discount Rate and Statistic** Climate Benefits Only Climate Benefits and Health Co-Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Option

1 In 2020 383 million metric tonnes CO 2 5% $4.9 $20 to $41 $19 to $38 3% $18 $33 to $54 $32 to $50 2.5% $26 $42 to $62 $40 to $59 3% (95 th percentile) $52 $68 to $89 $66 to $85 In 2025 506 million metric tonnes CO 2

5% $7.6 $29 to $58 $27 to $54 3% $25 $47 to $76 $45 to $71 2.5% $37 $59 to $88 $57 to $83 3% (95 th percentile) $77 $99 to $130 $97 to $120 In 2030 555 million metric tonnes CO 2 5% $9.5 $34 to $68 $32 to $63 3% $31 $56 to $89 $53 to $84 2.5% $44 $69 to $100 $67 to $98 3% (95 th percentile) $94 $120 to $150 $120 to $150 Option

2 In 2020 295 million metric tonnes CO 2 5% $3.8 $16 to $32 $15 to $30 3% $14 $26 to $42 $25 to $40 2.5% $20 $32 to $49 $31 to $46 3% (95 th percentile) $40 $53 to $69 $51 to $66 In 2025 376 million metric tonnes CO 2 5% $5.6 $22 to $44 $20 to $40 3% $19 $35 to $57 $34 to $54 2.5% $28 $44 to $66 $42 to $62 3% (95 th percentile) $57 $74 to $95 $72 to $92 In 2030 373 million metric tonnes CO 2

5% $6.4 $23 to $45 $22 to $42 3% $21 $37 to $60 $36 to $56 2.5% $30 $47 to $69 $45 to $65 3% (95 th percentile) $63 $80 to $100 $79 to $99 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The heal th co-benefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., fr om Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted pa rticles are not yet available. **Unless otherwise specified, it is the model average.ES-18 ES.6 Monetized Benefits, Compliance Costs and Ne t Benefits of the Proposed Guidelines for Existing Sources
In this summary, the EPA provides the estimates of the climate benefits, health co-benefits, compliance costs and net benefits of the proposed Option 1 and alternative Option 2 assuming a regional compliance approach and an alternative state compliance approach. In Table ES- 87
, the EPA estimates that in 2020 the proposed Option 1 regional compliance approach will yield
monetized climate benefits of approximately $17 billion using a 3 percent discount rate (model average, 2011$). The air pollution health co-benef its in 2020 are estimated to be $15 billion to $34 billion (2011$) for a 3 percent discount rate and $13 billion to $31 billion (2011$) for a 7 percent discount rate. The annual compliance costs
, including monitoring and reporting costs, are approximately $5. 4 5 billion (2011$) in 2020 [MRR costs to be added] . The quantified net benefits (the difference between monetized benefits and costs) are $26 billion to $ 46 45 billion (2011$) for 2020 (Table ES- 87 below) and $47 billion to $78 billion (2011$) for 2030 (Table
ES- 109 below), using a 3 percent discount rate (model average). For the Option 1 state compliance approach in 2020, the EPA estimates monetized climate benefits of approximately $18 billion using a 3 percent disco unt rate (model average). The air pollution health co-benefits in 2020 are estimated to be $15 billion to $36 billion for a 3 percent discount rate and $14 billion to $33 billion (2011$) for a 7 percent discount rate. The annual compliance costs including monitoring and reporting costs, are approximately $7. 4 5 billion (2011$) in 2020 [MRR costs to be added] . The quantified net benefits (the difference between monetized benefits and costs) are $26 billion to $46 billio n for 2020 (Table ES- 87 below) and $47 billion to $80 billion (2011$) for 2030 (Table ES- 109 below), Benefit and cost estimates for Options 1 and 2 regional and state compliance approaches for 2020, 2025, and 2030 are presented in Tables ES- 87 , ES- 98 , and ES910

.
The EPA could not monetize some important benefits of the guidelines. Unquantified
benefits include climate benefits from reducing emissions of non-CO 2 greenhouse gases and cobenefits from reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment. Upon considering these limitations and uncertainties, it remains cl ear that the benefits of this proposal are substantial and far outweigh the costs.ES-19 Table ES- 87 . Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2020 (billions of 2011$) a Option 1 - state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $4.9 $18 $26 $52 $3.8 $14 $20 $40 Air pollution health co-benefits c $15 to $36 $14 to $33 $12 to $29 $11 to $26 Total Compliance Costs d $7. 45 $5. 45 Net Benefits e $26 to $46 $24 to $43 $20 to $37 $19 to $34

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.5 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.2 tons of Hg Ecosystem Effects Visibility impairment Option 1 - regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $4.7 $17 $25 $51 $3.6 $13 $19 $39 Air pollution health co-benefits c $15 to $34 $13 to $31 $12 to $28 $11 to $25 Total Compliance Costs d $5. 45 $4. 23 Net Benefits e $26 to $ 4645 $25 to $42 $21 to $37 $20 to $34

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 0.9 tons of Hg Ecosystem Effects Visibility impairment

a All estimates are for 2020 and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this su mmary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount ra tes are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of co ncentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that al l fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

.

e The estimates of net benefits in this summary table are cal culated using the global SCC at a 3 percent discount rate (model average). The RIA includes combined climate and heal th estimates based on these additional discount rates.ES-20 Table ES- 98 . Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2025 (billions of 2011$) a Option 1 – state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $7.6 $25 $37 $77 $5.6 $19 $28 $57 Air pollution health co-benefits c $22 to $51 $20 to $46 $16 to $38 $15 to $35 Total Compliance Costs d $5.5 $5.5 Net Benefits e $41 to $70 $39 to $66 $30 to $52 $28 to $48

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 2.0 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $7.5 $25 $37 $76 $5.5 $18 $27 $56 Air pollution health co-benefits c $21 to $50 $19 to $45 $16 to $37 $14 to $34 Total Compliance Costs d $4.6 $4.5 Net Benefits e $42 to $70 $40 to $66 $30 to $51 $28 to $48

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment

a All estimates are for 2025 and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this su mmary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount ra tes are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of co ncentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

.

e The estimates of net benefits in this summary table are cal culated using the global SCC at a 3 percent discount rate (model average). The RIA includes combined climate and heal th estimates based on these additional discount rates.ES-21 Table ES- 109 . Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines –2030 (billions of 2011$) a Option 1 – state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate
Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.5 $31 $44 $94 $6.4 $21 $30 $63 Air pollution health co-benefits c $25 to $58 $23 to $53 $17 to $39 $15 to $36 Total Compliance Costs d $8.8 $3.1 Net Benefits e $47 to $80 $45 to $75 $34 to $57 $33 to $53

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 2.1 tons of Hg and 590 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.5 tons of Hg and 130 tons of HCl Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.3 $30 $44 $92 $6.0 $20 $28 $60 Air pollution health co-benefits c $24 to $55 $21 to $50 $16 to $37 $14 to $34 Total Compliance Costs d $7.3 $1.9 Net Benefits e $47 to $78 $44 to $73 $33 to $55 $32 to $51

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.7 tons of Hg and 580 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.1 tons of Hg and 30 tons of HCl Ecosystem Effects Visibility impairment

a All estimates are for 2030, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table re flect global impacts from CO2 emission changes and do not account for changes in non-CO2 GHG emissions. Also, differen t discount rates are applied to SCC than to the other estimates because CO2 emissions are long-lived and su bsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of co ncentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

.

e The estimates of net benefits in this summary table are cal culated using the global SCC at a 3 percent discount rate (model average). The RIA includes combined climate and heal th estimates based on these additional discount rates.ES-22 ES.7 Economic Impacts of the Proposed Emission Guidelines for Existing EGUs

The proposed guidelines have important energy market implications. Under Option 1, average nationwide retail electricity prices are projected to increase roughly 6 to 7 percent in 2020, and roughly 3 percent in 2030 (contiguous U.S.) , compared to base case price estimates modeled for these same years . Average monthly electricity bills are anticipated to increase by roughly 3 percent in 2020, but decline by roughly 9 percent by 2030 because increased energy efficiency will lead to reduced usage

.
The average delivered coal price to the power sector is projected to decrease by 16 to 17 percent in 2020 and roughly 18 percent in 2030, relative to the base case (Option 1). The EPA also projects that the electric power sector-deliver ed natural gas prices will increase by 9 to 12 percent in 2020, with negligible changes by 2030 relative to the base case . Natural gas use for electricity generation will increase by as much as 1.2 trillion cubic feet (TCF) in 2020 relative to the base case, declining over time.

The EPA projects coal production for use by the power sector, a large component of total coal production, will decline by roughly 25 to 27 percent in 2020 from base case levels. The use of coal by the power sector will decrease by roughly 30 to 32 percent in 2030.

Renewable energy capacity is anticipated to increase by roughly 12 GW in 2020 and by 9 GW in 2030 under Option 1. Energy market impacts from the guidelines are discussed more extensively in chapter 3 of this RIA.

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

Changes in price or demand for electricity, natural gas, coal, can impact markets for goods and services produced by sectors that use these energy inputs in the production process or supply those sectors. Changes in cost of production may result in changes in price, changes in quantity produced, and changes in profitability of firms affected. EPA recognizes that these guidelines provide significant flexibilities and states implementing the guidelines may choose to mitigate impacts to some markets outside the EGU sector. Similarly, demand for new generation orES-23 energy efficiency can result in changesshifts in production and profitability for firms that supply those goods and services and the guidelines provide flexibility for states that may want to enhance demand for goods and services from those sectors.

Executive Order 13563 directs federal agencies to consider the effect of regulations on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and jo b creation. It must be based on the best available science” (Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically included a separate analysis of regulation-i nduced employment impacts, during periods of sustained high unemployment, employment impacts are of particular concern and questions may arise about their existence and magnitude.

States have the responsibility and flexibi lity to implement policies and practices for compliance with Proposed Electric Generating Unit Greenhouse Gas Existing Source Guidelines. As such, gG iven the wide range of approaches that may be used, quantifying the associated employment impacts is difficult. EPA’s illustrative employment analysis includes an estimate of projected employment impacts associated with these guidelines for the electric power industry, coal and natural gas production, and demand side energy efficiency activities. These projections are derived, in part, from a detailed model of the electricity production sector used for this regulatory analysis, and U.S government data on employment and labor productivity
., In the electricity, coal, and natural gas sectors, the EPA estimates that these guidelines could have result in an increase of approximately employment impact of roughly 21,70028,000 to 25,40025,900 job-years in 2020 for Option 1, state and regional compliance approaches, respectively. For Option 2, the state and regional compliance approach estimates both an increase of approximate ly 29,800 to 26,70023,000 job-years in 2020. Demand-side energy efficiency employment impacts may range from an increase of approximately 78,800 jobs in 2020 for Option 1 for both state and regional compliance approaches. For Option 2 demand-side energy efficiency employment impacts are an increase of approximately 57,000 jobs in 2020 for state and regional compliance approaches. More detail about these analyses can be found in Chapter 7 of this RIA.ES-24 ES.9 Modified and Reconstructed Sources

The EPA is proposing emission limits for CO 2 emitted from reconstructed and modified EGUs under section 111(b) of the CAA. Based on historical information that has been reported to the EPA, the EPA anticipates few, if any, covered units will trigger the reconstruction or modification provisions in the period of analysis (through 2025). As a result, we do not anticipate any significant costs or benefits associ ated with this proposal. However, because there have been a few units that have notified EPA of modifications in the past, in chapter 9 of this RIA we present an illustrative analysis of the costs and benefits for a hypothetical unit if it were to trigger the modification provision.
Placeholder

ES. 910 References

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987- 2000.” Journal of the American Medical Association, 292(19): pp. 2372-8. Docket ID EPA-HQ-OAR-2009-0472-114577, Tec hnical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available at: < http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf >.ES-25 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised November 2013). Also available at: <http://www.whitehouse.gov/sites/default/files/ omb/assets/inforeg/tec hnical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>. Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 -Related Health Benefits of Emission Reductions for 17 Indust rial, Area and Mobile Emission Sectors Across the U.S.” Environment International , 49 41–151. Krewski D; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes and Y. Shi, et al. 2009. Extended FollowUp and Spatial Analysis of the American Ca ncer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC. Available at: <http://www.ipcc.ch/publications_and_data/pub lications_ipcc_fourth_assessment_report_synt hesis_report.htm>. Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives, July, 120(7): pp. 965-70. Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An Empiric Bayes Meta-regression Analysis.” Epidemiology, 16(4): pp. 458-68. National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on the Toxicological Effects of Methylmercury. Board on Environmental Studies and Toxicology. Washington, DC: National Academies Press. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: Nationa l Academies Press.ES-26 U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for Sulfur Oxides: Health Criteria (Final Report) . Research Triangle Park, NC: National Center for Environmental Assessment, September. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. EPA. 2008b. Final Ozone NAAQS Regulatory Impact Analysis . Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and Environmental Impacts Division, Air Benefit and Cost Group. (EPA document number EPA-452/R-08-003, March). Available at <http://cfpub.epa.gov/ncea/ cfm/recordisplay.cfm?deid=194645>.

U.S. EPA. 2008c. Integrated Science Assessmen t for Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. Available at <http://www.epa.gov/ttnecas1/re gdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. EPA. 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur: Ecological Criteria National (Final Report ). Research Triangle Park, NC: National Center for Environmental Assessment. (EPA document number EPA/600/R-08/139, December). Available at <http://cfpub.epa.gov/ncea/ cfm/recordisplay.cfm?deid=201485>.

U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report) . Research Triangle Park, NC: National Center for Envi ronmental Assessment, RTP Division. (EPA document number EPA-600-R-08-139F, December). Available at

< http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 >.
U.S. EPA. 2010d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standards to Incorporate Current Methods . Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/s3 -supplemental_analysis-updated_benefits11- 5.09.pdf >.

U.S. EPA. 2012a. Regulatory Impact Analysis for the Fina l Revisions to the National Ambient Air Quality Standards for Particulate Matter . Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and En vironmental Impacts Division. (EPA document number EPA-452/R-12-003, December). Available at <http://www.epa.gov/pm/2012/finalria.pdf>.

U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). Washington, DC: U.S. Environmenta l Protection Agency. (EPA document number EPA/600/R-10/076F). Available at <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>.ES-27 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of postneonatal infant mortality and particul ate air pollution in the United States.” Environmental Health Perspectives 105 (6): 608-612.

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## CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction
This document presents the expe cted benefits, costs, and eco nomic impacts of illustrative strategies states may implement to comply w ith the proposed Electric Utility Generating Unit (EGU) Existing Source GHG Guidelines in 202 0, 2025, and 2030. Furthermore, this document provides a separate description of the expected benefits, co sts, and economic impacts of proposed emission limits for reconstructed and m odified EGU sources. This chapter contains background information on these rules and an outline of the chapte rs in the report.

1.2 Legal, Scientific and Economic Basis for this Rulemaking
1.2.1 Statutory Requirement Section 111 of the Clean Air Act (CAA) require s performance standards for air pollutant emissions from categories of stationary source s that may reasonably contribute to endangerment of public health or welfare. In April 2007, the Supreme Court ruled in Massachusetts v. the Environmental Protection Agency (EPA) that greenhouse gases (GHGs) meet the definition of an “air pollutant” under the C AA. This ruling clarified that the authorities and requirements of the CAA apply to GHGs. As a result, the EPA must make decisions a bout whether to regulate GHGs under certain provisions of the CAA, based on relevant statutory cr iteria. The EPA issued a final determination that GHG emissions endanger both the public health and the public welfare of current and future generations in the Endange rment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the CAA (74 FR 66,496; Dec. 15, 2009).

Section 111(b) authorizes the EPA to issue new source performance standards (NSPS) for carbon dioxide (CO 2 

## ) from new, existing, and modified sources. In January 2014, under the authority of CAA section 111(b), the EPA proposed standards for emissions of CO 2 

## from new fossil fuel-fired electric steam generating units (utility boilers and in tegrated gasification combined cycle [IGCC] units) and for natural ga s-fired stationary comb ustion turbines. In a separate action, EPA is propos ing standards to address CO 2 

## emissions from reconstructed and modified power plants under th e authority of CAA section 111( b). When the EPA establishes section 111(b) standards of perf ormance for new sources in a pa rticular source category for a

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## pollutant that is not regulated as a criteria pollutant or haza rdous air pollutant, the EPA must establish requirements for existing sources in that source category for that pollutant under section 111(d). Under 111(d), the EPA devel ops “emission guidelines” that th e states must develop plans to meet. In this action, the EPA is proposi ng state goals for GHG emissions from existing
sources under section 111(d) of the CAA.

1.2.2 Health and Welfare Impac ts from Climate Change
In 2009, the EPA Administrator found that elevat ed concentrations of greenhouse gases in the atmosphere may reasonably be anticipated bo th to endanger public health and to endanger public welfare. These adverse impacts make it necessary for the EPA to regulate GHGs from EGU sources. This proposed rule is designe d to reduce the rate at which atmospheric concentrations of these gases are increasing, and therefore reduce the risk of adverse effects.

Climate change threatens public health in a number of ways. Heat is already the leading
cause of weather-related deaths in the United States. In the future, severe heat waves are projected to intensify in magnit ude and duration over the portions of the U.S. where these eventshave already been observed. If observed warming continues as projected, it will increase heatrelated mortality and illness, especially among the elderly, young, and frai l. There is also a relationship between climate change and other aspects of air quality. Climate change is expected to increase regional ozone pollution compared to the expected ozone levels in the absence of climate change. These increases are expected to increase associated risks for respiratory illnesses and premature death.

In addition to impacts on heat-related mortalit y and air quality, there is also the potential for increased deaths, injuries, in fectious diseases, stress-related disorders, and other adverse effects associated with social disruption and migration from more frequent extreme weather events. Climate change can also impact the spread of vecto r-borne diseases.

There are multiple pathways by which greenhouse gas emissions and the resultant climate change affect climate-sensitive economic sectors and the environment. These sectors include agriculture, forestry, water resources, sea level rise and coastal areas, energy, infrastructure, settlements, and ecosystems and wildlife. For exam ple, changes in the intensity and frequency of extreme weather events, such as droughts and hea vy storms, have the pote ntial to cause serious

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## negative impacts on U.S. agriculture. Additionally, climate change will likely further constrain already over-allocated water resources in so me regions of the Un ited States, increasing competition among agricultural, municipal, industria l, and ecological uses. Furthermore, climate change impacts in other regions of the world c ould raise national security concerns for the
United States.

The specific health and welfare impacts of climate change are addressed in more detail in Chapter 4 of this Regulator y Impact Assessment (RIA).

1.2.3 Market Failure
Many regulations are promulgated to correct ma rket failures, which lead to a suboptimal
allocation of resources within the free market . Air quality and polluti on control regulations address “negative externalities” whereby the market does not internalize the full opportunity cost of production borne by society as public goods such as air quality are unpriced.

GHG emissions impose costs on society, such as negative health and welfare impacts that
are not reflected in the market price of the goods produced through the polluting process. For this regulatory action the good produced is electricity. These social cost s associated with the health and welfare impacts are referred to as negative externalities. If an electricity producer pollutes the atmosphere when it generates electricity, this cost will be borne not by the polluting firm but by society as a whole. The market price of electric ity will fail to incorporate the full opportunity cost to society of generating electr icity. In this case, the quantity of electricity generated in a free
market will not be at the socia lly optimal level. More electric ity will be produced than would occur if the power producers had to account for the full opportunity cost of production including the negative externality. Consequent ly, absent a regulation on emissi ons, the marginal social cost of the last unit of electricity produced will exceed its marginal social benefit.

Clean air is an example of a public good. A pub lic good is one that is available to all and its consumption by one does not reduce the availab ility to others. The characteristics of a public good also mean that there may be weak incentiv es to provide it in a competitive market. Regulations are a mechanism to provide and protect public goods.

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## 1.3 Summary of Regulatory Analysis
In accordance with Executive Order 12866, Executive Order 13563, and the EPA’s “Guidelines for Preparing Economic Analyses,” the EPA prepared this RIA for this “significant regulatory action.” This action is an economically significant regulatory action because it is expected to have an annual effect on the econom y of $100 million or more or adversely affect in a material way the economy, a sector of th e economy, productivity, competition, jobs, the environment, public health or safety, or state, local, or tribal governments or communities.

This RIA addresses the potential costs, emi ssion reductions, and benefits of the existing source emission guidelines that are the focus of this action. Additionally, this RIA includes information on potential impacts on electricity markets, employment, and markets outside the electricity sector. Chapter 9 of this document provides a separate description of the expected benefits, costs and economic impacts of proposed emission limits for reconstructed and modified
EGU sources.

1.4 Background for the Proposed EGU Existing Source GHG Guidelines

1.4.1 Base Caseline and Years of Analysis
The rule on which the majority of the an alysis in this RIA is based proposes GHG emission guidelines for states to limit CO 2 

## emissions from certain existing EGUs. The base
caseline for this analysis, which uses the Integrated Planning Mode l (IPM), includes state rules that have been finalized and/or approved by a st ate’s legislature or environmental agencies, as
well as final federal rules. The IPM Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), 1 

## the Mercury and Air Toxics Rule (MATS), and other state and Federal regulations to the extent that they contain measures, permits, or other air-related limita tions or requirements.

1 

### EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving the 1997 National Ambient Air Quality Standards (NAAQS) for ozone (8-hour average of 0.08 ppm) and fine particles (24-hour average of 65 μ g/m3 or less and annual average of 15 μ g/m3 for particles of diameter 2.5 micrometers or less, i.e., PM2.5). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the U.S. Court of Appeals for the District of Columbia Ci rcuit in December 2008 and EPA was required to correct legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. CAIR remains in effect until replaced by EPA pursuant to the Court’s ruling. CAIR’s provision s were still in effect when EPA Base Case v.5.13 was released.

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## Additional legally binding and enforceable comm itments for GHG reductions considered in the
base caseline are discussed in the documentation for IPM. 2


## Costs and benefits are presented for compliance in 2020, 2025, and 2030. These years were selected because they represent partial and fu ll implementation dates for the two policy options analyzed. Analysis of employment impacts is presented for compliance in 2020, 2025, and 2030. All estimates are presented in 2011 dollars.

1.4.2 Definition of Affected Sources
This proposed rule under CAA section 111(d) will set emission guidelines for states to
limit CO 2 

## emissions from certain existing EGUs . This rulemaking does not address GHG emissions from new, reconstructed, or modifi ed sources. Section 111(b) emission limits for
reconstructed and modified sources are being proposed in a separa te action and are discussed in Chapter 9 of this RIA.

For the purposes of this analysisrule , an affected EGU is any fossil fuel-fired EGU that was in operation or had commenced constructi on as of January 8, 2014, and is therefore an “existing source” for purposes of CAA section 111, but in all other respects would meet the applicability criteria for coverage under the proposed GHG standards for new fossil fuel-fired EGUs published in the Federal Register on th at date. The proposed GHG standards for new EGUs generally define an affected EGU as any boiler, IGCC, or combustion turbine (in either simple cycle or combined cycle configuration) that (1) is capable of combusting at least 250 million British thermal units (Btu) per hour; (2) com busts fossil fuel for more than 10 percent of its total annual heat input; (3 ) sells the greater of 219,000 MWh pe r year or one-third of its potential electrical output to a utility distribution system; and (4) was not in operation or under construction as of January 8, 2014 (the date th e proposed GHG standards of performance for new EGUs were published in the Federal Register). The minimum fossil fuel consumption condition applies over any consecutive three- year period (or as long as the unit has been in operation, if less). The minimum electricity sales condition app lies over rolling three-year periods (or as long as the unit has been in operation, if less).

2 

### See Chapter 3 of EPA’s Base Case using IPM (v5.13) documentation, available at: http://www.epa.gov/powersect ormodeling/BaseCasev513.html

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## 1.4.3 Regulated Pollutant
This rule sets emission guidelines for CO 2 

## emissions from affected sources. The EPA is proposing these guidelines because CO 2 

## is a GHG and fossil fuel-fired power plants are the country’s largest stationary source emitters of GHGs. In 2009, the EPA found that by causing or contributing to climate change, GHGs endanger both the public health and the public welfare of current and future generations.

The EPA is aware that other GHGs such as nitrous oxide (N 2 

## O) (and to a lesser extent, methane [CH 4 

## ]) may be emitted from fossil-fuel-fired EGUs, especially from coal-fired circulating fluidized bed combustors and from units with selective cat alytic reduction and selective non-catalytic reduction syst ems installed for nitrogen oxide (NO X 

## ) control. The EPA is not proposing separate N 2 

## O or CH 4 

## guidelines or an equivalent CO 2 

## emission limit because of a
lack of available data for these affected s ources. Additional information on the quantity and significance of emissions and on the availability of cost effective controls would be needed before proposing standards for these pollutants.

1.4.4 Emission Guidelines
The EPA is proposing emission guidelines for stat es to use in developing plans to address greenhouse gas emissions from existing fossil fuel-f ired electric generating units. Specifically, the EPA is proposing state-specif ic rate-based goals for carbon dioxide emissions from the power sector, as well as guidelines for states to followuse in developing plans to attain the statespecific goals. (These state-based targets can be found in the preamble and Chapter 3 of this RIA.) The proposed emission guidelines provide st ates with options for meeting performance
standards in a manner that accommodates a dive rse range of state approaches. The proposed guidelines would also allow stat es to collaborate and to demo nstrate emission performance on a multi-state basis, in recognition of the fact that electricity is transmitte d across state lines, and measures often impact regional EGU CO 2 

## emissions. The illustrative compliance strategies presented in this RIA include bot h regional and state-le vel compliance scenario s for each of the regulatory options presented.

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## 1.5 Organization of the Regu latory Impact Analysis This report presents the EPA’s analysis of the potential benefits, cost s, and other economic effects of the proposed EGU Existing Source GHG Guid elines to fulfill the requirements of an RIA. This RIA includes the following chapters:

 Chapter 2, Electric Power Sector Profile

 Chapter 3, Control Strategies, Cost, Economic, and Energy Impacts

 Chapter 4, Estimated Climate Be nefits and Health Co-benefits

 Chapter 5, Economic Impacts – Market s Outside the Electricity Sector

 Chapter 6, Employment

 Chapter 7, Statutory and Executive Order Analyses

 Chapter 8, Summary of Benefits and Cost of the Proposed Regulation

 Chapter 9, Benefits, Costs, and Economic Impacts of Standards of Performance for Modified and Reconstructed Electric Generating Units

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## 1.6 References
40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findi ngs for Greenhouse Gases under Section 202(a) of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 15, 2009 / Rules and Regulations. U.S. Environmental Protection Agency. Power Sector Modeling Platform v.5.13. November 27, 2013. Available online at: http://www.epa .gov/powersectormodeling/BaseCasev513.html.2-1 

### CHAPTER 2: ELECTRIC POWER SECTOR PROFILE

2.1 Introduction
This chapter discusses important aspects of th e power sector that relate to the proposed EGU Existing Source GHG Standards, including the types of power-sector sources affected by the proposal, and provides background on the power sector and EGUs. In addition, this chapter provides some historical background on the EPA regulation of, and future projections for, the power sector.

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

2.2.1 Generation
Electricity generation is the first process in the delivery of electricity to consumers. Most of the existing capacity for generating electricity do es so by creating heat to create high pressure
steam that is released to rotate turbines whic h, in turn, create electricity. The existing power sector consists of over 18,000 generating units, comprising fossil-fuel-fired units, nuclear units, and hydroelectric and other renewable sources dispersed throughout the country (see Table 2-1).2-2 Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2012

Energy Source Number of
Generators Generator Nameplate Capacity (MW) Net Summer Capacity

(MW) Net Winter Capacity

(MW) Coal 1,309 336,341 309,680 312,293 Petroleum 3,702 53,789 47,167 51,239 Natural Gas 5,726 485,957 422,364 455,214 Other Gases 94 2,253 1,946 1,933 Nuclear 104 107,938 101,885 104,182 Hydroelectric Conventional 4,023 78,241 78,738 78,215 Wind 947 59,629 59,075 59,082 Solar Thermal and Photovoltaic 553 3,215 3,170 3,053 Wood and Wood-Derived Fuels 351 8,520 7,508 7,570 Geothermal 197 3,724 2,592 2,782 Other Biomass 1,766 5,527 4,811 4,885 Hydroelectric Pumped Storage 156 20,858 22,368 22,271 Other Energy Sources 95 2,005 1,729 1,739 Total 19,023 1,167,995 1,063,033 1,104,459

Source: Table 4.3, EIA Electric Power Annual , 2013a Note: This table presents generation capacity. Actu al net generation is presented in Table 2-3.
In 2012, electric generating sources produced net 3,890 billion kWh to meet electricity demand. Roughly 70 percent of this electricity was produced through the combustion of fossil fuels, primarily coal and natural gas, with coal accounting for the largest single share.2-3 Table 2-2. Electricity Net Generation in 2012 (billion kWh) Net Generation (Billion kWh) Fuel Source
Share Coal 1,500.6 38.57%
Petroleum 20.1 0.52% Natural Gas 1,132.8 29.12%
Other Gases 3.0 0.08%
Nuclear 769.3 19.78% Hydroelectric 268.9 6.91%
Other 195.7 5.03% Total 3,890 100%

Source: Tables 3.2.A and 3.3.A, EIA Electric Power Annual , 2013a Note: Excludes generation from commercial and industrial sectors. Retail sales are not equal to net generation because net generation includes net exported electricity and loss of electricity that occurs through transmission and distribution.

These electric generating sources provide el ectricity for commercial, industrial, and residential uses, each of which consumes roughly a quarter to a third of the total electricity produced (see Table 2-3). Some of these uses are highly variable, such as heating and air conditioning in residential and commercial buildings, while others are relatively constant, such as industrial processes that operate 24 hours a day.

Table 2-3. Total U.S. Electric Po wer Industry Retail Sales in 2012 (billion kWh) Sales/Direct Use (Billion kWh) Share of
Total End

Use

Retail Sales Residential 1,375 35.87% Commercial 1,327 34.63%
Industrial 986 25.72% Transportation 7.3 0.19%
Direct Use 138 3.59% Total End Use 3,832 100%

Source: Table 2.2, EIA Electric Power Annual , 2013a Comment [A1]: OMB: The table also shows transport, so suggest adding a sentence on how transport use is envisioned to grow.
EPA: This paragraph does not discuss potential growth in individual sectors, but instead gives examples of how the timing of demand can vary by sector. This is related to the discussion of base load and peak load sources.2-4 Coal-fired generating units have historically su pplied “base-load” electricity, the portion of electricity loads which are continually present, and typically operate throughout the day. Along with nuclear generation, these coal units meet the part of demand that is relatively constant. Although much of the coal fleet operates as base load, there can be notable differences across various facilities (see Table 2-4). For example, co al-fired units less than 100 megawatts (MW) insize compose 32 percent of the total number of coal-fired units, but only 4 percent of total coalfired capacity. Gas-fired generation is better able to vary output and is the primary option used to meet the variable portion of the electricity load and has historically supplied “peak” and “intermediate” power, when there is increased demand for electricity (for example, when businesses operate throughout the day or when people return home from work and run appliances and heating/air-conditioning), versus late at night or very early in the morning, when demand for electricity is reduced.

The evolving economics of the power sector, in particular the increased natural gas supply and subsequent relatively low natural gas prices, have resulted in more gas being utilized as base load energy in addition to supplying electricity during peak load. Projections of changes in capacity and the impact of this rule on the future need for construction of new generation capacity sources are discussed in more detail in Chapter 3 of this RIA.

Table 2- 14 . Coal Steam Electricity Generating Units in 2015, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate)

Unit Size Grouping (MW) No. Units % of All
Units Avg. Age Avg. Net
Summer Capacity

(MW) Total Net
Summer Capacity

(MW) % Total Capacity Avg. Heat
Rate (Btu/kWh)

0 To 25 133 14% 46 14 1,837 1% 11,860 >25 To 49 74 8% 38 37.5 2,775 1% 12,113 50 To 99 94 10% 44 72.0 6,765 2% 11,910
100 To 149 75 8% 49 124.4 9,329 3% 10,977
150 To 249 128 14% 47 191.3 24,492 9% 10,646 250 and up 432 46% 36 536.7 231,874 84% 10,336
Totals 1,266 316,480

Source: National Electric Energy Data System (NEEDS) v.5.132-5 Note: The average heat rate reported is the mean of the heat rate of the uni ts in each size category (as opposed to a generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel efficiency. Table is limited to coal-steam units online in 2013 or earlier, and excludes those units with planned retirements.

The locations of existing fossil units in NEEDS v.5.13 are shown in Figure 2.1.

Figure 2-1. Fossil Fuel-Fired Electrici ty Generating Facilities, by Size

Source: National Electric Energy Data System (NEEDS) v.5.13 Note: This map displays fossil capacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS reflects available fossil capacity on-line by the end of 2015. This include s planned new builds and planned retirements. In areas with a dense concentration of facilitie s, some facilities may be obscured.

2.2.2 Transmission

Transmission is the term used to describe the movement of electricity over a network of high voltage lines, from electric generators to substations where power is stepped down for local2-6 distribution. In the U.S. and Canada, there are three separate interconnected networks of high voltage transmission lines, 1 each operating synchronously. Within each of these transmission networks, there are multiple areas where the operation of power plants is monitored and controlled to ensure that electricity generation and load are kept in balance. In some areas, the operation of the transmission system is under the control of a single regional operator; in others, individual utilities coordinate the operations of their generation, transmission, and distribution systems to balance their common generation and load needs.

2.2.3 Distribution
Distribution of electricity involves networks of lower voltage lines and substations that take the higher voltage power from the transmission system and step it down to lower voltage
levels to match the needs of customers. The tr ansmission and distribution system is the classic example of a natural monopoly, in part because it is not practical to have more than one set of lines running from the electricity generating so urces to substations or from substations to
residences and businesses.

Transmission has generally been developed by th e larger vertically integrated utilities that typically operate generation and distribution networ ks. Often distribution is handled by a large number of utilities that purchase and sell electricity , but do not generate it. Over the last couple of decades, several jurisdictions in the United States began restructuring the power industry to separate transmission and distribution from genera tion, ownership, and operation. As discussed below, electricity restructuring has focused primarily on efforts to reorganize the industry to encourage competition in the generation segment of the industry, including ensuring open access of generation to the transmission and distribution services needed to deliver power to consumers. In many states, such efforts have also included separating generation assets from transmission
and distribution assets to form distinct economic entities. Transmission and distribution remain price-regulated throughout the country based on the cost of service.

1 These three network interconnections are the western US and Canada, corresponding approximately to the area west of the Rocky Mountains; eastern US and Canada , not including most of Texas; and a third network operating in most of Texas. These are commonly referred to as the Western Interconnect Region, Eastern Interconnect Region, and ERCOT, respectively.2-7 2.3 Deregulation and Restructuring
The process of restructuring and deregulation of wholesale and retail electric markets has changed the structure of the electric power industry. In addition to reorganizing asset management between companies, restructuring sought a functional unbundling of the generation, transmission, distribution, and ancillary services the power sector has historically provided, with the aim of enhancing competition in the generation segment of the industry.

Beginning in the 1970s, government policy shifted against traditional regulatory approaches and in favor of deregulation for many important industries, including transportation (notably commercial airlines), communications, an d energy, which were all thought to be natural monopolies (prior to 1970) that warranted g overnmental control of pricing. However, deregulation efforts in the power sector were most active during the 1990s. Some of the primary drivers for deregulation of electric power includ ed the desire for more efficient investment choices, the economic incentive to provide least-co st electric rates through market competition, reduced costs of combustion turbine technology that opened the door for more companies to sell power with smaller investments, and complexity of monitoring utilities’ cost of service and establishing cost-based rates for various customer classes.

The pace of restructuring in the electric power in dustry slowed signifi cantly in response to market volatility in California and financial turm oil associated with bankruptcy filings of key energy companies. By the end of 2001, restructuring had either been delayed or suspended in eight states that previously enacted legislation or issued regulatory orders for its implementation (shown as “Suspended” in Figure 2-2 below). Eight een other states that had seriously explored the possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA, 2003) (“Not Active” in Figure 2-2 below). Currentl y, there are 15 states where price deregulation of generation (restructuring) has occurred (“Active” in Figure 2-2 below). Power sector restructuring is more or less at a standstill; there have been no recent proposals to the Federal Energy Regulatory Commission (FERC) for actio ns aimed at wider restructuring, and no additional states have recently begun retail deregulation activity.2-8 Figure 2-2. Status of State Electrici ty Industry Restructuring Activities

Source: EIA “Status of Electricity Restructuring by State” 2010.

2.4 Emissions of Greenhouse Gases from Electric Utilities
The burning of fossil fuels, which generates about 70 percent of our electricity nationwide, results in emissions of greenhouse gases. The power sector is a major contributor of CO 2 in particular, but also contributes to emissions of sulfur hexafluoride (SF 6 ), CH 4 , and N 2 O. In
201 21 , the power sector accounted for 3 23 percent of total nationwide greenhouse gas emissions,
measured in CO 2 equivalent, a slight increase from its 30 percent share in 1990. Table 2-5 and Figure 2-3 show the contributions of the power s ector relative to other major economic sectors. Table 2-6 and Figure 2-4 show the contributions of CO 2 and other GHGs from the power sector.

Table 2- 25 . Domestic Emissions of Greenhouse Gase s, by Economic Sector (million metric tons of CO 2 equivalent)

Sector/Source 1990 20051995 20002008 200 59 201 01 2011 2012 Electricity Generation 1,866 1,9922,446 2, 402336 2, 187446 2, 303201 2,201 2,064 Transportation 1,553 1,6972,017 1,9 3527 2,0121,862 1,8 7629 1,852 1,837 Industry 1,53 19 1,5581,408 1, 372504 1, 221416 1,3 0132 1,298 1,278 Agriculture 458518 511584 501615 517605 547601 613 614 Field Code Changed2-9 Commercial 38 58 3 7091 37 96 3 8274 37 78 378 353
Residential 345 3 7167 3 6586 3 5871 3 605 7 354 321
U.S. Territories 34 4158 5046 5848 58 58 58 Total Emissions 6, 233183 6,5577,254 7, 118076 7,1956,663 6, 875702 6,753 6,523

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–201 21 . April 201 4 3.

Figure 2-6. Domestic Emissions of Greenhouse Gases, 201 21 (million metric tonnes of CO 2 equivalent)

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–201 21 . April 201 43

.

Table 2- 36 . Greenhouse Gas Emissions from the Electricity Sector (Generation, Transmission and Distribution), 201 21 (million metric tons of CO 2 equivalent)
Source Total Emissions

CO 2 from Electricity Generation 2,023
Coal 1,511
Natural Gas 492
Fuel Oil 19
Geothermal 0.4
CH 4 from Electricity Generation 0.5
Coal 0.1
Fuel Oil 0.1 

### Other 4,501 67% Electricity Generation 2,201 33% Other 4,459 68% Electricity Generation 2,064 32% Formatted: Complex Script Font: 12 pt
Formatted: Font: Times New Roman, 10 pt2-10 Natural Gas 0.4
Wood + N 2 O from Electricity Generation 18.3
Coal 9.1
Fuel Oil 0.3
Natural Gas 8.7
Wood 0.1
SF 6 from Electricity Transmission and Distribution 6.0
Total 2,048

Source Total
Emissions

CO 2 2,175.5
CO 2 from Fossil Fuel Combustion 2,158.5
Coal 1,722.7
Natural Gas 408.8
Petroleum 26.6
Geothermal 0.4
Incineration of Waste 12.4
Other Process Uses of Carbonates 4.6
CH 4 0.4 Stationary Combustion* 0.4
Incineration of Waste + N 2 O 18.3 Stationary Combustion* 17.9
Incineration of Waste 0.4
SF 6 ** 7.0
Electrical Transmission and Distribution 7.0
Total 2,201.2

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–201 21 . April 201 43

. * Includes only stationary combustion emissions related to the generation of electricity.

** SF 6 is not covered by this rule, which specifically regulates GHG emissions from combustion. + Does not exceed 0.05 Tg CO 2 Eq. or 0.05 percent. Comment [A2]: EPA should clarify what these are and why they are included in electric sector emissions.
EPA: The presentation of this data changed in the most recent GHG inventory and this category is no longer included as a line item. See table above.2-11 The amount of CO 2 emitted during the combustion of fossil fuels varies according to the carbon content and heating value of the fuel used (EIA, 2000) (see Table 2-7). Coal has higher carbon content than oil or natural gas and, thus, releases more CO 2 during combustion. Coal emits around 1.7 times as much carbon per unit of energy when burned as does natural gas (EPA 2013).

Figure 2-3. Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG Emissions (201 21 ) Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–201 21 . April 201 43

.

Table 2- 47 . Fossil Fuel Emission Factors in EPA Modeling Applications

Fuel Type Carbon Dioxide (lbs/MMBtu)

Coal

Bituminous 205.2 – 206.6

Subbituminous 212.7 – 213.1
Lignite 213.5 – 217.0

Natural Gas 117.1

Fuel Oil 38% 72%

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

CO2 SF6 N2O CH4 Other

Power Sector2-12 Distillate 161.4

Residual 161.4 – 173.9

Biomass* 195

Waste Fuels

Waste Coal 205.7

Table 2-7. Continued

Petroleum Coke 225.1

Fossil Waste 321.1

Non-Fossil Waste 0

Tires 189.5
Municipal Solid Waste 91.9

Source: Documentation for IPM Base Case v.513. See also Table 9.9 of IPM Documentation.

Note: CO 2 emissions presented here for biomass account forreflect combustion only . They do not include any other biogenic or fossil emissions/sequestration related to biomass growth, harvest, transportation or any other biomass or processing emissions. and do not reflect lifecycle emissions from initial photosynthesis (carbon sink) or harvesting activities and transportation (carbon source).

2.5 Improving GHG Performance at Existing EGUs
In proposing state goals, the analysis anticip ates that states will pursue a mix of carbonreducing strategies appropriate to each state’s uni que situation, developing an effective state plan that reflects the composition of the state’s economy, existing state programs and measures, and characteristics of the state’s existing electricity po wer system (e.g., utility regulatory structure, generation mix, transmission syst em and electricity demand). Th e analysis assumes states will develop plans involving four categories (“buildi ng blocks”) of demonstrated approaches to improve the GHG performance of existing EGUs in the power sector:

1. Lowering the carbon intensity of generation at individual affected EGUs through heat rate improvements 2. Reducing utilization of the most carbon-intensive EGUs by shifting generation to less carbon-intensive existing fossil fuel-fired EGUs through redispatch Comment [A3]: We suggest EPA say instead: CO2 emissions presented here for biomass account for combustion only and do not reflect lifecycle emissions from a baseline of letting the biomass decompose and emit methane and nitrous oxide, from the initial photosynthesis (carbon sink), or from harvesting activities and transportation (carbon source).

Note that IPM does not account for emissions that occur in the harvesting or transporting of fossil fuels either.

EPA: Proposed revision not accurate. Revised based on feedback from Climate Economics branch.2-13 3. Reducing utilization of the most carbon-intensive EGUs by expanding the amount of new, lower carbon-intensity generation capacity 4. Reducing utilization of the most carb on-intensive EGUs by increasing demand-side efficiency. This section briefly describes each of these four building block categories. Our analyses of the magnitude of the opportunity for improvements from each category, and more detail about how we used each state’s unique situation and availability of demonstrated approaches to improve GHG performance to determine state-speci fic emission goals, are presented in Chapter
3.

The first building block encompasses various improvements at existing EGUs that can
lower the amount of CO 2 they emit when generating power. Heat rate improvements result in any changes in equipment, operating procedures or maintenance practices that increase the efficiency of converting fuel energy into electricity by an EGU. Such efficiency changes result in more electricity being generated by each unit of fuel (e .g., ton of coal or cubic foot of gas), thereby lowering the amount of CO 2 per kWh of electricity produced as a byproduct of fuel combustion.

The second building block consists of improvements to lower the electric system’s overall carbon intensity by shifting generation among ex isting EGUs. The nation’s EGUs are connected by transmission grids extending over large regions. Through these interconnections, EGU owners and/or grid operators prioritize among EGUs when deciding which ones to operate (i.e., “dispatch”) to meet electricity demand at any tim e, subject to various constraints. Opportunities exist to lower significantly the electric system’s carbon intensity through redispatch among existing EGUs, particularly by shifting generation from coal units to natural gas combined cycle (NGCC) units. 2 Over the last several years, advances in the production of natural gas have helped reduce natural gas prices and improved the competitive position of gas-fired units relative to coal-fired units. Operators have already shifted significant quantities of generation from coal units to NGCCs, absent any federal CO 2 requirements. Additional redispatch opportunities exist to further reduce carbon intensity of the power syst em, with extent of the additional opportunities

2 We view opportunities to shift generation to existing re newable EGUs such as wind or solar units as limited because such units already tend to r un when capable of doing so due to their low variable operating costs.2-14 varying region, based on factors such as the mix of EGU types, the relative prices of coal and natural gas and the amount of available NGCC capacity

The third building block consists of the potential to increase the amount of lower carbon intensity generation by expanding low-carbon and renewable generating capacity. Adding new
NGCC nuclear or and new renewable generating capacity to the electric system would tend to shift generation to the new units from exis ting EGUs with higher carbon intensity. Such expansion is consistent with current trends. While not included in the goal setting for building block 3, the addition of new NGCC capacity would have a similar impact and is one option states may choose to achieve the goal.

The fourth building block consists of improv ing the GHG performance of the power sector indirectly by reducing the total amount of generation required – in other words, improving demand-side efficiency. Many studies have found that significant improvements in demand-side efficiency can be realized at less cost than the savings from avoided power generation. 3 These electricity demand reductions can be achieved through policies or programs, such as subsidies for the purchase of energy-efficient thatappliances, which incentivize investment in costeffective efficiency improvements by overcoming market imperfections that otherwise thwart these investments. States already employ a variety of mechanisms for this purpose. These include energy efficiency resource standards, building energy codes and appliance and equipment energy standards. Reducing electricity demand also enhances efficiency by reducing
the absolute amount of transmission and distribution losses th at occur across the grid between the electricity generation sites and the demand sites. Particularly when integrated into a comprehensive approach for addressing GHG em issions, demand-side efficiency improvements can improve the carbon profile of the electricity supply system.

3 See, e.g., “Unlocking Energy Efficiency in the U.S. Economy,” Hannah Choi Granade, et al., July 2009. (http://www.mckinsey.com/client_service/electric_power _and_natural_gas/latest_thinking/unlocking_energy_ef ficiency_in_the_us_economy) and Assessment of Achievable Potential fr om Energy Efficiency and Demand Response Programs in the U.S. (2010 – 2030): Executive Summary . EPRI, Palo Alto, CA: 2009. 1018363.2-15 2.6 GHG and Clean Energy Regulation in the Power Sector

2.6.1 State Policies
Several states recently established emission performance standards or other measures to limit emissions of GHGs from new EGUs that are comparable to this proposal in this rulemaking.

In 2003, then-Governor George Pataki sent a letter to his counterparts in the Northeast and Mid-Atlantic inviting them to participate in the development of a regional cap-and-trade program addressing power plant CO 2 emissions. This program, known as the Regional Greenhouse Gas Initiative (RGGI), began in 2009 and sets a regional CO 2 cap for participating states. The currently participating states include: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO 2 emissions from all fossil-fired EGUs greater than 25 MW in pa rticipating states, and limits total emissions to 91 million short tons in 2014. This emissions budget is reduced 2.5% annually from 2015 to 2020.

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

In 2006 Governor Schwarzenegger also signe d into law Assembly Bill 32, the Global Warming Solutions Act of 2006. This act incl udes a multi-sector GHG cap-and-trade program which covers approximately 85% of the state GHG emissions. EGUs are includes in phase I of the program, which began in 2013. Phase II begins in 20152020 and includes upstream sources. The cap is based on a 2 percent reduction from total 2012 expected emissions, and declines 2 percent annually through 2014, then 3 percent each year until 2020.

In May 2007, Washington Governor Gregoire signed Substitute Senate Bill 6001, which established statewide GHG emissions reduction goa ls, and imposed an emission standard that applies to any baseload electric generation that commenced operation after June 1, 2008 and is2-16 located in Washington, whether or not that generation serves load located within the state. Baseload generation facilities must initially comply with an emission limit of 1,100 lb
CO 2 /MWh.

In July 2009, Oregon Governor Kulongoski signed Senate Bill 101, which mandated that facilities generating baseload electricity, whether gas- or coal-fired, must have emissions equal to or less than 1,100 lb CO 2 /MWh, and prohibited utilities from entering into long-term purchase agreements for baseload electrici ty with out-of-state facilities th at do not meet that standard. Natural gas- and petroleum distillate-fired facilities that are primarily used to serve peak demand or to integrate energy from renewable resources are specifically exempted from the performance
standard.

In August 2011, New York Governor Cuomo signed the Power NY Act of 2011. This regulation establishes CO 2 emission standards for new and modified electric generators greater than 25 MW. The standards vary based on the type of facility: baseload facilities must meet a
CO 2 standard of 925 lb/MWh or 120 lb/MMBtu , and peaking facilities must meet a CO 2 standard of 1,450 lbs/MWh or 160 lbs/MMBtu.

Additionally, the majority of most states have implemented Renewable Portfolio Standards (RPS), or Renewable Electricity Standards (RES). These programs are designed to increase the renewable share of a state’s total electricity genera tion. Currently 30 states and the District of
Columbia have enforceable RPS or other mandat ory renewable capacity policies, and 7 states have voluntary goals. 4 These programs vary widely in structure, enforcement, and scope.

2.6.2 Federal Policies
In April 2007, the Supreme Court concluded that GHGs met the CAA definition of an air pollutant, giving the EPA the authority to regulate GHGs under the CAA contingent upon an agency determination that GHG emissions from new motor vehicles cause or contribute to air pollution that may reasonably be anticipated to endanger public health or welfare. This decision to regulate GHG emissions for motor vehicles set the stage for the determination of whether

4 EIA Today in Energy: Most states have Renewable Portfolio Standards

. 2012c.2-17 other sources of GHG emissions, including stationary sources, w ould need to be regulated as
well.

In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public Law 110– 161), the EPA issued the Mandatory Reporting of Greenhouse Gases Rule (74 FR 5620) which required reporting of GHG data and other releva nt information from fossil fuel suppliers and industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy-duty and off-road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG data to inform future policy decisions. As such, it did not require that sources control greenhouse gases, but sources above certain threshold levels must monitor and report emissions.

In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to Deseret Power Electric Cooperative, authorizing it to construct a new waste-coal-fired EGU near its existing Bonanza Power Plant, in Bonanza, Utah. The permit did not include emissions control requirements for CO 2 . The EPA acknowledged the Supreme Court decision, but found that decision alone did not require PSD permits to include limits on CO 2 emissions. Sierra Club challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) remanded the permit to the EPA to recons ider “whether or not to impose a CO 2 BACT (best available control technology) limit in light of th e ‘subject to regulation’ definition under the CAA.” The remand was based in part on EAB’s finding that there was not an established EPA interpretation of the regulatory phrase “subject to regulation.”

In December 2008, the Administrator issued a memo indicating that the PSD Permitting Program would apply to pollutants that are subj ect to either a provision in the CAA or a regulation adopted by the EPA under the CAA that requires actual control of emissions of that pollutant. The memo further explained that pollut ants for which the EPA regulations only require monitoring or reporting, such as the provisions for CO 2 in the Acid Rain Program, are not subject to PSD permitting. Fifteen organizations petiti oned the EPA for reconsideration, prompting the agency to issue a revised finding in March 2009. After reviewing comments, the EPA affirmed the position that PSD permitting is not triggered for a pollutant such as GHGs until a final nationwide rule requires actual control of emi ssions of the pollutant. For GHGs, this meant January 2011 when the first na tional rule limiting GHG emissions for cars and light trucks was2-18 scheduled to take effect. Therefore, a permit issued after January 2, 2011, would have to address
GHG emissions.

The Administrator signed two distinct findings in December 2009 regarding greenhouse gases under section 202(a) of the Clean Air Act. The endangerment finding indicated that current and projected concentrations of the six key well-mixed greenhouse gases — CO 2 , CH 4 , N 2 O, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF 6 — in the atmosphere threaten the public health and welfare of current and future generations. These greenhouse gases have long lifetimes and, as a result, become homogeneous ly distributed through the lower level of the Earth’s atmosphere (IPCC, 2001). This differentiates them from other greenhouse gases that are not homogeneously distributed in the atmosphere . The cause and contribute finding indicated that the combined emissions of these well-mixed greenhouse gases from new motor vehicles and new motor vehicle engines contribute to the greenhouse gas pollution which threatens public health and welfare. Both findings were published in the Federal Register on December 15, 2009 (Docket ID EPA-HQ-OAR-2009-0171). These findings did not themselves impose any requirements on any industry or other entities, but allowed the EPA to regulate greenhouse gases under the CAA (see preamble section II.E for regulatory background). This action was aprerequisite to implementing the EPA's proposed greenhouse gas emission standards for lightduty vehicles, which was finalized in January 2010. Once a pollutant is regulated under the CAA, it is subject to permitting requirements under the PSD and Title V programs. The 2009 Endangerment Finding and a denial of reconsideration were challe nged in a lawsuit; on June 26, 2012, the DC Circuit Court upheld the Endangerment Finding and the Reconsideration Denial, ruling that the Finding was neither arbitrary nor capricious, was consistent with Massachusetts v. EPA, and was adequately supported by the administrative record. The Court found that the EPA had based its decision on “substantial scientific evidence,” noted that the EPA’s reliance on
assessments was consistent with the methods decision-makers often use to make a science-based judgment, and stated that “EPA’s interpretation of the governing CAA provisions is unambiguously correct.”

In May 2010, the EPA issued the final Tailo ring Rule which set thresholds for GHG emissions that define when permits under the Ne w Source Review and Title V Operating Permit programs are required for new and existing industr ial facilities. Facilities responsible for nearly2-19 70 percent of the national GHG emissions from st ationary sources, including EGUs, were subject to permitting requirements under the rule. This rule was upheld by the D.C. Circuit in 2012.

The EPA entered into two proposed settlement agreements in December 2010 to issue rules that will address greenhouse gas emissions from fossil fuel-fired power plants and refineries. These two industrial sectors make up nearly 40 percent of the nation’s greenhouse gas emissions. On March 27, 2012, EPA proposed NSPS for new source natural gas, coal, and other
solid fossil-fired EGUs. After consideration of information provided in more than 2.7 million comments on this proposal, as well as consider ation of continuing changes in the electricity sector, the EPA determined that revisions in its proposed approach are warranted. This rule replaces that proposal. Existing source standards ar e not addressed in this action. Details of the settlement agreements can be found on the EPA website .5

On January 8, 2014 EPA proposed a new source performance standard (NSPS) for
emissions of carbon dioxide for new fossil fuel-fir ed electric utility generating units. This action proposes to establish separate standards for fossil fuel-fired electric steam generating units (utility boilers and Integrated Gasification Co mbined Cycle (IGCC) units) and for natural gasfired stationary combustion turbines. These proposed standards reflect separate determinations of the best system of emission reduction (BSER) ad equately demonstrated for utility boilers and IGCC units and for natural gas-fired stationary combustion turbines. This action proposes a standard of performance for utili ty boilers and IGCC units base d on partial implementation of carbon capture and storage (CCS) as the BSER. The proposed emission limit for those sources is 1,100 lb CO2/MWh. This action also proposes standards of performance for natural gas-fired stationary combustion turbines based on modern, efficient natural gas combined cycle (NGCC) technology as the BSER. The proposed emission limits for those sources are 1,000 lb CO2/MWh for larger units and 1,100 lb CO2/MWh for smaller units.

5 http://www.epa.gov/airquality/ghgsettlement.html2-20 2.7 Revenues, Expenses, and Prices
Due to lower retail electricity sales, total utility operating revenues declined in 2012 to $2 7181 billion from a peak of almost $300 billion in 2008. Despite revenues not returning to
2008 levels in 2011 , operating expenses were appreciably lower and as a result, net income also rose in comparison to both 2009 and 2010previous years (see Table 2-8). Recent economic

events and continued energy efficiency improvements have put downward pressure on electricity demand, thus dampening electricity prices and co nsumption (utility revenues), but have also reduced the price and cost of fossil fuels and other expenses. Electricity sales and revenues associated with the generation, transmission, an d distribution of electricity are expected to rebound and increase modestly by 2015, when revenues are projected to be roughly $359 billion (see Table 2-9).

Table 2-8 shows that investor-owned ut ilities (IOUs) earned income of about 1311.9 percent compared to total revenues in 201 21 . Based on EIA's Annual Energy Outlook 2013, Table 2-9 shows that the power sector is projected to derive revenues of $359 billion in 2015. Assuming the same income ratio from IOUs (with no income kept by public power), and using the same proportion of power sales from public power as observed in 201 21 , the EPA projects that the power sector will expend over $ 372320 billion in 2015 to generate, transmit, and distribute electricity to end-use consumers.

Over the past 50 years, real national average retail electricity prices have ranged from around 7 cents per kWh in the early 1970s, to around 11 cents, reached in the early 1980s. Generally, retail electricity prices do not cha nge rapidly and do not display the variability of other energy or commodity prices, although the frequency at which these prices change varies across different types of customers. Retail rate regulation has largely insulated consumers from the rising and falling wholesale electricity price signals whose variation in the marketplace on an hourly, daily, and seasonal basis is critical for driving lowest-cost matching of supply and demand. In fact, the real price of electricity t oday is lower than it was in the early 1960s and 1980s (see Figure 2-5).2-21 Figure 2-5. National Average Retail Electricity Price (1960 – 2011)

Source: EIA Annual Energy Outlook 2013 0 2 4 6 8 10 12 14

1960 1970 1980 1990 2000 2010cents/KWh(2011$)2-22 Table 2-8. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities for 2011 ($millions)

20 1 0 9 201 10 201 21 Utility Operating Revenues 285,512 276,124 280,520 285,512 280,520270,912 Electric Utility 260,119 249,303 255,573 260,119 255,573249,166 Other Utility 25,393 26,822 24,946 25,393 24,94621,745 Utility Operating Expenses 253,022 244,243 247,118 253,022 247,118235,694 Electric Utility 234,173 219,544 228,873 234,173 228,873220,722 Operation 166,922 154,925 161,460 166,922 161,460152,379
Production 128,831 118,816 122,520 128,831 122,520111,714
Cost of Fuel 44,138 40,242 42,779 44,138 42,77938,998
Purchased Power 67,284 67,630 61,447 67,284 61,44754,570
Other 17,409 10,970 18,294 17,409 18,29418,146
Transmission 6,948 6,742 6,876 6,948 6,8767,183
Distribution 4,007 3,947 4,044 4,007 4,0444,181
Customer Accounts 5,091 5,203 5,180 5,091 5,1805,086
Customer Service 4,741 3,857 5,311 4,741 5,3115,640
Sales 185 178 185 185 185221
Admin. and

General 17,120 15,991 17,343 17,120 17,34318,353
Maintenance 14,957 14,092 15,772 14,957 15,77215,489 Depreciation 20,951 20,095 22,555 20,951 22,55523,677
Taxes and Other 31,343 29,081 29,086 31,343 29,08629,177 Other Utility 18,849 24,698 18,245 18,849 18,24514,972 Net Utility Operating Income 32,490 31,881 33,402 32,490 33,40235,218 Source: Table 8.3, EIA Electric Power Annual , 201 3a1 Note: This data does not include information for public utilities.2-23Table 2-9. Projected Revenues by Service Category in 2015 for Public Power and InvestorOwned Utilities (billions) Generation $207
Transmission $40
Distribution $111
Total $359

Source: EIA 2013 b Note: Data are derived by taking either total electricity us e (for generation) or sales (transmission and distribution) and multiplying by forecasted prices by service categor y from Table 8 of EIA AEO 2013 (Electricity Supply, Disposition, Prices, and Emissions).

On a state-by-state basis, retail electricity prices vary considerably. The Northeast and California have average retail prices that can be as much as double those of other states (see Figure 2-6).2-24 Figure 2-6. Average Retail Electricity Price by State (cents/kWh), 2011

Source: EIA Annual Energy Outlook , 2012 a

2.8 Natural Gas Market
The natural gas market in the United States ha s historically experienced significant price volatility from year to year, between seasons with in a year, and can undergo major price swings during short-lived weather events (such as cold snaps leading to short-run spikes in heating demand). Over the last decade, gas prices (both Henry Hub prices and delivered prices to the Note: Data are displayed as 5 groups of 10 States and the District of Columbia. U.S. total average price per kilowatthour is 9.90 cents. Source: U.S. Energy Information Administration, Annual Energy Review

– Electricity Section, Table 4, September 27, 2012.2-25 power sector) have ranged from below $3 to nearly $10/mmBtu on an annual average basis (see Figure 2-6). During that time, the daily price of natural gas reached as high as $15/mmBtu. Recent forecasts of natural gas availability have also experienced consider able revision as new sources of gas have been discovered and have come to market, although there continues to be some uncertainty surrounding the pr ecise quantity of the resource base.

Current and projected natural gas prices are considerably lower than the prices observed over the past decade, largely due to advances in hydraulic fracturing and horizontal drilling techniques that have opened up new shale gas resources and substantially increased the supply of economically recoverable natural gas. According to an EIA “Energy in Brief” AEO 2012 (EIA
2012 b ):

Shale gas refers to natural gas that is trapped within shale formations. Shales are finegrained sedimentary rocks that can be rich sources of petroleum and natural gas. Over the past decade, the combination of horizontal drilling and hydraulic fracturing has allowed access to large volumes of shale gas that were previously uneconomical to produce. The production of natural gas from shale formations has rejuvenated the natural gas industry in the United States.

The U.S. Energy Information Administration's Annual Energy Outlook 2012 (Early Release) estimates that the United States posse ssed 2,214 trillion cubic feet (Tcf) of technically recoverable natural gas resources as of January 1, 2010. Natural gas from proven and unproven
shale resources accounts for 542 Tcf of this resour ce estimate. Many shale formations, especially the Marcellus, are so large that only small portion s of the entire formations have been intensively production-tested. Consequently, the estimate of technically recoverable resources is highly uncertain, and is regularly updated as more information is gained through drilling and production. At the 2010 rate of U.S. consumption (about 24.1 Tcf per year), 2,214 Tcf of natural gas is enough to supply over 90 years of use. Although the estimate of the shale gas resource base is lower than in the prior edition of th e Outlook, shale gas production estimates increased between the 2011 and 2012 Outlooks, driven by lower drilling costs and continued drilling in Comment [A4]: Seems a bit out of date. AEO 2014 is out. We would encourage EPA to update their analysis using the 2014 AEO.
EPA: EIA updates some data every year, while other information comes from special topics examined in a single year. This particular quote appeared in a special report.2-26 shale plays with high concentrations of natural gas liquids and crude oil, which have a higher value in energy equivalent terms than dry natural gas. 6

EIA’s projections of natural gas conditions did not change substantially in AEO 201 43
from the AEO 201 32 , and EIA is still forecasting abundant reserves consistent with the above findings. Recent historical data reported to EI A is also consistent w ith these trends, with 201 32 being the highest year on record for domestic natural gas production. 7 The average delivered natural gas price to the power sector was $ 4.493.44 per MMBtu in 201 32
, higher than in 2012 ($3.54/MMBtu), but still down from $4. 8978 /MMBtu in 2011. 8

EIA projections of future natural gas prices assume trends that are consistent with historical and current market behavior, technolo gical and demographic changes, and current laws and regulations. 9 Depending on actual conditions, there may be significant variation from the price projected in the reference case and the price observed. To address this uncertainty, EIA issues a range of alternative cases, including cases with higher and lower economic growth, which address many of the uncertainties inherent in the long-term projections. The EPA describes the AEO 2013 reference case and a number of relevant alternative cases in the analyses in Chapter 3.

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

7 http://www.eia.gov/dnav/ng/hist/n9010us2a.htm

8 http://www.eia.gov/dnav/ng/hist/n3045us3A.htm ; Assumes that 1 TCF = 1.023 MMBtu natural gas (http://www.eia.gov/tools/faqs/faq.cfm?id=45&t=8)

9 EIA 2010b.2-27 0 2 4 6 8 10 12

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

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

EIA Historical EIA Projected (AEO 2011) EIA Projected (AEO 2012)
EIA Projected (AEO 2013)2-28 Figure 2-7. Natural Gas Spot Price, Annual Average (Henry Hub)

Source: EIA 2010c, EIA 2011, EIA 2012 a , EIA 2013 b, EIA 2014

2.9 Electricity Demand and Demand Response
Electricity performs a vital and high-value f unction in the economy. Historically, growth in electricity consumption has been closely ali gned with economic growth. Overall, the U.S. economy has become more efficient over time, producing more output (gross domestic product – GDP) per unit of energy input, with per capita energy use fairly constant over the past 30 years (EIA, 2010d). The growth rate of electricity demanded has also been in overall decline for the past sixty years (see Figure 2- 87 ), with several key drivers that are worth noting. First, there has been a significant structural shift in the U.S. ec onomy towards less energy-intensive sectors, like 0 2 4 6 8 10 12

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

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

EIA Historical

EIA Projected (AEO 2012)

EIA Projected (AEO 2013)

EIA Projected (AEO 2014)2-29 services. 10 Second, companies have strong financial incentives to reduce expenditures, including those for energy. Third, companies are responding to the marketplace and continually develop and bring to market new technologies that redu ce energy consumption. Fourth, other policies, such as energy efficiency standards at the state and Federal level, have helped address certain market failures. These broader changes have alte red the outlook for future electricity growth.

10 EIA 2013 b ‐ 2% 0% 2% 4% 6% 8% 10% 12% 14%

1950 1960 1970 1980 1990 2000 2010 2020 2030 20402-30 Figure 2-8 . Electricity Growth Rate (3-Year Rolling Average) and Projections from the Annual Energy Outlook 201 43

Source: EIA 2009, EIA 201 43

Energy efficiency initiatives have become more common, and investments in energy efficiency are projected to continue to increase fo r the next 5 to 10 years, driven in part by the growing number of states that have adopted energy efficiency resource standards. These investments, and other energy efficiency policies at both the state and federal level, create incentives to reduce electricity consumption and peak load. According to EIA, demand-side management provided actual peak load reductions of 33.3 GW in 2010. For context, the current coal fleet is roughly 314 GW of capacity.

Demand for electricity, especially in the short run, is not very sensitive to changes in prices and is considered relatively price inelastic, although some demand reduction does occur in response to price. With that in mind, the EPA modeling does not typically incorporate a “demand response” in its electric generation modeling (Chapter 35 ) to the increases in electricity prices typically projected for EPA rulemakings. Electricity demand is considered to be constant in EPA modeling applications and the reduction in production costs that would result from lower demand is not considered in the primary analytical scenario that is modeled. This leads to some overstatement in the private compliance costs that the EPA estimates for rules where compliance costs are anticipated for a rulemaking. Note that this NSPS is not anticipated to create compliance costs for projected new EGU sources.2-31 2.10 References
Brennan, S.T.; R. C. Burruss; M.D. Merrill; P. A. Freeman and L. F. Ruppert. 2010. “A Probabilistic Assessment Methodology for th e Evaluation of Geologic Carbon Dioxide Storage: U.S. Geological Survey Open-File Report 2010–1127. 2010.” Available online at http://pubs.usgs.gov/of/2010/1127

. Interagency Task Force on Carbon Capture and Storage. 2010. “Report of the Interagency Task Force on Carbon Capture and Storage,” August 2010. Available online at: http://www.epa.gov/climatechange/downloa ds/CCS-Task-Force-Report-2010.pdf

. Intergovernmental Panel on Climate Change. 2001. “Climate Change 2001: The Scientific Basis. 2001.” Available online at: http://www.grida.no/publications/other/ip cc_tar/?src=/climate/ipcc_tar/wg1/218.htm

. International Energy Agency (IEA).2013. “Tracking Clean Energy Progress 2013 .” Input to the Clean Energy Ministerial. 2013. Available online at: http://www.iea.org/etp/tracking/

. National Energy Technology Laboratory (NETL). 2008. “Reducing CO 2 Emissions by Improving the Efficiency of Existing Coal-fire d Power Plant Fleet,” July 2008. Available online at: http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf

. National Energy Technology Laboratory (NETL). “Improving Domestic Energy Security and Lowering CO 2 Emissions with Next Generation CO 2 -Enhanced Oil Recovery (CO 2 -EOR),” June 20, 2011. DOE/NETL-2011/1504. Available online at: http://www.netl.doe.gov/energyanalyses/refshelf/PubDetails.aspx?Action=View&PubId=391

. National Energy Technology Laboratory (NETL). 2012. “The United States 2012 Carbon Utilization and Storage Atlas,” Fourth Edition. Available online at: http://www.netl.doe.gov/technologies/carbon_seq/refshelf/atlasIV/

. National Energy Technology Laboratory (NETL). 2013. “Energy Analyses: Cost and Performance Baselines for Fossil Energy Plants

. ” Available online at: http://www.netl.doe.gov/energy-analyses/baseline_studies.html

. Oil and Gas Journal . “Enhanced Oil Recovery Survey,” 2010. Volume 108, Issue 14, April 19, p.41. Pacific Northwest National Laboratory (PNNL). 2009.“An Assessment of the Commercial Availability of Carbon Dioxide Capture and St orage Technologies as of June 2009,” June. Available online at: http://www.pnl.gov/science/pdf/PNNL18520_Status_of_CCS_062009.pdf

.2-32 U.S. Energy Information Administration (U.S. EIA). 2000. “Carbon Dioxide Emissions from the Generation of Electric Power in the United States,” July. Available online at:

< ftp://ftp.eia.doe.gov/envi ronment/co2emiss00.pdf>

. U.S. Energy Information Administration (U.S. EIA). 2003. “Electric Power Annual 2003. 2003.” Available online at: < http://www.eia.gov/electricity/annual/archive/03482003.pdf>

. U.S. EIA. 2009. “Electric Power Annual 2009.” Available online at:

< http://www.eia.gov/electricity/annual/archive/03482009.pdf>

. U.S. EIA. 2013. “Electric Power Annual 2011.”Available online at: <http://www.eia.gov/electricity/annual/>.

U.S. EIA. 2010a. “Status of Electricity Restructuring by State.” 2010a. Available online at:

< http://www.eia.gov/cneaf/electricity/pag e/restructuring/restructure_elect.html>

. U.S. EIA. 2010b. “AEO 2010 Retrospective Review.” 2010b. Available online at: <http://www.eia.gov/forecasts /aeo/retrospective/>. U.S. EIA. 2010c. “Annual Energy Outlook 2010.”Available online at:

< http://www.eia.gov/oiaf/a rchive/aeo10/index.html>

. U.S. EIA. 2010d. “Annual Energy Review 2010.” Available online at:

< http://www.eia.gov /totalenergy/data/annual/pdf/aer.pdf>

. U.S. EIA. 2011. “Annual Energy Outlook 2011.” Available online at:

< http://www.eia.gov/forecasts/archive/aeo11/ >.
U.S. EIA. 2012 a . “Annual Energy Outlook 2012 (Early Release).” Available online at:

< http://www.eia.gov/forecasts/aeo/>

. U.S. EIA. 2012b. “Energy in Brief: What is Shale Gas and Why is it Important?” Available online at: <http://www.eia.gov/energy _in_brief/about_shale_gas.cfm>.

U.S. EIA. 2012 ac . “Today in Energy: Most states have Renewable Portfolio Standards.” Available online at: < http://www.eia.gov/todayinenergy/detail.cfm?id=4850>

. U.S. EIA. 2013 a . “Electric Power Annual 2011 2 .”Available online at: <http://www.eia.gov/electricity/annual/>.

U.S. EIA.2013 b . “Annual Energy Outlook 2013.” Available online at:

< http://www.eia.gov/forecasts/aeo/>

. U.S. EIA.2014. “Annual Energy Outlook 2014.” Available online at: <http://www.eia.gov/forecasts/aeo/>.2-33 U.S. Environmental Protection Agency. 201 43 . “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–201 21 ,” April. Available online at:

< http://www.epa.gov/climatecha nge/ghgemissions/usinventoryreport.htmlhttp://www.epa.gov /climatechange/Downloads/ghgemissions /US-GHG-Inventory-2013-Main-Text.pdf >

. U.S. Geological Survey Geologic (USGS). 20 13. “National Assessment of Geologic Carbon Dioxide Storage Resources – Summary: U.S. Geological Survey Factsheet 2013-3020.” Available online at: < http://pubs.usgs.gov/fs/2013/3020/>

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

3 -A- 1 1-1 

### CHAPTER 3: COST, ECONOMIC, AND ENERGY IMPACTS

3.1 Introduction



### 3.1 Introduction This chapter reports the compliance cost, economic, and energy impact analysis performed for the proposed rule. EPA used the Integrated Planning Model (IPM), developed by ICF International, to conduct its analysis. IPM is a dynamic linear programming model that can be used to examine air pollution control policies for CO 2 , SO 2 , NO X , Hg, HCl, and other air pollutants throughout the United States for the entire power system. The IPM analysis is complemented by an analysis of the cost and scope of reductions in electricity demand that can be achieved through energy efficiency programs.



### 3.1 3.2 Overview
EPA is proposing emission guidelines for states to follow in developing plans to address greenhouse gas emissions from existing fossil fuel-fired electric generating units. Specifically, the EPA is proposing state-specific rate-based goals for carbon dioxide emissions from the power sector, as well as guidelines for states to us e in developing plans to attain the state-specific goals. This rule, as proposed, would set in motion actions to lower the carbon intensity of power generation in the United States.
Over the last decade, EPA has conducted extensive analyses of regulatory actions affecting the power sector. These efforts support the Agency’s understanding of key variables that influence the effects of a policy and provide the framework for how the Agency estimates
the costs and benefits associated with its actions.
The estimated annual costs of the proposed action are between $5.4 and $7.4 billion in 2020 and between $7.3 and $8.8 billion in 2030 for the primary Option (Option 1). The alternative option (Option 2) has annual estimated costs of between $4.2 and $5.4 billion in 2020 and between $1.9 and $3.1 billion in 2030. 1

1 All costs represent real dollars ($2011). Comment [A1]: How much CO2 is abated by each BB? What is the MC and AC per BB? We think it is very important for EPA to provide this information.
EPA: The illustrative compliance scenarios presented in the RIA are focused on achieving the state goals (expressed as average CO2 rates), and affords flexibility over technologies (building blocks), time (multi-year compliance timeframe), and geography (the ability of states to partner together to reduce costs, shown in the regional scenarios). There is no explicit representation of each building block in the compliance modeling, and all interact together simultaneously to achieve the state goals. Thus, there is no way to firmly itemize the effect of each building block. In order to reflect what the illustrative impacts could be, we have capture the appropriate system costs and are not able to provide marginal costs, since we are not able to pinpoint particular policy dimensions that states may adopt and which have a more meaningful impact on marginal prices. Some additional supplemental materials and cost estimates have been added to particular TSDs and the preamble to support the various building blocks.
Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering, Tab stops: 0", Left
Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3, ... + Start at: 3 + Alignment: Left + Aligned at: 0" + Indent at: 0.25" Comment [A2]: Because IPM does not model electricity demand? How does EPA model changes in electricity demand due to price increases?
EPA: While IPM is capable of modeling pricedrive demand response, we have employed a different methodology in keeping with the core elements of BSER, and end-use EE in particular. The inclusion of EE into the ... [1] Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering Comment [A3]: Present costs separately from energy savings.
EPA: See subsequent response. Also, the EE costs are also broken out in the RIA, should one want to see those in isolation.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 2 1-1 

### 3.2 3.3 Power Sector Modelling Framework
IPM, developed by ICF Consulting, is a stat e-of-the-art, peer-rev iewed, dynamic linearprogramming model that can be used to project power sector behavior under future business-asusual conditions and examine prospective air pollution control policies throughout the United States for the entire electric power system. EPA us ed IPM, inclusive of the electricity demand reductions achieved through the energy efficiency scenario included in the proposed rule, to project likely future electricity market conditions with and without the proposed rule. The level of energy efficiency-driven reductions in electrici ty demand and the associated costs are reported
in Section 3.6.
The Integrated Planning Model (IPM) is a mu lti-regional, dynamic, deterministic linear programming model of the U.S. electric power sect or. It provides forecasts of least cost capacity expansion, electricity dispatch, and emission co ntrol strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. EPA has used IPM for over two decades to better understand power sector behavior under future business-as-usual
conditions and evaluate the economic and emis sion impacts of prospective environmental policies. The model is designed to reflect electrici ty markets as accurately as possible. EPA uses the best available information from utilities, indu stry experts, gas and coal market experts, financial institutions, and government statistic s as the basis for the detailed power sector modeling in IPM. The model documentation provid es additional information on the assumptions discussed here as well as all other model assumptions and inputs. 23

2 Detailed information and documentation of EPA’s Base Case using IPM (v5.13), including all the underlying assumptions, data sources, and architecture parameters can be found on EPA’s website at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

3 At the time the documentation was published, CO2 emi ssions from biomass were not included in EPA Base Case v.5.13. EPA is currently developing methods to estima te the amount of CO2 emitted on-site during biomass cofiring at coal fired power plants. For this analysis, the base case assumes an emissions factor of 195 lbs/MMBtu for all biomass. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 3 1-1 Although the Agency typically focuses on broad system effects when assessing the economic impacts of a particular policy, EPA’s application of IPM includes a detailed and sophisticated regional representation of key variables affecting power sector behavior.
The model incorporates a detailed representation of the fossil-fuel supply system that is used to forecast equilibrium fuel prices. The mo del includes an endogenous representation of the North American natural gas supply system thro ugh a natural gas module that reflects a partial supply/demand equilibrium of the North American gas market accounting for varying levels of potential power sector gas demand and corresponding gas production and price levels. 4 Thismodule consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas regasification facility locations that are tied togeth er by a series of linkages (i.e., pipelines) that represent the North American natural gas transmission and distribution network.
IPM also endogenously models the partial equilibrium of coal supply and EGU coal demand levels throughout the continental U.S., taking into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM, which are publicly available, were developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that power plants wo uld face if selecting that coal over the modeling time horizon. The IPM documentation outlines the methods and data used to quantify the economically recoverable coal reserves, character ize their cost, and build the 36 coal regions curves. The coal supply curves were developed in consultation with Wood Mackenzie, one of the leading energy consulting firms and specialists in coal supply. These curves have been independently reviewed by industry experts and have been made available for public review prior to this rulemaking process. 5

4 See Chapter 10 of EPA’s Base Case using IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

5 See Chapter 9 of EPA’s Base Case usi ng IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html Comment [A4]: Do the results show expansion of gas supply system from 2020 to 2030?
EPA: The results reflect this expansion, and language was added to section 3.7.4. For more information, see:http://www.epa.gov/airmarkets/progsregs/epaipm/docs/v513/Chapter_10.pdf (included in RIA as footnote 10).***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 4 1-1 EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the model ha s been used to forecast the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule, Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon
Pollution Standards for New Power Plants.
The model and EPA's assumptions input into the model undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variety of stakeholders, including owners and operato rs of capacity in the electricity sector that is represented by the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highly detailed review of key input assumptions, model representation, and modeling results. IPM has received extensive review by energy and environmental modeling experts in a variety of contexts. For example, in the late 1990’s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 prospective studies that are periodically condu cted. The model has also undergone considerable interagency scrutiny when it has been used to conduct over a dozen legislative analyses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of comparative modeling exercises sponsored by Stanford University’s Energy Modeling Forum over the past 15 years.
IPM has also been employed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, environmental groups, and industry, all of whom subject the model to their own review procedures.

1.2.1 3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.13)
This new IPM modeling platform (v.5.13) incorporates important structural improvements and data updates with respect to the previous version (v.4.10_MATS), and includes notable changes to the modeling architectur e. This is the fifth major iteration of EPA’s base case using IPM, and calibrates certain information and data from the Energy Information Agency's (EIA) Annual Energy Outlook (AEO), in this case AEO 2013 (hence the platform name of v5.13). Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 5 1-1 The current base case represents a projection of electricity sector activity that takes into account only those Federal and state laws and regul ations whose provisions were either in effect or enacted and clearly delineated at the time the base case was finalized in August 2013. The
EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), 6 the Mercury and Air Toxics Rule (MATS), and other state and Federal regulations to the extent that they contain measures, permits, or other air-related limitations or requirements.
EPA has also updated the National Electric Energy Data System (NEEDS). This database
contains the unit-level data that is used to constr uct the "model" plants that represent existing and
committed 7 units in EPA modeling applications of IPM. NEEDS includes basic geographic, operating, air emissions, and other data on these generating units. 8
Other routine updates were also adopted in v5.13. These include changes based on public comments that have been received over the last few years, updates reflecting planned new power plant construction, retirements, new power plan t cost and performance, pollution control costs and performance, emission rate assignments, and state rules and enforcement actions. The update also included further refinement to the modeled regions to reflect more recent power market structure based on NERC, FERC, EIA, and other data and planning sources, and now includes more regions that better reflect limitations on current power system dispatch and transmission
behavior.

6 EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving the 1997 National Ambient Air Quality Standards (NAAQS) for ozone (8-hour average of 0.08 ppm) and fine particles (24-hour average of 65 μ g/m3 or less and annual average of 15 μ g/m3 for particles of diameter 2.5 micrometers or less, i.e., PM2.5). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the U.S. Court of Appeals for the District of Columbia Ci rcuit in December 2008 and EPA was required to correct legal flaws in the regulations that had been cited in a ru ling by the Court in July 2008. CAIR remains in effect until replaced by EPA pursuant to the Court’s ruling. CAIR ’s provisions were still in effect when EPA Base Case v.5.13 was released.

7 v.5.13 includes planned units that had broken ground or secured financing and were expected to be online by the end of 2015; one geothermal unit and four nuclear units that are scheduled to come online after 2015 were also included. For more information, see Chapter 4 of EPA’s Base Case using IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

8 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.13), <http://www.epa.gov/powersector modeling/BaseCasev513.html>. Comment [A5]: Need to fix font in footnote
EPA: Thank you. The font is now corrected.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 6 1-1 

### 3.3 3.4 State Goals in this Proposal
In this action, the EPA is proposing state-speci fic rate-based goals to guide states in the development of their plans. The agency is proposing one option (Option 1) for state-specific goals and requesting comment on a second set of state-specific goals and compliance period (Option 2).

Table 3- 15 . Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO 2 Per Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2

State 9 Option 1 Option 2
Interim Goal (2020-2029) Final Goal (2030 Forward) Interim Goal (2020-2024) Final Goal (2025 Forward) Alabama 1,1471,113 1,0591,024 1,2701,243 1,2371,208 Alaska 1,0971,039 1,003878 1,1701,142 1,1311,086 Arizona * 735724 702691 779767 763751 Arkansas 9681,012 910950 1,0831,128 1,0581,101 California 556580 537558 582611 571599 Colorado 1,1591,164 1,1081,112 1,2651,274 1,2271,232 Connecticut 597588 540533 651640 627617 Delaware 913922 841848 1,0071,036 9831,010 Florida 794750 740692 907795 884771 Georgia 891885 834826 997992 964958 Hawaii 1,3781,388 1,3061,304 1,4461,464 1,4171,429 Idaho 244241 228225 261259 254252 Illinois 1,3661,391 1,2711,293 1,5011,529 1,4571,484 Indiana 1,6071,627 1,5311,549 1,7151,738 1,6831,704
Iowa 1,3411,351 1,3011,310 1,4361,447 1,4171,428
Kansas 1,5781,597 1,4991,516 1,6781,698 1,6251,644 Kentucky 1,8441,861 1,7631,778 1,9511,971 1,9181,937 Louisiana 9481,080 883993 1,0521,208 1,0251,171 Maine 393423 378407 418448 410440 Maryland 1,3471,325 1,1871,166 1,5181,495 1,4401,417 Massachusetts 655646 576567 715708 683675 Michigan 1,2271,337 1,1611,261 1,3491,470 1,3191,434 Minnesota 911891 873851 1,0181,001 999981

9 The EPA has not developed goals for Vermont and the Dist rict of Columbia because current information indicates those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. territories at this time. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 7 1-1 Mississippi 732717 692673 765754 743729 Missouri 1,6211,621 1,5441,544 1,7261,726 1,6941,694
Montana 1,8821,900 1,7711,781 2,0072,031 1,9601,980 Nebraska 1,5961,587 1,4791,471 1,7211,710 1,6711,661 Nevada 697697 647644 734736 713714 New Hampshire 546542 486485 598593 557553 New Jersey 647657 531531 722740 676688 New Mexico * 1,1071,088 1,0481,000 1,2141,203 1,1761,163 New York 635647 549557 736756 697715 North Carolina 1,0771,064 992977 1,1991,189 1,1561,145 Chapter 1 Table 3-51.
Continued

North Dakota 1,8171,821 1,7831,788 1,8821,887 1,8701,874 Ohio 1,4521,468 1,3381,349 1,5881,611 1,5451,566 Oklahoma 931916 895879 1,0191,022 986986 Oregon 407408 372375 450449 420418 Pennsylvania 1,1791,200 1,0521,067 1,3161,342 1,2701,293 Rhode Island 822816 782773 855854 840837 South Carolina 840802 772736 930889 897858 South Dakota 800794 741731 888885 861856
Tennessee 1,2541,234 1,1631,142 1,3631,345 1,3261,307
Texas 853932 791855 9571,059 9241,016 Utah * 1,3781,375 1,3221,318 1,4781,476 1,4531,451 Virginia 884869 810793 1,0161,011 962955 Washington 264294 215235 312350 284316 West Virginia 1,7481,766 1,6201,636 1,8581,877 1,8171,835 Wisconsin 1,2811,265 1,2031,185 1,4171,404 1,3801,366 Wyoming 1,8081,801 1,7141,701 1,9071,902 1,8691,862
* Excludes EGUs located in Indian country.
Note that the proposed state goals in Table 3-5 differ slightly from the state goals modelled in the illustrative compliance scenarios analyzed in this RIA. Any differences in the goals are minor and reflect small adjustments to the goal setting methodology due to minor adjustments in the assumed at-risk nuclear capacit y, fossil units included in Indian Country, the treatment of cogeneration in historical data, and the assumed applicability for some units. The resulting changes to the state goals are minor, and any differences in results obtained by analyzing these new goals would be negligible. Furthermore, these small changes in the goals Formatted: Font: Times New Roman, 10 pt***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 8 1-1 would be offset by comparable changes in the illustrative compliance scenarios (in which compliance is consistent with the assumptions used in setting state goals).



### 3.4 3.5 Compliance Scenarios Analyzed In order to estimate the costs, benefits, and impacts of implementing the proposed guidelines, the EPA modeled two illustrative compliance scenarios. One of these scenarios allows averaging of emission rates within each indivi dual state (these scenarios are referred to as “state”), and another set of scenarios where gr oups of states are assumed to collaborate and achieve compliance across larger regions (referred to as “regional”). Estimates of the benefits, costs, and economic impacts of this proposed action are presented for both state and regional compliance. 10 These illustrative compliance scenarios ar e designed to reflect, to the extent possible, the scope and nature of the proposed guidelines. However, there is considerable uncertainty with regard s to the precise measures that states will adopt to meet the proposed requirements, since there are consid erable flexibilities afforded to the states in developing their state plans. Nonetheless, the analysis of the bene fits, costs, and relevant impacts of the proposed rule attempts to encapsulate some of those flexib ilities in order to inform states and stakeholders
of the potential overall impacts of the proposal. The relevant impacts, costs, and benefits are provided for 2020, 2025, and 2030 for Options 1 and 2 (with both state and regional compliance).
It is also important to note that the an alysis does not specify any particular CO 2 reduction compliance measure to occur, with the exception of the level of demand-side energy efficiency (EE), which the model is not currently configured to include as an endogenous compliance option. In other words, aside from EE, the analysis allows the power system the flexibility to respond to the state rate-based goals in the most cost-effective manner determined by IPM , as specified below

. In the illustrative compliance scenarios analyzed, the average emissions rate of the source types included in the calculation of the state goals must be, on average, less than or equal to the proposed goals over the entire compliance period. That is, the sources assumed to be directly

10 For more details on the nature of the regulatory options, see the preamble. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 9 1-1 affected by the illustrative state compliance scen arios are only those included in the calculation of the state goals. 11 These affected sources are: 12

 Existing fossil steam boilers with nameplate capacity greater than 25 MW

 Existing NGCC units with nameplate capacity greater than 25 MW

 Simple cycle combustion turbines with nameplate capacity greater than 25 MW, and 33% capacity factor and 219,000 MWh of generation in 2012

 New and existing non-hydro renewable capacity

 At risk and under construction nuclear. 13

Furthermore, all compliance scenarios modeled include an assumption that affected sources within states are able to meet state goals collectively, by averaging all of their emissions relative to all of their generation. This approach enables some sources to emit at rates higher than the relevant goal, as long as there is corresponding generation coming from sources that emit at a lower rate such that the goal (in lbs/MWh) is met across all affected sources collectively. The
emissions rate at covered sources 14 ) must be less than or equal to the applicable state goal, on average, over the entire compliance period, but not in any particular year.
The illustrative compliance scenarios further as sume that the states adopt intertemporal averaging in the initial compliance period for both Option 1 and Option 2. That is, the average
emissions rate at covered sources in each state must be less than or equal to the applicable state goal over the compliance period. The initial compliance period for Option 1 is 2020 to 2029 and for Option 2 it is 2020 to 20245. After the in itial compliance period the average emission rate of

11 As discussed in the preamble, for compliance purposes stat es may be able to include sources of generation in the calculation of the state’s emission rate other than those sources of generation considered in constructing the building blocks for setting the state goals. However, this illustrative analysis does not include those options.

12 For the illustrative scenarios renewable generation includ es generation from wind, solar, geothermal, and biomass co-fired with coal. Dedicated biomass is not an af fected source in the illustrative scenarios.

13 See Technical Support Document – GHG Abatement Measures

14 Ttotal emissions from covered sources divided by the su m of total generation from covered sources plus demandside energy savings***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 10 1-1 the affected sources in each year must be less than or equal to the state goal in the illustrative compliance scenarios.
For EE, the megawatt-hour (MWh) savings and associated costs are specified exogenously for Option 1 and Option 2 compliance scenarios consistent with the EE "best practice" performance levels informing calculation of state goals under each option. EPA has determined that these performance levels are achievable at costs that are generally less than avoided power system costs

. even before the costs associated with achieving the state goals . via the other three building blocks are included. EPA has specified and imposed EE-related costs and changes in future electricity demand exogenously when modeling the compliance scenarios presented for this rule. Details of the implementation of th e demand reduction are reported in the following section.
All compliance scenarios modeled include an assumption that affected sources within states are able to meet state goals collectively, by averaging all of their emissions relative to all of their generation. 15 This approach enables some sources to emit at rates higher than the relevant goal, as long as there is corresponding generati on coming from lower emitting sources such that the goal (in lbs/MWh) is met across all affected sources collectively.
EPA also analyzed a set of compliance scenarios that assume greatera broader geographic flexibility for compliance where states may choos e to cooperate in order to achieve more costeffective outcomes, since some st ates can reduce their emissions below their state goal more easily relative to others. This scenario is modeled for both Option 1 and Option 2 and is referred to as the “Regional” scenario. The regional scenarios allow emission rate averaging across affected sources within six multi-state regions, informed by North American Electric Reliability Corporation (NERC) regions and Regional Tran smission Organizations (RTOs). These regions do not always follow state borders, however, so cert ain states that fall into more than one region were grouped in regions where there was a majority of geographic territory (area) or generation. While Florida and Texas each have unique NERC regions unto themselves, for purposes of this compliance analysis, those states were each grouped with other neighboring states.

15 The generating units assumed to be affected by the regul ation in the illustrative strategies are described in the preamble.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 11 1-1  West (WECC) (CA, WA, OR, ID, MT, UT, NV, CO, WY, NM, AZ)

 North Central (MISO) 16 – ND, SD, IA, MN, WI, MO, IL, IN, MI

 South Central (SPP + ERCOT) – NE, KS, OK, AR, TX, LA

 Southeast (SERC + FL) – KY, NC, SC, TN, MS, AL, GA, FL

 East Central (PJM) – OH, PA, WV, MD, DE, NJ, VA

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

Figure 3- 14 . NERC Regions

16 Note that the MISO region expanded to integrate Entergy territory at the end of 2013. Comment [A6]: Might want to include footnote recognizing the change in MISO that occurred at the end of 2013 – Louisiana’s Entergy integration into MISO footprint (MISO South).

EPA: Footnote added.
Formatted: Justified

Formatted: Font: 10 pt, Complex Script Font: 10 pt, Do not check spelling or grammar***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 12 1-1 Figure 3- 25 . NERC Assessment Areas

Figure 3- 36 . Regional Compliance Zones Used in this Analysis Formatted: Left***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 13 1-1 These regional groupings of states allow the state goals in each option analyzed to be met collectively by averaging emission rate performance across all affected units located in that region. Estimates and results are also presented for “State” scenarios (for both options), to illustrate potential impacts and benefits should states choose not to cooperate in a “Regional”

manner. In the regional scenarios, as in the state scenarios, affected sources in each state must respond to their respective state goal. However, the ability to average is extended to all affected sources in each compliance region. In these scenarios, the emissions rate from affected sources in each region 17 must be less than or equal to the weighted average 18 of the state goals in that region, over the compliance period. . Furthermore, as in the “State” scenarios, the regions are assumed to adopt intertemporal averaging in the initial compliance period for each Option.
The analysis in the illustrative scenarios does not assume that states use any specific policy mechanism to achieve the state goals. Wh ile IPM finds a least cost way to achieve the state goals to achieve the rate-based constraints imposed in the illustrative scenarios, there may be less costly approaches that the stat es may adopt to achieve their state goals.
In the compliance scenarios analyzed, the average emissions rate of the source types included in the calculation of the state goals must be less than or equal to the proposed goals.



### 3.5 3.6 Demand Side Energy Efficiency

1.5.1 3.6.1 Projected Demand-Side Energy Savings 19
To estimate the potential electricity demand reduction that could be achieved, and associated costs incurred, through implementation of demand-side energy efficiency policies, EPA developed scenarios that reflect increased levels of demand-side energy efficiency, rooted

17 total emissions from covered sources divided by the sum of total generation from covered sources plus demandside energy savings

18 where the weights are the generation of the covered sources plus the demand-side energy savings, in the respective state

19 For a more detailed discussion of the demand-side ener gy efficiency savings projections, refer to the Greenhouse Gas Abatement Measures TSD

. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering
Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 14 1-1 in what leading states have already accomplished or have requirements in place to accomplish. 20 For Option 1, adjustments were made to each stat e’s annual incremental reduction in electricity consumption by ramping up from an historical basis 21 to a target rate of 1.5% of electricity
demand annually over a period of years starting in 2017, and maintain that rate throughout the modeling horizon. Ten Twelve leading states have either achieved, or have established requirements that will lead them to achieve, this rate of increm ental electricity demand reduction, which we refer to as the “savings rate.” The p ace of improvement from the state’s historical value is assumed to be 0.2% per year, beginning in 2017 until the target rate of reduction from baseline electricity demand is achieved. States already at or above the 1.5% annual incremental savings rate are assumed to have a 1.5% rate beginning in 2017 and sustain that rate. For Option 2, the annual incremental savings rate is ramped up from an historical basis to a target rate of 1.0% and at a pace of improvement of 0.15% per year, beginning in 2017, until the target rate is achieved. States already at or above the 1.0% annual incremental savings rate are assumed to sustain a 1.0% rate beginning in 2017. Twenty leading states have either achieved, or have established requirements that will lead them to achieve, this rate of savings. The incremental savings rate for each state, for each year, is then used to derive cumulative annual energy savings based upon information/assumptions about the averag e life of EE measures and the distribution of measure lives within a state’s full portfolio of EE programs. The cumulative annual energy savings derived for Option 1 and Option 2 using this methodology are used consistently to set goals and to conduct power sector compliance modeling for each option. 22
To reflect the implementation of demand-side energy efficiency in modeling, the fixed total electricity demand in IPM was adjusted exogenously to reflect the estimated future-year

20 This scenario is intended to represent a feasible pa thway for additional EE resulting from accelerated use of energy efficiency policies, in all states, consistent with a level of performance that has already been demonstrated or required by policies (e.g., energy efficien cy resource standards) of leading energy efficiency implementing states, and consistent with a demonstrat ed annual pace of performance improvement from current levels. It does not represent an estimate of the full potential for end-use EE.

21 The historical basis of the percentage of reduced electri city consumption differs for each state and is drawn from the data reported in Energy Information Admi nistration (EIA) Form 861, 2012, available at http://www.eia.gov/electricity/data/eia861/.

22 See Greenhouse Gas Abatement Measures TSD and the State Goal Setting TSD Comment [A7]: This data set can be problematic. CEE and ACEEE troubleshoot and re-release this data. DOE could do the same if asked by EPA.
EPA: This data set is used as one input to the goal-setting for BB4 and not directly for the impacts assessment for meeting those goals. EPA is aware of the limitations of the dataset and discusses them in the goal-setting section of the GHG Abatement TSD, Chapter 5. Comment [A8]: The preamble uses the figure of 12 leading states on p. 152: “According to data and analyses from sources including EIA and the American Council for an Energy Efficient Economy (ACEEE), as well as the EPA’s own analysis, twelve leading states have either achieved

– or have established requirements that will lead them to achieve ‐ annual incremental savings rates of at least 1.5 percent of the electricity demand that would otherwise have occurred.”

EPA: Correct. Revision made. Thanks. Comment [A9]: Is this linear or compounding?

EPA: This is linear and described in detail in both the Abatement Measures TSD as well as the preamble.

Comment [A10]: When modeling, shouldn’t some sort of gross-to-net discount factor be applied to these savings figures when applied as a reduction in load vs. BAU?
EPA: The annual incremental savings that are used as the basis for projecting cumulative savings are NET values so cumulative savings are also NET.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 15 1-1 energy savings calculated from the approach described above. State energy savings in sales were scaled up to account for transmission losses and applied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency scenario energy savings. 23 The demand adjustments were applied proportionally across all segments (peak and non-peak) of the load duration curve. 24 In order to reflect the adjusted state-level demand within IPM model regions that cross state border s, energy savings from a bisected state were distributed between the applicable IPM model regions using a distribution approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities.

Table 3- 26 . Net Cumulative Savings as a Percent of Projected BAU Sales Option 1 Option 2 2020 2025 2030 2020 2025 2030 Alabama 1.36% 6.19% 10.09% 1.07% 4.54% 7.09% Arizona 5.24% 9.50% 11.72% 3.52% 6.45% 8.04% Arkansas 1.52% 6.46% 10.31% 1.24% 4.77% 7.27% California 4.95% 9.46% 11.90% 3.55% 6.57% 8.23% Colorado 3.92% 8.73% 11.39% 3.32% 6.35% 8.02% Connecticut 4.71% 9.55% 12.27% 3.61% 6.77% 8.56% Delaware 1.14% 5.94% 10.14% 0.86% 4.32% 7.10% District of Columbia 1.14% 5.94% 10.14% 0.86% 4.32% 7.10% Florida 2.03% 7.04% 10.50% 1.75% 5.26% 7.45% Georgia 1.76% 6.74% 10.40% 1.48% 5.01% 7.36% Idaho 3.80% 8.73% 11.50% 3.28% 6.38% 8.11% Illinois 4.36% 9.26% 12.03% 3.52% 6.66% 8.44% Indiana 3.20% 8.42% 11.59% 2.89% 6.26% 8.24% Iowa 4.65% 9.39% 12.04% 3.58% 6.67% 8.40%

23 That is, while the methodology for applying the energy efficiency savings rate was used consistently to set state goals and to conduct power sector co mpliance modeling for each option, the cumulative savings rate is applied to 2012 electricity consumption in the calculation of st ate goals whereas compliance modeling applies that rate to forecasted base case demand. Thus, for a given rate of demand reduction, the quantity of electricity demand reduced is greater in the compliance modeling than in se tting the state goals, consistent with forecasted base case demand growth from 2012 to 2020 and beyond.

24 For more information on load duration curves, see Chapter 2 of EPA’s Base Case using IPM (v5.13) documentation, available at: http://www.epa. gov/powersectormodeling/BaseCasev513.html Comment [A11]: This suggests that there is a slight disconnect between the absolute EE values used to set BSER and the values used as part of RIA modeling? BSER EE is based off of 2012 sales, whereas RIA modeling is based off of projected sales/forecasted demand in 2020 and beyond (incorporating growth factor). Any considerations as to how this might affect the RIA model results?
EPA: The fact that state goals are (1) based on historical 2012 generation and (2) in the form of an output-based emissions rate requires that any demand reduction included in the rates be relative to sales in 2012. The percentage-based sales reduction applied to state goal calculation is consistent with the sales reductions applied in future years, which include demand growth. The avoided MWh figure associated with the state goal setting is x% of 2012 sales, whereas the avoided MWh associated with future demand in the RIA model is x% of future year sales – so the MWh figure is different. But this is consistent with the building block 4 value – which is expressed as a percent of avoided sales. Naturally this MWh associated with the percent of avoided sales will vary with yearly changes in demand, but the emission rate form of the state goal helps ensure that changes in demand and proportional changes in avoided MWh still result in the same state goal.

Comment [A12]: Which states have to do more to comply with this rule?
EPA: States that are reporting greater levels of EE program impacts (incremental savings as % of sales), e.g., ME, end up with slightly higher BB4/EE-based goals than do states reporting lower levels of EE impacts, e.g., AL.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 16 1-1 Kansas 1.22% 6.05% 10.17% 0.94% 4.42% 7.14% Kentucky 1.91% 6.95% 10.57% 1.63% 5.18% 7.50%

Chapter 2 TABLE 3

‐ 2. CONTINUED

Louisiana 1.14% 5.88% 9.97% 0.85% 4.28% 6.98% Maine 5.37% 9.96% 12.48% 3.61% 6.77% 8.56% Maryland 4.21% 9.13% 11.92% 3.47% 6.61% 8.38% Massachusetts 4.43% 9.37% 12.18% 3.55% 6.73% 8.54% Michigan 4.59% 9.43% 12.16% 3.59% 6.73% 8.51% Minnesota 4.80% 9.49% 12.09% 3.58% 6.67% 8.40% Mississippi 1.40% 6.28% 10.20% 1.12% 4.62% 7.18% Missouri 1.58% 6.60% 10.53% 1.29% 4.88% 7.44% Montana 3.36% 8.41% 11.33% 3.01% 6.21% 8.02% Nebraska 2.20% 7.38% 10.95% 1.91% 5.51% 7.78% Nevada 2.95% 8.07% 11.15% 2.67% 6.00% 7.91% New Hampshire 2.84% 8.14% 11.52% 2.56% 6.08% 8.20% New Jersey 1.25% 6.10% 10.23% 0.96% 4.46% 7.19% New Mexico 3.10% 8.11% 11.03% 2.81% 6.02% 7.82% New York 4.42% 9.35% 12.17% 3.54% 6.73% 8.54% North Carolina 2.37% 7.45% 10.76% 2.09% 5.56% 7.64% North Dakota 1.39% 6.32% 10.35% 1.11% 4.65% 7.28% Ohio 4.17% 9.13% 11.97% 3.47% 6.63% 8.43% Oklahoma 1.86% 6.88% 10.54% 1.57% 5.13% 7.47% Oregon 4.66% 9.26% 11.76% 3.55% 6.55% 8.19% Pennsylvania 4.67% 9.42% 12.07% 3.58% 6.68% 8.42% Rhode Island 3.90% 9.02% 12.00% 3.35% 6.60% 8.48% South Carolina 2.32% 7.40% 10.73% 2.04% 5.52% 7.62% South Dakota 1.60% 6.62% 10.52% 1.32% 4.90% 7.43% Tennessee 2.21% 7.33% 10.79% 1.93% 5.47% 7.65% Texas 1.78% 6.79% 10.48% 1.50% 5.05% 7.42% Utah 3.62% 8.62% 11.44% 3.19% 6.33% 8.08% Vermont 5.37% 9.96% 12.48% 3.61% 6.77% 8.56% Virginia 1.23% 5.98% 9.95% 0.95% 4.37% 6.97% Washington 4.24% 9.01% 11.64% 3.45% 6.49% 8.16% West Virginia 1.77% 6.86% 10.71% 1.49% 5.11% 7.59% Wisconsin 4.68% 9.48% 12.17% 3.60% 6.73% 8.50% Wyoming 1.61% 6.55% 10.32% 1.33% 4.85% 7.28% Contiguous U.S. Total 3.05% 7.93% 11.14% 2.44% 5.76% 7.83% Alaska 1.22% 6.02% 10.09% 0.94% 4.40% 7.08% Hawaii 1.29% 6.13% 10.15% 1.01% 4.49% 7.13% U.S. Total 3.04% 7.92% 11.13% 2.43% 5.75% 7.83%***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 17 1-1 Source: See Greenhouse Gas Abatement Measures TSD

1.5.2 3.6.2 Demand-Side Energy Efficiency Total Costs 25
Total costs of achieving the demand-side energy efficiency scenarios for each year were determined at the state level, exogenous to power sector modeling. In addition to the energy savings data, the total cost was based upon first-year cost of saved energy, average measure life, distribution of measure lives, and cost escalation factors.
The first year cost of saved energy used in the cost calculation accounts for both the costs to the utilities that are funding the demand-side energy efficiency programs (known as the program costs), and the additional cost the end-user purchasing a more energy efficient technology (known as the participant costs). Total costs were found to be divided evenly, 50% each, between program costs and participant costs. 26 To account for the potential for increasing costs as states realize greater levels of energy savings, the first-year costs were escalated when the annual incremental savings in each state reach ed 0.5% (a 20% additional cost escalation is applied to subsequent investments) and a 1.0% (a 40% additional cost escalation is applied to subsequent investments).
To calculate total energy efficiency costs, first-year costs for each year for each state were levelized (at 3% and 7% discount rates) over the estimated distribution of measure lives and the results summed for each year for each state. Results were determined for Options 1 and 2 and are summarized below in Table 3-6. The total levelized cost of saved energy was calculated based upon the same inputs and using a 3% discount rate results in average values of a levelized cost per kWh of saved energy was calculated for each year of investment in each state. 27 This levelized cost per kWh is multipli ed by the cumulative annual savi ngs in kWh to derive a total cost estimate of EE for each state for each year. To account for the increasing costs of energy efficiency measure as states invest in increasin g amounts of demand-side energy efficiency over

25 For a more detailed discussion of the demand-side en ergy efficiency savings cost estimates, refer to the Greenhouse Gas Abatement Measures TSD.

26 For a more detailed discussion of the analysis of program versus participant costs, refer to the Greenhouse Gas Abatement Measures TSD.

27 All specified demand-side energy efficiency cost s in this document assume a 3% discount rate. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering

Comment [A13]: Please refer to comments on EE TSD pages 42-47. Use of alternate reasonable assumptions could lower these input costs by 20-50%. Please consider revisiting EE costs.

EPA: The EE TSD discusses the basis for EE costs in detail and acknowledges the conservatism of EPA’s estimates. We solicit comment in the Preamble on our basis/approach to estimating EE costs. Comment [A14]: Why is this a reasonable assumption? Did not see the discussion in the TSD.
EPA: This is discussed and supported in TSD. See Impacts Assessment section, Inputs, Step 3, Determine Ratio of Program to Participant costs.

Comment [A15]: All costs should be discounted both 3% and 7%.

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

3 -A- 18 1-1 time, the levelized cost per kWh is escalated when the annual incremental savings in each state reach 0.5% (a 20% additional cost escalation is applied to subsequent investments) and 1.0% (a 40% additional cost escalation is applied to subs equent investments). With these cost escalation factors applied to the increasing levels of EE savings over time under Options 1, the cost of saved energy averages 8.5 cents per kWh in 2020, 8.9 cents per kWh in 2025, and 9.0 cents per
kWh in 2030. 28
The utility funding for demand-s ide energy efficiency progra ms (to cover program costs) is typically collected through a standard per kWh surcharge to the rate-payer; the regional retail price impacts analyzed from this RIA’s compliance scenarios assumes accounts for the recovery of these program costs through the following procedure. 29 For each state, the first-year EE program costs are calculated for each year, equivalent to 50% of the total annual first-year EE total energy efficiency costs for that state. These EE program costs were distributed between the applicable IPM model regions using a distribution approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities . These regionalized EE program costs were then incorporated in to the regional retail price calculation. 30



### 3.6 3.7 Projected Power Sector Impacts

1.6.1 3.7.1 Projected Emissions Under the proposed rule, EPA projects annual CO 2 reductions between 17% and 18% below base case projections for Option 1 in 2020 (reaching 26% to 27% below 2005

28 This analysis does not capture the potential effect on the quantity of electricity demand of lower electricity prices induced by energy efficiency policies. That is, the modeling does not capture a “system wide” rebound effect of energy efficiency policies. This is due to IPM’s assu mption of fixed demand. However, the modeling also does not capture the effect of higher costs of producing el ectricity, which is attributable to other methods of complying with the state goals, on electricity demand for the same reason (i.e., fixed demand in IPM). For further discussion of these issues see the Limi tations section at the end of this chapter.

29 The full retail price analysis method is discussed in Section 1.7.9 of this Chapter.

30 The effect on equilibrium supply and demand of electricity due to changing retail rates to fund energy efficiency programs is not captured in the IPM modeling. Comment [A16]: Suggest that EPA also include estimates of cost reduction due to learning and technological change. Detailed comments are in EE TSD.

EPA: This is discussed in revisions to EE TSD. We are interested in identifying appropriate sources for this information.

Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering
Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 19 1-1 emissions 31 ), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005 emissions). For Option 2, EPA projects annual CO 2 reductions between 13% and 14% in 2020 (reaching 23% below 2005 emissions) and between 16% and 17% in 2030 (reaching 22% to 23% below 2005 emissions). For each Option, the regional scenario achieves fewer emissions reductions largely because the ability to average emissions regionally allows those states that were projected to emit below their compliance targ et in the base case to offset reductions that
other states would otherwise have made.

Table 3- 37 . Projected CO 2 Emission Impacts

CO 2 Emissions (MM Tonnes) CO 2 Emissions Reductions from Reference Base Case (MM Tonnes) CO 2 Emissions Reductions: Percent Change from ReferenceBase Case 2020 2025 2030 2020 2025 2030 2020 2025 2030 ReferenceBase Case 2,161 2,231 2,256

Option 1 Regional 1,790 1,730 1,711 371 501 545 17% 22% 24% Option 1 State 1,777 1,724 1,701 383 506 555 18% 23% 25% Option 2 Regional 1,878 1,862 1,903 283 368 353 13% 17% 16% Option 2 State 1,866 1,855 1,882 295 376 373 14% 17% 17% Source: Integrated Planning Model run by EPA, 2014
In 2020, EPA projects a 20% to 23% reduction of SO 2 , 22% to 24% reduction of NO X , and a 15% to 18% reduction of mercury, as a result of the proposed Option 1. EPA projects fewer emission reductions overall as a result of the proposed Option 2 in 2020: a 17% to 18%
reduction in SO 2 , 17% to 18% reduction in NO X , and an 11% to 14% reduction in mercury. The projected non-CO 2 reductions presented in Table 3-4Table 3-4 below demonstrate similar trends
in 2025 and 2030.

Table 3- 48 . Projected Non-CO 2 Emissions Reductions, 2020-2030

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

31 For purposes of these calculations, EPA has used historical CO 2 emissions from eGrid for 2005, which reports EGU emissions as 2,434 million metric tonnes in the contiguous US, and includes emissions from biomass. Comment [A17]: Power Sector Emissions were 2.54B metric tonnes in 2005: 384/2540=15% and and 555/2540=22% Are we missing something or are EPA’s numbers incorrect? In any case, this should be clarified.

EPA: See new footnote.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 20 1-1 2020 SO 2 (thousand tons) 1,476 1,184 1,140 1,231 1,208 -19.8% -22.7% -16.6% -18.1%

NO X (thousand tons) 1,559 1,213 1,191 1,290 1,277 -22.2% -23.6% -17.2% -18.0%
Hg (tons) 8.3 7.0 6.8 7.3 7.1 -15.3% -18.1% -11.3% -14.0%

PM 2.5 (thousand tons)
2025 SO 2 (thousand tons) 1,515 1,120 1,090 1,218 1,188 -26.1% -28.0% -19.6% -21.6%

NO X (thousand tons) 1,587 1,166 1,151 1,279 1,271 -26.5% -27.5% -19.4% -19.9% Hg (tons) 8.7 7.0 6.7 7.4 7.1 -19.5% -23.2% -14.9% -19.3%

PM 2.5 (thousand tons)
2030 SO 2 (thousand tons) 1,530 1,106 1,059 1,243 1,209 -27.7% -30.8% -18.8% -21.0%

NO X (thousand tons) 1,537 1,131 1,109 1,286 1,264 -26.4% -27.9% -16.4% -17.8%
Hg (tons) 8.8 7.0 6.6 7.7 7.2 -19.7% -24.1% -12.5% -17.6%

PM 2.5 (thousand tons) Source: Integrated Planning Model run by EPA, 2014
While the EPA has not quantified the climate impacts of these other pollutants for the proposed guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the compliance approaches examined in this RIA. The EPA assessed whether the net change in upstream methane emissions from natural gas and coal produc tion is likely to be positive or negative and also assessed the potential magni tude of changes relative to CO 2 emissions reductions anticipated at power plants. This assessment included CO 2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH 4 emissions from natural gas systems and coal mines and CO 2 emissions from flaring of methane will likely decrease under the proposed guidelines. Furthermore, the changes in upstream methane emissions are small***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 21 1-1 relative to the changes in direct emissions fro m power plants. The technical details supporting this analysis can be found in the Appendix to this chapter.

1.6.2 3.7.2 Projected Compliance Costs The power industry’s “compliance costs” are represented in this analysis as the change in electric power generation costs between the base case and policy case in which the sector pursues flexible compliance approaches to meet the proposed rule. In simple terms, these costs are the resource costs of what the power industry will expend to comply with EPA’s requirements. 32 Program and participant end-use EE costs are also included in the compliance
cost estimates.
EPA projects that the annual incremental compliance cost of the proposed rule’s Option 1 ranges from $5.4 to $7.4 billion in 2020 and from $7.3 to $8.8 billion in 2030. The projected annual incremental compliance cost of the proposed rule’s Option 2 ranges from $4.2 to $5.4 billion in 2020 and from $1.9 to $3.1 billion in 2030. The annual incremental cost is the projected additional cost of complying with the final rule in the year analyzed and includes the annualized cost of capital investment in new generating sources and heat rate improvements at coal steam facilities, 33 the ongoing costs of operating additional pollution controls, shifts between or amongst various fuels, demand-side energy efficiency measures, and other actions associated with compliance.

Table 3- 59 . Annualized Compliance Costs (billions of 2011$)
2020 2025 2030 Option 1 Regional 5.4 4.6 7.3 Option 1 State 7.4 5.5 8.8
Option 2 Regional 4.2 4.5 1.9
Option 2 State 5.4 5.5 3.1 Source: Integrated Planning Model run by EPA, 2014

32 The compliance costs also capture the effect of change s in equilibrium fuel prices on the expenditures of the electricity sector to serve demand.

33 See Chapter 8 of EPA’s Base Case usi ng IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering Comment [A18]: Please present total costs without fuel savings incorporated. Fuel savings should be presented the benefits section.
EPA: It is not clear what is meant by fuel savings. There are two primary costs to the proposal; system costs for the power sector, and the additional expenditures on EE. The costs reflected show the combined effect of these two elements, on an annualized basis. We have also captured the electricity savings by estimating the average electric bill impacts, in addition to the retail electricity price estimates. The EE costs are also broken out in the RIA, should one want to see those in isolation.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 22 1-1 EPA’s projection of $4.2 to $7.4 billion in additional costs in 2020 across the compliance scenarios evaluated for both options should be put into context for power sector operations. As shown in section 2.7, the power sector is expect ed in the base case to expend over $359 billion in 2020 to generate, transmit, and distribute electricity to end-use consumers. Therefore, the projected costs of compliance with the proposed rule amount to a one to two percent increase in the cost to meet electricity demand, while securing public health and welfare benefits that are several times more valuable (as described in Chapters 4 and 8).
The annual compliance costs presented in Table 3-5 Table 3-9 include the exogenously determined cost of demand-side energy efficiency measures, presented
here in Table 3-Table 6Table 3-6

.

Table 3- Table 610 . Annualized Gross Cost of Demand-Side Energy Efficiency ( at discount rates of 3% and 7%, discount rate, billions 2011$)

2018 2020 2025 2030 Option 1 at 3% 4.1 10.2 28.9 42.7 Option 1 at 7% 4.9 12.3 35.0 51.8 Option 2 at 3% 3.6 8.0 20.6 29.8 Option 2 at 7% 4.3 9.7 24.9 36.1 Source: Greenhouse Gas Abatement Measures TSD

1.6.3 3.7.3 Projected Compliance Actions for Emissions Reductions Heat rate improvements (HRI). EPA analysis assumes that the existing coal steam electric generating fleet has, on average, the ability to improve operating efficiency (i.e., reduce the average net heat rate, or the Btu of fuel energy needed to produce one kWh of net electricity output). All else held constant, HRI allow the EG U to generate the same amount of electricity using less fuel. The decrease in required foss il fuel results in a lower output-based CO 2 emissions rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling Comment [A19]: Please present results of 7% discount rate.

EPA: Done.

Comment [A21]: These costs are extremely large How can this possibly be the case? Please clarify.

EPA: See detailed discussion of basis for these cost estimates in EE TSD. Comment [A20]: Per above comments on EE input costs, even a few percent drop in input costs could make a large difference in the overall cost/benefit calculation. Please consider revisiting EE costs.

EPA: The EE TSD discusses the basis for EE costs in detail and acknowledges the conservatism of EPA’s estimates. We solicit comment in the preamble on basis/approach to estimating EE costs.

Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 23 1-1 conducted for these compliance scenarios, coal boilers have the choice to improve heat rates by 6% under Option 1 and 4% under Option 2, at capital cost of $100 per kW in both options. 34 The vast majority of existing coal boilers are projected to adopt the aforementioned heat rate improvements. EPA projects that 176 to 179 GW of existing coal steam capacity (greater than 25 MW) will improve operating efficiency (i.e., reduce the average net heat rate) under Option 1 in 2020. Under Option 2, EPA projects that 168 to 185 GW of existing coal steam capacity with improve operating efficiency in 2020.
Re-dispatch. Another approach for reducing the average emission rate from existing units is to shift some generation from more CO 2 -intensive generation to less CO 2 -intensive generation. Compared to the Base Case, existing coal steam capacity is projected to operate at a lower capacity factor for both Option 1 and Option 2, on average , although projected average capacity factors for existing coal steam boilers remains around 75% across scenarios . Existing natural gas combined cycle units, which are less carbon-intensive than coal steam capacity on an output basis, operate at noticeably higher capacity factor in Options 1 and 2, on average. See Table 3-
7Table 3-7

. The utilization of existing natural gas combined cycle capacity is lower than 70% on an annual average basis in these illustrative compli ance scenarios, reflecting the fact that states have the flexibility to choose among alternative CO 2 reduction strategies that were part of BSER, instead of employing re-dispatch to the maximum extent. In addition, future electric demand is considerably lower than the Base Case.

Table 3- 711 . Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity

Existing Coal Steam Existing Natural Gas Combined Cycle

2020 2025 2030 2020 2025 2030 ReferenceBase Case 78% 80% 79% 52% 48% 42% Option 1 Regional 77% 74% 73% 55% 54% 50% Option 1 State 76% 74% 73% 56% 55% 51% Option 2 Regional 75% 74% 76% 57% 55% 49%

34 See Technical Support Document – GHG Abatement Measures. Comment [A22]: Why are the two options the same price? Shouldn’t the lower level of improvement be cheaper?

EPA: See Abatement TSD for more information and detail on HRI. We have assumed, conservatively, the same cost.

Comment [A23]: This cost seems high. This implies a cost of $70/MWh (assuming a 7% cost of capital) and coal emits appox. 1 metric ton/MWh which is valued at an SCC of approximately $40. Am I missing something? I guess the co-benefits make it economically viable?

EPA: Chapter 2 in the GHG Abatement TSD includes an economic assessment that states all assumptions and the sensitivity of lower capital costs. Our calculations of $/tonne for HRI are considerably lower.

Comment [A24]: It is possible (likely?) that some plants making heat rate improvements will be dispatched more and thereby increase emissions triggering NSR,which would increase the cost of HRIs.
EPA: See preamble section VIII.A. Comment [A25]: Incremental cost?
EPA: See comment response above. Comment [A26]: Capacity factors of existing NGCC seem relatively low – compared to BSER re-dispatch up to 70%? Also, not clear as to why regional scenarios would not result in higher NGCC capacity factors? Suggest clarifying/explaining these results.
EPA: In a compliance setting, the system will find the optimal balance and least cost solution to meeting state goals. With some additional coal retirements, lower electric demand, new RE, etc, lower levels of redispatch are projected (compared to the BSER level of 70%). Regional scenarios result in slightly ... [2] Comment [A27]: I have to admit to some confusion between the “reference” case and the “baseline.” I went back in the text and looked for the distinction, but could not find it. Suggest that if there is a distinction between a reference case and a baseline case, then this would be a good place to put in a footnote, or change this text to “baseline.” ... [3]***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 24 1-1 Option 2 State 75% 73% 76% 57% 56% 49% Source: Integrated Planning Model run by EPA, 2014
Demand-Side Energy Efficiency. Another appr oach for reducing the average output-based
emission rate is to consider reductions in load attributable to demand-side energy efficiency savings, which will reduce the need for higher emitting generation. 35 In the compliance scenario analyses presented in this RIA, each state is cred ited for the total demand-side energy efficiency savings equal to the levels that are assumed in the state goals. See section 13.6.1 for a description of the levels assumed for Options 1 and 2. Again, these reductions in demand as a result of demand-side energy efficiency are made exogenously in the options and illustrative scenarios analyzed.
1.6.4 3.7.4 Projected Generation Mix

Table 3-89Table 3-12 and Figure 3-4Figure 3-7 show the generation mix in the base case and under the proposed rule . The ability for to average the em issions rate at covered sources (which exclude new NGCC) over the 2020-2030 compliance period provides an incentive to shift generation within the covered sources to less carbon-intensive generation. in 2020 for Option 1. In 2020, for Option 1, total generation declines approximately three percent under Option 1 as a result of the reduction in total de mand attributable to the demand-side energy savings under the proposed rule. Coal-fired generation is projected to decline 20% to 22% in 2020, and natural-gas-fired generation from existing combined cycle capacity is projected to increase four to six percent relative to the base case under Option 1. In 2030, the cumulative demand-side energy savings under the Option 1 re sult in an 11% reduction in total generation relative to the base case. The coal-fired fleet in 2030 generates between 25% and 27% less than in the base case, while natural-gas-fired genera tion from existing combined cycles increases 18% to 19% relative to the base case. Gas-fired generation from new combined cycle capacity increases in 2020, as projected new natural ga s combined cycle capacity replaces retired coal capacity. By 2030, generation from ne wly built natural gas combined cycle decreases between 36% and 40% relative to the base case, consistent with the decrease in new capacity (see

35 Because fossil fuel-fired EGUs typically have higher va riable costs than other EGUs (such as nuclear and renewable EGUs), they are typically the firs t to be displaced when demand is reduced. Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 25 1-1 section 03.7.6 ). Generation from non-hydro renewables increases two percent relative to the base
case in 2030.
Similar trends are projected for Option 2. In 2020, total generation declines two percent under Option 2 as a result of the reduction in tota l demand attributable to the demand-side energy savings under the proposed rule. Coal generation is projected to decrease between 16% and 17% in 2020, and natural gas-fired generation from existing combined cycle units is projected to increase nine percent, relative to the base case.
Under both options, additional pipeline capacity is projected to be built through 2020 relative to the base case. In all of the illustrative compliance scenarios, pipeline capacity is projected to increase four to eight percent beyond base case projections by 2020. In 2030, however, the total cumulative pipeline capacity built is projected to decrease under both options, consistent with the projected decrease in total natural gas use. The projected increase in pipeline capacity in the near term is largely the result of building pipeline capacity a few years earlier than in is projected in the base case. Given the relatively small amount of additional infrastructure development that is projected to shift to earlier years, construction of this additional capacity should be easily manageable and not raise any reliability or cost concerns.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 26 1-1 Table 3-8 912 . Generation Mix (thousand GWh)

Base Case Option 1 Option 2 Option 1 Option 2 Reg. State Reg. State Reg. State Reg. State
2020 Pulverized Coal 1,665 1,337 1,302 1,406 1,375 -20% -22% -16% -17% NG Combined Cycle (existing) 1,003 1,043 1,065 1,093 1,091 4% 6% 9% 9% NG Combined Cycle (new) 85 238 248 155 185 181% 192% 83% 119%
Combustion Turbine 19 33 33 33 32 74% 76% 75% 67% Oil/Gas Steam 52 15 14 18 16 -70% -73% -65% -69% Non-Hydro Renewables 299 321 323 313 316 7% 8% 5% 5% Hydro 280 282 281 282 281 1% 1% 1% 1% Nuclear 817 817 819 814 819 0% 0% 0% 0% Other 8 15 14 13 12 92% 88% 66% 61% Total 4,227 4,102 4,100 4,128 4,128 -3% -3% -2% -2% 2025 Pulverized Coal 1,702 1,275 1,250 1,383 1,353 -25% -27% -19% -20% NG Combined Cycle (existing) 919 1,022 1,035 1,055 1,068 11% 13% 15% 16% NG Combined Cycle (new) 280 257 266 218 231 -8% -5% -22% -17%
Combustion Turbine 27 37 37 37 37 36% 39% 36% 36% Oil/Gas Steam 37 13 15 16 16 -64% -59% -56% -58% Non-Hydro Renewables 335 347 346 342 341 4% 3% 2% 2% Hydro 280 282 281 282 282 1% 0% 0% 0% Nuclear 817 817 819 814 819 0% 0% 0% 0% Other 6 13 11 11 10 115% 86% 71% 64% Total 4,404 4,063 4,062 4,158 4,156 -8% -8% -6% -6% 2030 Pulverized Coal 1,668 1,249 1,216 1,419 1,377 -25% -27% -15% -17% NG Combined Cycle (existing) 810 955 961 932 936 18% 19% 15% 16% NG Combined Cycle (new) 599 359 384 372 409 -40% -36% -38% -32%
Combustion Turbine 23 32 31 30 25 43% 35% 32% 12% Oil/Gas Steam 23 10 12 14 13 -57% -46% -40% -42% Non-Hydro Renewables 350 356 356 355 353 2% 2% 1% 1% Hydro 280 281 280 281 281 0% 0% 0% 0% Nuclear 797 796 797 793 797 0% 0% -1% 0% Other 6 16 14 13 12 163% 132% 109% 94% Total 4,557 4,054 4,051 4,206 4,205 -11% -11% -8% -8% Note: “Other” mostly includes MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2014 Comment [A28]: Would you provide intuition on why there is decrease generation from new NGCC units whereas there is increase generation from existing NGCC?
EPA: The goals require a certain amount of redispatch to existing NGCC as a fundamental part of this rule, so we expect more to occur as a result. We also anticipate significant levels of EE, and much less new NGCC is needed to meet projected load.
Language added to text where appropriate.

Comment [A29]: Seems odd that the rule pushes out new NGCC generation in 2020 and then reduces it in 2025 and 2030 – please explain this result.
EPA: Some new NGCC is brought forward to help balance the coal retirements and new RE, but less is needed in the longer term as EE increases and there is lower overall electric demand.
Language added to text where appropriate. Comment [A30]: Why do renewables not increase by more in any of the analysis years? Based on the state goals I would have expected renewable generation to rise by quite a more.
EPA: Generation from both non-hydro renewables and existing NGCC increases as part of the model’s solution to meeting each state’s average emissions rate. At the level of CO 2 reductions in these scenarios, and with the projected fuel prices and capital costs for construction of new renewables, it makes sense that existing NGCC capacity is generally more competitive than new non-hydro renewables.
Also, note that demand is decreasing relative to the base case – so any generation increase relative to the base represents an even greater increase in total generation share. Comment [A31]: What comprises “other” in the categories listed for the table/figure?
EPA: “Other” includes largely waste fuels, with the exception of waste coal and petroleum coke, as well as fuel cells.

Footnote added.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-A-271-10.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. State Reg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclearCoal2020 2025 2030Option1Option2Option1 Option2Option1Option2***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-A-281-10.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. State Reg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclearCoal2020 2025 2030Option1Option2Option1 Option2Option1Option2***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-A-291-10.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. State Reg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclearCoal2020 2025 2030Option1Option2Option1 Option2Option1Option2***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-A-301-1Figure 3-47. Generation Mix with the BaseCase and 111(d) Options, 2020-2030(million GWh)Source: Integrated Planning Model run by EPA, 20140.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. State Reg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclearCoal2020 2025 2030Option1Option2Option1 Option2Option1Option2Comment [A32]:What comprises “other” inthe categories listed for the table/figure?EPA: See previous response.Comment [A33]:Would be helpful toinclude units on the y-axis: Million GWh.EPA: Included***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 0 1-1 1.6.5 3.7.5 Projected Incremental Retirements Relative to the base case, about 30 to 49 GW of coal-fired capacity is projected to be uneconomic to maintain (about 12% to 19% of all coal-fired capacity projected to be in service in the base case) by 2020 under the range of scenarios analyzed.
For the proposed rule, EPA examined whether these projected incremental retirements may adversely impact reserve margins and reliability planning. The IPM model is designed to ensure that generation resource adequacy is maintained in the projected results, and the model is required to meet reserve margin requirements in the 64 modeling regions in the contiguous US by retaining enough existing capacity and/or building enough new capacity. IPM also addresses reliable delivery of generation resources at a regional level by limiting the ability to transfer power between regions using the bulk power tr ansmission system. Within each model region, IPM assumes that adequate transmission capacity is available to deliver any resources located in, or transferred to, the region. 36
EPA examined the implications of each of the scenarios for regional resource adequacy and for potential concerns over on grid reliabilit y. To conduct this analysis, EPA examined key parameters from the IPM projections to assess whether concerns over regional resource adequacy would be likely to arise or whether changes in generation and flow pattern impacts would raise issues for reliability management. The key parameters analyzed were:
Operating Reserve Margins. The IPM model ensures that target reserve margins from NERC will be met, by maintaining existing capacity or by building additional new capacity if needed. Capacity that is otherwise uneconomic to oper ate will be retired only when planned reserve margins can be met from within the region or by transfers from other regions. Operational Capacity. Since IPM ensures that NERC target margins will be met, EPA analyzed the remaining operating capacity for 2020 through 2030 to determine what types of changes in the generation fleet were projected to occur through retirements, additional generation and energy efficiency. Although there were changes from the base case to the policy cases, none of

36 For more detail on IPM’s electric load modeling and power system operation, please see IPM documentation (http://www.epa.gov/powersector modeling). Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering

Comment [A34]: This comes up in the Goal TSD (p.31), when a statement is made that roughly 230 [coal?] EGUs are expected to close. I did not see closures in the state goal setting mathematics. Is EPA including anticipated closures in the state goal rate setting, and, if not, how would this many anticipated closures affect the stringency of the goals?
EPA: We are not including expected retirements in the goal setting. See state goals TSD. Comment [A35]: Are the assumptions in IPM equivalent to the requirements of RTOs, etc?
EPA: Yes, IPM ensures that target reserve margins will be met.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 1 1-1 the policy cases were found to raise concerns over regional resource adequacy. Moreover, the time horizon for compliance with this rule will permit environmental and reliability planners to coordinate these changes and address potential concerns before they arise. Interregional Power Flows and Capacity Transfers. IPM constrains both interregional energy flows and interregional transfers of capacity to meet reserve requirements. These limits are based on grid operator data from ISOs, RTOs and othe r planning entities. Although these limits keep projected flows within expected bounds, EPA further examined how the policy options impacted these flows and transfers to identify any large shifts from the base case. None of the interregional changes in the policy cases suggested that there would be increases in flows that would raise significant concerns about grid congestion or grid management. The increased energy efficiency anticipated with the proposed rule also contributes to meeting resource adequacy, by lowering the regional load and thereby lowering operating capacity needed to meet planning reserve margins. Energy efficiency also reduces the overall load on the grid and thus generally reduces the burden on the transmission infrastructure needed to maintain reliability. EPA conc ludes that the proposed rule will not raise significant concerns over regional resource adequacy or raise the potential for interregional grid problems. EPA believes any remaining local issues can be managed through standard reliability planning

processes. Capacity changes from the base case in 2020 are shown in Table 3-10
Table 3-13

. Comment [A36]: Does this analysis consider grid/ancillary services provided by affected EGUs, including those projected to be “uneconomic to maintain”? If not, such a discussion would be valuable in this context.
EPA: EPA concludes that the proposed rule will not raise significant concerns over regional resource adequacy or raise the potential for interregional grid problems. For example, combined overall generation from natural gas and coal are projected to fall by less than 5 percent at the national level in 2020 under the Option 1 regional case, indicating that the provision of ancillary services such as spinning reserves and load following can be managed without adverse impact of the grid. EPA believes any remaining local issues can be managed through standard reliability planning processes. See resource adequacy TSD for more details. Comment [A37]: For 2020, 2025, and 2030, the results show no changes in nuclear capacity in the base case. This appears to be in tension with the element of building block three that asserts a share of nuclear capacity is at risk of closure in a business-as-usual posture. Some explanation in this section (perhaps even a footnote) that recognizes and explains the difference may be warranted.
EPA: The modeling of nuclear in IPM is consistent with energy sector models, and reflects a deterministic, long-term (decades) solution to meet energy demand. The model has perfect foresight, and is not focused on how much nuclear is at risk or may retire (a detailed discussion of BB3 and nuclear power is discussed in the preamble and Abatement TSD). The nuclear fleet cannot be captured in a manner perfectly consistent with other technologies due to its unique characteristics (security, safety, capital expenditures, etc.), and the single most important element for nuclear operations in models is generally the license status.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 2 1-1 Table 3- 1013 . Total Generation Capacity by 2020-2030 (GW)

Base Case Option 1 Option 2 Option 1 Option 2 Reg. State Reg. State Reg. State Reg. State 2020 Pulverized Coal 244 198 195 214 211 -19% -20% -12% -14% NG Combined Cycle (existing) 219 216 217 218 218 -1% -1% -1% 0% NG Combined Cycle (new) 12 33 35 21 26 174% 190% 79% 117% Combustion Turbine 146 144 143 146 145 -2% -2% -1% -1% Oil/Gas Steam 83 66 66 72 71 -20% -20% -13% -14% Non-Hydro Renewables 93 105 105 101 102 13% 13% 9% 10% Hydro 101 101 101 101 101 0% 0% 0% 0% Nuclear 103 103 103 103 103 0% 0% 0% 0% Other 5 5 5 5 5 2% 2% 2% 2% Total 1,005 971 970 981 981 -3% -3% -2% -2% 2025 Pulverized Coal 243 197 193 214 211 -19% -21% -12% -13% NG Combined Cycle (existing) 219 216 217 218 218 -1% -1% -1% 0% NG Combined Cycle (new) 39 34 36 30 32 -11% -7% -23% -18% Combustion Turbine 149 145 144 148 147 -3% -3% -1% -2% Oil/Gas Steam 82 65 66 72 71 -20% -19% -12% -13% Non-Hydro Renewables 103 113 112 110 110 10% 8% 7% 7% Hydro 101 101 101 101 101 0% 0% 0% 0% Nuclear 103 103 103 103 103 0% 0% 0% 0% Other 5 5 5 5 5 2% 2% 2% 2% Total 1,044 980 977 1,000 997 -6% -6% -4% -4% 2030 Pulverized Coal 240 195 191 212 208 -19% -21% -12% -13% NG Combined Cycle (existing) 219 216 217 218 218 -1% -1% -1% 0% NG Combined Cycle (new) 84 49 52 53 58 -42% -38% -36% -31% Combustion Turbine 156 146 145 150 149 -7% -7% -4% -5% Oil/Gas Steam 82 65 66 72 71 -20% -19% -12% -13% Non-Hydro Renewables 107 117 115 113 114 9% 7% 6% 6% Hydro 101 101 101 101 101 0% 0% 0% 0% Nuclear 101 100 101 100 101 0% 0% -1% 0% Other 5 5 5 5 5 2% 2% 2% 2% Total 1,095 994 992 1,023 1,024 -9% -9% -7% -6% Source: Integrated Planning Model run by EPA, 2014

1.6.6 3.7.6 Projected Capacity Additions Due largely to a low growth rate anticipated for future electricity demand levels in the latest EIA forecast, and the demand reduction attributable to the demand-side energy savings Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 3 1-1 under the proposed rule, EPA proj ects less new natural gas combined cycle capacity built under the proposed rule than is built in the base cas e over the time horizon presented in this RIA. Conversely, EPA projects an increase in new non-hydro renewable capacity. As affected sources under the proposed rule, the generation from new non-hydro renewables are able to contribute to the average emissions rate in each state or region.
Under Option 1, new natural gas combined cycle capacity is projected to increase by 21 to 23 GW in 2020 (174% to 190% increase relative to the base case), and decrease by 32 to 35 GW (38% to 42% reduction relative to base case) by 2030. New non-hydro renewable capacity is projected to increase by about 12 GW (67% increase) above the base case in 2020, and 8 to 9 GW (24% to 28% increase) by 2030.
Under Option 2, new natural gas combined cycle capacity is projected to increase by about 9 to 14 GW (79% to 117% increase relative to base case) in 2020, and decrease between 26 and 31 GW (31% to 37% reduction) in 2030. New non-hydro renewable capacity is projected to increase by about 9 GW (50% increase) above the base case in 2020, and about 6 GW (18% increase) by 2030.

Table 3- 1114 . Projected Capacity Additions, Gas (GW) Cumulative Capacity Additions: Gas Combined Cycle Incremental Cumulative Capacity Additions: Gas Combined Cycle 2020 2025 2030 2020 2025 2030 ReferenceBase Case 11.9 38.9 83.8 Option 1 Regional 32.7 34.4 49.0 20.8 -4.4 -34.8 Option 1 State 34.7 36.1 51.6 22.7 -2.7 -32.1 Option 2 Regional 21.4 30.0 53.2 9.4 -8.9 -30.6 Option 2 State 25.9 32.0 57.8 14.0 -6.8 -26.0 Source: Integrated Planning Model run by EPA, 2014 Comment [A38]: Just to be clear, new NGCC is not in the state goal rate setting, but gets picked up in the IPM modeling?
EPA: New NGCC is not in the state goal rate, nor are emissions or generation from new NGCCs counted towards compliance with the goals. However, the model can build any technology that is determined to be economic.

Comment [A39]: Added ‘Reference’ label.

EPA: Corrected to “base case”***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 4 1-1 Table 3- 1215 . Projected Capacity Additions, Non-hydro Renewable (GW)
Cumulative Capacity Additions: Renewables Incremental Cumulative Capacity Additions: Renewables

2020 2025 2030 2020 2025 2030 ReferenceBase Case 17.8 28.4 32.7 Option 1 Regional 29.9 38.4 41.9 12.1 9.9 9.2 Option 1 State 29.6 37.0 40.6 11.8 8.5 7.9 Option 2 Regional 26.4 35.5 38.5 8.6 7.1 5.8 Option 2 State 26.7 35.0 38.7 8.8 6.6 6.0 Source: Integrated Planning Model run by EPA, 2014

1.6.7 3.7.7 Projected Coal Productions fo r the Electric Power Sector Coal production is projected to decrease in 2020 and beyond due to (1) improved heat rates (generating efficiency) at existing coal units, (2) demand reduction attributable to the demand-side energy savings under the proposed rule, and (3) a shift in generation from coal to less-carbon intensive generation. As shown in Table 3-13 Table 3-16 , the largest decrease in coal production is projected to occur in the western region. Waste coal production is projected to increase slightly under the proposed rule due to the operation under both options of less than 1 GW of coal steam capacity that is projected to retire under the base

case.

Table 3- 1316 . Coal Production for the Electric Power Sector, 2020 Coal Production (MM Tons) Percent Change from ReferenceBase Case
Base Case Option 1 Option 2 Option 1 Option 2 Reg. State Reg. State Reg. State Reg. State Formatted: Default

Comment [A40]: Reference or baseline? What is it referenced to?

EPA: Clarified “base case”

Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 5 1-1 Appalachia 140 87 91 97 100 -37% -35% -30% -28% Interior 249 231 222 234 226 -7% -11% -6% -9%
West 446 308 292 343 332 -31% -34% -23% -26% Waste Coal 9 10 10 11 11 8% 5% 15% 16% Imports 0 0 0 0 0
Total 844 636 616 684 668 -25% -27% -19% -21% Source: Integrated Planning Model run by EPA, 2014
Power sector gas use is projected to increase between 12% and 14% in 2020 under Option 1 and between 10% and 12% under Option 2. In later years, gas use declines under both options. These trends are consistent with the change in generation mix described above in section 03.7.4

. Table 3- 1417 . Power Sector Gas Use

Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use
2020 2025 2030 2020 2025 2030 ReferenceBase Case 8.35 8.88 9.89 Option 1 Regional 9.32 9.31 9.37 11.7% 4.8% -5.3% Option 1 State 9.54 9.52 9.61 14.3% 7.2% -2.9% Option 2 Regional 9.20 9.34 9.30 10.2% 5.1% -6.0% Option 2 State 9.35 9.52 9.53 12.0% 7.2% -3.7% Source: Integrated Planning Model run by EPA, 2014

1.6.8 3.7.8 Projected Fuel Price Impacts The impacts of the proposed rule on coal and natural gas prices before shipment are
shown below in Table 3-15Table 3-18 , Table 3-16Table 3-19 , Table 3-17Table 3-20 , and

Table 3-18

Table 3-21 and are attributable to policy-induced changes in overall power sector demand for
each fuel.
Coal demand decreases over the 2020-2030 time horizon, resulting in a decrease in the price of coal delivered to the electric power sector. In 2020, the increase in gas demand results in an increase in the price of gas delivered to the electric power sector. In 2030, gas demand and price decrease below the base case projections, due to the cumulative impact of demand-side energy efficiency savings and reduced overall demand. Field Code Changed

Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering
Formatted: 05 Body Text

Formatted: Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 6 1-1 IPM modeling of natural gas prices uses both short- and long-term price signals to balance supply and demand for the fuel across the modeled time horizon. As such, it should be understood that the pattern of IPM natural gas pr ice projections over time is not a forecast of natural gas prices incurred by end-use consumers at any particular point in time. The natural gas market in the United States has historically expe rienced some degree of price volatility from year to year, between seasons within a year, and during short-lived weather events (such as cold snaps leading to short-run spikes in heating demand). These short-term price signals are fundamental for allowing the market to successfully align immediate supply and demand needs. However, end-use consumers are typically shielded from experiencing these rapid fluctuations in natural gas prices by retail rate regulation and by hedging through longer-term fuel supply contracts by the power sector. IPM assumes these longer-term price arrangements take place “outside of the model” and on top of the “real-time” shorter-term price variation necessary to align supply and demand. Therefore, the model’s natural gas price projections should not be mistaken for traditionally experienced consumer price impacts related to natural gas, but a reflection of expected average price changes over the period of time represented by the modeling horizon.

Table 3- 1518 . Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)
Minemouth Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 ReferenceBase Case 1.73 1.88 2.06 2.62 2.80 2.98 Option 1 Regional 1.46 1.57 1.72 2.19 2.29 2.44 Option 1 State 1.45 1.56 1.70 2.18 2.30 2.44 Option 2 Regional 1.49 1.62 1.77 2.25 2.40 2.56 Option 2 State 1.49 1.62 1.76 2.26 2.41 2.56 Source: Integrated Planning Model run by EPA, 2014

Table 3- 1619 . Projected Average Minemouth and Delivered Coal Prices: Percent Change from Base Case Projections
Minemouth Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 Option 1 Regional -15.5% -16.6% -16.4% -16.3% -18.3% -18.1% Option 1 State -16.1% -17.0% -17.6% -16.5% -17.9% -18.2% Option 2 Regional -14.1% -14.1% -14.2% -13.8% -14.4% -14.2% Option 2 State -14.0% -14.1% -14.6% -13.6% -14.1% -14.2% Source: Integrated Planning Model run by EPA, 2014 Comment [A41]: How do IPM results compare with AEO projections? Are they sufficiently close enough?
EPA: Yes, EPA’s base cases power sector delivered gas price projections are very close to the projections in AEO 2014.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 7 1-1 Table 3- 1720 . Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu)
Henry Hub Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 ReferenceBase Case 4.98 5.68 6.00 5.36 6.11 6.39 Option 1 Regional 5.50 5.60 6.02 5.86 5.91 6.33 Option 1 State 5.61 5.57 6.07 5.98 5.90 6.39 Option 2 Regional 5.40 5.79 5.85 5.76 6.13 6.18 Option 2 State 5.43 5.80 5.86 5.80 6.16 6.22 Source: Integrated Planning Model run by EPA, 2014

Table 3- 1821 . Average Henry Hub (spot) and Delivered Natural Gas Prices: Percent Change from Base Case Projections
Henry Hub Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 Option 1 Regional 10.4% -1.5% 0.4% 9.3% -3.3% -0.9% Option 1 State 12.5% -2.0% 1.2% 11.5% -3.5% 0.0% Option 2 Regional 8.5% 2.0% -2.5% 7.5% 0.2% -3.2% Option 2 State 9.0% 2.1% -2.3% 8.1% 0.8% -2.6% Source: Integrated Planning Model run by EPA, 2014

1.6.9 3.7.9 Projected Retail Electricity Prices
EPA’s analysis projects an a near-term increase in the national average (contiguous U.S.) retail electricity price between 5.9% and 6.5% in 2020 and between 2.7% and 3.1% by 2030 under the proposed Option 1 , compared to the modeled baselinebase case price estimate in those

years.. Under Option 2, on average, EPA projects an average retail price increase ranging from 3.6% to 4.0% in 2020, and from 1.1% to 1.4% in 2030.
Retail electricity prices embody generation, transmission, distribution, and utility demand-side EE program costs. IPM modeling projects changes in regional wholesale power prices and capacity payments related to imposition of the represented policy that are combined with EIA regional transmission and distribution co sts to calculate changes to regional retail prices. There are many factors influencing the esti mated retail electricity price impacts, namely projected changes in generation mix, fuel prices, and development of new generating capacity. Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 8 1-1 These projected changes vary regionally under each compliance option in response to the goals, and can also vary depending upon retail electric ity market structure (e.g., cost-of-service vs. competitive).

Table 3- 1922 . 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2020 Projected Retail Price (c ents/kWh) Percent Change from ReferenceBase Case

Base Case Option 1 Regional Option 1 State Option 2 Regional Option 2 State Option 1 Regional Option 1 State Option 2 Regional Option 2 State
ERCT 9.9 10.6 10.8 10.4 10.5 7.4% 9.9% 5.3% 6.0%
FRCC 10.6 11.3 11.6 11.2 11.4 6.5% 8.7% 5.2% 7.1%
MROE 10.4 10.9 11.0 10.8 10.9 4.7% 5.8% 3.4% 4.2%
MROW 9.2 9.8 9.8 9.6 9.6 6.4% 7.0% 4.6% 4.9%
NEWE 13.8 15.2 15.4 14.5 14.8 10.1% 11.4% 5.1% 6.9%
NYCW 18.0 19.7 19.8 18.8 18.9 9.5% 10.0% 4.6% 5.0%
NYLI 14.7 16.1 16.1 15.3 15.3 9.3% 9.5% 3.7% 4.2%
NYUP 12.7 14.0 14.0 13.4 13.3 9.9% 9.8% 4.8% 4.5%
RFCE 12.2 13.3 13.2 12.6 12.8 8.6% 7.7% 3.2% 4.6%
RFCM 10.7 11.2 11.3 11.1 11.1 5.1% 6.0% 3.6% 3.9%
RFCW 10.1 10.7 10.8 10.4 10.3 6.1% 6.3% 2.5% 2.2%

Table 3-1822. Continued

SRDA 9.0 9.4 9.4 9.3 9.2 4.1% 4.5% 3.0% 2.8%
SRGW 9.3 9.7 9.7 9.5 9.5 4.9% 4.7% 2.1% 1.7%
SRSE 10.4 10.7 10.7 10.6 10.6 2.8% 3.4% 2.2% 2.2%
SRCE 8.2 8.4 8.4 8.3 8.4 1.5% 1.7% 1.3% 1.6%
SRVC 10.7 10.9 10.9 10.8 10.8 2.4% 2.2% 1.6% 1.5%
SPNO 10.6 11.7 10.7 11.4 10.7 9.9% 0.6% 7.5% 1.1%
SPSO 8.3 9.0 9.3 8.8 9.0 8.3% 11.7% 5.3% 7.7%
AZNM 10.5 11.1 11.3 10.9 11.1 5.3% 7.4% 3.9% 5.6%
CAMX 14.3 15.2 15.2 14.9 14.9 6.3% 6.4% 4.7% 4.5%
NWPP 7.3 7.7 7.8 7.6 7.6 6.1% 6.7% 4.5% 4.5%
RMPA 8.9 9.5 9.9 9.4 9.6 6.3% 10.4% 5.0% 6.9% Contiguous U.S. 10.4 11.1 11.1 10.8 10.9 5.9% 6.5% 3.6% 4.0%

Table 3- 2023 . 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2025 Projected Retail Price (c ents/kWh) Percent Change from ReferenceBase Case***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 9 1-1 Base Case Option 1 Regional Option 1 State Option 2 Regional Option 2 State Option 1 Regional Option 1 State Option 2 Regional Option 2 State
ERCT 11.2 11.5 11.4 11.6 11.6 2.9% 1.8% 3.3% 3.7%
FRCC 10.9 11.4 11.5 11.3 11.5 4.5% 5.4% 4.1% 5.4%
MROE 10.5 10.9 11.0 10.8 10.9 4.0% 4.2% 2.5% 4.0%
MROW 9.2 9.8 9.8 9.6 9.6 5.8% 5.7% 4.3% 4.4%
NEWE 14.2 14.4 14.5 14.3 14.5 1.4% 1.9% 0.2% 1.6%
NYCW 18.8 19.1 19.1 18.9 18.9 1.5% 1.3% 0.5% 0.4%
NYLI 15.6 15.5 15.4 15.4 15.3 -0.4% -1.4% -1.4% -1.5%
NYUP 13.2 13.3 13.2 13.2 13.1 0.2% -0.4% -0.3% -0.8%
RFCE 12.6 12.7 12.9 13.0 13.0 0.4% 2.2% 3.0% 3.1%
RFCM 10.7 11.2 11.2 11.1 11.1 4.0% 4.5% 3.1% 3.6%
RFCW 10.9 10.9 11.0 10.9 11.0 -0.4% 0.0% -0.1% 0.3%
SRDA 9.3 9.7 9.7 9.7 9.7 4.1% 3.7% 3.9% 3.6%
SRGW 10.1 10.1 10.1 10.1 10.1 -0.1% -0.1% -0.1% -0.1%
SRSE 10.3 10.7 10.7 10.6 10.7 3.5% 3.8% 2.8% 2.9%
SRCE 8.2 8.5 8.5 8.4 8.4 3.1% 3.2% 2.2% 2.2%
SRVC 10.6 10.9 10.8 10.8 10.8 3.0% 2.5% 2.4% 2.0%
SPNO 10.3 11.8 10.8 11.3 10.7 14.1% 4.5% 8.7% 3.0%
SPSO 8.8 9.5 9.7 9.4 9.5 7.4% 9.4% 6.0% 7.7%
AZNM 10.8 11.2 11.3 11.0 11.2 3.9% 5.0% 2.4% 4.0%

Chapter 3 TABLE 3

‐ 19. CONTINUED

CAMX 13.9 14.4 14.3 14.3 14.3 3.2% 2.9% 2.9% 2.9%
NWPP 7.4 7.8 7.7 7.6 7.6 5.0% 4.6% 3.4% 3.3%

RMPA 9.4 9.6 10.0 9.6 9.8 2.3% 6.4% 2.7% 4.8% Contiguous U.S. 10.8 11.1 11.1 11.0 11.1 2.7% 2.9% 2.4% 2.7%

Table 3- 2124 . 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 2030 Projected Retail Price (c ents/kWh) Percent Change from ReferenceBase Case Base Case Option 1 Regional Option 1 State Option 2 Regional Option 2 State Option 1 Regional Option 1 State Option 2 Regional Option 2 State
ERCT 11.6 12.0 12.0 11.8 11.8 3.6% 3.9% 1.9% 2.2%
FRCC 10.9 11.5 11.5 11.1 11.2 4.7% 5.6% 1.5% 2.7%
MROE 10.5 10.9 11.1 10.7 10.8 4.0% 5.9% 1.7% 3.3%
MROW 9.4 9.8 9.8 9.6 9.7 4.3% 4.3% 2.6% 3.2%
NEWE 15.1 15.1 15.3 14.9 15.1 -0.1% 1.0% -1.4% -0.6%
NYCW 19.9 20.1 20.1 20.0 19.9 1.0% 0.8% 0.4% -0.4% Comment [A42]: Some areas seem to have relatively higher percent price change from reference – might be helpful to provide insight (if any) as to why.
EPA: Language added where appropriate.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 10 1-1 NYLI 16.9 16.5 16.3 16.5 16.3 -2.3% -3.3% -2.5% -3.5%
NYUP 14.2 14.2 14.2 14.2 14.0 0.0% -0.4% -0.3% -1.8%
RFCE 12.4 12.8 12.9 12.6 12.7 3.4% 4.2% 1.4% 1.9%
RFCM 10.8 11.2 11.2 11.0 11.0 3.8% 4.5% 2.0% 2.2%
RFCW 11.2 11.3 11.3 11.2 11.2 0.4% 0.6% -0.1% 0.2%
SRDA 9.5 9.9 9.9 9.7 9.7 4.6% 4.5% 2.2% 1.8%
SRGW 10.4 10.3 10.2 10.3 10.4 -1.2% -1.6% -0.9% -0.5%
SRSE 10.4 10.7 10.8 10.5 10.5 3.2% 4.0% 1.0% 1.5%
SRCE 8.1 8.4 8.4 8.2 8.2 2.6% 2.7% 1.0% 0.7%
SRVC 10.4 10.7 10.6 10.5 10.5 3.0% 2.5% 1.3% 0.9%
SPNO 10.2 11.5 10.5 10.9 10.3 12.7% 3.1% 6.8% 1.2%
SPSO 9.1 9.7 9.9 9.4 9.6 7.2% 9.2% 3.7% 5.6%
AZNM 11.5 11.4 11.7 11.3 11.6 -0.2% 2.1% -1.0% 1.0%
CAMX 14.1 14.7 14.7 14.4 14.4 4.2% 3.9% 2.1% 2.2%
NWPP 7.4 7.7 7.7 7.6 7.5 2.8% 2.8% 1.5% 1.4%
RMPA 9.9 9.9 10.3 9.9 10.0 0.7% 4.7% 0.5% 1.8% Contiguous U.S. 10.9 11.2 11.3 11.1 11.1 2.7% 3.1% 1.1% 1.4%

Figure 3- 58 . Electricity Market Module Regions

Source: EIA < http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf >.

1.6.10 3.7.10 Projected Electricity Bill Impacts The electricity price changes addressed in section 3.7.9 combine with the significant policy-induced reductions in electricity demand to affect average electricity bills. The estimated Comment [A43]: Some areas seem to have relatively higher percent price change from reference – might be helpful to provide insight (if any) as to why.
EPA: See previous comment.

Formatted: 03 Second-Level Subheading BOLD, Space After: 0 pt, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 11 1-1 changes to average bills are summarized in Table 3-22 Table 3-25 . Under Option 1, EPA estimates an average bill increase of 2.7% to 3.2% in 2020 and an average bill decrease of 8.4% to 8.7% in 2030. Under Option 2, EPA estimates an average bill increase of 1.1% to 1.4% in 2020 and an average b ill decrease of 6.6% to 6,8% in 2030.

Table 3- 2225 . Projected Changes in Average Electricity Bills
2020 2025 2030 Option 1 Regional 2.7% -5.4% -8.7% Option 1 State 3.2% -5.3% -8.4% Option 2 Regional 1.1% -3.5% -6.8% Option 2 State 1.4% -3.2% -6.6%



### 3.7 3.8 Limitations of Analysis EPA’s modeling is based on expert judgment of various input assumptions for variables whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available information from engineering studies of air pollution controls, the ability to improve operating efficiency, and new capacity construction costs to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions.
The costs presented in this RIA include both the IPM-projected annualized estimates of private compliance costs as well as the estimated costs incurred by utilities and ratepayers to achieve demand-side energy efficiency improv ements. The IPM-projected annualized estimates of private compliance costs provided in this analysis are meant to show the increase in production (generating) costs to the power sector in response to the final rule. To estimate these annualized costs, EPA uses a conventional and widely-accepted approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental operating expenses. The CRF is derived from estimates of the cost of capital (private discount rate), the amount of insurance coverage required, local property taxes, and the life of capital. 37
The demand-side energy efficiency costs are developed based on a review of energy efficiency data and studies, and expert judgment. EPA recognizes that significant variation exists

37 See Chapter 8 of EPA’s Base Case usi ng IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 12 1-1 in these analyses reflecting data and methodological limitations. The method used for estimating the demand-side energy efficiency costs is discussed in more detail in the Greenhouse Gas Abatement Measures TSD. The TSD also discusses the economic literature of energy efficiency costs and related considerations, energy savings potential studies, and discusses the associated uncertainties. The evaluation, measurement and verification (EM&V) of demand-side energy efficiency is addressed in the State Plan Considerations TSD.
The base case electricity demand in IPM v.5.13 is calibrated to reference case demand in AEO 2013. AEO 2013 demand reflects, to some degr ee, a continuation of the impacts of state demand-side energy efficiency policies but does not explicitly represent many of the existing state policies in this area (e.g., energy effi ciency resource standards). To some degree the implicit representation of state policies in the EPA’s base case alters the impacts assessment, but the direction of change is not known with certainty . This issue is discussed in more detail in the
Greenhouse Gas Abatement Measures TSD.
Cost estimates for the proposed rule are ba sed on rigorous power sector modeling using ICF’s Integrated Planning Model. 38 IPM assumes “perfect foresigh t” of market conditions over the time horizon modeled; to the extent that ut ilities and/or energy regulators misjudge future conditions affecting the economics of pollution control, costs may be understated as well.



### 3.8 3.9 Significant Energy Impacts
The proposed rule (Option 1) would have a significant impact according to E.O. 13211: Actions that Significantly Affect Energy Supply, Distribution, or Use. Under the provisions of this rule, EPA projects that approximately 46 to 49 GW of additional coal-fired generation (about 19% of all coal-fired capacity and 4.6% of total generation capacity in 2020) may be removed from operation by 2020.

38 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling >. Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering

Comment [A44]: It should be clarified that this is incremental to base case number of retirements.

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

3 -A- 13 1-1 EPA also projects the average delivered coal price decreases by 16.3% to 16.5% with decreased production of 208 to 228 million tons (24.6% to 27.7% of US production) in 2020 and that electric power sector delivered natural gas prices will increase by about 9.3% to 11.5% with increased power sector consumption of between 979 to 1,194 billion cubic feet (BCF) in 2020. Average retail electricity price are projected to increase in the contiguous U.S. by 5.9% to 6.5%
in 2020.
3.10 Monitoring, Reporting, and Recordkeeping Costs EPA projected monitoring, reporting and recordkeep ing costs for both state entities and affected EGUs for the compliance years 2020, 2025 and 2030 . In calculating the costs for state entities, EPA estimated personnel costs to oversee compliance, and review and report annually to EPA on program progress relative to meeting the state’s reduction goal. To calculate the national costs, EPA estimated that 49 states and 1, 228 facilities would be affected.
In calculating the cost for affected EGUs to comply, EPA assumed that the state plan would utilize a rate-based emission limit. The EPA estimated that the majority of the cost to EGUs would be in calculating net energy output. Since the majority of EGUs do have some energy usage meters or other equipment available to them, EPA believes a new system for calculating net energy output is not needed. Howe ver, an affected EGU will still need to be modify their equipment to comply with a ra te-based limit as described in the emission guidelines. The EPA estimates that it would take 3 working months for a technician to retrofit any existing energy meters to meet the require ments set in the state plan. Additionally EPA believes that 1 hour will be needed for each EGU operator to read the rule and understand how the facility will comply with the rule. Also, af ter all modifications are made at a facility to measure net energy output, each EGUs Data Acquisition System (DAS) would need to be upgraded to supply the rate-based emissions value to either the state or EPA’s Emissions Collection and Monitoring Plan System (ECMPS). Please note the costs to develop net energy output monitoring and to upgrade each facility’s DAS system are one-time costs incurred in 2020. Recordkeeping and reporting costs substantially decrease for the period 2021-2030. The projected costs for 2020, 2025, and 2030 are summarized below.
In calculating the cost for states to comply, EPA estimates that each state will rely on the equivalent of 2 full time staff to oversee program implementation, assess progress, develop Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering, Tab stops: 0", Left***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 14 1-1 possible contingency measures, state plan revi sions and the subsequent public meetings if revisions are indeed needed, download data from the ECMPS for their annual reporting and develop their annual EPA report.
Table 3-2322. Years 2020, 2025 and 2030: Summary of Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$)

Nationwide Totals Total Annual Labor Burden (Hours) Total Annual Labor Costs Total Annualized

Capital

Costs Total Annual O&M Costs Total Annualized Costs Year 2020 852,160 $61,583,900 $0 $2,701,100 $64,285,000 Year 2025 217,280 $8,215,240 $0 $638,500 $8,853,740 Year 2030 217,280 $8,215,240 $0 $638,500 $8,853,740 The annual costs of this proposal Option1 and regulatory alternative Option 2 including monitoring reporting and recordkeeping costs are shown in Table 3-23 below. Table 3-2324. Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirem ents (billions of 2011$)
2020 2025 2030 Option 1 Regional 5.5 4.6 7.3 Option 1 State 7.5 5.5 8.8
Option 2 Regional 4.3 4.5 1.9
Option 2 State 5.5 5.5 3.1 Source: Integrated Planning Model run by EPA, 2014. Monitoring, reporting and recordkeeping costs calculated outside IPM.



### 3.9 3.11 Social Costs As discussed in the EPA Guidelines for Preparing Economic Analyses, social costs are the total economic burden of a regulatory action. This burden is the sum of all opportunity costs incurred due to the regulatory action, where an opportunity cost is the value lost to society of any goods and services that will not be produced and cons umed as a result of reallocating some resources towards pollution mitigation. Estimates of social costs may be compared to the social benefits expected as a result of a regulation to assess its net impact on society. The social costs of a Formatted: Indent: First line: 0"

Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 15 1-1 regulatory action will not necessarily be equivalent to the expenditures associated with compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section provides a qualitative discussion of the relation ship between social costs and compliance cost estimates presented in this chapter.
For the illustrative compliance scenarios cost esti mates presented in this Chapter are the sum of expenditures on end-use energy efficiency programs and the change in expenditures required by the electricity sector to comply with the proposed guidelines. These two components are estimated separately. The expenditures required to achieve the assumed demand reductions through end-use energy efficiency programs are estimated using historical data and expert judgment. The change in the expenditures required by the electricity sector to meet demand and maintain compliance are estimated by IPM and reflect both the reduction in electricity production costs due to the reduction in demand caused by the end-use energy efficiency programs and the increase in electricity produc tion costs required to achieve the additional emission reductions necessary to comply with the state goals.39
As described in section 3.6.1, the illustrative s cenarios generally assume that, in achieving their goals, states adopt energy efficiency programs which lead to net demand reductions in each year equivalent to those applied in the calculation of their respective goals. The estimated expenditures required to achieve those net demand reductions through end-use energy efficiency programs are presented in this chapter and detailed in the GHG Abatement Measures TSD chapter on end-use energy efficiency. The social cost of achieving these energy savings comes in the form of increased expenditures on technologi es and/or services that are required to lower end-user’s electricity consumption beyond the business as usual. Under the assumption of complete and well-functioning markets the ex penditures required to reduce electricity consumption on the margin will represent society’ s opportunity cost of the resources required to produce the energy savings.

39 As described in section 3.5, IPM provides the least-co st solution to attaining the constraints representing the regulation required used to achieve the state goals but for the component of the state goals that is achieved by end use energy efficiency programs.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 16 1-1 The social cost of achieving these net electricity demand reductions may differ from the expenditures associated with the end-use energy efficiency programs. For example, some participants in end-use energy efficiency programs might have chosen to adopt the energy efficiency improvements even in the absence of the program. 40 Therefore the expenditures, except for any program administration expenditures, associated with those demand reductions are not part of the program’s social costs, as they would have accrued even in its absence. 41 The compliance cost estimates for the illustrative scenar ios includes this limited share of the program costs that is expended on end-users who would have reduced their demand in the absence of the program. Thus, with respect to this particular issu e, the social cost of the illustrative scenarios would be less than the estimated compliance cost reported in Table 3-9 to a limited degree. If the program expenditures that do not lead to additional demand reductions are paid for by electricity rate or tax payer funded programs then that portion of program expenditures represents a transfer among electricity consumers and has no net welfare gain or loss to society as a whole.

Due to the flexibility held by states in implementing their compliance with the proposed standards these energy efficiency expenditures may be borne by end-users through direct participant expenditures or electricity rate increas es or by producers through reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the distributional impact of potential compliance strategies, it does not necessarily affect the opportunity cost required for the production of the energy savings from a social perspective. However, specific design elements of demand-side or end-use energy

40 For example if a rebate program is offered for energy effi cient appliances, some participants that claim the rebate may have purchased the more energy efficient applian ces even without the rebate and so the rebate and investment made by these end-users is not a social cost of the program.

41 The demand reductions assumed in the illustrative co mpliance scenarios only include those additional demand reductions motivated by the program costs. That is , the demand reductions assumed for the illustrative compliance scenarios already account for the potential that some program costs do not lead to additional energy efficiency investments by end-users. For more info rmation see the GHG Abatement Measures TSD chapter on end-use energy efficiency.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 17 1-1 efficiency programs included to address distributional outcomes may have an effect on the economic efficiency of the programs and therefore the social cost.
Another reason the expenditures associated with end-use energy efficiency programs may differ from social costs is due to differences in the services provided by more energy efficient technologies and services adopted under the program relative to the baseline. For example, if under the program end-users adopted more energy efficient products which were associated with quality or service attributes deemed less desirable, then there would be an additional welfare loss that should be accounted for in social costs but is not necessarily captured in the measure of expenditures. However, there is an analogous (and more common) possibility that in some cases the quality of services, outside of the energy sa vings, provided by the more energy efficient products and practices are deemed more desirable by some end-users. In either case these real welfare impacts are not fully captured by end- use energy efficiency expenditure estimates.
The fact that such quality and service differences may exist in reality but may not be reflected in the price difference between more and less energy efficient products is one potential hypothesis for the energy paradox. The energy paradox is the observation that end-users do not always purchase products that are more energy efficient when the additional cost is less than the reduction in the net present value of expected electricity expenditures achieved by those products.42 Such circumstances are present in the analysis presented in this chapter, whereby in some regions the baseline and illustrative scenarios suggest that cost of reducing demand through energy efficiency programs is less than the retail el ectricity price. In additi on to heterogeneity in product services and consumer preferences, there are other explanations for the energy paradox, falling both within and outside the neoclassical rational expectations paradigm that is used in benefit/cost analysis. The end-use energy effi ciency chapter of the GHG Abatement Measures TSD discusses the energy paradox in detail and provides additional hypothesis for why consumers may not make energy efficiency investments that ostensibly seem to be in their own interest. The TSD discussion also provides details on how the presence of additional market failures can lead to levels of energy efficiency investment that may be too low from society’s

42 An analogous situation is present when some EGUs ha ve assumed to have the ability to make heat rate improvements at a capital cost that is less th an the anticipated fuel expenditure savings.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3 -A- 18 1-1 perspective even if that is not the case for the end-user. In such cases there is the potential for properly designed energy efficiency programs to address the source of under-investment, such as principal-agent problems where there is a disconnect between those making the purchase decision regarding energy efficient investments an d energy use and those that would receive the benefits associated with reduced energy use through lower electricity bills.
The other component of compliance cost reported in this chapter is the change in resource cost (i.e., expenditures) required by the electricity sector to fulfill the remaining demand while making additional CO2 emissions intensity reductions necessary to comply with the state goals. Included in the estimate of these compliance costs, developed using IPM, are the cost savings associated with the reduction in required elect ricity generation due to the demand reductions from end-use energy efficiency programs and improvements in heat rate. By shifting the demand curve for electricity, energy efficiency programs reduce the production cost in the sector. The
resource cost estimates from IPM therefore account fo r the increased cost of providing electricity at a lower average emissions rate net of the reduction in production costs due to lower demand from end-use energy efficiency programs.
Under the assumption that impacts outside the elect ricity market and markets providing inputs to the electricity sector do not meaningfully affect the prices in those markets, and the assumption of fixed electricity demand, then the social cost s associated with the regulatory action would be equal to the resource costs measured by IPM (net of tax and subsidy payments and recovery of sunk costs). Under these assumptions the change in compliance cost will equal the reduction in producer 43 and consumer surplus 44 in the electricity sector from the pre-regulatory action market equilibrium. However, IPM forecasts production and price changes in fuel markets, which implies that there are changes in producer and consumer surplus in those markets, and therefore the resource cost estimate by IPM may differ from the social cost for this reason. For a

43 Producer surplus is a welfare measure representing the amount gained by producers from selling output at a market price higher than the price they are willing to accept.

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

3 -A- 19 1-1 theoretically consistent method for estimating partial-equilibri um changes in welfare where prices and outputs change in multiple mark ets see Chapter 9 of Just et al. (2004).

3.12 References US EPA (2010) EPA Guidelines for Preparing Economic Analyses, Chapters 8 and Appendix A. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.html To be completed Formatted: 02 First-Level Subheading BOLD, Space After: 0 pt, Add space between paragraphs of the same style, No bullets or numbering9-0Page 1: [1] Comment [A2] Author 

### Because IPM does not model electricity demand? How does EPA model changes in electricity demand due to price
increases?

EPA: While IPM is capable of modeling price-drive demand response, we have employed a different methodology in keeping with the core elem ents of BSER, and end-use EE in particular. The inclusion of EE into the analysis is based on a broader policy framework that seeks to recognize the important role EE has in reducing EGU emissions, and is premised on policy interventions that seek to harne ss this important resource, which is widely acknowledged as hampered by market failures and other barriers. We would not expect a market simulation model to successfully simulate this potential precisely because policies to circumvent mark et failures are necessary to realize these energy savings in practice.

Page 23: [2] Comment [A26] Author 

### Capacity factors of existing NGCC seem relatively low – compared to BSER re-dispatch up to 70%? Also, not clear as to why regional scenarios would not result in higher NGCC capacity factors? Suggest clarifying/explaining these results.



## EPA: In a compliance setting, the system will find the optimal balance and least cost solution to meeting state goals. With some additional coal retirements, lower elect ric demand, new RE, etc, lower levels of redispatch ar e projected (compared to the BSER level of 70%). Regional scenarios result in slightly fewe r reductions, due to flexibilities afforded when states can help other states meet their goals, where it is cost effective. Language was added were appropriate.

Page 23: [3] Comment [A27] Author 

### I have to admit to some confusion be tween the “reference” case and the “baselin e.” I went back in the text and looked for the distinction, but could not find it. Suggest th at if there is a distinction between a reference case and a baseline case, then this would be a good place to put in a footnote, or change this text to “baseline.”

EPA: Revised to “base case.”***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-1 

### CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN HEALTH CO-BENEFITS

4.1 Introduction Implementing the proposed Emission Guidelines for Greenhouse Gas (GHG) Emissions from Existing Stationary Sources: Electric Utility Generating Units (EGUs) (hereafter, EGU GHG Existing Source Guidelines) is expected to reduce emissions of carbon dioxide (CO 2 ) and have ancillary human health benefits (i.e., co-benefits) associated with lower ambient
concentrations to of criteria air pollutants. This chapter describes the methods used to estimate the monetized climate benefits and the monetized air pollution health co-benefits associated with reducing exposure to ambient fine particulate matter (PM 2.5 ) and ozone by reducing emissions of precursor pollutants (i.e., sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and directly emitted
PM 2.5 ). Data, resource, and methodological limitations prevent EPA from monetizing the benefits from several important co-benefit cate gories, including reducing direct exposure to SO 2 , NO 2 , and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. We qualitatively discuss these unquant ified benefits in this chapter.

This chapter provides estimates of the moneti zed climate benefits and air pollution health
co-benefits associated with emission reductions for two options with two illustrative compliance scenarios across several analysis years and discount rates. The estimated benefits associated with these emission reductions are beyond those achieved by previous EPA rulemakings, including the Mercury and Air Toxics Standards (MATS).

4.2 Estimated Climate Benefits from CO 2 The primary goal of the proposed guidelines is to reduce emissions of CO 2 . In this section, we provide an overview of the public health and public welfare effects associated with climate change. We also provide information regarding the economic valuation of CO 2 using the Social Cost of Carbon (SCC), a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given year. Table 4-1 summarizes the quantified and unquantified climate benefits in this analysis. Style Definition: Style biblio + Line spacing: single: Line spacing: Multiple 1.3 li Style Definition: Footnote 10 font: Line spacing: single Style Definition: Footnote Text,Footnote Text Char1,Footnote Text Char Char,Footnote Text Char1 Char Char,Footnote Text Char Char Char Char,Footnote Text Char Char1,ALTS FOOTNOTE,fn,Char,Char4,Footnote Text - Preamble,Char2,Footnote Text - Preamble1,Char3,ft: Indent: Before: 0", Hanging: 0.2", Space After: 6 pt Style Definition: 01 ChapterTitle***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-2 Table 4-1. Climate Effects Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Environment Reduced climate effects Global climate impacts from CO 2 —  SCC TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA * Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC *

* We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.
4.2.1 Public Health Effects from Climate Change Climate change threatens public health in a number of ways: direct temperature effects, the effect of higher atmospheric CO 2 concentrations on characteristics of air quality, changes in vector-borne diseases, and changes in the severity and frequency of extreme weather events. This section summarizes the 2009 Endangerment Findi ng’s conclusions for each of these effects in

turn. 1

Heat is already the leading cause of weather-re lated deaths in the United States, and heat
waves are associated with marked short-term incr eases in mortality and morbidity. It has already been observed that unusually hot days and heat waves are becoming more frequent, and that unusually cold days are becoming less frequent. In the future, severe heat waves are projected to further intensify in magnitude a nd duration over the portions of the United States where these events have already been observed. If this observed warming trend continues as projected, it is expected that heat-related mortality and morbid ity will increase. Different segments of the population are sensitive for different reasons, with older adults, the chronically sick, the very young, city-dwellers, and those taking medications that disrupt thermoregulation being the most sensitive to hot temperatures. Other groups that ha ve also been found to be sensitive to hot temperatures are the mentally ill, those lack ing access to air conditioning, those working or playing outdoors, and socially isolated persons . With continued warming over time, these adverse effects would be expected to increase as serious heat events become more frequent, prolonged, and extreme.

1 For more information on the impacts of climate change see the 2009 Endangerment Finding http://www.epa.gov/climatechange/endangerment/ Formatted Table

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

4-3 Conversely, increases in temperature are also expected to lead to some reduction in the risk of death, related to extreme cold. Evidence suggests that the net impact of changes in heat and cold mortality is likely to be adverse. The latest report from the U.S. Global Change Research Program (USGCRP, 2009) references a study by Medina-Ramon and Schwartz (2007) that analyzed daily mortality and weather data in 50 U.S. cities from 1989 to 2000. This report found that, on average, cold snaps in the United States increased death rates by 1.6 percent, while heat waves triggered a 5.7 percent increase in death rate s. While a single study is not conclusive, this study finds that increases in heat-related morta lity, due to global warming in the United States, are likely to be greater than decreases in cold-related mortality. The tendency for mortality rates to increase in winter occurs for reasons which are not strongly correlated with cold temperatures, such as influenza. Analyses of the effects of warmer winters on mortality must consider these relationships to avoid overestimating the effects of temperature increases during that season.

Climate change is expected to increase regional ozone pollution, relative to ozone levels in the absence of climate change. Increased levels of ozone are associated with risks of respiratory illnesses and premature death. In addition to human health effects, tropospheric ozone has significant adverse effects on crop yields, pasture a nd forest growth, and the composition of plant and animal species populations, pasture and fore st growth, and the composition of plant and animal species populations.

Peer reviewed modeling studies discussed in EPA’s Interim Assessment (U.S. EPA, 2009a) show that climate change is likely to cause increases in summertime ozone concentrations over substantial regions of the country, especially on the highest ozone days and in the largest metropolitan areas with the worst ozone problems. This effect was not uniform across the country, with some areas projected to have little change or slight decreases, though the decreases are expected to be less pronounced and occur in less popul o us regions than the increases. The key metric for regulating U.S. air quality is the maximum daily 8-hour average ozone concentration. For those regions where modeli ng projected climate-induced increases, the increase in 2050 was in the range of 2 to 8 ppb, averaged over the summer season. The increases were substantially greater than 2 to 8 ppb during the peak pollution episodes that tend to occur over a number of days each summer.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-4 In addition to direct temperature health effects and climate change induced air quality effects, there is also the potential for increased mortality rates, injuries, infectious diseases, and
stress-related disorders re sulting from more frequent extreme weather events.

Climate change is expected to lead to an increase in the frequency of heavy precipitation events, which would be associated with increas ed risk of deaths and injuries, as well as infectious, respiratory and skin diseases. Floods are low-probability, high-impact events that can overwhelm physical infrastructure, human resilience, and social organization. Floods can result in health impacts such as deaths, injuries, inf ectious diseases, toxic contamination, and mental health problems.

Climate change is expected to lead to increas es tropical cyclone intensity (hurricanes and tropical storms). Increases in tropical cyclone in tensity are linked to increases in the risk of deaths, injuries, waterborne and food borne diseas es, as well as post-traumatic stress disorders. Storm surge is the leading cause of death in coas tal storms, and the risk of death by drowning due to surge is expected to increase with rising sea levels and increased storm intensity as the result of climate change. Projected increases in flooding due to increased intensity of cyclonic events is also expected to lead to adverse public health impacts as noted above.

According to the assessment literature, under changing climate and environmentalconditions there will also likely be an increase in the spread of serial episodes of food and waterborne pathogens among susceptible populations, depending on the pathogens’ survival, persistence, habitat range and transmission. Food borne diseases show some relationship with temperature. The range of some zoonotic disease carriers, such as the Lyme disease-carrying tick, may increase with temperature.

Increases in atmospheric CO 2 concentrations directly as well as changes in the climate are expected to stimulate plants to increase photosynthesis, biomass, water use efficiency, and reproductive effort resulting in greater growth and distribution of weeds, grasses, and trees. Such increases could lead to greater production, di stribution, dispersion, and allergenicity of aeroallergens. These changes in aeroallergens and subsequent human exposures could affect the prevalence and severity of allergy symptoms. Howe ver, the scientific literature does not provide
definitive data or conclusions on how climat e change might impact aeroallergens and***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-5 subsequently the prevalence of allergenic illnesse s in the U.S. The IPCC concluded that pollens are likely to increase with elevated temperature and CO 2 (IPCC, 2007).

4.2.2 Public Welfare Effects from Climate Change
As with public health, there are multiple pathways in which the greenhouse gas air pollution and resultant climate change affect climate-sensitive economic sectors and environmental media. These sectors include agriculture; forestry; water resources; sea level rise in coastal areas; energy, infrastructure, and se ttlements; and ecosystems and wildlife. Impacts from climate change will also occur outside of the United States. This section summarizes the 2009 Endangerment Finding’s conclusi ons for each of these effects.

As noted above, elevated CO 2 concentrations and modest temperature increases may stimulate plants to increase photosynthesis, bi omass, water use efficiency, and reproductive effort. These impacts and the potential for longer growing seasons may result in increased agricultural productivity. But, the same effects may also enhance pest and weed growth. Further temperature increases, possible changes to precipitation patterns and variability, and any increases in ground-level ozone induced by higher temperatures are expected to reduce agricultural productivity. A USGCRP report (2009) concluded that while for some crops such as grain and oilseed crops there may be, on net, a beneficial effect over the next couple decades, as temperature rises, these crops will begin to experience failure, especially if climate variability increases and precipitation lessens or becomes more variable. Changes in the intensity and frequency of extreme weather events, such as droughts and heavy storms, have the potential to negatively impact U.S. agriculture. Changes in pr ecipitation patterns, in addition to increasing temperatures and longer growing seasons, can also change the demand for irrigation requirements and could potentially increase irrigation demand.

With respect to livestock, higher temperatur es will very likely reduce livestock production during the summer season in some areas, but these losses will very likely be partially offset by warmer temperatures during the winter season. The impact on livestock productivity due to increased variability in weather patterns, will likely be far greater than the effects associated with an absolute change in average climatic conditions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-6 For the forestry sector, the effects are similar to the agriculture sector. There is the potential for beneficial effects due to elevated concentrations of carbon dioxide, modest temperature increases, and nitrogen deposition, but there is also the potential for adverse effects from higher temperature increases, changing precipitation patterns, increased populations and spread of insects and disease, and the potential for more frequent and severe extreme weather events. According to the science assessment reports upon which the Administrator relied for the 2009 Endangerment Finding, climate change ha s very likely increased the size and number of wildfires, insect outbreaks, and increased tree mort ality in the Interior West, the Southwest, and Alaska, and will continue to do so.

If existing trends in precipitation continue, it is expected that forest productivity will likely decrease in the Interior West, Southwest, eastern portions of the Southeast and Alaska. F Forest productivity will likely increase in the northeastern United States, Lake States, and in western portions of the Southeast. An increase in forecast drought events, as a result of climate change, is also likely to reduce forest productivity where such events occur.

The connection of water resources to climate change is very important, given the increasing demand for adequate water supplies and services for agricultural, municipal, energy, and industrial uses, coupled with existing strain s on this scarce resource in many parts of the country. According to the assessment literature , climate change has already altered, and will likely continue to alter the water cycle, affecting where, when, and how much water is available for all uses. With higher temperatures, the water-holding capacity of the atmosphere and evaporation into the atmosphere increase, and this favors increased climate variability, with more intense precipitation and more droughts.

Climate change is and will continue to cause the shrinking of the snowpack, induced by increasing temperatures. In the western United St ates, there is already well-documented evidence of a shrinking snowpack due to climate warming. Earlier meltings, with increased runoff in the winter and early spring, increase flood concerns and also result in substantially decreased summer flows. This pattern of reduced snowpack and changes to the flow patterns pose serious risks to major population regions, such as California, which rely on snowmelt-dominated watersheds for their water supply. Increased precipitation is expected to increase total water flow***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-7 in some eastern areas leading to increased flood risk and potential water pollution. Despite the increased average flow, increased variability may still enable periods of drought.

Climate change will likely further constrain ov er-allocated water resources in some regions of the United States, increasing competition among agricultural, municipal, industrial, and ecological uses. Increased incidence of extrem e weather and floods may also overwhelm or damage water treatment and management systems, resulting in water quality problems.

According to the assessment literature, sea level is rising along much of the U.S. coast and the rate of change will very likely increase in the future, exacerbating the impacts of progressive inundation, storm-surge flooding, and shoreline erosion. A large percentage of the U.S. population lives in these coastal areas. The most vulnerable areas are the Atlantic and Gulf Coasts, the Pacific Islands, and parts of Alaska . Cities such as New Orleans, Miami, and New York are particularly at risk, and could have diffi culty coping with the sea level rise projected by the end of the century under a higher emissions scenario. Population growth and the rising value of infrastructure increases the vulnerability of coastal areas to climate variability and future climate change. Adverse impacts on islands present concerns for Hawaii and the U.S. territories. Reductions in Arctic sea ice increases extreme coastal erosion in Alaska, due to the increased exposure of the coastline to strong wave action. In the Great Lakes, where sea level rise is not a concern, both extremely high and low water levels, resulting from changes to the hydrological cycle, have been damaging and disr uptive to shoreline communities.

Coastal wetland loss is being observed in the United States where these ecosystems are squeezed between natural and artificial landward boundaries and rising sea levels. Up to 21 percent of the remaining coastal wetlands in the U. S. mid-Atlantic region are potentially at risk of inundation between 2000 and 2100. Stress will increase on coastal habitats through the interaction of climate change with developm ent and pollution relate d to development.

Although increases in mean sea level over the 21 st century and beyond are projected to inundate unprotected, low-lying areas, the most devastating impacts are likely to be associated with storm surge. In combination, expected rates of sea level rise, projected storm intensity, wave height, and storm surge all suggest more severe coastal flooding and erosion hazards. Higher sea level provides an elevated base from which storm surges occur and diminishes the***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-8 rate at which low-lying areas drain, increasing the risk of flooding from rainstorms. In New York City and Long Island, flooding from a combination of sea level rise and storm surge could be several meters deep. Projections suggest that the recurrence period of a 100-year flood event in this area might be reduced to 4–60 years by the 2080s. Additionally, some major urban centers in the U.S., such as areas of New Orleans are si tuated in low-lying flood plains, presenting increased risk from storm surges.

With respect to infrastructure, climate change vulnerabilities of industry, settlement, and society are more related to changes in the intens ity and frequency of extreme weather events than gradual changes in temperature. Extreme weather events could threaten U.S. energy infrastructure (transmission and distribution), tr ansportation infrastructure (roads, bridges, airports and seaports), water infrastructure, and other built aspects of human settlements. Moreover, soil subsidence caused by the melting of pe rmafrost in the Arctic region is a risk to gas and oil pipelines, electrical transmiss ion towers, roads, and water systems.

Within settlements experiencing climate change stressors, certain parts of the population may be especially vulnerable based on their circumstances. These include the poor, the elderly, the very young, those already in poor health, the disabled, those living alone, and/or indigenous populations dependent on one or a few resources. In Alaska, indigenous communities are likely to experience disruptive impacts, including shifts in the range or abundance of wild species crucial to their livelihoods and well-being.

Climate change is exerting major influences on natural environments and biodiversity, and these influences are generally expected to grow with increased warming. Observed changes in the life cycles of plants and animals include shifts in habitat ranges, timing of migration patterns, and changes in reproductive timing and behavior.

The underlying assessment literature finds with high confidence that substantial changes in the structure and functioning of terrestrial ecosystems are very likely to occur with global warming greater than 2 to 3 °C above pre-industrial levels, with predominantly negative consequences for biodiversity and the provisioning of ecosystem goods and services. With global average temperature changes above 2 °C, many terrestrial, freshwater, and marine species (particularly endemic species), are at far greater risk of extinction than in the geological past.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-9 Climate change and ocean acidification will likely impair a wide range of planktonic and other
marine calcifiers such as corals. Even without oc ean acidification effects, increases in sea surface temperature of about 1 to 3 °C are projected to result in more frequent coral bleaching events and widespread coral mortality. In the Arctic, wildlife faces great challenges from the effects of climatic warming, as projected reductions in s ea ice will drastically shrink marine habitat for polar bears, ice-inhabiting s eals, and other animals.

Some common forest types are projected to expand, others are projected to contract, and others, such as spruce-fir, are likely to disappear from the contiguous United States. Changes in plant species composition in resp onse to climate change can incr ease ecosystem vulnerability to other disturbances, including wild fires and biological invasion. Di sturbances such as wildfires and insect outbreaks are increasing in the United States and are likely to intensify with warmer winters, drier soils and longer growing seasons in the future. The areal extent of drought-limited ecosystems is projected to increase 11 percent per °C warming in the United States. In California, temperature increases greater than 2 °C may lead to conversion of shrubland into desert and grassland ecosystems and evergreen conifer forests into mixed deciduous forests. Greater intensity of extreme events may alter disturbance regimes in coastal ecosystems leading to changes in diversity and ecosystem functionin g. Species inhabiting salt marshes, mangroves, and coral reefs are likely to be particularly vulnerable to these effects.

According to the USGCRP report of June 2009 and other sources, climate change impacts in certain regions of the world may exacerbate problems that raise humanitarian, trade, and national security issues for the United States. 2 The IPCC (2007) identifies the most vulnerable regions of the world as:

1. Africa, especially the sub-Saharan region, because of current low adaptive capacity as

well as climate change

2. Small islands due to high exposure of population and infrastructure to risk of sea-level rise and increased storm surge

2 ‘‘In an increasingly interdependent world, U.S. vulnerability to climate change is linked to the fates of other nations. For example, conflicts or mass migrations of people resulting from food scarcity and other resource limits, health impacts or environmental stresses in other parts of the world could threaten U.S. national security.’’ (Karl et al., 2009).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-10 3. Asian mega-deltas due to large populations and high exposure to sea level rise, storm surge, and river flooding

4. The Arctic because of the effects of high rates of projected warming on natural systems Climate change has been described as a potent ial threat multiplier wi th regard to national security issues. While some of these internationa l risks do not readily le nd themselves to precise analyses or future projections, given the unavoidable global nature of the climate change problem it is appropriate and prudent to consider how impacts in other world regions may present risks to the U.S. population.

4.2.3 Social Cost of Carbon
We estimate the global social benefits of CO 2 emission reductions expected from the proposed guidelines using the federal government’s SCC estimates. 3 The SCC is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given year. It includes a wide range of anticip ated climate impacts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is typically used to assess the a voided damages as a result of regulatory actions (i.e., benefits of rulemakings that have an incremental achieve marginal impact on cumulative global reductions in CO 2 emissions).

The federal estimates of SCC used in this analysis were developed over many years, using the best science available, and with input from the public. An interagency process that included
the EPA and other executive branch entities used three integrated assessment models (IAMs) to develop SCC estimates and selected four global values for use in regulatory analyses. The federal government first released the estimates in February 2010 and updated them in 2013 using
new versions of each IAM. The 2010 SCC T echnical Support Document (SCC TSD) provides a

3 The federal government concluded that a global measure of the benefits from reduci ng U.S. GHG emissions should be the standard practice when conduc ting regulatory impact analysis in support of federal rule makings. See Technical Support Document: Technical Update of the Soc ial Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon (2013) and Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Wo rking Group on Social Cost of Carbon (2010).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-11 complete discussion of the methods used to develop the estimates, and the 2013 SCC TSD presents and discusses the updated estimates. 4,5

Notably, the 2013 process did not revisit the 2010 interagency modeling decisions (e.g., with regard to the discount rate, reference case socioeconomic and emission scenarios or equilibrium climate sensitivity). Rather, improv ements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves and used in peer-reviewed publications. The model updates that are relevant to the SCC estimates include: an explicit representation of sea level rise damages in the Dynamic Integrated Climate and Economy (DICE) and Policy Analysis of the Greenhouse Effect (PAGE) models; updated adaptation assumptions, revisions to ensure damages are constrained by GDP, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in the DICE model; and updated damage functions for sea level rise imp acts, the agricultural sector, and reduced space heating requirements, as well as changes to the transient response of temperature to the buildup
of GHG concentrations and the inclusion of indire ct effects of methane emissions in the Climate Framework for Uncertainty, Negotiation, and Di stribution (FUND) model. The 2013 SCC TSD provides complete details.

When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenge s. A report from the National Academies of

4 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advi sers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, a nd Department of Treasury (February 2010). Available at: http://www.whitehouse.gov/sites/default/files/omb/in foreg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf

5 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, De partment of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised November 2013). Available at: http://www.whitehouse.gov/sites/default/files/omb/asse ts/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf

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

4-12 Science (NRC, 2009) points out that any assessme nt will suffer from uncertainty, speculation, and lack of information about (1) future emissi ons of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. 6 As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including theincomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatme nt of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Current integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of clim ate change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. The limited amount of research linking climate impacts to economic damages makes the modeling exercise
even more difficult. These individua l limitations do not all work in the same direction in terms of their influence on the SCC estimates, though taken together they suggest that the SCC estimates are likely conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that “It is very likely that [SCC estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts.”

Nonetheless, these estimates and the discussi on of their limitations represent the best
available information about the social benefits of CO 2 reductions to inform benefit-cost analysis.
The new versions of the models used to esti mate the values presen ted below offer some improvements in these areas, although further work remains warranted. Accordingly, the EPA and other agencies continue to engage in research on modeling and valuation of climate impacts with the goal to improve these estimates. Additional details are provided in the SCC TSDs.

6 National Research Council (2009). Hidden Costs of Ener gy: Unpriced Consequences of Energy Production and Use. National Academies Press. See docket ID EPA-HQ-OAR-2009-0472-11486.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-13 The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per
metric ton of CO 2 emissions in the year 2020 (2011$). 7 The first three values are based on the average SCC from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SCC
estimates for several discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred by different generations). The fourth value is the 95 th percentile of the SCC from all three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts from temperature change further out in the tails of the SCC distribution (representing less likely, but potentially catastrophic, outcomes).

Table 4-2 presents the updated global SCC estimates for the years 2015 to 2050. In order
to calculate the dollar value for emission reducti ons, the SCC estimate for each emissions year would be applied to changes in CO 2 emissions for that year, and then discounted back to the analysis year using the same discount rate used to estimate the SCC. 8 The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climate change. Note that the interagency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. Tables 4-3 through 4-5 report the incremental climate benefits estimated in three analysis years (2020, 2025, and 2030) for the two illustrative compliance scenarios (i.e., state and regional) for two options evaluated.

7 The 2010 and 2013 TSDs present SCC in 2007$. The estimates were adjusted to 2011$ using GDP Implicit Price Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-02/pdf/ECONI-2013-02-Pg3.pdf

. 8 This analysis co nsidered the climate impacts of only CO 2 emission change. As discussed below, the climate impacts of other pollutants were not calculated for the proposed guidelines. Furthermore, the U.S. Interagency Working Group on the Social Cost of Carbon has so far only considered estimates for the social cost of CO 2 . While CO 2 is the dominant GHG emitted by the sector, we reco gnize the representative facilities within these comparisons may also have different emission rates for ot her climate forcers that will serve a minor role in determining the overall social cost of generation.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-14 Table 4-2. Global Social Cost of CO 2 , 2015-2050 (in 2011$)*

Year Discount Rate and Statistic 5% Average 3% Average 2.5% Average 3% (95th percentile) 2015 $12 $39 $61 $116 2020 $13 $46 $68 $137 2025 $15 $50 $74 $153 2030 $17 $55 $80 $170 2035 $20 $60 $85 $187 2040 $22 $65 $92 $204 2045 $26 $70 $98 $220 2050 $28 $76 $104 $235 * The SCC values vary depending on the year of CO 2 emissions and are defined in real terms , i.e., adjusted for

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

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

Discount Rate and Statistic Option 1 – state Option 1 – regional Option 2 – state Option 2 - regional Million metric tonnes of CO 2 reduced 383 371 295 283 5% (average) $4.9 $4.7 $3.8 $3.6 3% (average) $18 $17 $14 $13 2.5% (average) $26 $25 $20 $19 3% (95 th percentile) $52 $51 $40 $39 * The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of climate impacts.

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

Discount Rate and Statistic Option 1 – state Option 1 - regional Option 2 – state Option 2 - regional Million metric tonnes of CO 2 reduced 506 501 376 368 5% (average) $7.6 $7.5 $5.6 $5.5 3% (average) $25 $25 $19 $18 2.5% (average) $37 $37 $28 $27 3% (95 th percentile) $77 $76 $57 $56 * The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of climate impacts.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-15 Table 4-5. Estimated Global Climate Benefits of CO 2 Reductions for Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$)*

Discount Rate and Statistic Option 1 – state Option 1 - regional Option 2 – state Option 2 - regional Million metric tonnes of CO 2 reduced 555 545 373 353 5% (average) $9.5 $9.3 $6.4 $6.0 3% (average) $31 $30 $21 $20 2.5% (average) $44 $44 $30 $28 3% (95 th percentile) $94 $92 $63 $60 * The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of climate impacts. It is important to note that the climate benef its presented above are associated with changes
in CO 2 emissions only. Implementing these guide lines, however, will have an impact on the emissions of other pollutants that will affect th e climate. The climate impacts of these other pollutants, however, can be complex, and have not been calculated for the proposed guidelines. 9

These other pollutants include other greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases and decreases) can be a direct result of changes in electricity generation, upstream fossil fuel extraction and transport, and/or downstream secondary market impacts. Reductions in black car bon or ozone precursors would lead to further cooling, but reductions in the other aerosol sp ecies and precursors would lead to warming. Therefore, changes in non-CO 2 pollutants could potentially au gment or offset the climate benefits calculated here. These pollutants can act in different ways and on different timescales than carbon dioxide. For example, aerosols refl ect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. These aerosols can also affect climate indirectly by altering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and acce lerating melting. In terms of lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of years, many of these other pollutants are short lived and remain in the atmosphere

9 The federal government’s SCC estimates are designed to as sess the climate benefits associated with changes in CO 2 emissions only.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-16 for short periods of time ranging from days to weeks and can therefore exhibit large spatial and temporal variability.

While the EPA has not quantified the climate impacts of these other pollutants for the proposed guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the compliance scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the pote ntial magnitude of changes relative to CO 2 emissions reductions anticipated at pow er plants. This assessment included CO 2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 emissions, such as emissions associated with dr illing, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH 4 emissions from natural gas systems and coal mines and CO 2 emissions from flaring of methane will likely decrease under the proposed guidelines. Furthermore, the changes in upstream
methane emissions are small relative to the ch anges in direct emissions from power plants.

4.3 Estimated Human Health Co-Benefits

In addition to CO 2 , implementing these proposed guidelines is expected to reduce
emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted fine particles. 10 Therefore, reducing these emissions would also reduce human exposure to ambient PM 2.5 and the incidence of PM 2.5 -related health effects. In addition, in the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Reducing NO X emissions would also reduce human exposure to ozone and the incidence of ozone-related health effects. Although we do no t have sufficient data to quantify these impacts in this analysis, reducing emissions of SO 2 and NOx would also reduce ambient exposure to SO 2 and NO 2 , respectively. In this section, we provide an overview of the monetized PM 2.5 and

10 We estimate the health co-benefits associated with emission reductions of two categories of directly emitted particles: elemental carbon plus organic carbon (EC+OC) and crustal. Crustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represen ts particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-17 ozone-related co-benefits estimated for the proposed guidelines. The estimated co-benefits
associated with these emission reductions are beyond those achieved by previous EPA rulemakings, including MATS. A full description of the underlying data, studies, and assumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d).

There are several important considerations in assessing the air pollution-related health cobenefits for a climate-focused rulemaking. Firs t, these estimated health co-benefits do not account for any climate-related air quality chan ges (e.g., increased ambient ozone associated with higher temperatures) but rather changes in precursor emissions affected by this rulemaking.Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM 2.5 -related health co-benefits would be impacted by excluding climate-related air quality changes since the scie nce is unclear as to how climate change may
affect PM 2.5 exposure. Second, the estimated health co-benefits also do not consider temperature
modification of PM 2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, the estimated climate benefits reported in this RIA reflect global benefits, while the estimated health co-benefits are calculated for the contiguous U.S. only. Excluding temperature modification of air pollution risks and internati onal air pollution-related h ealth benefits implies that the quantified health co-benefit s likely lead to underestimation.

Implementing these guidelines may lead to reductions in ambient PM 2.5 concentrations below the National Ambient Air Quality Standard s (NAAQS) for PM and ozone in some areas and assist other areas with attaining thes e NAAQS. Because the NAAQS RIAs (U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences worth noting in the design and an alytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a re vised air quality standard nationwide based on an array of emission reduction strategies for di fferent sources including known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction strategies that States may c hoose to enact when implementi ng a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, EPA’s NAAQS RIAs are merely illustrative and the estimated costs and benef its are not intended to be added to the costs***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-18 and benefits of other regulations that result in specific costs of control and emission reductions. However, it is possible that some costs and bene fits estimated in this RIA may account for the same air quality improvements as estim ated in the illustrative NAAQS RIAs.

Similar to NAAQS RIAs, the emission reduc tion scenarios estimated for the proposed guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the illustrative compliance scenarios would occur in one well-characterized sector (i.e., the EGU sector). In general, EPA is more confident in the magnitude and location of the emission reductions for implementation rules, which typically require specific emission reductions in a specific sector. As such, emission reductions achieved under promulgated implementation rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the
incremental costs and benefits associated with attaining revised future NAAQS. EPA does not re-issue illustrative RIAs outside of the rulemaking process that retroactiv ely update the baseline to account for implementation rules promulgated after an RIA was completed. For more information on the relationship between illustrativ e analyses, such as for the NAAQS and this proposal, and implementation rules, please see Section 1.3 of the PM NAAQS RIA (U.S. EPA, 2012a).

4.3.1 Health Impact Assessment for PM 2.5 and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b)
identified the human health effects associated with ambient PM 2.5 exposure, which include premature morality and a variety of morbidity effects associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical
Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with ambient ozone exposure, which include premature morality and a variety of morbidity effects associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified co-benefit categories captured in EPA’s health co-benefits estimates for reduced exposure to
ambient PM 2.5 and ozone. Although the table below does not list unquantified health effects such as exposure to SO 2 , NO 2 , and mercury nor welfare effects such as acidification and nutrient enrichment, these effects are described in deta il in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chap ter. It is important to emphasize that the list of unquantified benefit categories is not exhaustive , nor is quantification of each effect complete.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-19 Table 4-6. Human Health Effects of Ambient PM 2.5 and Ozone

Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Human Health Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)   PM ISA

Infant mortality (age <1)   PM ISA

Reduced incidence of morbidity from exposure to PM 2.5 Non-fatal heart attacks (age > 18)   PM ISA Hospital admissions—respiratory (all ages)   PM ISA Hospital admissions—cardiovascular (age >20)   PM ISA Emergency room visits for asthma (all ages)   PM ISA Acute bronchitis (age 8-12)   PM ISA Lower respiratory symptoms (age 7-14)   PM ISA Upper respiratory symptoms (asthmatics age 9-11)   PM ISA Asthma exacerbation (asthmatics age 6-18)   PM ISA Lost work days (age 18-65)   PM ISA Minor restricted-activity days (age 18-65)   PM ISA Chronic Bronchitis (age >26) — — PM ISA Emergency room visits for cardiovascular effects (all ages) — — PM ISA 1
Strokes and cerebrovascular disease (age 50-79) — — PM ISA 1 Other cardiovascular effects (e.g., other ages) — — PM ISA 2 Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc) — — PM ISA 2,3
Cancer, mutagenicity, a nd genotoxicity effects — — PM ISA 2,3
Reduced incidence of mortality from exposure to ozone Premature mortality based on short-term study estimates (all ages)   Ozone ISA Premature mortality based on long-term study estimates (age 30–99) — — Ozone ISA 1

Reduced incidence of morbidity from exposure to ozone Hospital admissions—respiratory causes (age > 65)   Ozone ISA Hospital admissions—respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted-activity days (age 18–65)   Ozone ISA School absence days (age 5–17)   Ozone ISA Decreased outdoor worker productivity (age 18–65) — — Ozone ISA 1 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA 2

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

1 We assess these co-benefits qualitatively due to data a nd resource limitations for this analysis, but we have quantified them in sensitivity analyses for other analyses.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods. Formatted: Footnote 10 font***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-20 3 We assess these co-benefits qualitatively because current evidence is only suggest ive of causality or there are other significant concerns over the strength of the association.

We follow a “damage-function” approach in calculating benefits, which estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming i ndependence of the values for those individual endpoints. Because EPA rarely has the time or resources to perform new research to measure directly, either health outcomes or their values for regulatory analyses, our estimates are based on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estima te benefits for the environmental quality change under analysis. Because we did not conduct scenario-specific air quality modeling for the proposed guidelines, wW e use a “benefit-per-ton” approach to estimate the PM 2.5 and ozone cobenefits in this RIA. This section describes the underlying basis for the health and economic valuation estimates, and the subsequent section describes generationprovides an overview of the benefit-per-ton estimates , which are described in detail in the appendix to this chapter

. The health impact assessment (HIA) quantifie s the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 and ozone. We use the environmental Benefits Mapping and Analysis Program (BenMAP) (version 4.0.66) to systematize health impact analyses by applying a database of key input parameters, including population projections, health impact functions, and valuation functions (Abt Associates, 2012). For this assessment, the HIA is limited to those health effects that are directly linked to ambient
PM 2.5 and ozone concentrations. There may be other indirect health impacts associated with reducing emissions, such as occupational health exposures. Epidemiological studies generally provide estimates of the relative risks of a partic ular health effect for a given increment of air pollution (often per 10 μg/m 3 for PM 2.5 or ppb for ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 to changes in the incidence of a health effect. We refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the epidemiology studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature mortality below. The size of the mortality eff ect estimates from epidemiological studies, the***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-21 serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant h ealth endpoint quantified in this analysis.

4.3.1.1 Mortality Concentra tion-Response Functions for PM 2.5 Considering a substantial body of published scie ntific literature and reflecting thousands of epidemiology, toxicology, and cl inical studies, the PM ISA documents the association between
elevated PM 2.5 concentrations and adverse health effects, including increased premature mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee of EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 2009c), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM 2.5 based on the entire body of scientific evidence. The PM ISA also
concluded that the scientific literature consis tently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. In addition to adult mortality discussed in more detail below, we use effect coefficients from Woodruff et al. (1997) to estimate PM-related infant mortality.

For adult PM-related mortality, we use the eff ect coefficients from the most recent epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al., 2012). The PM ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts provide the strongest evidence of the association between long-term PM 2.5 exposure and premature mortality with support from a number of additional cohort st udies. The SAB’s Health Effects Subcommittee (SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths and weaknesses, we present PM 2.5 co-benefits estimates using relative risk estimates from both these cohorts (Krewski et al ., 2009; Lepeule et al., 2012).

As a characterization of uncertainty regarding the adult PM 2.5 -mortality relationship, EPA graphically presents the PM 2.5 co-benefits derived from EPA’s expert elicitation study (Roman et al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-22 mortality among the adult U.S. population resulting from reductions in ambient annual average
PM 2.5 concentrations. In that study, twelve experts provided independent opinions regarding the
PM 2.5 -mortality concentration-response function. Because the experts relied upon the ACS and Six Cities cohort studies to inform their concentration-response functions, the benefits estimates derived from the expert responses generally fall between results derived from the these studies (see Figure 4-1). We do not combine the expert results in order to preserve the breadth and diversity of opinion on the expert panel. This presentation of the expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA emphasize that “scientific differences existed only with respect to the magnitude of the effect of
PM 2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports
the conclusion that the benefits of PM 2.5 control are very likely to be substantial”. Although it is possible that newer scientific literature could revi se the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.

4.3.1.2 Mortality Concentrati on-Response Functions for Ozone
In 2008, the National Academies of Science (NRC, 2008) issued a series ofrecommendations to the EPA regarding the quantification and valuation of ozone-related shortterm mortality. Chief among these was that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the commit tee recommended that “ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest emphasis be placed on the multicity and NMMAPS [National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the co-benefits of avoiding short-term ozone mort ality using the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect estimates from the three meta-analyses (Bel l et al. (2005), Levy et al. (2005), and Ito et al. (2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). 11 For simplicity, we report the ozone mortality estimates in this RIA as a

11 Since EPA received NAS advice, EPA published the Ozon e ISA (U.S. EPA, 2013b) and the second draft Ozone Health Risk and Exposure Assessment (U.S. EPA, 2014a). Th erefore, the ozone mortality studies applied in this analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most updated Formatted: Footnote Text,Footnote Text Char1,Footnote Text Char Char,Footnote Text Char1 Char Char,Footnote Text Char Char Char Char,Footnote Text Char Char1,ALTS FOOTNOTE,fn,Char,Char4,Footnote Text - Preamble,Char2,Footnote Text - Preamble1,Char3,ft, Indent: Before: 0", First line: 0"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-23range from Bell et al. (2004) to Levy et al. (2005) to represent the lowest and the highest cobenefits estimates based on these six ozone mortality studies. In addition, we graphically present
estimated co-benefits derived from all six studies mentioned above as a characterization of uncertainty regarding the ozone -mortality relationship in Figure 4-1.

4.3.2 Economic Valuation for Health Co-benefits
After quantifying the change in adverse health impacts, we estimate the economic value of these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. Therefore, the appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. For some health effects, such as hospital admissions, WTP estimates are generally not available, so we use the cost of treating or mitigating th e effect. These cost-of-illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the di rect expenditures related to treatment but not the value of avoided pain and suffering from the health effect. The unit values applied in this analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and over 90 percent of monetized ozone-related co-benefits. The economics literature concerning the appropriate method for valuing re ductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in th e risk of premature mortality is the subject of continuing discussion within the economics and public policy analysis community. Following
the advice of the SAB’s Environmental Econom ics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL ) approach in calculating estimates of mortality benefits, because we believe this calculation provi des the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.

literature available. The selection of ozone mortality studies used to estimate benefits in RIAs will be revisited in the forthcoming RIA accompanying the on-going review of the Ozone NAAQS.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-24 EPA continues work to update its guidance on valuing mortality risk reductions, and the Agency consulted several times with the SAB-EE AC on this issue. Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently, best reflects the SAB-EEAC advice it has received. Therefore, EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 12 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 13 We then adjust this VSL to account for the currency year and to account for income growth from 1990 to the analys is year. Specifically, the VSLs applied in this analysis in 2011$ after adjusting for income gr owth are $9.9 million for 2020 and $10.1 million
for 2025 and 2030. 14

The Agency is committed to using scientifically sound, appropriately reviewed evidence invaluing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). EPA is taking SAB’s recommendations under advisement.

In valuing PM 2.5 -related premature mortality, we disc ount the value of premature mortality occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that there is a “cessation” lag between changes in PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the

12 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA re tained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future.

13 In 1990$, this base VSL is $4.8 million.

14 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 estimates use income growth only through 2024 and are therefore likely underestimates. Comment [A1]: OMB comment: Marker for a chat.
EPA response: We are happy to talk about VSL.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-25 SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20
after the reduction in PM 2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Because short-term ozone-related premature mortality occurs within the analysis year, the estimated
ozone-related co-benefits are identical for all discount rates.

4.3.3 Benefit-per-ton Estimates for PM 2.5 Due to analytical limitations, it was not possi ble to conduct scenario-specific air quality modeling for this proposal. Instead, wW e used a “benefit-per-ton” approach to estimate the
PM 2.5 co-benefits in this RIA. EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b). These benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of premature mortality and premature morbidity), of reducing one ton
of PM 2.5 (or PM 2.5 precursor such as NO X or SO 2 ) from a specified source. Specifically, in this analysis, we multiplied the estimates for th e EGU sector by the corresponding emission reductions based on regional (i.e., East, West, and California) benefit-per-ton estimates.

The method used to calculate the regional bene fit-per-ton estimates is a slight modification of the national benefit-per-ton estimates described in the TSD: Estimating the Benefit per Ton of Reducing PM 2.5 Precursors from 17 Sectors (U.S. EPA, 2013a). The national estimates were derived using the approach published in Fann et al. (2012), but they have since been updated to reflect the epidemiology studies and Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012a). The approach in Fann et al. (2012) is similar to the work previously published by Fann et al. (2009), but the newer study includes improvements that provide more refined estimates of PM 2.5 -related health benefits for emissions reductions in the various sectors. Specifically, the air quality modelin g data reflect industrial sectors that are more narrowly defined. In addition, the updated air quality modeling data reflects more recent emissions data (2005 rather than 2001) and has higher spatial resolution (12km rather than 36 km grid cells). 15 For this rulemaking, to generate the regi onal benefit-per-ton estimates we simply

15 Although the modeling underlying the benefit-per-ton es timates does not reflect emission reductions anticipated from MATS, the EGU contribution to ambient PM 2.5 and ozone on a per-ton basis would be similar. (Fann,***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-26 aggregated the EGU impacts in BenMAP to the region (i.e., East, West, and California) rather than aggregating to the nation as was done in Fann et al. (2012). We then divided the regional benefits by the regional emissions rather than the national emissions. The appendix to this chapter provides additional detail regarding these calculations.

As noted below in the characterization of un certainty, all benefit-per-ton estimates have inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution
of the modeled sector emissions, which may not match the emission reductions anticipated by the proposed guidelines, and they may not refl ect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location. In addition, these estimates reflect the regional average benefit-per-ton for each ambient
PM 2.5 precursor emitted from EGUs, which assumes a linear atmospheric re sponse to emission reductions. The regional benefit-per-ton estimates, although less subject to these types of uncertainties than national estimates, still should be interpreted with caution. Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure.

4.3.4 Benefit-per-ton Estimates for Ozone

Similar to PM 2.5 , we used a “benefit-per-ton” approach in this RIA to estimate the ozone co-benefits, which represent the total monetized human health co-benefits (the sum of premature mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also
consistent with the PM 2.5 estimates, we generated regional benefit-per-ton estimates for ozone for the EGU sector using the air quality modeling data described in Fann et al. (2012) and using the updated Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012a). In
contrast to the PM 2.5 estimates, the ozone estimates are not based on changes to annual emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NO X emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate

Fulcher, and Baker, 2013) Because th e emission reductions in this RIA are calculated from an IPM base case that includes MATS (see Chapter 3), there is no do uble-counting concern with the resulting co-benefits estimates. Formatted: 05 BodyText, Indent: First line: 0"

Formatted: 05 BodyText, Indent: First line: 0", Space After: 0 pt, Line spacing: single***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-27ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with a longer ozone season s such as southern California and Texas. These
estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx
alone. Because EGUs emit little VOC relative to NO X emissions, it is unlikely that VOCs emitted by EGUs would contribute substantially to regional ozone formation. All benefit-per-ton
estimates have inherent limitations and should be interpreted with cau tion. We provide more detailed information regarding the generation of th ese estimates in the appendix to this chapter.

4.3.5 Estimated Health Co-Benefits Results Tables 4-7 through 4-9 provide the national and regional benefit-per-ton estimates for 2020, 2025, and 2030. Tables 4-10 through 4-12 provide the emission reductions estimated to occur in three analysis years (2020, 2025, and 2030) for two illustrative compliance scenarios (i.e., state and regional) for two options by region (i.e., East, West, and California). 16 Tables 4-13 through 4-15 summarize the national monetized PM and ozone-related health co-benefits estimated to occur in three analysis years (2020, 2025, and 2030) for the options by precursor pollutant using discount rates of 3 percent and 7 percent. Tables 4-16 through 4-18 provide
national summaries of the reductions in health incidences estimated for the options associated with these pollution reductions in 2020, 2025, and 2030. Figure 4-1 provides a visual representation of the range of estimated PM 2.5 and ozone-related co-benefits using concentrationresponse functions from different studies and expert opinion for the options evaluated in 2020 as an illustrative analysis year. Figures 4-2 and 4-3 provide a breakdown of the monetized health co-benefits for each of the options evaluated in 2020 as an illustrative analysis year by precursor pollutant and region, respectively.

16 See Chapter 3 of this RIA for more information regarding the expected emission reductions used to calculate the

health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrative compliance scenarios. Formatted: 05 BodyText, Indent: First line: 0"

Formatted: Space After: 3 pt***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-28 Table 4-7. Summary of National and Regional Benefit-per-ton Estimates for EGUs in 2020 (2011$)*

Pollutant Discount Rate National Regional East West California

SO 2 3% $38,000 to $86, 000 $40,000 to $90 ,000 $7,800 to $18,000 $160,0 00 to $320,000 7% $34,000 to $77, 000 $36,000 to $82 ,000 $7,100 to $16,000 $140,0 00 to $320,000 Directly emitted PM 2.5 (EC+OC) 3% $140,000 to $320,000 $140,000 to $320,0 00 $56,000 to $130,000 $ 280,000 to $570,000 7% $130,000 to $290,000 $130,000 to $280,0 00 $50,000 to $110,000 $ 250,000 to $570,000 Directly emitted PM 2.5 (crustal) 3% $18,000 to $40, 000 $18,000 to $41 ,000 $11,000 to $25,000 $110,000 to $220,000 7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000

NO X (as PM 2.5 ) 3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $34,000 7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000 NO X (as Ozone) N/A $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000 * The range of estimates reflects the range of epidem iology studies for avoided premature mortality for PM 2.5 and

ozone. All estimates are rounded to two significant figur es. The monetized co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the be nefit per ton estimates vary depending on the location and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton estimates for ozone are based on ozone season NO X emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals ar e unavailable for this analysis because of the benefitper-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Table 4-8. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2025 (2011$)*

Pollutant Discount Rate National Regional East West California

SO 2 3% $41,000 to $93, 000 $44,000 to $98 ,000 $8,800 to $20,000 $180,0 00 to $410,000 7% $37,000 to $84, 000 $39,000 to $89 ,000 $8,000 to $18,000 $160,0 00 to $370,000 Directly emitted PM 2.5 (EC+OC) 3% $150,000 to $350,000 $150,000 to $340,0 00 $64,000 to $140,000 $ 320,000 to $720,000 7% $140,000 to $310,000 $140,000 to $310,0 00 $58,000 to $130,000 $ 290,000 to $650,000 Directly emitted PM 2.5 (crustal) 3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000 7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000

NO X (as PM 2.5 ) 3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000 7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000 NO X (as Ozone) N/A $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000 * The range of estimates reflects the range of epidem iology studies for avoided premature mortality for PM 2.5 and

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

4-29 Table 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*

Pollutant Discount Rate National Regional East West California

SO 2 3% $44,000 to $100,000 $47,0 00 to $110,000 $9,800 to $2 2,000 $200,000 to $450,000 7% $40,000 to $90, 000 $42,000 to $95 ,000 $8,800 to $20,000 $180,0 00 to $410,000 Directly emitted PM 2.5 (EC+OC) 3% $170,000 to $370,000 $160,000 to $370,0 00 $71,000 to $160,000 $ 360,000 to $800,000 7% $150,000 to $340,000 $150,000 to $330,0 00 $64,000 to $150,000 $ 320,000 to $730,000 Directly emitted PM 2.5 (crustal) 3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000 7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000

NO X (as PM 2.5 ) 3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000 7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000 NO X (as Ozone) N/A $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000 * The range of estimates reflects the range of epidem iology studies for avoided premature mortality for PM 2.5 and

ozone. All estimates are rounded to two significant figur es. The monetized co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the be nefit per ton estimates vary depending on the location and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton estimates for ozone are based on ozone season NO X emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals ar e unavailable for this analysis because of the benefitper-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2020 (thousands of short tons) Region SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC)* Directly emitted PM 2.5 (crustal)* Option 1 - State

East 311 315 135 West 25 51 22 California 0 1 1 National Total 335 367 157 Option 1 - Regional

East 279 305 130 West 10 32 13 California 2 8 3 National Total 292 345 146 Option 2 - State East 247 240 101 West 20 40 18 California 0 1 1 National Total 267 281 119 Option 2 - Regional East 234 235 97 West 8 25 11 California 2 8 3 National Total 244 268 111 *Emissions of directly emitted particles not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-30 Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2025 (thousands of short tons) Region SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC)* Directly emitted PM 2.5 (crustal)* Option 1 - State

East 395 378 164 West 30 53 23 California 1 5 2 National Total 425 436 190 Option 1 - Regional

East 376 372 160 West 16 34 15 California 3 16 5 National Total 395 421 180 Option 2 - State

East 301 271 114 West 25 42 20 California 1 4 2 National Total 327 317 136 Option 2 - Regional

East 281 270 113 West 13 24 11 California 3 14 5 National Total 297 309 129 *Emissions of directly emitted particles not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-31 Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2030 (thousands of short tons) Region SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC)* Directly emitted PM 2.5 (crustal)* Option 1 - State

East 441 376 163 West 30 52 24 California 0 0 0 National Total 471 428 187 Option 1 - Regional

East 406 366 158 West 16 33 15 California 2 7 3 National Total 424 407 176 Option 2 - State

East 299 238 104 West 22 36 16 California 0 0 0 National Total 321 274 120 Option 2 - Regional

East 275 227 98 West 11 20 9 California 1 4 3 National Total 287 252 110 *Emissions of directly emitted particles not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-32 Table 4-13. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2020 (millions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate Option 1 - State

SO 2 $13,000 to $29,000 $11,000 to $26,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $2,200 to $4,900 $2,000 to $4,400 NOx (as Ozone) $640 to $2,700 $640 to $2,700 Total $15,000 to $36,000 $14,000 to $33,000 Option 1 - Regional

SO 2 $12,000 to $26,000 $11,000 to $24,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $2,200 to $5,000 $2,000 to $4,500 NOx (as Ozone) $630 to $2,700 $630 to $2,700 Total $15,000 to $34,000 $13,000 to $31,000 Option 2 - State

SO 2 $10,000 to $23,000 $9,100 to $21,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $1,700 to $3,800 $1,500 to $3,400 NOx (as Ozone) $480 to $2,100 $480 to $2,100 Total $12,000 to $29,000 $11,000 to $26,000 Option 2 - Regional

SO 2 $9,800 to $22,000 $8,900 to $20,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $1,700 to $3,900 $1,600 to $3,500 NOx (as Ozone) $470 to $2,000 $470 to $2,000 Total $12,000 to $28,000 $11,000 to $25,000 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairme nt. 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 moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Emissions of directly emitted particles not yet available. Comment [A2]: OMB comment: Given projected PM emissions post MATS (and now CSAPR) for the US, including the transportation sector, how many tons (and thus, benefits) remain to be mitigated? Does EPA have any sense of the cost reduction curves associated with those remaining tons?
EPA response: In Table 3-4 of this RIA, we provide the estimated tons of criteria pollutant emissions in the IPM base case and remaining after implementation of the proposed guidelines. Cost curves associated with the remaining criteria pollutant tons are not relevant to the co-benefits analysis or the proposed guidelines.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-33 Table 4-14. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2025 (millions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate Option 1 - State

SO 2 $18,000 to $40,000 $16,000 to $36,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $2,900 to $6,500 $2,600 to $5,800 NOx (as Ozone) $1,000 to $4,400 $1,000 to $4,400 Total $22,000 to $51,000 $20,000 to $46,000 Option 1 - Regional

SO 2 $17,000 to $38,000 $15,000 to $35,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $3,000 to $6,800 $2,700 to $6,100 NOx (as Ozone) $1,000 to $4,300 $1,000 to $4,300 Total $21,000 to $50,000 $19,000 to $45,000 Option 2 - State

SO 2 $14,000 to $30,000 $12,000 to $27,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $2,100 to $4,700 $1,900 to $4,200 NOx (as Ozone) $720 to $3,100 $720 to $3,100 Total $16,000 to $38,000 $15,000 to $35,000 Option 2 - Regional

SO 2 $13,000 to $29,000 $12,000 to $26,000 Directly emitted PM 2.5 (EC+OC) #NUM! to #NUM! #NUM! to #NUM! Directly emitted PM 2.5 (crustal) #NUM! to #NUM! #NUM! to #NUM! NOx (as PM 2.5 ) $2,200 to $5,000 $2,000 to $4,500 NOx (as Ozone) $730 to $3,100 $730 to $3,100 Total $16,000 to $37,000 $14,000 to $34,000 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairme nt. 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 moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx emissions . Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Emissions of directly emitted particles not yet available. Formatted Table

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

4-34 Table 4-15. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2030 (millions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate Option 1 - State

SO 2 $21,000 to $47,000 $19,000 to $43,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $2,900 to $6,600 $2,600 to $6,000 NOx (as Ozone) $1,100 to $4,600 $1,100 to $4,600 Total $25,000 to $58,000 $23,000 to $53,000 Option 1 - Regional

SO 2 $20,000 to $44,000 $18,000 to $40,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $3,000 to $6,700 $2,700 to $6,100 NOx (as Ozone) $1,100 to $4,500 $1,100 to $4,500 Total $24,000 to $55,000 $21,000 to $50,000 Option 2 - State

SO 2 $14,000 to $32,000 $13,000 to $29,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $1,900 to $4,200 $1,700 to $3,800 NOx (as Ozone) $680 to $2,900 $680 to $2,900 Total $17,000 to $39,000 $15,000 to $36,000 Option 2 - Regional

SO 2 $13,000 to $30,000 $12,000 to $27,000 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $1,800 to $4,200 $1,700 to $3,800 NOx (as Ozone) $660 to $2,800 $660 to $2,800 Total $16,000 to $37,000 $14,000 to $34,000 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairme nt. 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 moneti zed co-benefits incorporate the conv ersion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Emissions of directly emitted particles not yet available. Formatted Table

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

4-35 Table 4-16. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 2020* Option 1 - state Option 1 - regional Option 2 - state Option 2 - regional PM 2.5 -related Health Incidences Avoided Premature Mortality Krewski et al. (2009) (adult) 1,600 1,500 1,300 1,300 Lepeule et al. (2012) (adult) 3,700 3,400 2,900 2,900 Woodruff et al. (1997) (infant) 4 3 3 3 Avoided Morbidity Emergency department visits for asthma (all ages) 860 800 680 670 Acute bronchitis (age 8–12) 2,400 2,200 1,900 1,800 Lower respiratory symptoms (age 7–14) 30,000 28,000 24,000 24,000 Upper respiratory symptoms (asthmatics age 9–11) 44,000 41,000 35,000 34,000 Minor restricted-activity days (age 18–65) 1,200,000 1,100,000 940,000 930,000 Lost work days (age 18–65) 200,000 190,000 160,000 160,000 Asthma exacerbation (age 6–18) 98,000 91,000 78,000 76,000 Hospital admissions—respiratory (all ages) 480 450 380 370 Hospital admissions—cardiovascular (age > 18) 590 550 470 460 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 1,900 1,700 1,500 1,400 Pooled estimate of 4 studies 200 190 160 160 Ozone-Related Health Incidences Avoided Premature Mortality Bell et al. (2004) (all ages) 36 35 27 27 Levy et al. (2005) (all ages) 170 160 120 120 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 220 210 160 160 Hospital admissions—respiratory causes (ages < 2) 100 98 76 74 Emergency room visits for asthma (all ages) 120 110 89 87 Minor restricted-activity days (ages 18-65) 210,000 210,000 160,000 160,000 School absence days 72,000 71,000 55,000 54,000 * All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. Co-benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 95 th percentile confidence interval for the health im pact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 per cent based on Lepeule et al. (2012). Formatted Table***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-36 Table 4-17. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 2025* Option 1 - state Option 1 - regional Option 2 - state Option 2 - regional PM 2.5 -related Health Incidences Avoided Premature Mortality Krewski et al. (2009) (adult) 3,200 2,100 2,500 1,600 Lepeule et al. (2012) (adult) 7,200 4,800 5,500 3,700 Woodruff et al. (1997) (infant) 6 4 5 3 Avoided Morbidity Emergency department visits for asthma (all ages) 1,600 1,100 1,200 830 Acute bronchitis (age 8–12) 4,800 3,100 3,700 2,300 Lower respiratory symptoms (age 7–14) 61,000 39,000 47,000 30,000 Upper respiratory symptoms (asthmatics age 9–11) 88,000 57,000 68,000 43,000 Minor restricted-activity days (age 18–65) 2,400,000 1,500,000 1,800,000 1,100,000 Lost work days (age 18–65) 400,000 260,000 310,000 190,000 Asthma exacerbation (age 6–18) 200,000 130,000 160,000 96,000 Hospital admissions—respiratory (all ages) 920 660 710 490 Hospital admissions—cardiovascular (age > 18) 1,100 800 850 600 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 3,600 2,500 2,800 1,900 Pooled estimate of 4 studies 390 280 300 210 Ozone-Related Health Incidences Avoided Premature Mortality Bell et al. (2004) (all ages) 93 92 65 66 Levy et al. (2005) (all ages) 420 420 300 300 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 600 600 430 430 Hospital admissions—respiratory causes (ages < 2) 250 240 180 180 Emergency room visits for asthma (all ages) 290 290 210 210 Minor restricted-activity days (ages 18-65) 520,000 520,000 370,000 370,000 School absence days 180,000 180,000 130,000 130,000 * All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. Co-benefits for ozone are based on ozone season NOx emissions. In general, the 95 th percentile confidence interval for the he alth impact function alone ranges from approximately ±30 percen t for mortality incidence based on Krewski et al. (2009) and ±46 pe rcent based on Lepeule et al. (2012). Formatted Table***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-37 Table 4-18. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030* Option 1 - state Option 1 - regional Option 2 - state Option 2 - regional PM 2.5 -related Health Incidences Avoided Premature Mortality Krewski et al. (2009) (adult) 2,600 2,400 1,700 1,600 Lepeule et al. (2012) (adult) 5,800 5,400 3,900 3,600 Woodruff et al. (1997) (infant) 5 4 3 3 Avoided Morbidity Emergency department visits for asthma (all ages) 1,300 1,200 850 800 Acute bronchitis (age 8–12) 3,500 3,300 2,300 2,200 Lower respiratory symptoms (age 7–14) 45,000 42,000 30,000 28,000 Upper respiratory symptoms (asthmatics age 9–11) 64,000 61,000 43,000 41,000 Minor restricted-activity days (age 18–65) 1,700,000 1,600,000 1,100,000 1,100,000 Lost work days (age 18–65) 290,000 270,000 190,000 180,000 Asthma exacerbation (age 6–18) 150,000 140,000 99,000 93,000 Hospital admissions—respiratory (all ages) 810 760 540 510 Hospital admissions—cardiovascular (age > 18) 970 910 650 610 Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 3,100 2,900 2,100 1,900 Pooled estimate of 4 studies 330 320 220 210 Ozone-Related Health Incidences Avoided Premature Mortality Bell et al. (2004) (all ages) 97 96 62 60 Levy et al. (2005) (all ages) 440 430 280 270 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 670 660 430 410 Hospital admissions—respiratory causes (ages < 2) 250 240 160 150 Emergency room visits for asthma (all ages) 290 290 190 180 Minor restricted-activity days (ages 18-65) 510,000 510,000 330,000 320,000 School absence days 180,000 180,000 120,000 110,000 * All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. Co-benefits for ozone are based on ozone season NOx emissions. In general, the 95 th percentile confidence interval for the he alth impact function alone ranges from approximately ±30 percen t for mortality incidence based on Krewski et al. (2009) and ±46 pe rcent based on Lepeule et al. (2012). Formatted Table***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-38 Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU GHG Existing Source Guidelines in 2020 *

*The PM 2.5 graphs show the estimated PM 2.5 co-benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. (2009) study and the Lepe ule et al. (2012) study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005). Ozone co-benefits o ccur in the analysis year, so they are the same for all discount rates. These estimates do not include climate benefits. The monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. Results would be similar if the state compliance scenario was shown. PM 2.5 Ozone

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4-39 Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% Discount Rate for each Option (Regional Compliance) for Proposed EGU GHG Existing Source Guidelines in 2020* * “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) mortality study for ozone with the Krew ski et al. (2009) mortality study for PM 2.5 . “High Health Co-benefits” refers to the combined health co-b enefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 . Emission reductions of directly emitted particles are not yet available. Results would be similar if the state compliance scenario was shown. SO2 81% Directly emitted PM2.5 (EC+OC)

0% Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5)

15%

NOx (as Ozone)

4% 

### Option 1 ‐ Regional

SO2 77% Directly emitted PM2.5 (EC+OC)

0%
Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

15%

NOx (as Ozone)

8% 

### Option 1 ‐ Regional

SO2 82% Directly emitted PM2.5 (EC+OC)

0% Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5)

14% NOx (as Ozone)

4% 

### Option 2 ‐ Regional

SO2 79% Directly emitted PM2.5 (EC+OC)

0%
Directly emitted PM2.5 (Crustal)

0% NOx (as PM2.5)

14% NOx (as Ozone)

7% 

### Option 2 ‐ Regional Low Health Co

‐ benefits High Health Co

‐ benefits

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4-40 Figure 4-3. Breakdown of Monetized Health Co-Benefits by Region at a 3% Discount Rate for each Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020* * “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) mortality study for ozone with the Krew ski et al. (2009) mortality study for PM 2.5 . “High Health Co-benefits” refers to the combined health co-b enefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 . Results would be similar if the state compliance scenario was shown. 4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits
In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis includes many data sources as inputs, includi ng emission inventories, air quality data from models (with their associated parameters an d inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing co-benefits, East 95.5% West 0.9%

California

3.6% 

### Option 1 ‐ Regional

East 95.5% West 0.9%

California

3.6% 

### Option 1 ‐ Regional

East 94.9% West 0.8%

California

4.2% 

### Option 2 ‐ Regional

East 94.8% West 0.9%

California

4.3% 

### Option 2 ‐ Regional Low Health Co

‐ benefits High Health Co

‐ benefits

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4-41 and assumptions regarding the future state of th e world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the estimate of co-benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benef its. Therefore, the estimates of co-benefits in each analysis year should be viewed as representative of the general magnitude of co-benefits of the illustrative compliance scenarios, rather than the actual co-benefits anticipated from implementing the proposed guidelines.

This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NA AQS RIA (U.S. EPA, 2008b) because we lack the necessary air quality modeling input and/or monitoring data to run the benefits model. However, the results of the quantitative and qualita tive uncertainty analyses presented in the PM NAAQS RIA and Ozone NAAQS RIAs can provide some information regarding the uncertainty inherent in the estimated co-benefits results pres ented in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indicat e that alternate cessation lag assumptions could change the estimated PM 2.5 -related mortality co-benefits discounted at 3 percent by between 10 percent and –27 percent and that alternate income growth adjustments could change
the PM 2.5 -related mortality co-benefit s by between 33 percent and

− 14 percent. Although we generally do not calculate confidence in tervals for benefit-per-ton estimates and they can provide an incomplete picture about the overall uncertainty in the benefits estimates , the PM NAAQS RIA can provide an indication of the random sampling error in the health impact and economic valuation functions using Monte Carlo methods. In general, the 95 th percentile confidence interval
for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the
central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

Unlike RIAs for which EPA conducts air quality modeling, we do not have data
information on the specific location of the air qualit y changes associated with the proposed guidelines. As such, it is not feasible to estimate the proportion of co-benefits occurring in different locations, such as designated nonattai nment areas. Instead, we applied benefit-per-ton estimates, which reflect specific geographic patt erns of emissions reductions and specific air***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-42 quality and benefits modeling assumptions. For example, these estimates may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over-estimat e or under-estimate of the actual co-benefits of controlling PM and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct air quality modeling. Great care should be ta ken in applying these estimates to emission reductions occurring in any specific location, as these are all based on broad emission reductionscenarios and therefore represent average benefits-per-ton over the entire region. The benefitper-ton for emission reductions in specific locations may be very different than the estimates presented here. To the extent that the geogra phic distribution of the emissions reductions achieved by implementing the proposed guidelines is different than the modeled regional emissions in the sector modeling, the co-benefits may be underestimated or overestimated. For more information regarding the limitations of benefit-per-ton estimates derived from the sector modeling, see the TSD describing the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013a) and Fann et al. (2012). In addition, the appendix to this chapter provides additional uncertainty information re garding the benefit-per-ton estimates ap plied in this RIA, including an
evaluation of the similarities and differences in the spatial distribution of EGU emissions in the sector modeling and the IPM base case discussed in Chapter 3 of this RIA.

Our estimate of the total monetized co-benefits is based on EPA’s interpretation of the best available scientific literature and methods and supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for
PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 health
co-benefits.

1. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differ entiation of effect es timates by particle type. The PM ISA concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-43 2. We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates include health co-benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle stan dard and those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of theSAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates.
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 observed PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in
the risk we estimate from simulated PM 2.5 concentrations that fall be low the bulk of the observed data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML] , or one standard deviation below the mean of the air quality data in the study , etc. ) allow readers to determine the portion of population exposed to annual mean PM 2.5 levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA
does not view these concentration benchmarks as a concentration threshold below which we would not quantify health co-benefits of air quality improvements. 17 Rather, the co-benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associated with the emission reduction strategies

. and becauseThe PM
ISA concluded that the scientific evidence collectively is sufficient to conclude that the

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

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 -related Mortality (U.S. EPA, 2010b). Formatted: 05 BodyText, Line spacing: single

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4-44 relationship between long-term PM 2.5 exposures and mortality is causal and that overall the studies support the use of a no-threshold log-linear model to estimate the current body of
scientific literature indicates that a no-thr eshold model provides the best estimate of PM-related long-term mortality (U.S. EPA, 2009b)

. In other words, although we may have less confidence in the magnitude of the risk at concentrations below these benchmarks, we still have high confidence that PM2.5 is causally associated with risk at those lower air quality concentrations.

For this analysis, policy-specific air quality data is not available due to time or resource
limitations . For this RIA, we are unable to estimate the percentage of premature mortality
associated with the emission reductions at each PM 2.5 concentration. However, we believe that it is still important to characterize the distributio n of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at
each PM 2.5 level in the baseline of the source appo rtionment modeling used to calculate the benefit-per-ton estimates for this sector. 18 It is important to note th at baseline exposure is only one parameter in the health impact function, al ong with baseline incidence rates population and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction po licy. The most important aspect, which we are unable to quantify without rule-specific air quality modeling, is the shift in exposure anticipated by implementing the proposed guidelines. Therefor e, caution is warranted when interpreting the
LML assessment in this RIA because these results are not consistent with results from RIAs that had air quality modeling.

Table 4-19 provides the percentage of the population exposed above and below two concentration benchmarks (i.e., LML and 1 one standard deviation below the mean) in the modeled baseline for the sector modeling. Figure 4-4 shows a bar chart of the percentage of the population exposed to various air quality levels in the baseline, and Figure 4-5 shows a

18 As noted above, theThe source apportionment modeling used to generate the benefit-per-ton estimates does not reflect emission reductions anticipated from the EGU sector as a result of the recently promulgated MATS rule or the vacatur of the CSAPR rule. (F ann, Fulcher, and Baker, 2013)

. Therefore, the baseline PM 2.5 concentrations in the LML assessment are higher than would be expected if MATS was reflected. Formatted: Complex Script Font: Not Bold, Subscript

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4-45 cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.

Table 4-19. Population Exposure in the Baseline Modeling Above and Below Various Concentrations Benchmarks in the Underlying Epidemiology Studies *

Epidemiology Study Below 1 Standard Deviation. Below AQ Mean At or Above 1 Standard Deviation Below AQ Mean Below LML At or Above LML
Krewski et al. (2009) 89% 11% 7% 93% Lepeule et al. (2012) N/A N/A 23% 67%

* One standard deviation below the mean is equivalent to the middle of the range between the 10 th and 25 th

percentile. For Krewski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3 . For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one st andard deviation below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM 2.5 do not occur.

Figure 4-4 . Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling Among the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 5% 10% 15% 20% 25%

<1 1

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

4-46 Figure 4-5 . Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling

4.4 Combined Climate Benefits and Health Co-benefits Estimates
In this analysis, we were able to monetize the estimated co-benefits associated with the
decreased emissions of CO 2 and reduced exposure to PM 2.5 and ozone, but we were unable to monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, carbon monoxide, SO 2 , and NO 2 , as well as ecosystem effects and visibility impairment. Specifically, we estimated the combined climate benefits at discount rates of 5 percent, 3 percent, 2.5 percent, and 3 percent (95 th percentile) (as estimated by the interagency working group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by
EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e) and OMB’s Circular A4 [OMB, 2003]).

Among the populations exposed to PM 2.5 in the baseline: 93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Cumulativepercentageofpopulationexposed

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

4-47 Different discount rates are applied to SCC than to the health co-benefit estimates
because CO 2 emissions are long-lived and subsequent damages occur over many years. Moreover, several rates are applied to SCC beca use the literature shows that it is sensitive to assumptions about discount rate and because no consensus exists on the appropriate rate to use in an intergenerational context. The SCC interagency group centered its attention on the 3 percent discount rate but emphasized the importance of considering all four SCC estimates. 19 The EPA has evaluated the range of potential impacts by co mbining all SCC values w ith health co-benefits values at the 3 percent and 7 percent discount rates. To be consistent with concepts of intergenerational discounting, values for health benefits, which occur within a generation, would only be combined with SCC values using a lower discount rate (e.g., the 7 percent health benefit estimates would be combined with 5 percent or lower SCC values, but the 3 percent health benefit would not be combined with the 5 percent SCC value). While the 5 percent SCC and 3 percent health benefit estimate falls within the range of values we analyze, this individual estimate should not be used independently in an analysis, as it is represents a combination of discount rates that is unlikely to occur. Combin ing the 3 percent SCC values with the 3 percent
health benefit values assumes that there is no difference in discount rates between intragenerational and inte rgenerational impacts.

Tables 4-20 through 4-22 provide the combined climate and health benefits for each option evaluated for 2020, 2025, and 2030. Figure 4-6 shows the breakdown of the monetized benefits by pollutant for each option evaluated in 2020 as an illustrative analysis year using a 3 percent discount rate.

19 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or

http://www.whitehouse.gov/sites/default/files/omb/in foreg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for details.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-48 Table 4-20. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2020 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate Benefits and Health Co-Benefits (Discount Rate Applied to Health Co-Benefits)

3% 7% Option 1 - state 383 million metric tonnes CO 2 5% $4.9 $20 to $41 $19 to $38 3% $18 $33 to $54 $32 to $50 2.5% $26 $42 to $62 $40 to $59 3% (95 th percentile) $52 $68 to $89 $66 to $85 Option 1 - regional 371 million metric tonnes CO 2 5% $4.7 $19 to $39 $18 to $36 3% $17 $32 to $51 $30 to $48 2.5% $25 $40 to $59 $38 to $56 3% (95 th percentile) $51 $65 to $85 $64 to $81 Option 2 - state 295 million metric tonnes CO 2 5% $3.8 $16 to $32 $15 to $30 3% $14 $26 to $42 $25 to $40 2.5% $20 $32 to $49 $31 to $46 3% (95 th percentile) $40 $53 to $69 $51 to $66 Option 2 - regional 283 million metric tonnes CO 2 5% $3.6 $16 to $32 $15 to $29 3% $13 $25 to $41 $24 to $38 2.5% $19 $31 to $47 $30 to $45 3% (95 th percentile) $39 $51 to $67 $50 to $64 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include redu ced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emission reduct ions of directly emitted par ticles are not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-49 Table 4-21. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2025 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate Benefits and Health Co-Benefits (Discount Rate Applied to Health Co-Benefits)

3% 7% Option 1 - state 506 million metric tonnes CO 2 5% $7.6 $29 to $58 $27 to $54 3% $25 $47 to $76 $45 to $71 2.5% $37 $59 to $88 $57 to $83 3% (95 th percentile) $77 $99 to $130 $97 to $120 Option 1 - regional 501 million metric tonnes CO 2 5% $7.5 $29 to $57 $27 to $53 3% $25 $46 to $75 $44 to $70 2.5% $37 $58 to $86 $56 to $82 3% (95 th percentile) $76 $98 to $130 $96 to $120 Option 2 - state 376 million metric tonnes CO 2 5% $5.6 $22 to $44 $20 to $40 3% $19 $35 to $57 $34 to $54 2.5% $28 $44 to $66 $42 to $62 3% (95 th percentile) $57 $74 to $95 $72 to $92 Option 2 - regional 368 million metric tonnes CO 2 5% $5.5 $21 to $43 $20 to $39 3% $18 $34 to $56 $33 to $52 2.5% $27 $43 to $64 $42 to $61 3% (95 th percentile) $56 $72 to $93 $71 to $90 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment . Emission reductions of directly emitted particles are not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-50 Table 4-22. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate Benefits and Health Co-Benefits (Discount Rate Applied to Health Co-Benefits)

3% 7% Option 1 - state 555 million metric tonnes CO 2 5% $9.5 $34 to $68 $32 to $63 3% $31 $56 to $89 $53 to $84 2.5% $44 $69 to $100 $67 to $98 3% (95 th percentile) $94 $120 to $150 $120 to $150 Option 1 - regional 545 million metric tonnes CO 2 5% $9.3 $32 to $62 $30 to $57 3% $30 $53 to $83 $51 to $78 2.5% $44 $67 to $97 $65 to $92 3% (95 th percentile) $92 $120 to $150 $110 to $140 Option 2 - state 373 million metric tonnes CO 2 5% $6.4 $33 to $65 $31 to $60 3% $21 $54 to $86 $52 to $81 2.5% $30 $67 to $99 $65 to $94 3% (95 th percentile) $63 $120 to $150 $110 to $140 Option 2 - regional 353 million metric tonnes CO 2 5% $6.0 $22 to $43 $20 to $40 3% $20 $35 to $56 $34 to $53 2.5% $28 $44 to $65 $43 to $62 3% (95 th percentile) $60 $76 to $97 $74 to $93 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment . Emission reductions of directly emitted particles are not yet available.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-51 Figure 4-6. Breakdown of Combined Monetized Climate and Health Co-benefits of Proposed EGU GHG Existing Source Guidelines in 2020 by Option (Regional Compliance) and Pollutant (3% discount rate)* * “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) mortality study for ozone with the Krew ski et al. (2009) mortality study for PM 2.5 . “High Health Co-benefits” refers to the combined health co-b enefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 . Results would be similar if the state compliance scenario was shown. 4.5 Unquantified Co-benefits The monetized co-benefits estimated in this RIA only reflect a subset of co-benefits
attributable to the health effect reductions associ ated with ambient fine particles and ozone. Data, time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the co-benefits from several important benefit cate gories, including co-benefits associated with the CO2 54% SO2 37% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5)

7% NOx (as Ozone) 2% 

### Option 1 ‐ Regional

CO2 33% SO2 52% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5) 10%

NOx (as Ozone) 5% 

### Option 1 ‐ Regional

CO2 52% SO2 39% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0%

NOx (as PM2.5)

7% NOx (as Ozone) 2% 

### Option 2 ‐ Regional

CO2 32% SO2 54% Directly emitted PM2.5 (EC+OC) 0%

Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5)

9%

NOx (as Ozone) 5% 

### Option 2 ‐ Regional Low Health Co

‐ benefits High Health Co

‐ benefits

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4-52 potential for exposure to several HAP ( including mercury and hydrogen chloride ) and , carbon monoxide, SO 2 carbon monoxide, SO 2 , Nand NO 2 , and ozone , as well as ecosystem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analysis. This does not imply that there are no co-benefits associated with these emission reductions. In this section, we provide a qualitative description of these benefits, which are listed
in Table 4-23.

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

Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information Improved Human Health

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

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

Reduced incidence of morbidity from exposure to CO Cardiovascular effects — — CO ISA 1,2 Respiratory effects — — CO ISA 1,2,3 Central nervous system effects — — CO ISA 1,2,3 Premature mortality — — CO ISA 1,2,3 Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 1

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

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

Improved Environment Formatted: Subscript

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4-53 Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Table 4-12. Continued 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 — — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1
Damage to urban ornamental plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2

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

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

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

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

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2

Injury to vegetation from NO x exposure — — NO x SO x ISA 2

Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2
Commercial, subsistence and recreational fishing — — Mercury Study RTC 1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA. 2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or

methods. 3 We assess these co-benefits qualitatively because current evidence is only suggest ive of causality or there are other significant concerns over the strength of the association. Formatted Table***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-54 4.5.1 HAP Co-benefits
Due to methodology and resource limitations, we were unable to estimate the co-benefits associated with reducing emissions of the hazard ous air pollutants in this analysis. EPA’s SABHES concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollut ants (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 2009, EPA convened a workshop to address the inherent co mplexities, limitations, and
uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose-res ponse relationships (Gwinn et al., 2011).

Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated with HAP emitted by EGUs. Below we describe th e health effects associated with the two HAP for which we were able to quantify emission reductions for the proposed guidelines: mercury and hydrogen chloride. We estimate that the illustrative compliance scenarios for the proposed guidelines would reduce mercury emissions by up to 2.1 tons and hydrogen chloride by up to 590 tons by 2030.

4.5.1.1 Mercury Mercury in the environment is transforme d into a more toxic form, methylmercury (MeHg). Because Hg is a persistent pollutant, Me Hg accumulates in the food chain, especially the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS Study was issued which provides a thorough review of the effects of MeHg on human health (NRC, 2000)

. 20

. Many of the peer-reviewed articles cited in this section are publications originally cited in the Mercury Study. 21 In addition, EPA has conducted literature searches to

20 National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury . Washington, DC: National

Academies Press.

21 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress , EPA–HQ–OAR–

2009–0234–3054. December. Available on the In ternet at <http://www.epa.gov/hg/report.htm>. Formatted: 05 BodyText, Indent: First line: 0"

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4-55 obtain other related and more recent publications to complement the material summarized by the
NRC in 2000.

In its review of the literature, the NAS foun d neurodevelopmental effects to be the most sensitive and best documented endp oints and appropriate for establishing a reference dose (RfD) (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report noted th at studies in animals reported sensory effects as well as effects on brain development and memory functions and support the conclusions based on epidemiology studies. The NAS noted that their recommended endpoints for an RfD are associated with the ability of children to learn and to succeed in school. They concluded the following: “The population at highest risk is the children of women who consumed large amounts of fish and seafood during pregnancy. The committee concludes that the risk to that population is likely to be sufficient to result in an increase in the number of children who have to struggle to keep up in school.”

The NAS summarized data on cardiovascular effects available up to 2000. Based on these and other studies, the NRC concluded that “Although the data base is not as extensive for cardiovascular effects as it is for other end poin ts (i.e., neurologic effects), the cardiovascular system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated
that “additional studies are needed to better charac terize the effect of meth ylmercury exposure on blood pressure and cardiovascular function at various stages of life.”

Additional cardiovascular studies have been published since 2000. EPA did not to develop a quantitative dose-response assessment for cardi ovascular effects associated with MeHg exposures, as there is no consensus among scientists on the dose-response functions for these effects. In addition, there is inconsistency among available studies as to the association between MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet received the review and scrutiny of the more well-established neurotoxicity data base.

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing chromosomal damage in a number of experimental systems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-56 studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim et al., 2000) reported a direct relationship betw een MeHg concentration in hair and DNA damage in lymphocytes, as well as effects on chromosomes. 22 Long-term MeHg exposures in this population were believed to occur through consumption of fish, suggesting that genotoxic effects (largely chromosomal aberrations) may result fr om dietary, and chronic MeHg exposures similar to and above those seen in the Faroes and Seychelles populations.

Although exposure to some forms of Hg can result in a decrease in immune activity or an autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is limited (NRC, 2000) 23

. Based on limited human and animal data, MeHg is classified as a “possible” human carcinogen by the International Agency for Research on Cancer (IARC, 1994) 24 and in IRIS (U.S. EPA, 2002). 25 The existing evidence supporting the possibility of carcinogenic effects in humans from low-dose chronic exposures is te nuous. Multiple human epidemiological studies have found no significant association between Hg exposure and overall cancer incidence, although a few studies have shown an association between Hg exposure and specific types of cancer incidence (e.g., acute leukemia and liver cancer) (NRC, 2000).

There is also some evidence of reproductive and renal toxicity in humans from MeHg exposure. However, overall, human data regarding reproductive, renal, and hematological toxicity from MeHg are very limited and are based on either studies of the two high-dose

22 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte.

2000. Cytogenetic damage related to low levels of met hyl mercury contamination in the Brazilian Amazon. An. Acad. Bras. Ciênc. 72(4): 497-507.

23 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S.

Department of Health and Human Services, Public Health Service, Atlanta, GA.

24 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of

Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000).

25 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on

Methylmercury. National Center for Environmental Assessm ent. Office of Research and Development. Available at http://www.epa.gov/iris/subst/0073.htm

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4-57 poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of chronic exposures at the levels of interest in this analysis.

4.5.1.2 Hydrogen Chloride
Hydrogen chloride (HCl) is a corrosive ga s that can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. 26 The greatest impact is on the upper respiratory tract; exposure to high conc entrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seri ously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can lead to Reactive Airways Dysfunction Syndrome (R ADS), a chemically, or irritant-induced type of asthma. Children may be more vulnerable to corrosive agents than adults because of the relatively smaller diameter of their airways. Children may also be more vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 27

4.5.2 Additional NO 2 Health Co-Benefits
In addition to being a precursor to PM 2.5 and ozone, NOx emissions are also associated with a variety of adverse health effects associated with direct exposure. Unfortunately, we were
unable to estimate the health co-benefits associated with reduced NO 2 exposure in this analysis. Therefore, this analysis only quantified and monetized the PM 2.5 and ozone co-benefits
associated with the reductions in NO 2 emissions.

Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Ox ides of Nitrogen —Health Criteria (NOx ISA) (U.S. EPA, 2008c) concluded that there is a likely causal relationship between respiratory health effects and short-term exposure to NO 2 . The NOx ISA concluded that the evidence “is sufficient

26 Agency for Toxic Substances and Disease Registry (A TSDR). Medical Management Guidelines for Hydrogen

Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02

. 27 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This material is available at http://www.epa.gov/iris/subst/0396.htm

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4-58 to infer a likely causal relationship between short-term NO 2 exposure and adverse effects on the respiratory system.” These epidemiologic and experimental studies encompass a number of endpoints including emergency department visi ts and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also concluded that the relationship between short-term NO 2 exposure and premature mortality was “suggestive but not sufficient to infer a causal rela tionship,” because it is difficult to attribute the mortality risk effects to NO 2 alone. Although the NOx ISA stat ed that studies consistently reported a relationship between NO 2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM.

4.5.3 Additional SO 2 Health Co-Benefits
In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variety of adverse health effects associated with direct exposure. Unfortunately, we were unable to
estimate the health co-benefits associated with reduced SO 2 in this analysis because we do not have air quality modeling data available. Therefore, this analysis only quantifies and monetizes
the PM 2.5 co-benefits associated with the reductions in SO 2 emissions.

Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO 2 ISA) concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO 2 (U.S. EPA, 2008a). The immediate effect of SO 2 on the respiratory system in
humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associat ed with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified four short-term morbidity endpoints that the SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and respiratory-related hospitalizations. The diffe ring evidence and associated strength of the
evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short-term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is di fficult to attribute the mortality risk effects to Formatted: 05 BodyText, Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-59 SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these co-benefits due to data
constraints.

4.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur — Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO 2 and NOx emissions also contribute to a variety of adverse welfare effects, including acidi c deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern U.S., the surface waters affected by acidification are a
source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesth etic and educational services and recreational fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temp eratures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial ac idification affects several important ecological services, including declines in habitat for threat ened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in
forest soil erosion and reductions in water rete ntion (cultural and regulating). (U.S. EPA, 2008d)

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an important source of food production, particular ly fish and shellfish production, and a variety of cultural ecosystem services, including water-based recreational and
aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets the balance between native and nonnative plants, changing the ability of an area to support biodiversity. When the composition of species changes, then fire frequency and intensity can Formatted: 05 BodyText, Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-60 also change, as nonnative grasses fuel more fre quent and more intense wildfires. (U.S. EPA, 2008d)

4.5.5 Ozone Welfare Co-Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature (U.S. EPA, 201 3b). Sensitivity to ozone is highly variable across species, with over 65 plan sp ecies identified as “ozone-sensitive”, many of which occur in state and national parks and forests. These effects include those that damage or impair the intended use of the plant or ecosystem. Such effects are considered adverse to the public welfare and can include reduced growth and/or biomass pro duction in sensitive pl ant species, including forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, ins ect pest infestation, and competition), species composition shift, and changes in ecosystems and associated ecosystem services.

4.5.6 Carbon Monoxide Co-Benefits CO in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO is “likely to have a causal relationship” with cardi ovascular morbidity, particularly in individuals with coronary heart disease. Epidemiologic st udies associate short-term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmona ry disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and Formatted: 05 BodyText, Indent: First line: 0"

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4-61 respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects follow ing long-term exposure to CO, and for central nervous system effects linked to short- and long-term exposure to CO.

4.5.7 Visibility Impairment Co-Benefits
Reducing secondary formation of PM 2.5 would improve levels visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light-extin ction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. Without air quality modeling, we are unable to estimate visibility related benefits, and we are also unable to determine whether the emission reductions associated with the proposed guidelines would be likely to have a significant im pact on visibility in urban areas or Class I

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4-62 4.6 References
40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act,” Federal Register, Vo l. 74, No. 239, Tuesday, December 15, 2009, Rules and Regulations. Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices . Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987- 2000.” Journal of the American Medical Association , 292(19): p. 2372-8. Bell, M.L.; F. Dominici and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the Na tional Morbidity, Mortality, and Air Pollution Study.” Epidemiology, 16(4): pp. 436-45. Fann, N.; C.M. Fulcher and B.J. Hubbell. 2009. “The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution.” Air Quality, Atmosphere, & Health 2:169–176. Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 -related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 41–151. Gwinn, M.R.; J. Craig; D.A. Axelrad; R. Cook; C. Dockins; N. Fann; R. Fegley; D.E. Guinnup; G. Helfand; B. Hubbell; S.L. Mazur; T. Palma; R.L. Smith; J. Vandenberg and B. Sonawane. 2011. “Meeting report: Estimating the benefits of reducing hazardous air pollutants— summary of 2009 workshop and future considerations.” Environmental Health Perspectives Jan; 119(1): p. 125-30. Huang, Y.; F. Dominici; M. Bell. 2005. “Bayes ian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics , 16, 547-562. Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at: <http://www.epa.gov/ttn/ecas/regdata/Uncertain ty/pm_ee_tsd_expert_interview_summaries.p df>. Formatted: Line spacing: Multiple 1.3 li***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-63 Industrial Economics, Incorporated (IEc). 2009. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study: Health Benefits of Benzene Reductions in Houston, 1990–2020. Final Report, July 14, 2009. Available at: <http://www.epa.gov/air/sect812/dec09/812C AAA_Benzene_Houston_Final_Report_July_20 09.pdf>. Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC

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‐ analysis of the Benefits of Attaining Alternative Ozone Standards to Incorporate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3 -supplemental_analysis-updated_benefits11- 5.09.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic Analyses . EPA 240-R-10-001. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/v wAN/EE-0568-50.pdf/$file/EE-0568- 50.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. Available at: <http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at: <http://www.epa.gov/ttn/ecas/regda ta/RIAs/matsriafinal.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-67 U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters. February. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/boilersriafinal110221_psg.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011d. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA-452/D-11-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. April. Available at: <http://www.epa .gov/ttn/naaqs/standards/pm/data/20110419pmpafinal.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Ai r Quality Standards for Particulate Matter . EPA452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis: Petroleum Refineries New Source Performance Standards Ja. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/ refineries_nsps_ja_final_ria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013a. Technical Support Document: Estimating the Benefit per ton of Reducing PM 2.5 Precursors from 17 sectors . Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13 .pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/076F. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. U.S. Environmental Protection Agency (U.S. EPA). 2014a. Welfare Risk and Exposure Assessment for Ozone Second External Review Draft . EPA-452/P-14-003a. Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <http://www.epa.gov/ttn/naaqs/standards/ ozone/data/20140131welfarerea.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-68 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2002. Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: Developing Best Estimates of Dose-Response Functions An SAB Workshop Report of an EPA/SAB Workshop (Final Report). EPA-SAB-EC-WKSHP-02-001. January. Available at: <http://yosemite.epa.gov/sab%5CSAB PRODUCT.NSF/34355712E C011A358525719A005B F6F6/$File/ecwkshp02001%2Bappa-g.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available at: <http://yosemite.epa.gov/sab/sabproduct. nsf/0/43B91173651AED9E85257487004EA6CB/$F ile/EPA-COUNCIL-08-002-unsigned.pdf>. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2000. An SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at: <http://yosemite.epa.gov/sab%5CSAB PRODUCT.NSF/413345241 48BCCD6852571A70051 6498/$File/eeacf013.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$Fil e/council_ltr_05_001.pdf>. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990–2020 . EPA-COUNCIL-08-001. July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$Fi le/EPA-COUNCIL-08-001-unsigned.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008) . EPA-COUNCIL-09-008. May. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73 ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-69 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15 1B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010a. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act . EPA-COUNCIL-10-001. June. Available at: < http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA 39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductio ns for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 July

. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/ EPA-SAB-11-011-unsigned.pdf>. U.S. Global Change Research Program (USGCRP). 2009. Global Climate Change Impacts in the United States . Thomas R. Karl, Jerry M. Melillo, and Thomas C. Peterson, (eds.). Cambridge University Press. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship between Selected of postneonatal infant mortality and particul ate air pollution in the United States.” Environmental Health Perspectives 105 (6): 608-612.

### CHAPTER 5: ECONOMIC IMPACTS – MARKETS OUTSIDE THE ELECTRICITY SECTOR

5.1 Introduction
The energy sector impacts presented in Chapter 3 of this regulatory impact analysis (RIA) include projected changes in the prices for elect ricity, natural gas, and coal resulting from the proposed Electric Utility Generating Unit (EGU) Existing Source Greenhouse Gas (GHG) Guidelines. This chapter addresses the impact of these changes on other markets and discusses some of the determinants of the magnitude of these impacts.

Under the proposed EGU Existing Source GHG Guidelines states are not required to use any of the measures that the EPA determines cons titute BSER, or use those measures to the same degree of stringency that the EPA determines is achievable and cost-effective; rather, CAA section 111(d) allows each state to determine the appropriate combination of, and the extent of its reliance on, measures for its state plan, by way of meeting its state-specific goal. Given the flexibilities afforded states in complying with the emission guidelines, the benefits, cost and economic impacts reported in this RIA are illustra tive of compliance actions states may take. The compliance approaches adopted by the states will ultimately drive the magnitude and timing of impacts on the prices of electricity, natural gas, a nd coal and in turn other markets that use these commodities in the production process. The flexib ility afforded to states by the Clean Air Act also allows them to adopt programs which includ e design elements that may mitigate or promote particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas Initiative use the revenues from allowance auctions to support direct bill assistance for retail consumers, fund investments in clean energy and electricity demand reduction for business consumers, and support employment in the development of clean and renewable energy technologies. In its recent regulations to limit GHG emissions, California’s Air Resources Board designated a portion of allowances to be allo cated to electric distribution companies for the purpose of mitigating electricity rate increases an d their associated impacts. Other states may choose to comply with particularly robust deployment of renewables, energy efficiency, or natural gas in order to promote manufacturing demand or employment in those sectors. Style Definition: Footnote Text,Footnote Text Char Char,Footnote Text Char1 Char Char,Footnote Text Char Char Char Char,Footnote Text Char Char1,ALTS FOOTNOTE,fn,Char,Char4,Footnote Text - Preamble,Char2,Footnote Text - Preamble1,Char3,ft,f: Line spacing: singleTo support the Transport Rule proposed in the summer of 2010, among other rulemakings,
the EPA used the Economic Mode l for Policy Analysis (EMPAX) to estimate the effect of impacts projected by the Integrated Planning M odel (IPM) on markets outside the electricity sector. EMPAX is a dynamic computable general equilibrium (CGE) model that forecasts a new equilibrium for the entire economy after a policy intervention. While the external Council on Clean Air Compliance Analysis (Council) peer revi ew of this study stated that inclusion of benefits in an economy-wide model, specifically adapted for use in that study, “represent[ed] a significant step forward in benefit-cost analys is,” EPA recognizes that serious technical challenges remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models. Consiste nt with the Council’s advice regarding the importance of including benefit-side effects demonstrated by the 1990 to 2020 Clean Air Actstudy, and the lack of available multi-year air quality projections needed to include these benefitside effects, EPA has not conducted CGE modeling for this proposal.

However, the EPA recognizes that serious technical challenges remain when attempting to evaluate the impacts of potential regulatory actions using economy-wide models. The EPA is therefore establishing a new Science Advisory Board (SAB) panel on economy-wide modeling to consider the technical merits and challenges of using this analytical tool to evaluate costs, benefits, and economic impacts in regulat ory development. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefit-cost and economic impact analyses that are used to inform decision-making at the agency. The panel will also be asked to identify potential paths forward for improvements that could address the challenges posed when economy-wide models are us ed to evaluate the effects of regulations.

The advice from the Science Advisory Board (SAB) panel formed specifically to address the subject of economy-wide modeling will not be available in time for this proposal. Given the ongoing SAB panel on economy-wide modeling, the uncertain nature of the ultimate energy price impacts due to the compliance flexibility for states, and the ongoing challenges accurately representing costs, energy efficiency savings and economic benefits in economy-wide modeling, this Chapter proceeds by presenting the energy im pacts associated with the illustrative scenariosanalyzed in Chapter 3. This Chap ter then presents a qualitative discussion of the factors that will, in part, determine the timing and magn itude of effects in other markets.

5.2 Summary of Secondary Market Impacts of Energy Price Changes
Electricity, natural gas, and coal are important inputs to the production of other goods and services. Therefore, changes in the price of these commodities will shift the production costs for sectors that use electricity, natural gas, and co al in the production of other goods and services. Such changes in production costs may lead to changes in the quantities and/or prices of the goods or services produced and changes in imports and exports. We refer to these changes as secondary market impacts.

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on the illustrative compliance approaches modeled for this proposal. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector that is described in more detail in Chapter 3. The prices are average prices weighted by the amount used. Table 6-1 shows these estimated price changes. For other results generated by IPM, please refer to Chapter
3.

There are many factors influencing the projected natural gas prices. Firstly, as discussed in Chapter 3, there are key differences between the two regulatory options (both stringency and over time). Second, IPM (and its integrated gas resource and supply module) will develop natural gas reserves and bring to market appropriate natural gas supplies based on a multitude of factors. Since the model has perfect foresight, it can anticipate future demand for natural gas and respond accordingly. In addition, IPM (and the natural gas module) are viewing a very long time horizon (through 2050, longer than the compliance timeframe), such that the impacts in certain years may be responsive to other modeling assumptions or drivers. The modeling framework is simultaneously solving for all of these key market and policy parameters (both electric and natural gas), resulting in the impacts shown. For more information on the modeling framework see http://www.epa.gov/airmarkt/progsregs/epa-ipm/BaseCasev513.html. Formatted: Font: Not Italic
Formatted: Indent: First line: 0.4"
Formatted: Font: Not ItalicTable 5-1. Estimated Percentage Changes in Average Energy Prices, by Energy Type and Regulatory Option 2020 2025 2030
Option 1 Regional Compliance:

Electricity Price Change: 5.9% 2.7% 2.7% Delivered Natural Gas Price Change: 9.3% -3.3% -0.9% Delivered Coal Price Change: -16.3% -18.3% -18.1%
Option 2 Regional Compliance 2020 2025 2030

Electricity Price Change: 3.6% 2.4% 1.1% Delivered Natural Gas Price Change: 7.5% 0.2% -3.2% Delivered Coal Price Change: -13.8% -14.4% -14.2%
Option 1 State Compliance: 2020 2025 2030

Electricity Price Change: 6.5% 2.9% 3.1% Delivered Natural Gas Price Change: 11.5% -3.5% 0.0% Delivered Coal Price Change: -16.5% -17.9% -18.2%
Option 2 State Compliance 2020 2025 2030

Electricity Price Change: 4.0% 2.7% 1.4% Delivered Natural Gas Price Change: 8.1% 0.8% -2.6% Delivered Coal Price Change: -13.6% -14.1% -14.2%

For options and years when the price of electricity, natural gas, or coal increased, one would expect decreases in production and increases in market prices in sectors for which these commodities are inputs, ceteris paribus. Conversel y, in options and years when prices of these inputs decreased, one would expect increases in production and decreases in market prices within these sectors. Smaller changes in input price changes are assumed to lead to smaller impacts within secondary markets. However, a number of factors in addition to the magnitude and sign of the energy price changes, influence the magnitude of the impact on production and market prices for sectors using electricity, natural gas, or co al as inputs to production. These factors are
discussed below.

5.3.1 Share of Total Production Costs
The impact of energy price changes in a partic ular sector depends, in part, on the share of total production costs attr ibutable to those commodities. For sectors in which the directly Comment [A1]: Would EPA provide intuition on why gas price changes are so different between option 1 and 2?affected inputs are only a small portion of producti on costs, the impact will be smaller than for sectors in which these inputs make up a greater po rtion of total production costs. Therefore more energy-intensive sectors would potentially experi ence greater cost increases when electricity, natural gas, or coal prices increase, but would also experience greater savings when these input prices decrease. 1

5.3.2 Ability to Substitute between Inputs to the Production Process
The ease with which producers are able to s ubstitute other inputs for electricity, natural gas, or coal, or even amongst those commodities , influences the impact of price changes for these inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partially, offset the increased cost of these inputs resulting in smaller market impacts. Similarly, when prices fo r electricity, natural gas, or coal decrease, some sectors may choose to use more of these input s in place of other more costly substitutes.

5.3.3 Availability of Substitute Goods and Services
The ability of producers in sector s experiencing changes in thei r input prices to pass along the increased costs to their customers in the form of higher prices for their products depends, in part, on the availability of substitutes for the sectors’ products. Substitutes may either be other domestic products or foreign imports. If close substitutes exist, the demand for the product will in general be more elastic and the producers will be less able to pass on the added cost through a price increase.

Such substitution can also take place between foreign and domestic goods within the same sector. Changes in the price of electricity, natura l gas, and coal can influence the quantities of goods imported or exported from sectors us ing these inputs. When the cost of domestic production increases due to more expensive inputs , imports may increase as consumers substitute towards relatively less costly foreign-produced goods. If imports increase as a result of a regulation and those imports come from countries wi th fewer emission controls, this can result in foreign emission increases that offset some po rtion of domestic decreases, an effect commonly

1 The net direct effect of this rulemaking on the production co sts of a sector that is attributable to a change in the electricity price also depends on the expenditures the s ector makes to reduce its demand for electricity under any energy efficiency program that was adopted to achieve a state goal. Comment [A2]: Trade exposed industries will also have a more difficult time passing the costs along as well.referred to as “leakage.” The potential for leakage is noteworthy for global pollutants such as carbon dioxide (CO 2 ) and other GHG emissions. Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO 2 emissions are not affected by the location from which those emissions originate. EPA does not anticipate significant leakage in the EGU sector to occur from this regulation as discussed in Chapter 3. because the nature of electricity transm ission does not lend itself to significant imports or exports of electricity.

5.4 Conclusions
Changes in the price of electricity, natural gas, and coal can impact markets for goods and services produced by sectors that use these energy inputs in the production process. The direction and magnitude of these impacts is influenced by a number of factors. Changes in cost of production may lead to changes in price, quantity produced, and profitability of firms within secondary markets. If regulation results in changes in domestic markets that lead to an increase in imports, increases in production in countries with more energy-intensive production may lead to emissions leakage. Comment [A3]: Would EPA provide the page numbers in Chapter 3 that leads to this conclusion?***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-1 

### CHAPTER 6: EMPLOYMENT IMPACT ANALYSIS

6.1 Introduction
Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and jo b creation. It must be based on the best available science” (Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, 1 during periods of sustained high unemployment, employment impacts are of particular concern and questions may arise about their existence and magnitude. The chapter discusses and projects potential employment impacts of the Proposed Elect ric Generating Unit Greenhouse Gas (EGU GHG) Existing Source Guidelines for the electric power industry, coal and natural gas production, and demand-side energy efficiency.
Section 6.2 presents the overview of the guidelines and a general description of the associated building block framework. Section 6.3 describes the theoretical framework used to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 6.4 presents an overview of the peer-reviewed literature relevant to evaluatin g the effect of environmental regulation on employment. Section 6.5 provides background regarding recent employment trends in the electricity generation, coal and natural gas extraction, renewable energy, and demand-side energy efficiency-related sect ors. Section 6.6 presents the EPA’s quantitative projections of potential employment impacts in these sectors. Th ese projections are based in part on a detailed model of the electricity production sectors used for this regulatory analysis. Additionally, this section discusses projected employment impacts du e to demand-side energy efficiency activities.
Section 6.7 offers several conclusions.
6.2 Overview of the Proposed EGU GHG Existing Sour ce Guidelines
The EPA is proposing emission guidelines for st ates to use in developing plans to address greenhouse gas emissions from existing fossil fuel -fired EGUs. Specifically, EPA is proposing

1 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-2 state-specific rate-based goals for carbon dioxide emissions from the power sector, as well as emission guidelines for states to use in developi ng plans to attain the state-specific goals. The guidelines, as proposed, will lower carbon intens ity of power generation in the United States. Under the Clean Air Act (CAA) section 111(d), state plans must establish standards of performance that reflect the degree of emission limitation achievable through the application of the “best system of emission reduction” (BSER) that, taking into account the cost of achieving such reductions and non-air quality health and environmental impact and energy requirements, the Administrator determines has been adequately demonstrated. Consistent with CAA section 111(d), this proposed rule contains state-speci fic goals that reflect the EPA’s calculation of emission reductions that a state can achieve through the cost-effective application of BSER. The EPA is using four building blocks as a basis to determine state-specific goals.
6.2.1 Determining State Goals Utilizing the Four Building Blocks

In proposing state goals, EPA is applying the following four building blocks. Each represents a demonstrated approach to improving the GHG performance of existing EGUs in the power sector: 2

1. lowering the carbon intensity of generation at individual affected EGUs (e.g., through heat rate improvements);

2. reducing emissions of the most carbon-intensive affected EGUs to the extent that this can be accomplished cost-effectively by shifting generation to less carbon-intensive existing fossil fuel-fired EGUs, including natural gas combined cycle (NGCC) units under construction;

3. reducing emissions of carbon-emitting EGUs to the extent that this can be accomplished cost-effectively by expanding the amount of new, lower (or no) carbon-intensity generation; and

4. reducing emissions of carbon-emitting EGUs to the extent that this can be accomplished by cost-effectively increasing demand-side energy efficiency.

2 Refer to Chapter 3 for information regarding estimates of emissions reductions achievable by each building block and the determination of state-specific goals.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-3 6.3 Economic Theory and Employment
Regulatory employment impacts are difficult to disentangle from other economic changes affecting employment decisions
, over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc). The unit of measurement (e.g., number of jobs, types of job hours worked, and earnings)
, may affect observability of that response. Net employment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly regulated sector, upstream and downstream sectors, et c.) over time. In light of these diff iculties, economic theory provides a constructive framework for analysis.
Microeconomic theory describes how firms adjust their use of inputs in response to changes in economic conditions. 3 Labor is one of many inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints. 4,5
Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002), adapt this model to analyze how environmental regulations affect labor demand. 6 They model environmental regulation as effectively requiring certain factors of production, such as pollution abatement capital, at levels that firms would not otherwise choose.
Berman and Bui (2001, pp. 274-75) model two components that drive changes in firmlevel labor demand: output e ffects and substitution effects. 7 Regulation affects the profit-

3 See Layard and Walters (1978), a standard microeconom ic theory textbook, for a discussion, in Chapter 9.

4 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization.

5 In this framework, labor demand is a function of quan tity of output and prices (of both outputs and inputs).

6 Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002) use a cost-minimization framework, which is a special case of profit-maximization with fixed output quantities.

7 The authors also discuss a third component, the impact of re gulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor-shift effect.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-4 maximizing quantity of output by changing the marginal cost of production. If regulation causes marginal cost to increase, it will place upward pr essure on output prices, leading to a decrease in demand, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulation increases marginal cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting
, and more efficient equipment that lowers marginal production costs. In such a case, output could increase. For example, in the context of the current rule, improv ing the heat rate of utility boiler increases fuel efficiency, lowering marginal production costs, and thereby potentially increasing the boiler’s generation. An unregulated profit-maximizing firm may not have chosen to install such an efficiency-improving technology if th e investment cost were too high. 8
The substitution effect describes how, holding output constant, regul ation affects laborintensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection re quirements, specialized waste handling, or pollution technologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman an d Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures required by the regulation and the corresponding change in labor-intensity of production.
In summary, as output and substitution eff ects may be positive or ne gative, theory alone cannot predict the direction of the net effect of regulation on labor demand at the level of the regulated firm. Operating within the bounds of standard economic theory, however, empirical estimation of net employment effects on regulated firms is possible when data and methods of sufficient detail and quality are available. The literature, however, illustrates difficulties with empirical estimation, For example, studies so metimes rely on confidential plant-level employment data from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that are too da ted to be reliably informative. In addition, the most commonly used empirical methods do not permit estimation of net effects.

8 An unregulated profit-maximizing firm may not have chosen to install such an efficien cy-improving technology if the investment cost were too high.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-5 The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry. Employ ment impacts at an individual plant do not necessarily represent impacts for the sector as a whole. The approach must be modified when applied at the industry level.
At the industry-level, labor demand is more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. 9 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. 10 In this case the output effect may be small, while the substitution effect depends on input substitutability. Suppose, for example, that new equipment for heat rate improvements requires labor to install and operate. In this case the substitution effect may be positive, and with a small output effect, the total effect may be positive. As with potential effects for an indivi dual firm, theory cannot determine the sign or magnitude of industry-level regulatory effects on labor demand . Determining these signs and magnitudes requires additional s ector-specific empirical study. For environmental rules, much of the data needed for these empirical studies are not publicly available, would require significant time and resources in order to access confidential U.S. Census data for research, and also would not be necessary for other components of a typical RIA.
In addition to changes to labor demand in the regulated industry, net employment impacts encompass changes in other related sectors. For example, the proposed guidelines may increase demand for heat rate improving equipment and services. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated industry is purchasing the equipment and these costs may impact labor demand at regulated firms. Therefore, it is important to consider the net effect of compliance actions on employment across multiple sectors or industries.

9 See Ehrenberg & Smith, p. 108.

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

6-6 If the U.S. economy is at full employment, even a large-scale environmental regulation is unlikely to have a noticeable impact on aggregate net employment. 11 Instead, labor would primarily be reallocated from one productive use to another (e.g., from producing electricity or steel to producing high efficiency equipment), and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another). 12
Affected sectors may experience transitory effects as workers change jobs. Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals and communities just as localized increases may have positive impacts.
If the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitude of the net impact of environmental regulation on employment; it could cause either a short-run net increase or short-run net decrease (Schmalansee and Stavins, 2011). An important research question is how to accommodate unemployment as a structural feature in economic models. This feature may be important in assessing large-scale regulatory impacts on employment (Smith 2012).
Environmental regulation may also affect labor supply. In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to work. 13 While the theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study empirically. There is a small emerging literature , described in the next
section that uses detailed labor and enviro nmental data to assess these impacts.
To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net employment effect incorporates expected

11 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do so at prevailing wages is actively employed. The une mployment rate at full employment is not zero.

12 Arrow et. al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can be important, as discussed in late r paragraphs of this section.

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

6-7 employment changes (both positive and negative) in the regulated sector and elsewhere. Labor demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be eith er negative or positive. Estimation of net employment effects for regulated sectors is possi ble when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.
6.4 Current State of Knowledge Based on the Peer-Reviewed Literature
The labor economics literature contains an extensive body of peer-reviewed empirical work analyzing various aspects of labor demand, relying on the theoretical framework discussed in the preceding section. 14 This work focuses primarily on effects of employment policies such as labor taxes and minimum wages. 15 In contrast, the peer-reviewed empirical literature specifically estimating employment effects of en vironmental regulations is more limited.
Empirical studies, such as including Berman and Bui (2001) and Morgenstern et al (2002) , suggest that net employment impacts weremay not be statistically different from zero or slightly positive but small even in the regulated sector. Other research suggests that more highly regulated counties may generate fewer jobs than less regulated ones (Greenstone 2002 , Walker
2011 ). Environmental regulations may affect sectors that support pollution reduction earlier than the regulated industry. Rules are usually announced well in advance of their effective dates and then typically provide a period of time for firms to invest in technologies and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution control equipment and services to enable compliance when the regulation
becomes effective. Estimates of short-term incr eases in demand for specia lized labor within the environmental protection sector have been prepared for several EPA regulations in the past, including the Mercury and Air Toxics Standards (MATS). 16 Overall, the peer-reviewed literature does not contain evidence that environmental regu lation has a large impact on net employment (either negative or positive) in the long run across the whole economy.

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

15 See Ehrenberg & Smith (2000), Chapter 4: “Employment Ef fects: Empirical Estimates” for a concise overview.

16 U.S. EPA (2011b).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-8 6.4.1 Regulated Sector

Berman and Bui (2001) examine how an increase in local air quality regulation affects manufacturing employment in the South Coast Air Quality Management District (SCAQMD), which includes Los Angeles and its suburbs. From 1979 to 1992 the SCAQMD enacted some of the country’s most stringent air quality re gulations. Using SCAQMD’s local air quality regulations, Berman and Bui identify the effect of environmental regulations on net employment in regulated manufacturing industries relative to other plants in the same 4-digit SIC industries but in regions not subject to local regulations. 17 The authors find that “while regulations do impose large costs, they have a limited effect on employment” (Berman and Bui, 2001, p. 269). Their conclusion is that local air quality regula tion “probably increased labor demand slightly” but that “the employment effects of both compli ance and increased stringency are fairly precisely estimated zeros, even when exit and dissuaded entry effects are included” (Berman and Bui, 2001, p. 269). 18
The few studies in peer-reviewed journals evaluating employment impacts of policies
that reduce CO 2 emissions in the electric power generation sector are in the European context. In a sample of 419 German firms, 13 percent of which were in the electricity sector, Anger and Oberndorfer (2008) find that the initial allocation of emission permits did not significantly affect employment growth in the first year of the EU Emissions Trading Scheme (ETS). Examining European firms from 1996-2007, Commins et al. (2011) find that a 1 percent increase in energy taxes is associated with a 0.01 percent decrease in employees in the electricity and gas sector. Chan et al. (2013) estimate the impact of the EU ETS on a panel of almost 6,000 firms in 10 European countries from 2005-2009. They find that firms in the power sector that participated in the ETS had 2-3 percent fewer employees relative to those that did not participate, but this effect is not statistically significant.
This literature suggests that the employment impacts of controlling CO 2 emissions in the European power sector were small. The degree to which these studies’ results apply to the U.S.

17 Berman and Bui include over 40 4-digit SIC industries in their sample. Like Morgenstern, Pizer, and Shih (2002), tT hey do not estimate the number of jobs creat ed in the environmental protection sector.

18 Including the employment effect of exiting plants and plants dissuaded from opening will increase the estimated impact of regulation on employment. This employment effect is not included in Morgenstern et. al. (2002)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-9 context is unclear. European policies analyzed in these studies effectively put a price on emissions either through taxes or tradable permits. A performance standard may not generate similar employment effects. Moreover, European firms face relative fuel prices and market regulatory structures different from their U.S. counterparts, further complicating attempts to transfer quantitative results from the EU experience to evaluate this rule.
A small literature examines impacts of environmental regulations on manufacturing employment. Kahn and Mansur (2013) study environmental regulatory impacts on geographic distribution of manufacturing employment, controlli ng for electricity prices and labor regulation (right to work laws). Their methodology identifies employment impacts by focusing on neighboring counties with different air quality regulations. They find limited evidence that environmental regulations may cause employment to be lower within “county-border-pairs.” This result suggests that regulation may cause an effective relocation of labor across a county border, but since one county’s loss is another’s gain
, such shifts cannot be transformed into an estimate of a national net effect on employment . Moreover this result is sensitive to model specification choices.
6.4.2 Labor Supply Impacts

The empirical literature on environmental regulatory employment impacts focuses primarily on labor demand. However, there is a nascent literature focusing on regulation-induced effects on labor supply. This literature is limited by empirical challenges. Researchers have found that lost work days and mortality are reduced when air quality improves. 19 The EPA’s study of benefits and costs of implementing clean air regulations used these studies to predict how increased labor availability would increase the labor supply and improve productivity and the economy. 20 Worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2013) review the research studying these productivity effects, focusing on how health and human capital may be affected by environmental quality, particularly air pollution. In

19 The Benefits and Costs of the Clean Air Act from 1990 to 2020 Final Report – Rev. A U.S. Environmental Protection Agency, Office of Air and Radiation, April 2011a. Available at:

< http://www.epa.gov/air/sect812/feb11/fullreport_rev_a.pdf>.

20 The Benefits and Costs of the Clean Air Act from 1990 to 2020 Final Report – Rev. A, U.S. Environmental Protection Agency, Office of Air and Radiation, April 2011a. Available at:

< http://www.epa.gov/air/sect812/feb11/fullreport_rev_a.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-10 previous research, 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 variati on in monitored ozone levels on productivity. They find “ozone levels well below federal air quality standards have a significant impact on productivity: a 10 parts per billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654).
This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment estimates that would a ppropriately capture the way in which costs, compliance spending, and environmental benefits propagate through the macro-economy. Quantitative estimates are further complicated by the fact that macroeconomic models often have little sectoral detail and usually assume that the economy is at full employment. The EPA is currently seeking input from an independent expert panel on modeling economy-wide regulatory impacts, including employment effects. 21

6.5 Recent Employment Trends
The U.S. electricity system includes employ ees that support electric power generation, transmission and distribution, the extraction of fossil fuels, renewable energy generation and supply-side and demand-side energy efficiency. Th is section describes recent employment trends in the electricity system.

6.5.1 Electric Power Generation

In 2013 the electric power generation, transmi ssion and distribution sector (NAICS 2211) employed 394,000 workers in the U. S. 22 Installation, maintenance, and repair occupations

21 For further information see: < https://www.federalregister.gov/articles/2014/02/05/2014-02471/draft-supportingmaterials-for-the-science-advisory-board-p anel-on-the-role-of-economy-wide-modeling>.

22 U.S. Bureau of Labor Statistics. “Current Employm ent Survey Seasonally Adjusted Employment for Electric Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Data extracted on: February 19, 2014. Available at: < http://www.bls.gov/ces/data.htm>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-11 accounted for the largest share of workers (30 percent). 23 These categories include inspection, testing, repairing and maintaining of electrical e quipment and/or installation and repair of cables used in electrical power and distribution syst ems. Other major occupation categories include office and administrative support (17 percent), production occupations (15 percent), architecture and engineering (11 percent), business and financial operations (7 percent) and management (6 percent).
As shown in Figure 6.1, employment in the Electric Power Industry averaged 435,000 workers in the early 2000s, declining to an average of 400,000 workers later in the decade, and to 394,000 workers in 2013.

Figure 6.1. Electric Power Industry Employment

23 U.S. Bureau of Labor Statistics, Occupational Employment Statistics, May 2012 National Industry-Specific Occupational Employment and Wage Estimates, Electri c Power Generation, Transmission, and Distribution (NAICS 2211). Available at: < http://www.bls.gov/oes/current/naics4_221100.htm >.350400450500

2000 2005 2010 2015 Year 

### Electric Power Industry national employment (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-12 6.5.2 Fossil Fuel Extraction

6.5.2.1 Coal Extraction
The coal extraction sector is primarily engaged in coal mining and coal mine site development, excluding metal ore mining and nonmetallic mineral mining and quarrying. There are two sources of U.S. government data on coal mining employment, the BLS Current Employment Statistics (NAICS 2121), and the Department of Labor’s Mine Safety and Health Administration (MSHA). 24 Both sources show similar national levels and trends, though one is survey-based (BLS) and the other is census-based (MSHA). MSHA tracks direct coal mine employment and independent contractor employ ment, whereas BLS does not track contractors. Contractor employment reported by MSHA focuses primarily on mine development, construction, reconstruction or demolition of mine facilities, construction of dams, excavation or earth moving, equipment installation, service or repair, and material handling, drilling, or blasting. 25 In 2013, BLS reported 79,000 coal mining employees, and MSHA reported 80,000 coal mining employees and 32,000 contractors. 26 Both sets of data reveal a stable trend in employment over the past 10 years, with the exception of a small temporary increase in 2011.
See Figure 6.2 below.

24 U.S. Bureau of Labor Statistics. “Current Employment Statistics Seasonally Adjusted Employment for Coal Mining (national employment),” NAICS 2121, Series ID: CES1021210001. Data extracted on: February 19, 2014. Available at: < http://www.bls.gov/ces/data.htm>.

25 Mine Safety and Health Administration, CFR Part 50 Ti tle 30 Employment Data – sele cted contract employment is included, p. 2. Available at:

< http://www.msha.gov/Stats/Part50/WQ/MasterFiles/MIWQ%20Master_20133.pdf> . U.S. Bureau of Labor Statistics, Current Employment Statistics – cont ract employment not included. Available at:

< http://www.bls.gov/ces/idcf/forme_sp.pdf>

.

26 Annual averages calculated for: (i) BLS monthly coal mining employment data for 2013, and (ii) MSHA Part 50 quarterly data for 2013. Available at: < http://www.msha.gov/STATS/PART50/P50Y2K/AETABLE.HTM >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-13 Figure 6.2. Coal Production Employment

6.5.2.2 Oil and Gas Extraction
In 2013, there were 198,000 employees in the oil and gas extraction sector (NAICS 211). 27 This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of hydrocarbon liquids. Activities include the development of gas and oil fields, exploration activities for crude petroleum and natural gas, drilling, completing, and equipping wells, and other production
activities. 28 In contrast with coal, and looking at Figure 6.3, there has been a sharp increase in employment in this sector over the past decade.

27 BLS, Current Employment Statistics. Seasonally adjusted employment for oil and gas extraction (national employment), NAICS 211. Series ID: CES1021100001. Data extracted on: February 19, 2014. Available at:

< http://www.bls.gov/ces/data.htm>.

28 U.S. Bureau of Labor Statistics. 20014. Available at: < http://www.bls.gov/iag/tgs/ia g211.htm Accessed Feb. 19>.20406080100

2000 2005 2010 2015 Year BLS - employees MSHA - employees MSHA - contractors 

### Coal mining employment - national (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-14 Figure 6.3. Oil and Gas Production Employment

6.5.3 Clean Energy Employment Trends

Clean energy resources, such as energy efficiency and renewable energy, are used to meet energy demand, reduce peak electricity system loads, and reduce reliance on the most carbon-intensive sources of electricity. However, there is not a single clean energy sector in
standard national accounts classifications. Renewabl e generation is not reported to the Bureau of Labor and Statistics separately from other elect ric power generation. Similarly, manufacturers of energy efficient appliances are not reported separately from conventional appliance manufacturers and green building design is not se parate from the construction sector. Instead, clean energy technology and services are supported by industries throughout the economy.
Without a specific industrial classification, it is difficult to quantify the exact number of clean energy-related jobs or document the trends. Employees engaged in clean energy can span many job classifications, such as experts requi red to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians who inst all service or operate100150200250

2000 2005 2010 2015 Year 

### Oil and Gas Extraction national employment (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-15 equipment. As such, there are a variety of definitions of clean or green jobs used, some more expansive than others.
6.5.3.1 Defining Clean Energy Jobs
Two U.S. Government sources, the 2010 Department of Commerce (DOC) report, Measuring the Green Economy and the 2010 and 2011 Bureau of Labor Statistics (BLS) Green
Goods and Services surveys have subdivided industrial classifications into “green” categories. In both cases the approach was to determine which product classifications, rather than industries, were green. They multiplied green production by product revenue and defined an industrial sector as green if it met a threshold of green revenue as a proportion of total revenue.
DOC broadly defined green jobs in 2010 as those “created and supported in businesses that produce green products and services.” 29 They further classified green jobs into a broad and a narrow category. The narrow category includes only products deemed to be green without disagreement, while the broad category is le ss conservative. DOC analysts applied their definition of green goods and services to over 22,000 product codes in the 2007 Economic
Census to estimate their contribution to the U. S. economy. The report found that the number of green jobs in 2007 ranged from 1.8 million to 2. 4 million jobs, accounting for between 1.5 and 2 percent of total private sector employment. 30
BLS used an expansive definition of clean or green jobs in 2010 and 2011. It goes beyond direct clean energy-related investments and includes “those in businesses that produce goods and provide services that benefit the environment or conserve natural resources. These goods and services, which are sold to customers, include research and development, installation, and maintenance services for renewable energy and energy efficiency and education and training related to green technologies and practices” but also include recycling and natural resource

29 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy ,” April. Available at: < http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf >.

30 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy, April. Available at: < http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf>

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

6-16 conservation, such as forestry management. 31 Based on surveys across the 325 industries it identified as potential producers of green goods and services, BLS counts approximately 2.3 million jobs in the green eco nomy in 2010, rising 7.4 pe rcent to 2.5 million in 2011, 32 compared to increases of about one percent across all occupations in the entire economy over the same period. 33 The table below, Table 6-1, presents BLS green job estimates nationally and for the utility sector.
Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average)

Total GGS Employment Utility GGS Employment Total GGS Growth 2010-11 Utility GGS Growth 2010-11

2010 2,342,562 69,031 NA NA

2011 2,515,200 71,129 7.4% 3.0%

Source: Bureau of Labor Statistics

6.5.3.2 Renewable Electricity Generation Employment Trends
The DOC report does not separate renewable energy data and the BLS data include only privately owned electricity generating facilities

. As such, neither source isolates renewable electricity generation employment. For historical tr ends in this sector, we therefore, rely on a Brookings Institution study, Muro et al. (2011). This study built a national database of “clean economy” jobs from the bottom up, verifying each company individually. 34 They include a list of

31 BLS has identified 325 detailed industries (6-digit NA ICS) as potential producers of green goods and services. Available at: < http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, l ast modified date: March 19, 2013.

32 U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). 2011. “Green Goods and Services 2010-2011.” (Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>.

33 U.S. Department of Labor, U.S. Bureau of Labor St atistics. 2010. National Occ upational Employment and Wage Estimates, United States. Available at: < http://www.bls.gov/oes/ 2010/may/oes_nat.htm>, May. National Occupational Employment and Wage Estimates, United States http://www.bls.gov/oes/2010/may/oes_nat.htm

.

34 < http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-17 categories similar but not identical to that of BLS, including agricultural and natural resources conservation, education and compliance, energy and resource efficiency, greenhouse gas reduction, environmental management and recycling, and renewable energy. This study found about 138,000 jobs in the renewable energy sector in 2010, with an overall average annual growth rate of 3.1 percent from 2003-2010. Table 6-2 details the national results by energy

source.

Table 6-2. Renewable Electri city Generation-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate

Biofuels/Biomass 20,680 8.9
Geothermal 2,720 6.7 Hydropower 55,467 -3.6 Renewable Energy Services 1,981 6.3
Solar Photovoltaic 24,152 10.7
Solar Thermal 5,379 18.4 Waste-to-Energy 3,320 3.7

Wave/Ocean Power 371 20.9
Wind 24,294 14.9
Total 138,364 3.1

Source: http://www.brookings.edu/~/media/Seri es/resources/071 3_clean_economy.pdf , Appendix A.

6.5.3.3 Employment Trends in Demand

- Side Energy Efficiency Activities
U.S. government data used for calculating the historical trends in the demand-side energy efficiency sector come from the BLS green goods and services surveys. BLS reports an energy efficiency category, finding 1.49 million private sector energy efficiency jobs in 2010 and 1.64
million in 2011.
For a longer term trend the Brookings Institu tion study (Muro et al. 2011) built a national database of “clean economy” jobs from the bottom up, verifying each company individually. 35

35 < http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-18 This study found about 428,000 jobs in the Energy and Resource Efficiency sector in 2010, with an overall average annual growth rate of 2.6 percent from 2003-2010. Table 6-3 details the results by energy sector.

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

Sector Jobs, 2010 2003-2010 Average

Annual Growth Rate
Appliances 36,608 -3.1 Energy-saving Building Materials 161,896 2.5 Energy-saving Consumer Products 19,210 -2.9
Green Architecture and Construction Services 56,190 6.4 HVAC and Building Control Systems 73,600 3.3 Lighting 14,298 -1.8 Professional Energy Services 49,863 6.9
Smart Grid 15,987 8.6
Total 427,652 2.6

Source: http://www.brookings.edu/~/media/Ser ies/resources/0713_clean_economy.pdf , Appendix A

In addition, other research institutes and industry groups have clean economy or clean energy employment databases. While definitions and timeframes vary, all show positive employment trends of 1.9 percent or more growth in clean energy-related jobs annually. For more information about these studies, see Appendix 6.B.2 Other Approaches to Exploring Clean Energy Employment Trends.
6.6 Projected Sectoral Employment Changes due to Proposed Guidelines
EGUs may respond to these proposed guidelines by placing new orders for efficiencyrelated or renewable energy equipment and services to reduce GHG emissions. Installing and operating new equipment or improving heat rate efficiency could increase labor demand in the electricity generating sector itself, as well as associated equipment and services sectors.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-19 Specifically, the direct employment effects of supply-side initiatives include changes in labor demand for manufacturing, installing, and ope rating higher efficiency or renewable energy electricity generating assets supported by the initiative while reducing the demand for labor that would have been used by less efficient or higher emitting generating assets. Once implemented, increases in operating efficiency would impact th e power sector’s demand for fuel and plans for
EGU retirement and new construction.
In addition, EPA expects state compliance plans to also include demand-side energy efficiency policies and programs that typically change energy consumption patterns of business and residential consumers by reducing the quantity of energy required for a given level of production or service. Demand-side initiatives generally aim to increase the use of cost-effective energy efficiency technologies (e.g., including mo re efficient appliances and air conditioning systems, more efficient lighting devices, more efficient design and construction of new homes and businesses), and advance efficiency improvements in motor systems and other industrial processes. Demand-side initiatives can also directly reduce energy consumption, such as through programs encouraging changing the thermostat during the hours a building is unoccupied or motion-detecting room light switches. Such dema nd-side energy efficiency initiatives directly affect employment by encouraging firms and consumers to shift to more efficient products and processes than would otherwise be the case. Empl oyment in the sectors that provide these more efficient devices and services would be expected to increase, while employment in the sectors that produce less efficient devices would be expected to contract.
This analysis uses the cost projections from the engineering-based Integrated Planning Model (IPM) to project labor demand impacts of the proposed guidelines for the electricity generation sector (fossil, renewable, and nuclear), and the fuel production sector (coal and natural gas). These projections include effects attributable to heat rate improvements, construction of new EGUs, changes in fuel use, and reductions in electricity generation due to demand side energy efficiency activities. To project labor requirements for demand side energy efficiency activities, the analysis uses a different approach that combines data on historic changes in employment and expenditures in the energy efficiency sector with projected changes in expenditures in the sector arising from st ate implementation of the proposed guidelines.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-20 We project labor impacts for two options for establishing the “best system of emission reduction” (BSER) for GHG em issions from existing EGUs. The EPA is proposing a BSER goal approach referred to as Option 1 and taking comment on a second approach referred to as Option 2. Option 1 has a relatively stringent target, but allows a longer timeframe to comply (2030). Option 2 has a less stringent target with a shorter timeframe (2025). This analysis estimates labor impacts of illustrative state and regional compliance approaches for the goals set for Options 1 and 2. With the state compliance approach, states are assumed to comply with the guidelines by implementing measures solely within the state. Under the regional approach, states may form regional groups to comply with the guidelines.
6.6.1 Projected Changes in Employment in Elect ricity Generation and Fossil Fuel Extraction

The analytical approach used in this analysis is a bottom-up engineering method combining EPA’s cost analysis of the proposed guidelines with data on labor productivity
, engineering estimates of the amount and types of labor needed to manufacture, construct, and operate different types of gene rating units, and prevailing and wage rates for skilled and general labor categories . This approach is different from the types of economic analyses discussed in Section 6.3. Rather than projecting employme nt impacts throughout the U.S. economy, the engineering based analysis focuses on the direct impact on labor demand in industries closely involved with electricity generation. The engine ering approach projects labor changes measured as the change in each analysis year in job-years 36 employed in the power generation and directly related sectors (e.g., equipment manufacturing, fuel supply and generating efficiency services). For example, this approach projects the amounts and types of labor required to implement improvements in generating efficiency. It then uses the EPA’s estimated effect of efficiency improvements on fuel demand to project reductions in the amount of labor required to produce coal and gas.
This analysis relies on projections and costing analysis from the IPM, which uses industry-specific data and assumptions to estimate costs and energy impacts of the proposed guidelines (see Chapter 3). The EPA uses the IPM to predict coal generating capacity that is

36 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time individual for one year. For example, 20 job-years in 202 0 may represent 20 full-time jobs or 40 half-time jobs in that year.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-21 likely to undertake improvements in heat rate efficiency (HRI). 37 IPM also predicts the guidelines’ impacts on fuel use, retirement of existing units, and construction of new ones.
The methods we use to estimate the labor impacts are based on the analytical methods used in the Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). While the methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with operating and main taining generating units, as well as labor needed to mine coal and natural gas) are the same as we used in MATS (with updated data where available), the
methods used to estimate the labor associated with installing new capacity and implementing heat rate improvements were developed for the GHG guidelines analysis.
The bottom up engineering based labor analysis in the MATS RIA primarily was concerned with the labor needs of retrofitting pollution control equipment. A central feature of the GHG guidelines labor analysis, however, involves the quantity and timing of the labor needs of building new renewable (primarily wind) and natural gas combined cycle (CC) generating capacity. The EPA IPM analysis finds that by 20 20 a significantly larger amount of renewable and CC capacity will be built to implement the GH G guidelines under all of the option scenarios. For example, in the base case IPM estimates th at 11 GW of non -hydro renewable capacity will be built between 2016 and 2020, while under Option 1 with regional compliance almost twice as much (21.3 GW) renewable capacity will be built. Similarly, in the base case 7.9 GW of CC capacity is built between 2016 and 2020, while almost 3 times as much (24.43 GW) is built under Option 1 with regional compliance.
An important aspect of building new units is that all of the construction-related labor occurs before the new units become operational. While the financial costs of building the new units are amortized and recouped over the book life of the new equipment, the labor involved with manufacturing equipment and constructing the new units occurs, and is actually paid for, in a concentrated amount of time before the new capacity begins to generate electricity. IPM

37 Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to generate a net electrical output. Assuming all other things be ing equal, a lower heat rate is more efficient because more electricity is generated from each ton of coal.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-22 assumes 38 that new CC units take 3 years to build, and both natural gas combustion turbines and wind powered renewables take 2 years.
In addition to the amount of labor needed to build new generating capacity, IPM also estimates that there will be significant labor imp acts in later years from avoiding having to build additional new capacity. Because of the demand-side energy efficiency programs, the total amount of electricity needed by 2030 is substant ially lower with all of the Options than in the base case. For example, in Option 1 with regi onal compliance only half the total new CC capacity is built between 2016 and 2030 (40.6 GW), compared with 79.7 GW in the base case. The avoided new capacity results in both a significant net cost savings to consumers and the power sector, as well reduced emissions of both CO 2 and precursor pollutants from fossil fuel generation. The avoided needed to build new capacity, however, also has significant labor impacts. A portion of the employme nt that would have been used to build the new capacity in the base case will not occur with the implementatio n of the GHG guidelines. Similarly, less labor involved with operating and providing fuel for new units will be needed with the GHG guidelines than in the base case.
Overall, the impact of much more rapid construction of new renewable and fossil generation capacity in the early years of implementing the GHG guidelines, followed by the need to build substantially less capacity in later years as the demand-side energy efficiency programs reduce the overall electricity demand relative to the base case, creates an important but complex temporal dynamic to the supply side labor analysis. In the early years of implementation there will be a sizable net increase in the amount of labor needed to construct and operate the new generating capacity. However in later years the reduced need for additional newly built capacity
results in a sizable net decrease in the amount of labor needed relative to the base case.
The changes in the timing and overall need for new capacity haves direct labor impacts not only on the construction-related, one time labor, but also on the subsequent labor needs for operating and fuel supply labor to operate the plants. In the case of avoided new capacity, the labor impacts include the loss of those ongoing la bor needs. In addition, there are similar labor impacts from the loss of operating and fuel rela ted jobs arising from the retirement of existing coal generating capacity.

38 Table 4.7, IPM 5.13 Documentation. Formatted: Subscript***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-23 A critical component of the overall labor imp acts of implementing the GHG guidelines is the impact of the labor associated with the demand-side energy efficiency activities. The demand side labor impacts are presented in Section 6.6.2. All of the labor impacts of the demand-side energy efficiency ac tivities are increases in labor needs, which more than offset the loss of supply-side jobs associated with the decreasing demand for electricity Section 6.6.2 projects additional labor required fo r demand-side efficiency activities. arising from the demandside programs.

The IPM labor expenditure projections are distributed across different labor categories (e.g., general construction labor, boilermakers and engineering) using data from engineering analyses of labor’s overall share of total expenditu res, and apportionment of total labor cost to various labor categories. Hourly labor expenditures (including wages, fringe benefits, and employer-paid costs including taxes , insurance and administrative costs ) for each category are used to estimate the labor quantity (measured in full time job-years) consistent with the compliance scenario projections. Projected labor impacts arising from changes in fuel demand are primarily derived from labor productivity data for coal mining (tons mined per employee hour) and natural gas extraction (MMBtu produced/job-year). Tables 6.4 and 6.5 present projected changes relative to the baseline of four labor categories : for 2020, 2025 and 2030:

1. manufacturing, engineering and construction for building, designing and implementing heat rate improvements;
2. manufacturing and construction for new generating capacity;
3. operating and maintenance for existing generating capacity; and
4. extraction of coal and natural gas fuel.

All of the employment estimates presented in Tables 6-4 and 6-5 are estimates occurring in a single year. For the construction-related (one time) labor impacts, including the installation of HRI, Tables 6-4 and 6-5 present the average annual impact occurring in each year of three different intervals of years. The multi-year intervals correspond with the analytical years reported by IPM. The three intervals are from 2017 through 2020 (a four year interval), from 2021 through 2025 (5 years), and 2026 through 2030 (5 years). The construction-related labor***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-24 analysis are based on the IPM estimates of the net change in capital investment that occurs during each multi-year interval to fund building new units completed during that interval. The new built labor analysis uses the net change in capital investment to estimate the amount and type of labor needed during the interval to build the new capacity. The analysis assumes that the new built labor within each interval is evenly di stributed throughout the interval. Tables 6-4 and 6-5 reflect this assumption by presenting the aver age labor utilization per year during each of the
3 intervals. The HRI-related labor impact are estimated based on the assumed capital cost of $100/kw (see Section 3.7.3). Note that all of the HRI-relate d labor impacts occur in the first interval (2017 to 2020), and are assumed to be occur evenly throughout that four year interval. Therefore, the HRI-related labor estimates in Tables 6-4 and 6-5 are the annual average labor impacts for of the 4 years. There are no HRI improvements made after 2020.
The labor estimates for operating and mainta ining generating units annually are based on IPMs estimates of Fixed Operating and Mainte nance (FOM) Costs. IPM estimates FOM for each year individually, so the net changes in O&M-related labor estimates in Tables 6-4 and 6-5 are single year estimates for 2020, 2025 and 2030. These O&M labor estimates are not the average annual averages labor needs throughout each multi-year interval. There are O&M labor changes occurring in the all years throughout the entire period 2017-2030, but labor impact changes each year. The fuels-related labor estimates are also single-year estimates, and not multi-year averages. The labor analysis uses IPM’s estimates of the net changes in the amount of coal and natural gas in 2020, 2025 and 2030, which are inherently estimates of the fuel usage in a single year. As with the O&M labor impacts, the fuels-related labor impacts occur in every year throughout 2017-2030, and the labor impact changes every year.
It should be noted that the supply side labor impact estimates in Tables 6-4 and 6-5 reflect all the supply side changes that will o ccur with each alternative option and compliance alternative. These labor impacts include not only the impacts of Building Blocks 1 through 3, but also the changes in total generation needed that result from the demand-side energy efficiency activities in Building Block 4. The additio nal upstream labor impacts from the demand side activities are presented below in Section 6.2.2.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-25 More details on methodology, assumptions, and data sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A.
The quantified labor impacts include both one-time and recurring employment effects. One-time employment effects include construction activities, such as increases in labor demand for making heat rate improvements or decreases in labor demand from building fewer generating units due to reduced electricity demand. Recurring employment effects include annual changes in labor needed to extract coal or gas, or labor that would have operated a new generating unit that is no longer necessary.
All of the employment estimates presented in Tables 6.4 and 6.5 are estimates occurring in a single year. For the construction-related (one time) labor impacts, including the installation of the heat rate improvements, Tables 6.4 and 6.5 present the average annual impact occurring in each year in three different intervals of years. The intervals are selected to correspond with the analytical years reported by IPM. The three intervals are from 2017 through 2020, from 2021 through 2025, and 2026 through 2030. The labor analysis based on the IPM results directly estimates the total amount of labor needed throughout each interval.In addition to projected changes for labor in 2020, 2025 and 2030, changes in labor are also expected to occur in each year from 2017 to beyond 2030. For example, all HRI installation is projected to take place from 2017- 2020. Theis labor analysis assumes that 1⁄4 of the labor required to install the HRI occurs in each of those four yearslabor within each interval is evenly distributed throughout the interval. There are no estimated HRI-related labor impacts after the initial four year installation phase. Hence the HRI-related labor impacts are entirely one time effects.

In contrast, changes in labor utilization from changes in new capacity construction, increased plant retirements, and reduced fuel use include both one time (construction) and reoccurring (operations and fuel) employment effects. Quantifying the labor impacts from capacity changes is more complex, therefore, than the HRI-related labor impacts. For example, the IPM estimates show that implementing the guid elines will result in the amount of some types of additional new generating capacity will increa se in some years, but the amount of new construction of the same type of capacity will be reduced in other years. The IPM estimates show that the amount of new non-hydro renewa ble generation capacity (primarily wind) will***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-26 increase substantially over the baseline estimates for several years after the guidance is issued. With the Option 1 alternative and assuming regional compliance, by 2020 the amount of new renewable capacity is estimated increase by 30 GW, compared with only 18 GW in the base case. This initial 43% increase in building new renewable capacity with Option 1 will require a
commiserate 43% increase in the amount of construction labor. After this initial surge of additional renewable capacity construction is over, however, the rate of building additional renewable capacity with Option 1 slows relative to the renewable capacity building expected to
occur in the base case. IPM estimates that between 2021 and 2030 an additional 14.9 GW of renewable capacity will be built in the base case, but only an additional 12.0 GW will be built with the Option 1 alternative. Hence with Option 1 between 2021 and 2030 modestly less construction labor for needed annually for renewable capacity construction than in the base case. So while the cumulative amount of labor through 2030 needed for renewable capacity construction is 34% greater with Option 1 (w ith 42 GW of new renewable capacity built by 2030, which is 34% greater than the 32.7 GW in the base case), because Option 1 substantially “front loads” the new renewable construction towards the earlier years, Option 1 does not require more construction labor in every year. These initia l labor impacts of change s in new capacity are the changes in the one time employment used during construction. These labor impacts do not all occur in the year the new capacity is (or would have been) brought online. Increasing or avoiding the need for building new capacity impacts jobs over several years during the construction phase. For example, constructing a new advanced combined cycle gas unit has a three year lead time during which construction-related labor is used. 39 Hence building an additional new gas unit increases construction labor needs for 3 years, while avoiding the need for a new CC gas unit that was expected in the base case will decrease labor needs for 3 years. The supply side labor impacts shown below in Tables 6-4 and 6-5 account for the lead time effects; the quantified labor impacts in the tables are of the estimated change s in labor occurring within the year indicated.
The second labor impact from the either building or avoiding building new capacity is the reoccurring change in the operating and main tenance labor that will be (or would have been) employed throughout that unit’s lifetime. A similar reoccurring labor impact occurs in the fuel sector, driven by changes in th e amount of fuel needed by an y changes in additional capacity.

39 Table 4.7, IPM 5.13 Documentation.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-27 Labor impacts from reduced fuel use and avoided new capacity resulting from the demand-side efficiency are presented in Tables 6-4 (regional compliance) and 6-5 (state compliance). Section 6.6.2 projects additional labor required for demand-side efficiency
activities.
More details on methodology, assumptions, and data sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A (write-up pending) for
this RIA.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-28 Table 6-4. Engineering-Based a Changes in Labor Demand Utilization, in 2020, 2025 and 2030, Regional Compliance Approach - ( Number of Job-Years b of Employment in A Single Year)

Category Option 1 Option 2
One-timeConstructionrelated (One-time) Changes * 2017- 2020 2021- 2025 2026- 2030 2017- 2020 2021- 2025 2026- 2030 Heat Rate Improvement: Total 32,900 0 0 33,900 0 0

Boilermakers and General Construction 22,800 0 0 23,600 0 0
Engineering and Management 6,000 0 0 6,200 0 0
Equipment-related 2,900 0 0 3,000 0 0 Material-related 1,100 0 0 1,100 0 0 New Capacity Construction: Total 24,20024,7 00 -33,300 -37,000 14,70010,6 00 -23,100 -29,600

Renewables 17, 05 00 -4,700 -2,100 11,6009,50 0 -3,100 -4,000

Natural Gas 76 ,700 -28,600 -34,900 3,1001,100 -20,000 -25,600 Recurring Changes ** 2020 2025 2030 2020 2025 2030 Operation and Maintenance: Total -22,900 -23,800 -23,700 -15,300 -15,500 -16,600
Changes in Gas 2,300 -600 -3,400 1,000 -1,000 -3,900 Retired Coal -22,600 -20,800 -18,200 -14,600 -13,100 -11,500 Retired Oil and Gas -2,600 -2,400 -2,100 -1,700 -1,400 -1,200 Fuel Extraction: Total -8,800 -14,900 -19,200 -6,600 -10,600 -13,300 Coal -13,700 -17,000 -16,600 -10,900 -12,900 -10,300 Natural Gas 4,900 2,100 -2,600 4,300 2,300 -3,000 Supply Side Employment Impacts - Quantified 25, 94 00 -72,000 -79,900 2 6,72,6 00 -49,200 -58,600

a Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas). b All job-year estimates on this are full time equivalent (FTE) jobs. Job estimates in the Demand-Side energy efficiency section (below) include both full time and part time jobs. *Construction-related job-year changes are one-time impact s, occurring during each year of the 2 to 4 year period during which construction and HRI instal lation activities occur. Figures in tabl e are average job-years during each of the years in each range. Negative job-y ear estimates when additional generating capacity must be built in the base case, but is avoided in the Guideline implementation s cenarios due to HRI or Demand-side energy efficiency programs. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stre am of positive job-years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated. Formatted: Superscript

Formatted: Centered

Formatted: Superscript
Formatted Table
Formatted: Font: 10 pt, Font color: Black, Complex Script Font: 10 pt, Bold
Formatted: Not Superscript/ Subscript Formatted: Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-29 Table 6-5. Engineering-Based a Changes in Labor Utilization, State Compliance Approach - ( Number of Job-Years of Employment in Year)

Category Option 1 Option 2Construction-related (Onetime) Changes* 2017-2020 2021-2025 2026-2030 2017-2020 2021-2025 2026-2030 Heat Rate Improvement: Total 32,200 0 0 30,800 0 0

Boilermakers and General Construction 22,400 0 0 21,400 0 0
Engineering and Management 5,900 0 0 5,700 0 0 Equipment-related 2,900 0 0 2,800 0 0

Material-related 1,000 0 0 1,000 0 0 New Capacity Construction: Total 28,200 -38,000 -36,100 23,000 -29,500 -25,300

Renewables 19,100 -8,900 -2,200 15,800 -6,300 -2,400 Natural Gas 9,100 -29,100 -33,900 7,200 -23,200 -22,900 Recurring Changes** 2020 2025 2030 2020 2025 2030 Operation and Maintenance: Total -24,100 -25,300 -24,900 -16,800 -17,100 -16,800
Changes in Gas 2,500 -500 -3,200 1,500 -800 -2,600

Retired Coal -24,000 -22,500 -19,700 -16,400 -14,800 -12,900 Retired Oil and Gas -2,600 -2,300 -2,000 -1,900 -1,500 -1,300 Fuel Extraction: Total -8,300 -14,600 -19,400 -6,500 -10,300 -13,400 Coal -14,300 -17,800 -18,000 -11,500 -13,500 -11,600 Natural Gas 6,000 3,200 -1,400 5,000 3,200 -1,800 Supply Side Employment Impacts – Quantified 28,000 -77,900 -80,400 29,800 -56,900 -55,500

a Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas) b All job-year estimates on this are Full Time Equivalent (FTE) jobs. Job estimates in the Demand-Side energy efficiency section (below) include both full time and part time jobs *Construction-related job-year changes are one-time impact s, occurring during each year of the 2 to 4 year period during which construction and HRI instal lation activities occur. Figures in tabl e are average job-years during each of the years in each range. Negative job-year estimates when additional generating capacity must be built in the base case, but is avoided in the Guideline implementation s cenarios due to HRI or Demand-side energy efficiency programs. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stre am of positive job-years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-30 6.6.2 Projected Changes in Employment in Demand-Side Energy Efficiency Activities
EPA anticipates that this rule will stimulate investment in clean energy technologies and services, resulting in considerable increases in en ergy efficiency in particular. We expect these increases in energy efficiency, specifically, to support a significant number of jobs existing in
related industries.
In this section, we project employment impacts in demand-side energy efficiency activities arising from these guidelines. The approach uses information from power sector modeling and projected impacts on energy efficiency investments analyzed as part of building block four (see Chapter 3), and U.S. government data on employment and expenditures in energy efficiency. This approach is limited by the fact that we do not know which options states will choose for demand-side energy efficiency activities. These employment projections are gross, in the sense that they do not include impacts of any shift in resources from other sectors. Nor does this analysis attempt to qua ntify employment impacts arising from changes in consumer expenditures away from energy towards other sectors. EPA requests public comment on all aspects of this proposed approach to partially quantifying demand-side management and energy efficiency employment impacts.
Investments in demand-side energy efficiency reduce energy required for a given activity by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing energy improvements for existing systems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air conditi oning during periods of high electricity demand).
Employment impacts of demand-side energy efficiency programs have not been extensively studied in the peer-reviewed, published economics literature. Instead, most research has focused on consumer response to and amount of energy savings achieved by these programs (e.g., Allcott (2011a, 2011b), Arimura et. al (2012)). Results suggest that demand-side energy efficiency programs reduce energy use and generate small increases in consumer welfare. These policy impacts are due to market failures that cause inefficiently low investment in energy efficiency as described in “energy paradox” literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)). 40

40 For more information on this “ efficiency paradox ,” see Chapter 3.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-31 Two recent articles discuss employment effects of demand-side energy efficiency programs. Aldy (2013) describes clean energy investments funded by the American Recovery and Reinvestment Act of 2009, which “included mo re than $90 billion for strategic clean energy investments intended to promote job creation and the deployment of low-carbon technologies” (p. 137), with nearly $20 billion for energy e fficiency investments. The Council of Economic Advisors (CEA) (2011) estimated higher economic activity and employment than would have otherwise occurred without the American Recovery and Reinvestment Act. Using CEA’s methods to quantify job creation for the Recovery Act, Aldy uses the share of stimulus funds for clean energy investments to estimate job-years supported by the Recovery Act. The largest sources of job creation in clean energy are thos e that received the largest shares of stimulus funds: renewable energy, energy efficiency, and transit. Aldy’s estimates, while informative, are not directly applicable for employment analysis in this rulemaking as there are important differences in expected employment impacts from a historically large fiscal stimulus specifically targeting job creation during a period of exceptionally high unemployment versus environmental regulations taking effect several years from now.
Yi (2013) analyzes clean energy policies and employment for U.S. metropolitan areas in 2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing
an additional state clean ener gy policy tool (renewable energy policies, greenhouse gas emissions policies, and energy efficiency polices such as energy efficiency resource standards, appliance or equipment energy efficiency standards, tax incentives, and public building energy efficiency standards) is associated with 1% mo re clean energy employment within that MSA.
These estimates are not transferable to this rulema king since states are likely to change intensity as well as number of clean energy programs.
Outside of the peer-reviewed, published economics literature, analysts have estimated employment effects of existing and proposed stat e and regional demand-side energy efficiency policies, programs and investments. While these studies may use modeling approaches that would not be appropriate for national-level anal yses or may include job impacts not considered in this chapter, they are often used by state government officials in their decision-making process. As such, they can provide useful insight s as to how states analyze demand-side energy efficiency activities. Appendix A6.C.1 Other Approaches to Estimating Employment Effects of***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-32 Demand-side Energy Efficiency Policies and Programs, Including State Examples presents several of these studies.
Lacking a peer-reviewed methodology for estimating employment impacts of investments in demand-side energy efficiency , we propose the following approach to illustrate potential effects on labor demand in the energy efficiency sector due to demand side management strategies. . We use U.S. government data to divide the historical change in employment in the energy efficiency sector by th e historical change in expenditure in the sector and multiply this fraction by projected expenditure in the sector undertaken in response to these proposed guidelines.
Data used for calculating the numerator of the fraction comes from the “energy efficiency” category of the 2010 and 2011 BLS Green Goods and Services surveys. 41 BLS does not report the denominator of the job per additional dollar fraction, however. Instead, the data include the fraction of green revenues received relative to total revenues in each NAICS code. 42 We multiply data on tota l revenues by NAICS by the fraction of green revenues reported by BLS to obtain green revenues. The only U.S. Government data source containing this revenue
information for all NAICS sectors is the U.S. Economic Census. This Census is conducted at 5- year intervals (the latest available year is 2007), however, making it unsuitable for identifying the change in revenues from 2010 to 2011. Instead, we use data from the Annual Survey of Manufacturers. The disadvantage of this data source is that the manufacturing sector makes up only 50 percent of the 132 NAICS codes belonging to the energy efficiency sector as defined by the BLS Green Goods and Services surveys, with the remainder in the construction or service

sectors. Thus, this analysis implicitly assumes th at the same number of jobs per dollar are supported in construction and service sectors as in manufacturing. Using this approach we obtain a factor of 2.56 additional demand side energy efficiency jobs per additional million 2011 dollars of expenditure.
Having calculated the fraction of additional jobs per additional dollar of energy efficiency expenditure, we use energy sector model projections of the first-year cost s required for states to

41 For more details on these surveys, see section 7.5.3.

42 See detailed listing available here: http://www.bls.gov/ggs/naics_2012.xlsx. Comment [A1]: This job analysis is limited to directly affected sectors. In contrast ImSet uses implan to predict economy
‐ wide effects of shifting consumption away from energy towards other sectors. EPA is waiting SAB advice before engaging in economy
‐ wide modeling.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-33 attain the goal of demand side efficiency improvements set by building block four. 43 Multiplying this dollar e xpenditure by the jobs per additional dollar figure results in projected employment impacts for demand-side energy effi ciency activities of 78,800 in 2020, 112,000 in 2025, and 111,800 in 2030 for both the proposed Option 1 regional and state compliance approaches. The estimates for Option 2 are shown on Table 6-6 below.
Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts For Option 1 and Option 2 for Both Regional and State Compliance Approaches Employment iI mpact (jobs)*
Option 1 * Option 2 *

Source Factor 2020 2025 2030 2020 2025 2030

BLS GGS additional jobs per additional million dollars 2.56 78,800 112,000 111,800 57,000 76,200 77,200

* Demand-side energy efficiency job numbers calculated for this table assume all activities financed by these expenditures are additional to what would have been undertaken in the ab sence of these programs. Additional details can be found in the Appendix of the document entitled “GHG Abatement Measures Technical Support Document – Chapter 5: Demand-side Energy Efficiency (EE) as a CO 2 Abatement Measure for Existing Power Plants”.*S ince these figures represent number of employees (full or part time) they should not be added to the full-time equivalent job-years reported in Table 6-5. Although this approach has the advantage of illuminatcalculat ing the change in jobs for an incremental change in expenditures, this appr oach is limited by its focus on manufacturing

sectors and. direction of bias (oO verestimation or underestimation ) cannot be determined at this time. The EPA is requesting comment on this met hod, data, identification of related studies and peer reviewed articles and other methods.
There is more uncertainty involved in this approach than the standard bottom-up engineering analysis used to estimate electricity generation and fuel production employment impacts of this rulemaking. For those, the EPA was able to identify a limited set of activities (e.g., constructing a new NGCC power plant), and study associated labor requirements. Demand

side energy efficiency improveme nts, in contrast, encompass a wi de array of activities (subsidies for efficient appliances, “smart meters,” etc.). In addition, there is considerable uncertainty regarding which activities a state will choose. Thus , the validity of the jobs per dollar approach

43 See Greenhouse Gas Abatement Measures TSD, Appendix 5-4.Section XX.YY Comment [A2]: Footnote moved to caveat section.

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

6-34 used here relies on the assumption that states will use a mix of activities similar to the 2011 composition of energy efficiency sectors identified by BLS.
In addition, the EPA does not have access to bottom-up information regarding labor requirements for these activities. Use of a constant job per dollar fraction is at best a crude approximation of these labor requirements. The EPA has identified several other limitations of this approach, outlined below.
Job Reclassification. Job numbers in this chapter represent gross changes in the affected sector. As such they may over-estimate impacts to th e extent that jobs created displace workersemployed elsewhere in the economy. For demand-side efficiency activities this potential overstatement is may be higher than in other sector s. If states encourage consumers to purchase ENERGY STAR appliances, for example, currently employed workers in factories and retail outlets may simply be given a different task. This approach, however, would count these workers as jobs created Imports. The job per additional dollar fraction used in the employment projection is calculated based on jobs per dollar of revenue for domestic firms only. To the extent that spending on demand-side energy efficiency activities goes to ward the purchase of imported goods this projection will overstate the U.S. employment impact of those expenditures.

Fixed Coefficient. Implicit in this approach is the assumption that employment impacts can be projected decades into the future on the basis of a single calculation from 2010-2011 data. The labor intensity of demand side energy efficien cy will likely change with technological innovation in the sector. In addition, even absent technological change, labor intensity of expenditures will likely change over time as states alter their portfolio of efficiency activities (e.g., by moving to higher cost activities after exhausting opportuniti es for low cost efficiency gains).

Non-additional Activities. Here we assume that all activities financed by demand side energy efficiency expenditures are add itional to what would have been undertaken in the absence of these programs. If utilities finance some actions customers would have undertaken in the absence of these programs (e.g., if a customer receives a rebate for an energy efficient appliance that would have been purchased without the rebate), these numbers would ov er-estimate employment impacts of the proposed guidelines. Comment [A3]: New "additionality" caveat***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-35 6.7 Conclusion
This chapter presents qualitative and quantitative discussions of potential employment impacts of the proposed guidelines for electricity generation, fuel production, and demand

- side energy efficiency sectors. The qualitative disc ussion identifies challenges associated with estimating net employment effects and discusses anticipated impacts related to the rule. It includes an in-depth discussion of economic theory underlying analysis of employment impacts. Labor demand impacts for regulat ed firms can be decomposed into output and substitution effects, both of which may be positive or negativ e. Consequently, theory alone cannot predict the direction or magnitude of a regulation’s employment impact. It is possible to combine theory with empirical study specific to the regulated firms and other relevant sectors if data and methods of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible.
We examine the peer-reviewed economics literature analyzing various aspects of labor demand, relying on the above theoretical framework. Determining the direction of employment effects in regulated industries is challenging because of the complexity of the output and substitution effects. Complying with a new or mo re stringent regulation may require additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant i ndustries) in their production processes. The
available literature illustrates some of the diffic ulties for empirical estimation: studies sometimes rely on confidential plant-level employment data from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that are too date d to be reliably informative. In addition, the methods do not permit estimati on of net economy-wide effects. Empirical analysis at the industry level requires estimates of product demand elasticity; production factor substitutability; supply elasticity of production fact ors; and the share of to tal costs contributed by wages, by industry, and perhaps even by facility. For environmental rules, many of these data items are not publicly available, would require significant time and resources in order to access confidential U.S. Census data for research, and also would not be necessary for other components of a typical RIA. Econometric studies of environmental rules converge on the finding that employment effects, whether positiv e or negative, have been small in regulated

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

6-36 Quantitative analysis in this chapter projects a subset of potential employment impacts in the electricity generation, fuel production, and demand-side energy efficiency sectors. States have the responsibility and flexibility to impl ement policies and practices for compliance with Proposed Electric Generating Unit Greenhouse Ga s Existing Source Guidelines. As such, given the wide range of approaches that may be used, quantifying the associated employment impacts is difficult. EPA’s employment analysis incl udes projected employment impacts associated withthese guidelines for the electric power industry, coal and natural gas production, and demandside energy efficiency activities. These projections are derived, in part, from a detailed model of the electricity production sector used for this regulatory analysis, and U.S government data on employment and labor productivity. In the electricity, coal, and natural gas sectors, the EPA estimates that these guidelines could have an employment impact of roughly 25,40025,900 jobyears in 2020 for Option 1 and 22,60026,700 for Option 2 of that same year (see Tables 6-4 and 6-5) . Employment impacts from demand-side energy efficiency activities are based on historic data on jobs supported per dollar of expenditure. Demand-side energy efficiency employment impacts would approximately be 78,800 jobs in 2020 for both Option 1 regional and state compliance approaches (see Table 6-6)

. The IPM-generated job-year numbers for the electricity, coal and natural gas sectors should not be added to the demand-side efficiency job impacts since the former are reported in full time equivalent jobs, whereas the latter do not distinguish between full and part time employment. This is an illu strative analysis and both employment estimates provides useful information. Finally, note again that this is an illustrative analysis, and CAA section 111(d) allows each state to determine the appropriate combination of, and the extent of its reliance on, measures for its state plan, by way of meeting its state-specific goal. Given the flexibilities afforded states in complying with the emission guidelines, the impacts reported in this chapter are illustrative of compliance actions states may take.

There are additional employment impacts that we cannot quantify and they are represented qualitatively in the tables below. Formatted: Space Before: 0 pt, Line spacing: 1.5 lines***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-37 Table 6-7. Supply Side and Demand-Side Employment Impacts in 2020, 2025 and 2030, Regional Compliance Approach - ( Number of Job-Years – Supply Side. Jobs – Demand-Side)

Category Option 1 Option 2 2020 2025 2030 2020 2025 2030 Supply Side Employment Impacts – Quantified – Job Years 25,400 -72,000 -79,900 22,600 -49,200 -58,600
Demand-Side Energy Efficiency Employment Impacts – Quantified - Jobs* 78,800 112,000 111,800 57,000 76,200 77,200

Un-quantified – Supply and Demand-Side Energy Efficiency Employment Impacts  Other additional direct and indirectly related jobs in the manufacturing and materials sectors.

 Additional upstream and downstream effects of EE investments (e.g. related to changes in demand for steel, increased spending in non-energy categories).

* Demand-side energy efficiency job numbers calculated for this table assume all activities financed by these expenditures are additional to what would have been undertaken in the ab sence of these programs. Additional details can be found in the Appendix of the document entitled “GHG Abatement Measures Technical Support Document – Chapter 5: Demand-side Energy Efficiency (EE) as a CO 2 Abatement Measure for Existing Power Plants”.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-38 Table 6-8. Supply Side and Demand-Side Employment Impacts in 2020, 2025 and 2030, State Compliance Approach - ( Number of Job-Years – Supply Side. Jobs – Demand-Side)

* Demand-side energy efficiency job numbers calculated for this table assume all activities financed by these expenditures are additional to what would have been undertaken in the ab sence of these programs. Additional details can be found in the Appendix of the document entitled “GHG Abatement Measures Technical Support Document – Chapter 5: Demand-side Energy Efficiency (EE) as a CO 2 Abatement Measure for Existing Power Plants”. Cate g or y Option 1 Option 2 2020 2025 2030 2020 2025 2030 Supply Side Employment Impacts – Quantified – Job Years 21,700 -77,900 -80,400 23,300 -56,900 -55,500
Demand- Side Energy Efficiency Employment Impacts – Quantified – Jobs* 78, 800 112,000 111,800 57,000 76,200 77,200

Un-Quantified - Supply and Demand- Side Energy Efficiency Employment Impacts  Other additional direct and indirectly related jobs in the manufacturing and materials sectors.

 Additional upstream and downstream effects of EE investments (e.g. related to changes in demand for steel, increased spending in non-energy categories).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-39 6.8 References
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6-40 Bezdek, R. H.; R. M. Wendling and P. Diperna. 2008. “Environmental Protection, the Economy, and Jobs: National and Regional Analyses.” Journal of Environmental Management 86 (1): 63-79.
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< http://www.e2.org/jsp/controller;jsessionid=8287977D43FA05E237DD5FEF58B00B3A?do cName=cleanjobsarchives >. Environmental Entrepreneurs. 2012. “What Clean Energy Jobs? These Clean Energy Jobs: Third Quarter 2012.” Clean Energy Jobs Roundup

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6-41 Global Insight. 2008. U.S. Metro Economies: Current and Potential Green Jobs in the U.S. Economy. U.S. Conference of Mayors. Available at:

< http://www.usmayors.org/pressreleases/uploads/greenjobsreport.pdf >. Goldman, C.; M. C. Fuller; E. Stuart; J. S. Peters; M. McRae and N. Albers, et al. 2010. Energy Efficiency Services Sector: Workforce Size and Expectat ions for Growth. Lawrence Berkeley National Laboratory. Zivin, G.; and J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.” American Economic Review , 102(7), pp. 3652-73. Zivin, G. and J. and M. Neidell. 2013. “Environment, Health, and Human Capital.” Journal of Economic Literature , September 2013, v. 51, iss. 3, pp. 689-730. Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy 110(6): 1175-1219.
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. Morgenstern, R. D.; W. A. Pizer and J.S. Sh ih. 2002. “Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management, 43(3): 412-436. Muro, M.; J. Rothwell; D. Saha & Partnership, w. B. 2011. Sizing the Clean Economy: A National and Regional Green Jobs Assessment. Brookings Institution, Metropolitan Policy Program. Available at: http://www.brookings.edu/~/media/series/resources/0713_clean_economy.pdf

. Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the Transport Rule.” White Paper. Boston, MA. Exelon Corp.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-42 Smith, K. 2012. “Reflections: In Search of Crosswalks between Macroeconomics and Environmental Economics.” Review of Environmental Economics and Policy , 6(2), pp 298- 317. Southwest Energy Efficiency Project. 2002. “The New Mother Lode: The Potential for More Efficient Electricity Use in the Southwest.” Available at: < http://www.swenergy.org/nml >. The Pew Charitable Trust. 2009. 2009. “The Clean Energy Economy: Repowering Jobs, Businesses and Investments Across America

. ” Available at:

< http://www.pewenvironment.org/uploadedFile s/PEG/Publications/Report/Clean%20Energy %20Economy.pdf >. The Solar Foundation in partnership with BW Research and Cornell University. 2012. “The National Solar Jobs Census.” Available at: < http://thesolarfoundation.org/research/nationalsolar-jobs-census-2012 >. Sommers, D. 2013. “BLS Geen Jobs Overview,” Monthly Labor Review. U.S. Bureau of Labor Statistics, January, p. 3-16. Available at:

< http://www.bls.gov/opub/mlr/2013/01/art1full.pdf>. U.S. Department of Commerce, Economics and Statistics Administration. 2010a. “Measuring the Green Economy.” Available at: < http://www.esa.doc.gov/Reports/measuring-greeneconomy>

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< http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c?OpenDocument>. U.S. Department of Labor, Bureau of Labor Statistics. 2012a. Databases, Tables & Calculators by Subject. Acccessed on April 16, 2012 and available at:

< http://data.bls.gov/timeseries/LNS14000000>. U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “The Employment Situation—March 2012.” News Release. Accessed April 16, 2012 and available at:

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< http://www.bls.gov/emp/ep_table_102.htm >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-43 U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). “Green Goods and Services, 2010-2011.” Retrieved January 14, 2014, and available at:

< http://www.bls.gov/ggs/ggsoverview.htm>. U.S. Energy Information Agency. 2013. “Annual Energy Outlook 2014: Early Release.” Available at: http://www.eia.gov/forecast/aeo/er/early_elecgen.cfm

. U.S. Environmental Protection Agency (EPA). 2 011a. “The Benefits and Costs of the Clean Air Act from 1990 to 2020 Final Report – Rev. A.” Office of Air and Radiation. April.Available
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< http://www.epa.gov/ttn/ecas/re gdata/RIAs/matsriafinal.pdf>

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< http://www.usitc.gov/publications/332/pub4389.pdf> Walker, R. 2011.“Environmental Regulation and Labor Reallocation." American Economic Review, Papers and Proceedings, 101(2).
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. Yi, H. 2013. “Clean Energy Policies and Green Jobs: An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy , 56: 644-65***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6-44Appendix 6A-1 

### APPENDIX 6A: ESTIMATING SUPPLY-SIDE EMPLOYMENT IMPACTS OF THE PROPOSED EGU GHG EXISTING SOURCE GUIDELINES

This appendix presents the methods used to estimate the supply-side employment impacts of the EGU GHG Existing Source Guidelines. The focus of the employment analysis is limited to the direct changes in the amount of labor needed in the power, fuels and generating equipment sectors directly influenced by compliance with the Guidelines. It does not include the ripple effects of these impacts on the broader economy (i.e., the “multiplier” effect), nor does it include the wider economy-wide effects of the changes to the energy markets, such as changes in electricity prices. The methods used to estimate the supply-side employments are based on methods previously developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact Analysis (RIA). The methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with operating and maintaining gene rating units, as well as labor needed to mine coal and natural gas) are the same as was used in MATS (with updated data where available). The labor analysis in the MATS RIA was primarily concerned with the labor needs of retrofitting pollution control equipment. The EGU GHG Existing Source guidelines labor analysis, however, involves the quantity and timing of the labor needs of building new renewable and natural gas, as well as making heat rate improvements at existing coal fired EGUs. These construction-related compliance activities in th e EGU GHG Existing Source guidelines required developing additional appropriate analytical methods that were not needed for the MATS analysis. The newly developed analytical methods for the construction-related activities are similar in structure and overall approach to the methods used in MATS, but required additional data and engineering information not needed in the MATS RIA. A6.1 General Approach The analytical approach used in this analysis is a bottom-up engineering method combining the EPA’s cost analysis of the proposed guidelines with data on labor productivity, engineering estimates of the amount and types of labor needed to manufacture, construct, and operate different types of generating units, and prevailing wage rates for skilled and general labor categories. The approach involved using power sector projections and various energy market implications under the proposed EGU GHG Existing Source guidelines from modeling conducted with the EPA Base Case version 5.13, using the Integrated Planning Model (IPM ® ) [1] , along with data from secondary sources, to estimate the first order employment impacts for 2020, 2025, and 2030.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-2 Throughout the supply-side labor analysis the engineering approach projects labor changes measured as the change in each analysis year in job-years [2] employed in the power generation and directly related sectors (e.g., equipment manufacturing, fuel supply and generating efficiency services). Job-years are not individual jobs, nor are they necessarily permanent nor full time jobs. Job-years the amount of work performed by one full time equivalent (FTE) employee in one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in that year , or any combination of full and part time workers such that total 20 FTEs. 6A.1.1 Employment Effects Included In the Analysis The estimates of the employment impacts (both positive and negative) are divided into five categories:

 additional employment to ma ke heat rate improvements [3] (HTI) at existing coal fired EGUs;

 additional construction-related employment to manufacture and install additional new generating capacity (renewables, and natural gas combined cycle or combustion turbine units) when needed as part of early compliance actions;

 lost construction-related employment opportuniti es due to reductions in the total amount of new generating capacity needed to be built in the later years because of reduced overall demand for electricity because of demand-side energy efficiency activities;

 lost operating and maintenance employment op portunities due to increased retirements of coal and small oil/gas units;

 changes (both positive and negative) in coal mining and natural gas extraction employment due to the aggregate net changes in fuel demands arising from all the activities occurring due to compliance with the proposed guidelines.

Some of the changes are one-time labor effects which are associated with the building (or avoiding building) new generating capacity and in stalling HRI. This type of employment effects involves project-specific labor that is used for 2 to 4 years to complete a specific construction and installation type of project. There are other labor effects, however, which continue year after year. For example, bringing new generating capacity online creates an ongoing need for labor to operate and maintain the new generating capacity throughout the expected service life of the unit. New generating capacity also creates a need for additional employment to provide the fuel***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-3 annually to run the new capacity. There are also continuing effects from the lost O&M and fuel sector labor opportunities from decisions to retire existing cap acity, as well as similar lost labor opportunities from decisions to reduce a portion of the amount of additiona l capacity needed in the base case. A6.2 Employment Changes due to Heat Rate Improvements The employment changes due to heat rate improvement (HRI) were estimated based on the incremental MW capacity estimated to implement such improvements by 2020 as indicated by the analysis conducted by EPA. The heat rate improvement job impacts were assumed to have all occurred by 2020 and thus this study assumes there will be no HRI related jobs after 2020 (i.e. no permanent O&M related jobs due to HRI for 2025 or 2030). EPA modeled the heat rate improvements exogenously in IPM using the assumption that all “relevant” units can improve their heat rate by 6 percent at a capital cost of $100/kW. This study assumes that these investments will occur over a four-year period culmin ating in 2020. Hence, the per-year cost of heat rate was calculated to be $25/kW, and this cost was used in the next step. This cost was then allocated to four categories based on the estimates provided by Andover Technology Partners (ATP), which we re adapted from proxy projects involving installation of combustion control retrofits, su ch as those installed under the Best Available Retrofit Technology (BART) submissions from co al-fired power plants located in Wyoming and Arizona. For more details, refer to the Staudt (2014) report. [4] These proxies were chosen to ensure that the types of activities involved and their associated costs would be representative of those investments EPA expects power plants to undertake for efficiency upgrades. Information on cost for these proxies were then extrapolated to approximate the labor requirements for four broad categories of la bor – boilermakers and general construction, engineering and management support labor, labor required to produce the equipment in upstream sectors, and labor required to supply the materials (assumed to be primarily steel) in upstream sectors. More details about these estimates are provided in the Staudt (2014) report. Based on the cost allocated in each categories and output per worker figures for respective industries in 2020, the employment gains for heat rate improvement were estimated [5] for 2020 using the assumptions summarized in Table 6A-1 below. Output per workers in future years were adjusted to account for growth in labor productivity, based on historical evidence of productivity growth rates for the relevant sectors. Table 6A-1 Labor Productivity Grow th Rate due to Heat Rate Improvement***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-4 Share of the Total Capital Cost Output/Worker (2020) Labor Productivity Growth Rate

Boilermaker and Gen. Const. 40% $78,500 0%

Management/Engineering 20% $141,000 1.3% Equipment 30% $458,000 3.2%

Materials 10% $424,000 -1.2%

For these output per worker figures, a power sector construction industry (NAICS 237130) was used for general construction and boilermakers, Engineering Services (NAICS 54133) was used for the engineering and management component, Machinery Manufacturing (NAICS 333) was used for the equipment sect or, and steel manufacturing (NAICS 33121) was used for materials. Use of machinery manufact uring for equipment and steel for materials was based on an analysis of the types of materials and equipment needed for these projects, and what EPA determined to be the most appropriate industry sectors for those. For more details, refer to the Staudt (2014) report. A6.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity Employment changes due to new generation units were based on the incremental changes in capacity (MW), capital costs ($MM), and fixed operations and maintenance (FOM) costs ($MM) between the policy scenario and the base case in a given year. New capacities were aggregated by generation type into the following categories: • Combined Cycle,
• Combustion Turbine, and • Renewables (which includes biom ass, geothermal, landfill gas, onshore wind, and solar). For each category, the analysis estimated the impacts due to both the construction and operating labor requirements for corresponding capacity changes. The construction labor was estimated using information on the capital costs, while the operating labor was estimated using the FOM costs. Because IPM outputs provide annualized capital costs ($MM), EPA first converted the annualized capital costs to changes in the total capital investment using the corresponding capital***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-5 charge rates. [6] These total capital investments were then converted to annual capital investments using assumptions about the estimated duration of the construction phase, in order to estimate the annual impacts on construction pha se labor. Duration estimates were based on assumptions for construction lengths used in EPA’s IPM modeling. [7] Specific assumptions used for different generating technologies are shown in Table 6A-2 below. Table 6A-2 Capital Charge Rate and Duration Assumptions

New Investment Technology Capital Charge Rate Duration (Years)

Advanced Combined Cycle 10.3% 3

Advanced Combustion Turbine 10.6% 2

Renewables

Biomass 9.5% 3
Wind (Onshore) 10.9% 3

Landfill Gas 10.9% 3

Solar 10.9% 3

Geothermal 10.9% 3

Annual capital costs for each generation type were then broken down into four categories: equipment, material (which is assume d to be primarily steel), installation labor, and support labor in engineering and management. The percentage breakdowns shown in Table 6A-3 were estimated using information provided by Staudt (2014), based primarily on published budgets for new unit assembled in a study fo r the National Energy Technology Laboratory (NETL). For more details, refer to the Staudt (2014) report. Annual capital costs for each generation type provided by the IPM output were allocated according to this breakdown. Table 6A-3 Expenditure Breakdown due to New Generating Capacity Equipment Material Labor Eng. and Const. Mgt

Renewables 54% 6% 31% 9%***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-6 Combined Cycle 65% 10% 18% 7%

Combustion Turbine 65% 10% 18% 7%

The short-term construction labor of the new generation units were based on output ($ per worker) figures for the respective sectors. The total direct workers per $1 million of output for the baseline year 2007 were forecasted to the years under analysis using the relevant labor productivity growth rate. Table 6A-4 shows the figures for each of the five productivities: general power plant construction; engineering an d management; material use; equipment use; and plant operators. The resulting values were multiplied by the capital costs to get the job impact. Table 6A-4 Labor Productivity due to New Generating Capacity Labor Productivity Growth Rate Workers per Million $ (2007)

General Power Plant Construction 0.0% 5.7

Engineering and Management 1.3% 5.2

Material Use (Steel) -1.2% 2.0 Equipment Use (Machinery) 3.2% 3.3

Plant Operators 2.8% 10.8

General installation labor, assumed to be mostly related to th e general power plant construction phase, was matched with the power industry specific construction sector. Engineering/management was matched to the engi neering services sector to determine their respective output per worker. For materials, EPA assumed steel to be the proxy and used the steel manufacturing sector for this productivity. Equipment was assumed to primarily come from machinery manufacturing sector (such as turbines, engines and fans).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-7 The net labor impact for construction labor for a given year was adjusted to account for changes in capacity that has already taken place in the prior IPM run year. Because IPM reports cumulative changes for new generating capacity for any given run year, this adjustment ensured that the short-term construction phase job impacts in any given run year does not reflect the cumulative effects of prior construction changes for the given policy scenario. The estimated amount of the change in construction-related labor in a single IPM run year (e.g., 2025) represents the average labor impact that occurs in all years between that IPM run year and the previous run year (i.e., the labor estimates derived from the 2025 IPM run year are the average annual labor impacts in 2021 through 2025). The construction labor results for 2020 represent the average labor impacts in 2017 through 2020. The plant operating employment estimates used a simpler methodology as the one described above. The operating employment estimates use the IPM estimated change in FOM costs for the IPM run year. Because the FOM costs are inherently estimates for a single year, the operating employment estimates are for a single year only. While there are obviously operating employment effects occurring in every year throughout the entire IPM estimation period (2017- 2030), the labor analysis only estimates the single year labor impacts in the IPM run years: 2020, 2025 and 2030. The total direct workers for $1 million and labor productivity growth rate provided for plant operators in Table 6A-4 were used to estimate the employment impact. A6.2.2 Employment Changes due to Coal and Oil/Gas Retirements Employment changes due to plant retirements were calculated using the IPM projected changes in retirement capacities for coal and oil/ga s units for the relevant year and the estimated changes in total FOM costs due to those retiring units. Thus, the basic assumption in this analysis was that increased retirements (over the base case) will lead to reduced FOM expenditures at those plants which were assumed to lead to direct job losses for plant workers. In order to estimate the total FOM changes due to retirements, EPA first estimated the average FOM costs ($/kW) for existing coal-fired and oil/gas-fired units in the base case, as shown in Table 6A-5 below. It was assumed that the average FOM cost of existing units in the base case can be used as a proxy for the lost economic output due to fossil retirements. Thus, changes in the FOM costs for these retiring unit s were derived by taking the product of the incremental change in capacity and the average FOM costs. These values were converted to lost employment using data from the Economic Census and BLS on the output/worker estimates for the utility sector. [8] Table 6A-5 Average FOM Cost Assumptions***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-8 2020 2025 2030

Coal 65 68 69

Oil and Gas 21 22 22

Note that the retirement related employment losses are assumed to include losses directly affecting the utility sector, and do not include loss es in upstream sectors that supply other inputs to the EGU sector (except fuel related job losses, which are estimated separately and discussed in the next section). A6.2.3 Employment Changes due to Coal and Oil/Gas Retirements

Two types of employment impacts due to projected fuel use changes were estimated in this section. First, employment losses due to either reductions or shifts in coal demand were estimated using an approach similar to EPA’s co al employment analyses under Title IV of the Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for various coal supplying regions were taken from EPA’s base and policy case runs for the proposed EGU GHG NSPS. These changes were converted to job-years using EIA data on regional coal mining productivity (in short tons per employee hour), using 2008 labor productivity estimates. [9] [10] Specifically, the incremental changes to coal demand were calculated based on the coal supply regions in IPM -- Appalachia, Interior, and West and Waste Coal (which was estimated using US total productivity). Worker productivity values used for estimating coal related job impacts are shown in Table 6A-6 below. Table 6A-4 Labor Productivity due to New Generating Capacity Labor Productivity
Coal (Short tons/ employee hour) Appalachia 2.91

Interior 4.81***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-9 West 19.91

Waste 5.96 Natural Gas (MMBtu/ employee hour) 126 Pipeline Construction (Workers per $Million) 5.1

For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike coal labor productivities used above. Most secondary data sources (such as Census and EIA) provide estimates for the combin ed oil and gas extraction sector. This section thus used an adjusted labor productivity estim ate for the combined oil and gas sector that accounts for the relative contributions of oil and natural gas in the total sector output (in terms of the value of energy output in MMBtu). This estimate of labor productivity was then used with the incremental natural gas demand for the respective IPM runs to estimate the job-years for the specific year (converting the TCF of gas used projected by IPM into MMBtu using the appropriate conversion factors). In addition, the pipeline construction costs were estimated using endogenously determined gas market model parameters in IPM used by EPA for the MATS rule (using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for additional pipeline would be proportionate to thos e projected for the MATS rule and were hence extrapolated from those estimates. [11] The job-years associated with the pipeline construction were included in the natural gas employment estimates.

[1] Results for this analysis were developed using various outputs from EPA’s Base Case v.5.13 using ICF’s Integrated Planning Model (IPM®). This case includes all of the underlying modeling that was developed by EPA with technical support from ICF International, Inc. See http://www.epa.gov/powersectormodeling/BaseCasev513.html for more information.
[2] Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in that year.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-10 [3] Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to generate a net electrical out put. Assuming all other things being equal, a lower heat rate is more efficient because less fuel is needed per unit of electric output.

[4] Staudt, James,Andover Technology Partners, Inc. Estimating Labor Effects of Heat Rate Improvements. Report prepared for the proposed EGU GHG Existing Source Guidelines, March 6, 2014.
[5] Total value of shipments or receipts in 2007 and total employees were taken from 2007 Economic Census, Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity was taken from the Bureau of Labor Statistics. Average growth rate calculated for years 1992-2007, applied to 2007 productivity to determine 2020 estimates of productivity. For the construction sector, BLS productivity growth rate data was unavailable. Because of this, and lack of reliable data on construction sector productivity growth, the output per worker for the construction sector was not forecasted to 2020, and the most recent available value from 2007 was used.

[6] Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v.5.13 Using the Integrated Programming Model.
[7] Ibid
[8] The same specific sources as cited before, however, used workers and total payroll.
[9] From US Energy Information Administration (EIA) Annual Energy Review, Coal Mining Productivity Data. Used 2008.
[10] Unlike the labor productivity estimates for various equipment resources which were forecasted to 2020 using BLS average growth rates, this study uses the most recent historical productivity estimates for fuel sectors. In general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly high er degree of variability in recent years than the manufacturing sectors, which would have introdu ced a high degree of uncertainty in forecasting productivity growth rates for future years. [11] See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015”. Technical Support Document, March 2011.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-11***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

Appendix 6B-12 APPENDIX 6B: OTHER APPROACHES TO ESTIMATING EMPLOYMENT EFFECTS OF DEMAND –SIDE ENERGY EFFICIENCY

A6.B.1 Other Approaches to Estimating Employment Effects of Demand –Side Energy Efficiency Policies and Programs, Including State Examples
EPA expects states to increase their investment s in energy efficiency as a cost-effective means of compliance to help them meet energy demand and reduce peak electricity loads which can lower greenhouse gas emissions. States have flexibility in how they will comply with this rule and will use energy efficiency in varying degrees across the states. States have significant experience implementing energy efficiency policies and programs and analysts have used a variety of methods to estimate employment effects.
While these studies on state policies may not typically be published in the peer-reviewed, economics literature and they use modeling appr oaches that would not be appropriate or available for national-level analyses, or may focus differently than the direct job impacts addressed in this chapter, they are often used by state government officials when deciding which program and policy options are cost-effective.
In a study commissioned by Lawrence Berkeley National Laboratory, Goldman et al. (2010) quantified energy efficiency-related employment. They focused on quantifying energy efficiency-related employment and acknowledged that energy efficiency-related employment cuts across many aspects of the ‘industry’, including “ product development, manufacturing, wholesale and retail distribution, deployment (e .g., project design, construction, and evaluation of savings) and operations and maintenance .” 44 In the study, they focused only on the deployment and installation of energy efficiency products and services, excluding jobs related to the manufacturing, wholesale and retail distribut ion or operations and maintenance of energy efficiency equipment. They examined five diff erent energy efficiency employment activities: ratepayer-funded efficiency activity, low income weatherization, energy service companies (ESCOs), insulation, and federal and state government energy efficiency offices. They estimated

44 Goldman, C., Fuller, M. C., Stuart, E., Peters, J. S., McRae, M., Albers, N., et al. (2010). Energy Efficiency Services Sector: Workforce Size and Expectations for Growth. Lawrence Berkeley National Laboratory.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

Appendix 6B-13 2008 employment levels using self-reported data from energy efficiency-related providers, 11 state case studies, nearly 300 interviews with tr ade officials and industry experts, and publicly available data. LBNL calculated that in 2008 about $18 billion was spent on energy efficiency, resulting in about 114,000 person-years of employment. 45
California has a long history of running energy efficiency programs since the early 1970s. They have implemented a suite of policies and programs to increase energy efficiency, including standards, loading order requirements for energy efficiency and decoupling policies. A 2008 analysis by Roland-Holst of UC Berkeley estimated the employment impacts of California energy efficiency measures from 1972 – 2006 by analyzing the consumption patterns from BEA data, the historic economic structure of the stat e and sector-specific employment data. California programs enabled California households to redirect their expenditures toward other goods and services, creating about 1.5 millio n full-time equivalent (FTE) jobs with a total payroll of $45
billion. 46 California noted that “The same efficiency measures resulted in slower (but still positive) growth in energy supply chains, including oil, gas, and electric power. For every new job foregone in these sectors, however, more than 50 new jobs have been created across the state’s diverse economy.” The analysis went on to explore the potential from increasing investments in energy efficiency. 47 Using the Berkeley Energy and Resources (BEAR) California-specific computable general equilibrium model, the study found that “The economic benefits of energy efficiency innovation have a compounding effect. The first 1.4 percent of annual efficiency gain produced about 181,000 addition al jobs, while an additional one percent yielded 222,000 more. It is reasonable to assume that the marginal efficiency gains will be more costly, but they have more intensive economic growth benefits. 48 "

45 Goldman, C.; M. C. Fuller; E. Stuart; J. S. Peters; M. McRae and N. Albers, et al. 2010. Energy Efficiency Services Sector: Workforce Size and Expectations for Growth. Lawrence Berkeley National Laboratory.

46 Roland-Holst, D. 2008. “Energy Efficiency, Innovation, and Job Creation in California Working Paper,” October. University of California Berkeley, Center for Energy Resources and Economic Sustainability.

47 Ibid

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

Appendix 6B-14 New York also has a history of running energy efficiency programs and estimating related employment effects. The New York State Energy Research and Development Authority (NYSERDA) is required to submit an annual report to the New York Public Services Commission documenting the impacts, including employment effects, of spending systems benefit charge money to enable NY to meet their energy efficiency resource standard (EERS). 49 This funding is spent on energy efficiency-related programs across the state to lower demand for electricity. As of their June 2013 SBC3 Post -Program Annual Report, NYSERDA had spent $963.4 million, and achieved savings of 2,140.5 GWh. 50 Using the Regional Economic Models, Inc. (REMI) Policy Insight model, NYSERDA estimated that the EERS had and created 4,205 net additional jobs cumulati vely through December 2012. 51
In 2011, The Analysis Group conducted an independent analysis of the Regional Greenhouse Gas Initiative (RGGI) program, with guidance from a multidisciplinary technical advisory group. Nine states participate in RGGI which addresses power sector CO 2 emissions
and was established in 2009. 52 Approximately 90 percent of CO 2 allowances are auctioned by the
RGGI states 53 and from 2009 through 2012, the RGGI states invested auction proceeds of more than $900 million in programs that lower costs for energy consumers and reduce CO2
emissions. 54 Through 2012, the RGGI states invested approximately $460 million of the

49 Define SBC

50 New York State Energy Research and Development Au thority. 2013. “System Benefits Charge Program SBC3 Post-Program Annual Report,” June 4.

51 New York State Energy Research and Development Au thority. 2013. “System Benefits Charge Program SBC3 Post-Program Annual Report,” June 4.

54 Ibid. The REMI Policy Insight model is a regional economic forecasting model that integrates input-output, computable general equilibrium, econometric and econo mic geography methodologies to project changes in prices, competitiveness factors and business activity over time. 55 States currently participating in RGGI include Connecticut, Delaware, Maine, Maryland, Massachuset ts, New Hampshire, New York, Rhode Island, and Vermont.

53 Regional Greenhouse Gas Initiative: Allowance Allocation. 2013. Available at: <http://rggi.org/design/overview/allowance-allocation/2013-allocation>.

54 Regional Investments of RGGI CO2 Allowance Proceeds, 2012 (2014), available at http://www.rggi.org/docs/Documents/2012 -Investment-Report.pdf ; Note that this number includes NJ proceeds; NJ has since dropped out of RGGI.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

Appendix 6B-15 proceeds into energy efficiency programs. 55 The Analysis Group traced the flow of proceeds from the first three years of the program (2009 – 2011) through the economies of the RGGI states, modeled the electric sector outcomes a nd then modeled macroeconomic impacts using the
IMPLAN model. 56 They concluded that 16,000 new net jobs years were created throughout the economy, with job types including “engineers who perform efficiency audits, workers who install energy efficiency measures in commercial buildings, staff performing teacher training on energy issues; or the workers in state-funded programs that might have been cut had a state not used RGGI funds to close the gap.” 57 These job estimates include jobs created directly and indirectly.
While none of these studies directly correspond to this proposed rule, studies on existing state policy experiences with demand-side energy efficiency can be informative in the sense that they show a range of employment impacts states are considering as they develop their energy efficiency policies and programs.
A6.B.2 Other Approaches to Exploring Clean Energy Employment Trends

In addition to the studies discussed in ch apter 6 above, other research institutes and numerous industry groups have developed databases of employment in the clean economy or in clean energy. While the definition s and timeframes vary, all show positive employment trends of 1.9 percent or more growth in clean energy-related jobs annually. Below are a few such examples.
In a 2009 report, Pew Charitable Trust estimated the number of clean energy jobs in the U.S. between 1998 and 2007. They defined “clean energy economy jobs” as those “aimed at developing clean, renewable sources of ener gy, increasing energy efficiency, reducing greenhouse gas emissions that cause global warming, and conserving water and other natural

55 Of the $707 million in auction proceeds invested by RGGI participating states through 2012, 65 percent supported end-use energy efficiency programs. See Regional Gr eenhouse Gas Initiative, “Regional Investments of RGGI CO 2 Allowance Proceeds, 2012” (2014), available at) http://www.rggi.org/docs/Documents/2012-InvestmentReport.pdf.

56 IMPLAN is a social accounting /input-output model.

57 The Analysis Group, The Economic Impacts of the Regional Greenhouse Gas Initiative 33(2011),***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

Appendix 6B-16 resources” and counted “businesses and jobs supplying products and services generated by the clean energy economy, not the companies using these products and services to make themselves “greener” (i.e., ...on the supply side, not the demand side, of the clean energy economy).” 58 Pew built a national database of jobs from the bottom up, counting companies that they “could verify online as being actively engaged in the clean energy economy.” They concluded that jobs in the clean energy economy grew over that time period at a faster rate than overall U.S. jobs; they found that the number of clean energy jobs in the U.S. as a whole grew at a rate of 9.1% over that time, compared to overall job growth of 3.7%. 59
The Solar Foundation conducts an annual voluntary online survey of the solar industry to track solar jobs. The survey cove rs jobs related to solar insta llation, manufacturing, sales and distribution, project development (beginning in 2012), and a catchall “other” category. Their latest survey results indicate a positive growth rate in employment from 2010-2013. 60
Another effort has been tracking trends in clean energy job announcements since 2011. Instead of compiling data on employees actively working in clean energy, E2 Entrepreneurs monitors clean energy project development and job announcements in 30 states and reports on them quarterly. Their reports cover clean power generation, manufacturing, building efficiency, recycling, public transportation, biofuels, elect ric vehicles and smart grid/transmission. Their latest report is for the third quarter of 2013 and it shows an increase in the number of announcements when compared to the third quarter of 2012. 61

58 The Pew Charitable Trusts. “The Clean Energy Economy,” 2009. http://www.pewenvironment.org/uploadedFiles/PEG/Publications/Report/Clean%20Energy%20Economy.pdf

59 The Pew Charitable Trusts. “The Clean Energy Economy,” 2009. http://www.pewenvironment.org/uploadedFiles/PEG/Publications/Report/Clean%20Energy%20Economy.pdf

60 The Solar Foundation in partnership with BW Research and Cornell University. (2012). The National Solar Jobs Census. http://thesolarfoundation.org/research/national-solar-jobs-census-2012

61 Environmental Entrepreneurs. (2013). Clean Energy Works For US:2013 Third Quarter Clean Energy/Clean Transportation Jobs Report. http://www.e2.org/jsp/controller;jsessionid=8287977D4 3FA05E237DD5FEF58B00B3 A?docName=cleanjobsarchiv es. Environmental Entrepreneurs. (2012). What Clean Energy Jobs? These Clean Energy Jobs: Third Quarter 2012 Clean Energy Jobs Roundup.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

Appendix 6B-17 For a list of other sources, including unpublished models from experts in the field, the interested reader may wish to refer to the meta-analysis conducted by Wei, et al. (2010). The authors surveyed 15 studies of renewable energy, energy efficiency, nuclear power, carbon capture and sequestration, coal, and gas. 62***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-1 

### CHAPTER 7: S TATUTORY AND EXECUTIVE 

### O RDER 

### A NALYSIS
7.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, Improving Regulation and Regulatory Review
Under Section 3(f) (1) of Executive Order 12866 (58 FR 51735, October 4, 1993), this action is an "economically significant regulatory ac tion” because it is likely to have an annual effect on the economy of $100 million or more or to adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or state, local, or tribal governments or communities. The $100 million threshold can be triggered by either costs or benefits, or a combination of them. The EPA submitted this action to OMB for review under Executive Orders 12866 and 13563 (76 FR 3821, January 21, 2011), and any changes made in response to OMB recommendations have been documented in the docket for this action. The EPA also prepared an analysis of the potential costs and benefits associated with this action in this Regulatory Impact Analysis (RIA).
Consistent with EO 12866 and EO 13563, the EPA estimated the costs and benefits for illustrative compliance scenarios of implementing the proposed guidelines. This proposal sets goals to reduce CO 2 emissions from the electric power industry. Actions taken to comply with the proposed guidelines will also re duce the emissions of directly emitted PM 2.5 , sulfur dioxide (SO 2 ) and nitrogen oxides (NO X ). The benefits associated with these PM, SO 2 and NO X reductions are referred to as co-benefits, as these reductions are not the primary objective of this
rule.
The EPA has used the U.S. government’s social cost of carbon (USG SCC) estimates (i.e., the monetary value of impacts associated with a marginal change in CO 2 emissions in a given year), to analyze CO 2 climate impacts of this rulemaking. The four USG SCC estimates are associated with different discount rates (m odel average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent), and each increases over time. In this summary, EPA provides the estimate of climate benefits associated with the SCC value deemed to be central by the USG (the model average at 3% discount rate). The EPA estimates that in 2020, the Option 1 regional compliance approach will yield monetized climate benefits of approximately $17 billion with a 3 percent mode l average (2011$). The air pollution health cobenefits in 2020 are estimated to be $15 billion to $34 billion for a 3 pe rcent discount rate, and Comment [A1]: This chapter was revised to reflect edits made to the preamble.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-2 $13 billion to $31 bil lion (2011$) for a 7 percent discount rate. The annual illustrative compliance costs estimated by IPM, inclusive of demand side energy efficiency program and participant costs and MRR costs, are approximately $5.5 billion (2011$) in 2020. The quantified net benefits (the difference between monetized bene fits and costs) in 2020, are estimated to be $26 billion to $45 billio n using a 3 percent discount rate (model average) and $25 to $42 billion using a 7 percent discount rate (model average, 2011$). For Option 1 state compliance approach, the climate benefits are estimated to be $18 b illion (2011$). The air pollution health co-benefits in 2020 for the Option 1 state compliance approach are estimated to be $15 billion to $36 billion for a 3 percent discount rate, an d $14 billion to $33 billion (2011$ ) for a 7 percent discount rate. The annual illustrative compliance costs estimate d by IPM, inclusive of demand side energy efficiency program and participant costs and MR R costs, are approximately $7.5 billion (2011$) in 2020. The quantified net benefits in 2020, are estimated to be $26 billion to $46 billion using a 3 percent discount rate (model average) and $24 to $43 billion using a 7 percent discount rate (model average, 2011$).
For Option 1 regional compliance approach in 2030, the EPA estimates this proposal will yield monetized climate benefits of approximatel y $30 billion (3 percent, model average, 2011$). The air pollution health co-benefit s in 2030 are estimated to be $2 4 billion to $55 billion for a 3 percent discount rate, and $21 billion to $50 bill ion (2011$) for a 7 percent discount rate. The annual illustrative compliance costs estimated using IPM, inclusive of demand side energy efficiency program and participant costs and MR R costs, are approximately $7.3 billion (2011$) in 2030. The quantified net benefits (the difference between monetized benefits and costs) in 2030, are estimated to be $47 billion to $78 billi on using a 3 percent discount rate and $44 to $73 billion assuming a 7% discount rate (model average, 2011$). For the Option 1 state compliance approach, net benefits in 2030 are estimated to be $47 to $80 billion (3 percent discount rate) and $45 to 75 billion (7 percent discount rate (2011$). Compliance costs are estimated to be $8.8 billion (2011$). Based upon the fore going discussion, it remains clear that the benefits of this proposal are substantial, and far exceed the costs. Additional benefit, cost and net benefit estimates for Option 1 and Option 2 are provided in Chapter 8 of this report.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-3 7.2 Paperwork Reduction Act
The information collection requirements in this proposed rule have been submitted for approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act
, 44 U.S.C. 3501 et seq . The Information Collection Request (ICR) document prepared by the EPA has been assigned the EPA ICR number [insert #].
The information collection requirements are based on the recordkeeping and reporting burden associated with developing, implementing, and enforcing a state plan to limit CO 2 emissions from existing sources in the power sector. These recordkeeping and reporting requirements are specifically authorized by C AA section 114 (42 U.S.C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR part 2, subpart B.
The annual burden for this collection of information for the states (averaged over the first 3 years following promulgation of this proposed action) is estimated to be a range of 315,554 hours at a total annual labor cost of $22,334,119, to 632,338 hours at a total annual labor cost of $44,755,317. The lower bound estimate reflects the assumption that some states already have energy efficiency and renewable energy programs in place. The higher bound estimate reflects the assumption that no states have energy efficiency and renewable energy programs in place.
The annual burden for this collection of informati on for the states (averaged over the first 3 years following promulgation of this proposed action) is estimated to be a range of 315,554 hours at a total annual labor cost of $22,334,119, to 632,338 hours at a total annual labor cost of $44,755,317. The lower bound estimate reflects the assumption that some states already have energy efficiency and renewable energy programs in place. The higher bound estimate reflects the assumption that no states have energy efficiency and renewable energy programs in place. The total annual burden for the federal govern ment (averaged over the first 3 years following promulgation of this proposed action) is estimated to be 53,267 hours at a total annual labor cost of $2,958.005 Burden means the total time, effort, or financial resources expended by persons to generate, maintain, retain, or disclose or provide information to or for a Federal agency. This includes the time needed to review instructions; develop, acquire, install, and utilize technology and systems for the purposes of collecting, validating, and verifying information, processing and***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-4 maintaining information, and disclosing and prov iding information; adjust the existing ways to comply with any previously appli cable instructions and requirements; train personnel to be able to respond to a collection of information; search data sources; complete and review the collection of information; and transmit or otherwise disclose the information.
An agency may not conduct or sponsor, and a person is not required to respond to a collection of information unless it displays a currently valid OMB control number. The OMB control numbers for the EPA’s regulations are listed in 40 CFR part 9 and 48 CFR chapter 15.
To comment on the agency's need for this information, the accuracy of the provided burden estimates, and any suggested methods for minimizing respondent burden, the EPA has established a public docket for this rule, which includes this ICR, under Docket ID Number EPA-HQ-OAR-2013-0602. The final rule will respond to any OMB or public comments on the information collection requirements contained in this proposal.
7.3 Regulatory Flexibility Act
The Regulatory Flexibility Act (RFA) genera lly requires an agency to prepare a regulatory flexibility analysis of any rule , subject to notice and comment rulemaking requirements, under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a signifi cant economic impact on a substantial number of
small entities. Small entities include small businesses, small orga nizations, and small governmental jurisdictions.
For purposes of assessing the impacts of this ru le on small entities, small entity is defined

as:

1. A small business is defined by the SBA’s regulations at 13 CFR 121.201 (for the electric power generation industry, the sm all business size standard is an ultimate parent entity with less than 750 employ ees). The NAICS codes for the affected industry are in Table 14 below.
2. A small governmental jurisdiction that is a government of a city, county, town, school district or special district w ith a population of less than 50,000. A small organization that is any not-for-pr ofit enterprise that is independently owned and operated and is not dominant in its field.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-5 Table 7-1 Potentially Regulated Categories and Entities a

Category NAICS Code Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel electric power generating units.

State/Local Government 221112 b Fossil fuel electric power generating units owned by municipalities.

a Include NAICS categories for source categories that own and operate electric power generating units (includes

boilers and stationary combined cycle combustion turbines).

b State or local government-owned and operated establishment s are classified according to the activity in which they are engaged.

After considering the economic impacts of this proposed rule on small entities, EPA certifies that this action will not have a signifi cant economic impact on a substantial number of
small entities.
The proposed rule will not impose any requi rements on small entities. Specifically, emission guidelines established under CAA section 111(d) do not impose any requirements on regulated entities and, thus, will not have a significant economic imp act upon a substantial number of small entities. After emission guidelines are promulgated, states establish standards on existing sources and it is those state require ments that could potentially impact small entities. Our analysis here is consistent with the analysis of the analogous situation arising when the EPA establishes NAAQS, which do not impose any requirements on regulated entities. As here, any impact on small entities would only ar ise when states take subsequent action to maintain and/or achieve the NAAQS through thei r state implementation plans. See American Trucking Assoc. v. EPA, 175 F.3d 1029, 1043-45 (D.C. Cir. 1999) national standards for allowable concentrations of particulate matter in ambient air as required by section 109 of the
CAA. See also American Trucking Associations v. EPA. 175 F.3d at 1044–45 (NAAQS do not***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-6 have significant impacts upon small entities becau se NAAQS themselves impose no regulations upon small entities).
Nevertheless, the EPA is aware that there is substantial interest in the proposed rule among small entities (municipal and rural electric cooperatives). As detailed in Section III.A.of the preamble, the EPA has conducted an unprecedented amount of stakeholder outreach on setting emission guidelines for existing EGUs. While formulating the provisions of the proposed rule, the EPA considered the input provided over the course of the stakeholder outreach. Section III.B. of the preamble describes the key messages from stakeholders. In addition, as described in the RFA section of the preamble to the proposed standards of performance for GHG emissions from new EGUs (79 FR 1499-1500, January 8, 2014), the EPA conducted outreach to representatives of small entities while formulati ng the provisions of the proposed standards. Although only new EGUs would be affected by those proposed standards, the outreach regarded planned actions for new and existing sources. We invite comments on all aspects of the proposal and its impacts, including potential impacts on small entities.
7.4 Unfunded Mandates Reform Act (UMRA)
This proposed action does not contain a federal mandate that may result in expenditures of $100 million or more for State, local, and tribal governments, in the aggregate, or the private sector in any one year. Specifically, emission guidelines established under CAA section 111(d) do not impose any direct compliance requirements on regulated entities. Thus, this proposed rule is not subject to the requirements of section 202 or section 205 of UMRA.
This proposed rule is also not subject to the requirements of section 203 of UMRA because it contains no regulatory requirements that might significantly or uniquely affect small governments.
In light of the interest among governmental entities, the EP A initiated consultations with governmental entities while formulating the provisions of the proposed standards for new EGUs. Although only new EGUs would be affected by those proposed standards, the outreach regarded planned actions for new and existing sources. As described in the UMRA discussion in the preamble to the proposed standards of performa nce for GHG emissions from new EGUs (79 FR 1500-1501, January 8, 2014), the EPA consulted with the following 10 national organizations representing state and local elected officials:***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-7  National Governors Association

 National Conference of State Legislatures

 Council of State Governments

 National League of Cities

 U.S. Conference of Mayors

 National Association of Counties

 International City/County Management Association

 National Association of Towns and Townships

 County Executives of America

 Environmental Council of States

On February 26, 2014, the EPA re-engaged with those governmental entities to provide a pre-proposal update on the emission guidelines for existing EGUs and emission standards for
modified and reconstructed EGUs.
While formulating the provisions of these pr oposed emission guidelines, the EPA also considered the input provided over the course of the extensive stakeholder outreach conducted by the EPA.

7.5 Executive Order 13132, Federalism
Under Executive Order 13132, EPA may not issue an action that has federalism implications, that imposes substantial direct complia nce costs, and that is not required by statute, unless the Federal government provides the funds necessary to pay the direct compliance costs incurred by State and local governments, or the EPA consults with state and local officials early in the process of developing the proposed action.
The EPA has concluded that this action may have federalism implications, because it may impose substantial direct compliance costs on State or local governments, and the Federal government will not provide the funds necessary to pay those costs. As discussed in the Supporting Statement found in the docket for this rulemaking, the development of state plans will entail many hours of staff time to develop and coordinate programs for compliance with the proposed rule, as well as time to work with state legislatures as appropriate, and develop a plan
submittal.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-8 The EPA consulted with state and local officials early in the process of developing the proposed action to permit them to have meaningf ul and timely input into its development. As described in the Federalism discussion in the preamble to the proposed standards of performance for GHG emissions from new EGUs (79 FR 1501, January 8, 2014), the EPA consulted with state and local officials in the process of developing the proposed standards for newly constructed EGUs. This outreach regarded planned actions for new, reconstructed, modified and existing sources. The EPA invited the following 10 national organizations representing state and local elected officials to a meeting on April 12, 2011, in Washington DC: (1) National Governors Association; (2) National Conference of State Le gislatures, (3) Council of State Governments, (4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of Counties, (7) International City/County Manageme nt Association, (8) National Association of Towns and Townships, (9) County Executives of America, and (10) Environmental Council of States. These 10 organizations representing elected state and local officials have been identified by the EPA as the “Big 10” organizations appropriate to contact for purpose of consultation with elected officials. On February 26, 2014, the EPA re-engaged with those governmental entities to provide a pre-proposal update on the emission guidelines for existing EGUs and emission standards for modified and reconstructed EGUs. In addition, extensive stakeholder outreach conducted by the EPA allowed state leaders, including governors, environmental commissioners, energy officers, public utility commissioners, and air directors, opportunities to engage with EPA officials and provide input regarding redu cing carbon pollution from power plants.
A detailed Federalism Summary Impact Statement (FSIS) describing the most pressing issues raised in pre-proposal and post-proposal co mments will be forthcoming with the final rule, as required by section 6(b) of Executive Order 13132. In the spirit of Executive Order 13132, and consistent with EPA policy to promote co mmunications between EPA and State and local governments, EPA specifically solicits comment on this proposed action from State and local
officials.
7.6 Executive Order 13045, Protection of Children from Environmental Health Risks and Safety
The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those regulatory actions that concern health or safety risks, such that the analysis required under***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-9 section 5-501 of the Order has the potentia l to influence the regulation. This action is not subject to EO 13045 because it does not involve decisions on environmental health or safety risks that may disproportionately affect chil dren. The EPA believes that the CO 2 emission reductions resulting from implementation of the proposed guidelines, as well as substantial ozone and PM 2.5 emission reductions as a co-benefit, would further improve children’s health.
7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, Distribution, or Use
Executive Order 13211 (66 FR 28355; May 22, 2001) requires the EPA to prepare and submit a Statement of Energy Effects to the Ad ministrator of the Office of Information and Regulatory Affairs, OMB, for ac tions identified as ‘‘significan t energy actions.’’ This action, which is a significant regulatory action under EO 12866, is likely to have a significant effect on the supply, distribution, or use of energy. We ha ve prepared a Statement of Energy Effects for
this action as follows. We estimate a 4 to 7 percent increase in retail electricity prices, on average, across the contiguous U.S. in 2020, and a 16 to 22 percent reduction in coal-fired electricity generation as a result of this ru le. The EPA projects that electric power sector delivered natural gas prices will increase by about 8 to 12 percent in 2020 and reflect no change to a decrease of approximately 3 percent in 2030.

Additional information is ava ilable in Chapter 3 of this RIA.
7.8 National Technology Transfer and Advancement Act (NTTAA)
Section 12(d) of the NTTAA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) directs the EPA to use Voluntary Census Standard s in their regulatory and procurement activities unless to do so would be inconsistent with app licable law or otherwise impractical. Voluntary consensus standards are technical standards (e .g., materials specifications, test methods, sampling procedures, business practices) developed or adopted by one or more voluntary
consensus bodies. The NTTAA directs the EPA to provide Congress, through annual reports to the OMB, with explanations when an agency does not use available and applicable VCS. This proposed rulemaking does not involve technical standards.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-10 The EPA welcomes comments on this aspect of the proposed rulemaking and specifically invites the public to identify potentially-applicable VCS and to explain why such standards
should be used in this action.

7.8 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations
Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive policy on environmental justice. Its main provision directs federal agencies, to the greatest extent practicable and permitted by law, to make en vironmental justice part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human health orenvironmental effects of their programs, policies and activities on minority populations and lowincome populations in the U.S.
Section II.A of the preamble summarizes the public health and welfare impacts from GHG emissions that were detailed in the 2009 Endangerment Finding under CAA section 202(a)(1). 1 As part of the Endangerment Finding, the Administrator considered climate change risks to minority or low-income populations, find ing that certain parts of the population may be especially vulnerable based on their circumstances. These include the poor, the elderly, the very young, those already in poor health, the disa bled, those living alone, and/or indigenous populations dependent on one or a few resources. The Administrator placed weight on the fact that certain groups, including children, the elderl y, and the poor, are most vulnerable to climaterelated health effects.
Strong scientific evidence that the potential impacts of climate change raise environmental justice issues is found in the major assessment reports by the U.S. Global Change Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IPCC), and the National Research Council (NRC) of the National Academies, summarized in the record for the Endangerment Finding. Their conclusions incl ude that poor communities can be especially vulnerable to climate change imp acts because they tend to have more limited adaptive capacities and are more dependent on climate-sensitive resources such as local water and food supplies. In

1 “Endangerment and Cause or Contribute Findings for Green house Gases Under Section 202(a) of the Clean Air Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-11 addition, Native American trib al communities possess unique vulnerabilities to climate change, particularly those on established reservations that are restricted to reservation boundaries and therefore have limited relocation options. Tr ibal communities whose health, economic wellbeing, and cultural traditions depend upon the na tural environment will likely be affected by the degradation of ecosystem goods and services a ssociated with climate change. Southwest native cultures are especially vulnerable to water quality and availability impacts. Native Alaskan communities are likely to experience disruptive impacts, including shifts in the range or abundance of wild species crucial to their livelihoods and well-being. The most recent assessments continue to strengthen scientific understanding of climate change risks to minority and low-income populations.
This proposed rule limits GHG emissions by establishing CO 2 emission guidelines for existing fossil fuel-fired EGUs . In addition to reducing CO 2 emissions, implementing the proposed rule would reduce other emissions from EGUs which are dispatched less under a state’s 111(d) program because of their relatively low en ergy efficiency. These emission reductions will
include SO 2 and NOx, which form ambient PM 2.5 and ozone in the atmosphere, and hazardous air pollutants (HAP), such as mercury. In the final rule revising the annual PM 2.5 standard 2 , the EPA identified persons with lower socioeconomic st atus as an at-risk population for experiencing adverse health effects related to PM exposures. Persons with lower socioeconomic status have been generally found to have a higher prevalence of pre-existing diseases, limited access to medical treatment, and increased nutritional deficiencies, which can increase this population’s
risk to PM-related and ozone-related effects. 3 Therefore, in areas where this rulemaking reduces exposure to PM 2.5 , ozone, and methylmercury persons with low socioeconomic status would also benefit. Chapter 4 of this RIA provides ad ditional information rega rding the health and ecosystem effects associated with these emission reductions.

2 U.S. EPA (2013). National Ambient Air Quality Standards for Particulate Matter, Final Rule. Federal Register 78 (15 January 2013): 3086-3287.

3 U.S. Environmental Protecti on Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cf m/recordisplay.cfm?deid=216546 >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-12 While there will be many locations with improved air quality for PM 2.5 , ozone, and methylmercury, there may also be EGUs whose emissions of one or more of these pollutants ortheir precursors increase as a result of the proposed emission guidelines for existing fossil fuelfired EGUs. This may occur at EGUs that are dispatched more intensively than in the past because they become more energy efficient. The EPA has considered the potential for such increases and the environmental justice implications of such increases.
As we noted in the NSR discussion in the preamble, as part of a state’s CAA section 111(d) plan, the state may require an affected EGU to undertake a physical or operational change to improve the unit’s efficiency that results in an increase in the unit’s dispatch and an increase in the unit’s annual emissions of GHGs and/or other regulated pollutants. As we noted in the NSR discussion in the preamble, a state can take st eps to avoid increased utilization of particular EGUs and thus to avoid any significant increas es in emissions including emissions of other regulated pollutants whose environmental effects would be more localized around the affected EGU. To the extent that states take this pa th, there would be no new environmental justice concerns in the areas near such EGUs. For any EGUs that make modifications that do trigger NSR permitting, the applicable local, state, or federal permitting program will ensure that there are no new NAAQS violations and that no existing NAAQS violations are made worse. For those EGUs in a permitting situation for which the EPA is the permit reviewing authority, the EPA will consider environmental justice issu es as required by Executive Order 12898.
In addition to some EGUs possibly being requ ired by a state to make modifications for increased energy efficiency, another effect of the proposed CO 2 emission guidelines for existing
fossil fuel-fired EGUs would be increased utiliza tion of other, unmodified EGUs with relatively low GHG emissions per unit of electrical output, in particular high efficiency gas-fired EGUs. Because such EGUs would not have been modified physically nor changed their method of operation, they would not be subject to review in the NSR permitting program. Such plants would have more hours in the year in which they operate and emit pollutants, including pollutants whose environmental effects if any would be localized rather than global as is the case with GHG emissions. Changes in utilization al ready occur now as demands for and sources of electrical energy evolve, but the proposed CO 2 emission guidelines for existing fossil fuel-fired EGUs can be expected to cause more such changes. Because gas-fired EGUs emit essentially no***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-13 mercury, increased utilization would not increase methylmercury concentrations in their vicinities. Increased utilization generally woul d not cause higher peak concentrations of PM 2.5, NOx, or ozone around such EGUs than is already occurring because peak hourly or daily emissions generally would not change, but increas ed utilization may make periods of relatively high concentrations more frequent. It should be no ted that the gas-fired sources that are likely to be dispatched more frequently than at present have very low emissions of primary particulate matter, SO 2 and hazardous air pollutants per unit of el ectrical output, such that local (or regional) air quality for these pollutants is likely to be affe cted very little. For natural gas-fired EGUS, the EPA found that regulation of HAP emissions “is not appropriate or necessary because the impacts due to HAP emissions from such units are negligible based on the results of the study documented in the utility RTC” 65 FR 79831. In studies done by DOE/NETL comparing cost and performance of coal- and NG-fired generation, they assumed SO 2 , PM (and Hg) emissions to be “negligible.” Their studies predict NOx emis sions from a NGCC unit to be approximately 10 times lower than a subcritical or supercritical coal-fired boiler. Many are also very well controlled for emission of NOx through the appli cation of after combustion controls such as selective catalytic reduction, although not all gas-fired sources are so equipped. Depending on the specificity of the state CAA section 111(d) plan , the state may be able to predict which EGUs and communities may be in this type of situation and to address any concerns about localized
NO 2 concentrations in the design of the CAA section 111(d) program, or separately from the CAA section 111(d) program but before its im plementation. In any case, existing tracking systems will allow states and the EPA to be aware of the EGUs whose utilization has increased most significantly, and thus to be able to priori tize our efforts to assess whether air quality has changed in the communities in the vicinity of such EGUs. There are multiple mechanisms in the CAA to address situations in which air quality has degraded significantly.
In conclusion, this proposed rule will resu lt in regional and national pollutant reductions; however, there likely will also be some locations with more times during the year of relatively higher concentrations of pollu tants with potential for effect s on localized communities than would be experienced in the absence of the proposed rule. The EPA cannot exactly predict how emissions from specific EGUs would change as an outcome of the proposed rule due to the stateled implementation. Therefore, the EPA has conc luded that it is not practicable to determine***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-14 whether there would be disproportionately high and adverse human health or environmental effects on minority, low income, or indigenous populations from this proposed rule.
In order to provide opportunities for meaningful involvement early on in the rule making process, the EPA has hosted webinars and conference calls on August 27, 2013 and September 9, 2013 on the proposed rule specifically for environmental justice communities and has taken all comments and suggestions into consideration in the design of the emission guidelines.
The public is invited to submit comments or id entify peer-reviewed studies and data that assess effects of exposure to the pollutant addressed by this proposal.

7.10 Executive Order 13175 - Consultation and Coordination with Indian Tribal
Governments
This action does not have tribal implications, as specified in Executive Order 13175 (65 FR 67249, November 9, 2000). It would not impose substantial direct compliance costs on tribal governments that have affected EGUs located in their area of Indian country. Tribes are not required to, but may, develop or adopt CAA programs. Tribes are not required to develop plans to implement the guidelines under CAA section 111(d) for affected EGUs. To the extent that a tribal government seeks and attains treatment in a manner similar to a state (TAS) status for that purpose and is delegated authority for air quality planning purposes, these proposed emission guidelines would require that planning requirements be met and emission management implementation plans be executed by the tribes. The EPA is aware of three coal-fired EGUs and one natural gas-fired EGU located in Indian country but is not aware of any affected EGUs that are owned or operated by tribal entities. The EPA notes that this proposal does not directly impose specific requirements on EGU sources, including those located in Indian country, such as the three coal-fired EGUs and one natural gas-fire d EGU, but provides guidance to any tribe with delegated authority to address CO 2 emissions from EGU sources foun d subject to section 111(d) of the CAA. Thus, Executive Order 13175 does not apply to this action.

The EPA conducted outreach to tribal environmental staff and offered consultation with tribal officials in developing this action. Because the EPA is aware of tribal interest in this proposed rule, prior to the April 13, 2012 proposal (77 FR 22392-22441), the EPA offered***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-15 consultation with tribal officials early in the process of developing the proposed regulation to permit them to have meaningful and timely inpu t into its development. The EPA's consultation regarded planned actions for new and existing sources. In addition, on April 15, 2014, prior to proposal, the EPA met with Navajo Energy Development Group officials. For this proposed action for existing EGUs, a tribe that has one or more affected EGUs located in its area of Indian Country 4 would have the opportunity, but not the obligation, to establish a CO 2 performance standard and a CAA section 111(d) plan for its area of Indian country.
Consultation letters were sent to 584 tribal leaders. The letters provided information regarding the EPA's development of both the NSPS and emission guidelines for fossil fuel-fired
EGUs and offered consultation. No tribes have requested consultation. Tribes were invited to participate in the national informational webinar held August 27, 2013. In addition, consultation/outreach meeting was held on September 9, 2013, with tribal representatives from
some of the 584 tribes. The EPA also met with tr ibal environmental staff via National Tribal Air Association teleconferences on July 25, 2013, and December 19, 2013. In those teleconferences, the EPA provided background information on the GHG emission guidelines to be developed and a summary of issues being explored by the agency. Tribes have expressed varied points of view. Some tribes raised concerns about the impacts of the regulations on EGUs and the subsequent impact on jobs and revenue for their tribes. Other tribes expressed concern about the impact the regulations would have on the cost of water to their communities as a result of increased costs to the EGU that provide energy to transport the water to the tribes. Other tribes raised concerns about the impacts of climate change on their co mmunities, resources, life ways and hunting and treaty rights. The tribes were al so interested in the scope of the guidelines being considered by the agency (e.g., over what time period, relationship to state and multi-state plans) and how tribes will participate in these planning activities. In addition, the EPA held a series of listening sessions prior to development of this proposed action. In 2013, tribes participated in a session with the state agencies, as well as a separate session with tribes.

4 The EPA is aware of at least four affected EGUs located in Indian country: two on Navajo lands, the Navajo Generating Station and the Four Corners Generating Station; one on Ute lands, the Bonanza Generating Station; and one on Fort Mojave lands, the South Point Energy Center. The affected EGUs at the first three plants are coal-fired EGUs. The fourth affected EGU is an NGCC facility.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7-16 During the public comment period for this pr oposal, the EPA will hold meetings with
tribal environmental staff to info rm them of the content of this proposal, as well as offer further
consultation with tribal elected officials where it is appropriate. We specifically solicit comment from tribal officials on this proposed rule.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

8-1 

### CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

8.1 Comparison of Benefits and Costs

The illustrative benefits, costs, and net bene fits of compliance with Options 1 and 2 are presented in this chapter of the Regulatory Impact Analysis (RIA). As discussed in Chapter 3, the EPA is proposing two options for state-specific rate-based CO 2 goals that reflect application of cost-effective measures from all four building blocks discussed. Options 1 and 2, presented in this RIA, reflect possible complian ce approaches with state-specific CO 2 goals. These Options are a reflection of what compliance could look like for Options 1 and 2, assuming states comply
with the CO 2 goals on an individual state basis (referre d to as state approach) or a number of states comply collectively on a regional ba sis (referred to as regional approach). EPA is proposing Option 1 and taking comment on Option 2. The guidelines allow flexibility of compliance, and EPA recognizes that actual compliance may differ from the illustrative approaches reflected in this RIA.

The EPA has used the U.S. government’s social cost of carbon (USG SCC) estimates (i.e., the monetary value of impacts associated with a marginal change in CO 2 emissions in a given year), to analyze CO 2 climate impacts of this rulemaking. The four USG SCC estimates are associated with different discount rates (m odel average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent), and each increases over time. In this comparison of benefits and costs, the EPA provi des the estimate of climat e benefits associated with the SCC value deemed to be central by the USG (the model average at 3% discount rate). As Table 8-1 shows, the EPA estimates that under the proposed Option 1, regional approach monetized climate benefits (in 2011$) are approximately $17 billion in 2020, assuming a 3 percent discount rate (model average) . The air pollution health co-benefits in 2020 are estimated to be $15 billion to $34 billion for a 3 percen t discount rate and $13 billion to $31 billion (2011$) for a 7 percent discount rate. The annual, illustrative compliance costs estimated by IPM and inclusive of demand side energy efficiency program and participant costs and Monitoring Reporting and Recordkeeping (MRR) costs [MRR costs to be added] , are approximately $5. 4 5 billion (2011$) in 2020. The quantified net benefits (the difference between monetized benefits and costs) in 2020, are $26 billion to $ 46 45 billion using a 3 percent discount rate and $ 25 24***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

8-2 billion to $ 42 43 billion assuming a 7 percent discount rate (2011$). In comparison, net benefits for the Option 1 state compliance approach in 2020 ar e estimated to be $26 billion to $46 billion, using a 3 percent discount rate and $24 billion to $43 billion assuming a 7 percent discount rate. Option 2 regional and state compliance approach benefits, costs and net benefits for 2020 are
also shown on Table 8-1. Table 8-2 reflects estim ates for the Options 1 and 2 regional and state compliance approaches for 2025. For the regional compliance approach to the proposal Option 1 in 2030, the EPA estimates this proposal will yield monetized climate benefits of approximately $30 billion (2011$) (using 3 percent discount rate (model average) as shown on Table 8-3. The air pollution health co-benefits in 2030 are esti mated to be $24 billion to $55 billion for a 3 percent discount rate and $21 billion to $50 bill ion (2011$) for a 7 percent discount rate. The annual illustrative compliance costs estimated using IPM, inclusive of demand side energy efficiency program and participant costs and MRR costs [MRR costs to be added] , are approximately $7.3 billion (2011$) in 2030. The qu antified net benefits for the Option 1 regional in 2030 are $47 billion to $78 billion, assuming a 3 percent discount rate, and $44 to $73 billion assuming a 7 percent discount rate (2011$). In 2030, quantified net benefits are $47 billion to $80 billion, assuming a 3 percent discount ra te, and $45 to $75 billion assuming a 7 percent discount rate (2011$) for the Option 1 state complia nce approach. This table also reflects benefit, cost, and net benefit estimates in 2030 for Option 2 regional and state compliance approaches.

Based upon the foregoing discussion, it remains cl ear that the benefits of this proposal are substantial and far outweigh the costs for the all regulatory options and compliance approaches. The EPA could not monetize important categorie s of impacts. Unquantified impacts include those associated with changes in emissions of ot her pollutants that affect the climate, such as methane. In addition, the analysis does not quantify co-benefits from reducing exposure to SO 2 , NOx, and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

8-3 Table 8-1. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2020 (billions of 2011$) a Option 1 - state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $4.9 $18 $26 $52 $3.8 $14 $20 $40 Air pollution health co-benefits c $15 to $36 $14 to $33 $12 to $29 $11 to $26 Total Compliance Costs d $7. 45 $5. 45 Net Benefits e $26 to $46 $24 to $43 $20 to $37 $19 to $34

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.5 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.2 tons of Hg Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $4.7 $17 $25 $51 $3.6 $13 $19 $39 Air pollution health co-benefits c $15 to $34 $13 to $31 $12 to $28 $11 to $25 Total Compliance Costs d $5. 45 $4. 23 Net Benefits e $26 to $ 4645 $25 to $42 $21 to $37 $20 to $34

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 0.9 tons of Hg Ecosystem Effects Visibility impairment

a All estimates are for 2020, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduc tion in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally pot ent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative compliance costs estimated, using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

.

e The estimates of net benefits in this summary table are calculated using the global social cost of carbon at a 3 percent discount rate (model average) . The RIA includes combined climate and health estimates based on these additional discount rates. Formatted: Line spacing: single

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8-4 Table 8-2. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2025 (billions of 2011$) a Option 1 – state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $7.6 $25 $37 $77 $5.6 $19 $28 $27 Air pollution health co-benefits c $22 to $51 $20 to $46 $16 to $38 $15 to $35 Total Compliance Costs d $ 4.65.5 $5.5 Net Benefits e $41 to $70 $ 40 39 to $66 $30 to $52 $28 to $48

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 2.0 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $7.5 $25 $37 $76 $5.5 $18 $27 $56 Air pollution health co-benefits c $21 to $50 $19 to $45 $16 to $37 $14 to $34 Total Compliance Costs d $4.6 $4.5 Net Benefits e $42 to $70 $40 to $66 $30 to $51 $28 to $48

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment

a All estimates are 2025, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduc tion in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative complian ce costs estimated, using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

.

e The estimates of net benefits in this summary table are calculated using the global social cost of carbon at a 3 percent discount rate (model average) . The RIA includes combined climate and health estimates based on these additional discount rates. Formatted: Line spacing: single

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8-5 Table 8-3. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines –2030 (billions of 2011$) a Option 1– state Option 2– state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.5 $31 $44 $94 $6.4 $21 $30 $63 Air pollution health co-benefits c $25 to $58 $23 to $53 $17 to $39 $15 to $36 Total Compliance Costs d $8.8 $3.1 Net Benefits e $47 to $80 $45 to $75 $34 to $57 $33 to $53

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 2.1 tons of Hg and 590 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.5 tons of Hg and 130 tons of HCl Ecosystem Effects Visibility impairment Option 1– regional Option 2– regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.3 $30 $44 $92 $6.0 $20 $28 $60 Air pollution health co-benefits c $24 to $55 $21 to $50 $16 to $37 $14 to $34 Total Compliance Costs d $7.3 $1.9 Net Benefits e $47 to $78 $44 to $73 $33 to $55 $32 to $51

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 1.7 tons of Hg and 580 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.1 tons of Hg and 30 tons of HCl Ecosystem Effects Visibility impairment

a All estimates are 2030 for Option 1 and 2025 for Option 2, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

c The air pollution health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduc tion in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally pot ent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Emission reductions of directly emitted particles are not yet available.

d Total costs are approximated by the illustrative compli ance costs estimated using the Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs [MRR costs to be added]

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8-6 e The estimates of net benefits in this summary table ar e calculated using the global social cost of carbon at a 3 percent discount rate (model average) . The RIA includes combined climate and health estimates based on these additional discount rates.

## 10-1 

## CHAPTER 9: B ENEFITS 

## , C OSTS 

## , AND 

## E CONOMIC 

## I MPACTS OF 

## S TANDARDS OF 

## P ERFORMANCE FOR 

## R ECONSTRUCTED AND 

## M ODIFIED 

## E LECTRIC 

## U TILITY 

## G ENERATING 

## U NITS


## 9.1 This chapter will be transmitted with the 111(b) rulemaking package.3A-1 

### A PPENDIX 

### 3A: A NALYSIS OF 

### P OTENTIAL 

### U PSTREAM 

### M ETHANE 

### E MISSIONS 

### C HANGES IN 

### N ATURAL 

### G AS 

### S YSTEMS AND 

### C OAL 

### M INING

The purpose of this appendix is to describe the methodology for estimating upstream methane (CH 4 ) emissions related to natural gas systems and coal mining sectors that may result from the compliance approaches examined in the Regulatory Impact Analysis (RIA). The US Environmental Protection Agency (EPA) assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the potential magnitude of these upstream changes relative to CO 2 emissions reductions anticipated at power plants from the compliance strategies examined in the RIA. In addition to estimating changes in upstream methane emissions, tT his assessment included estimating CO 2 emissions from vented, fugitive and flared emissions associated with fuel production, processing, transmission, storage, and distribution of fuels pr ior to fuel combustion in electricity plants, and from the flaring of methane, but did not examine other potential changes in other upstream greenhouse gas emissions changes from naturalturalnatural gas systems and coal mining sectors.

The methodologies used to project upstream emissions were previously developed for the purpose of the Sixth U.S. Climate Action Report, and were subject to peer review and public review as part of the publication of that report. In Section 3A.1, the overall approach is described in brief. In Section 3A.2, results are presented. In Section 3A.3, detailed methodologies are presented for how CH 4 and CO 2 projections were calculated for coal mining and natural gas systems. Finally, Section 3A.4 contains a bibliography of cited resources.

3A.1 General Approach

3A.1.1 Analytical Scope
Upstream CH 4 and CO 2 emissions associated with coal mining and natural gas systems were estimated for 2020 through 2030 using methodologies developed for the 2014 U.S. Climate Action Report (U.S. Department of State 2014). The base year for the projections is 2011, as reported in the 2013 U.S. GHG Inventory (EPA 2013a). The projection methodologies use activity driver data outputs such as coal and natural gas production from the baseline and policy Comment [A1]: The original language was too abbreviated, and seemed to be missing a section on what the assessment did cover, which I cut and paste from the next page. Comment [A2]: We revised this language to be more clear on what the assessment covered.

Formatted: Subscript
Formatted: Subscript3A-2 scenarios generated by the Integrated Planning Model (IPM), which was used in the RIA to model compliance strategies. The projection methodologies use similar activity data and emissions factors as are used in the U.S. Greenhouse Gas (GHG) Inventory. The projection methodologies estimate reductions associated with both voluntary and regulatory programs affecting upstream emissions. In the case of the voluntary programs, the rate of reductions is based on the historical average over recent years. In the case of regulatory reductions, the
reductions are based on the reduction rates estimated in the relevant regulatory impact analysis. The methodologies were subject to expert peer review and public review in the context of the Sixth U.S. Climate Action Report. For more information on the review, or for the detailed methodologies used for non-CO 2 source projections in that report, see “Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO 2 and Non-Energy CO 2 Sources” (EPA, 2013b). Uncertainties and limitations are discussed, incl uding a side case which incorporates additional geographic information for estimating CH 4 from coal mining.

The term “upstream emissions” in this memo refers to vented, fugitive and flared emissions associated with fuel production, proces sing, transmission, storag e, and distribution of fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on upstream CH 4 from the natural gas systems and coal mining sectors. In addition, the analysis
included CO 2 resulting from flaring in natural gas production. This analysis does not assess other upstream GHG emissions changes, such as CO 2 emissions from the combustion of fuel used in natural gas and coal production activities or other non-combustion CO 2 emissions from natural gas systems, such as vented CO 2 and CO 2 emitted from acid-gas removal processes..

Also, the EPA assessed potential upstream methane emissions from natural gas systems and coal mining sectors within the domestic US, but did not examine emissions from potential changes in upstream emissions generated by changes in natural gas and coal production
, processing, and transportation activities outside of the US. 1 activities related to imported natural

1 While the analysis does not estimate methane emissions changes outside of the United States, activity factors include imports and exports of natural gas to help es timate domestic methane emissions related to trade of natural gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural gas within the US (or transporting natural gas w ithin the US while en route for export).. Formatted: Subscript

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Formatted: Subscript3A-3 gas or coal . Last, the EPA did not assess potential changes in other upstream non-GHG emissions, such as nitrogen oxides, volatile organic compounds, and particulate matter. Table 3A-1 presents estimates of the upstream emission s discussed in this memo for 2011, based on the 2013 U.S. GHG Inventory.

. EPA defined the boundaries of this assessment in order to provide targeted insights into the potential net change in methane emissions from natural gas systems and coal production activities specifically. CO 2 emissions from flared methane are included because regulatory and voluntary programs influence the rate of methane flaring over time, but the CO 2 remaining after flaring is still, from the perspective of this assessment, a methane-related GHG. EPA chose to limit the scope of the upstream analysis to the important and well-defined issue of methane emissions from natural gas systems and coal production.

3A.1.2 Coal Mining Source Description
Within coal mining, this analysis covers fugitive CH 4 emissions from coal mining (including pre-mining drainage) a nd post-mining activities (i.e., coal handling), including both underground and surface mining. Emissions from abandoned mines are not included. Energyrelated CO 2 emissions, such as emissions from mining equipment and vehicles transporting coal
are not included. CH 4 , which is contained within coal seams and the surrounding rock strata, is released into the atmosphere when mining operations reduce the pressure above and/or surrounding the coal bed. The quantity of CH 4 emitted from these opera tions is a function of two primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of the coal, with higher coal ranks corresponding to higher carbon content and generally higher CH 4 content. Pressure increases with depth and prevents CH 4 from migrating to the surface; as a result, underground mining operations typically emit more CH 4 than surface mining. In addition to emissions from underground and surface mi nes, post-mining processing of coal and
abandoned mines also release CH 4 . Post-mining emissions refer to CH 4 retained in the coal that is released during processing, storage, and transport of the coal.

3A.1.3 Natural Gas Systems Source Description
Within natural gas systems, this analysis covers vented and fugitive CH 4 emissions from the production, processing, transmission and storag e, and distribution segments of the natural gas Comment [A3]: Discussion added in response to comment provided in separate comment document: “Scope (Chapter 3A). This needs much more clarity on why EPA is including some sources and not others as part of the analysis. EPA clearly states what it includes and what it doesn’t but there is often no justification for why some sources are not included in the calculations. For example, why focus only on upstream methane emissions, as opposed to allupstream greenhouse gas emissions? Energyrelated CO2 emissions from NG systems are non-trivial (e.g., roughly 9% of U.S. natural gas end-use occurs during its production,processing and transmission; EIA). Also, noncombustion CO2 emissions (e.g., vented during acid-gas removal ) are not included in this analysis.”3A-4 system. It also includes CO 2 from flaring of natural gas. Not included are vented and fugitive
CO 2 emissions from natural gas systems, such as vented CO 2 emissions removed during natural gas processing, or energy-related CO 2 such as emissions from stationary or mobile combustion. The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds of processing facilities, and over a million miles of transmission and distri bution pipelines. CH 4 and
non-combustion 2 CO 2 emissions from natural gas systems are generally process-related, with normal operations, routine maintenance, and system upsets being the primary contributors. There are four primary stages of the natural gas system which are briefly described below.

Production: In this initial stage, wells are used to withdraw raw gas from underground formations. Emissions arise from the wells themselves, gathering pipelines, and well-site gas treatment facilities (e.g., dehydr ators, separators). Major emissi ons source categories within the production stage include pneumatic devices, gas wells with liquids unloading, and gas well completions and re-completions (i.e., workovers) with hydraulic fracturing (EPA 2013). Flaring emissions account for the majority of the non-combustion CO 2 emissions within the production stage.

Processing: In this stage, natural gas liquids and various other constituents from the raw gas are removed, resulting in “pipeline-qu ality” gas, which is then inject ed into the transmission system. Fugitive CH 4 emissions from compressors, includi ng compressor seals, are the primary emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion
CO 2 emissions in the processing stage come from acid gas removal units, which are designed to
remove CO 2 from natural gas.

Transmission and Storage: Natural gas transmission involv es high-pressure, large-diameter pipelines that transport gas long distances from field production and processing areas to distribution systems or large-volume customers such as power plants or chemical plants. Compressor station facilities, which contain large reciprocating and turbine compressors, are

2 In this document, consistent with IPCC accounting te rminology, the term “combustion emissions” refers to the emissions associated with the combustion of fuel for useful heat and work, while “non-combustion emissions” refers to emissions resulting from othe r activities, including flaring and CO 2 removed from raw natural gas.3A-5 used to move the gas throughout the U.S. transmission system. Fugitive CH 4 emissions from these compressor stations and from metering and regulating stations account for the majority of the emissions from this stage. Pneumatic devices and non-combusted engine exhaust are also
sources of CH 4 emissions from transmission facilities. Natu ral gas is also injected and stored in underground formations, or liquefied and stored in above-ground tanks, during periods of lower demand (e.g., summer), and withdrawn, processed, and distributed during periods of higher demand (e.g., winter). Compressors and dehydrators are the primary contributors to emissions from these storage facilities. Emissions from LNG import terminals are included within the transportation and storage stage.

Distribution: Distribution pipelines take the high-press ure gas from the transmission system at “city gate” stations, reduce the pressure, and then distribute the gas through primarily underground mains and service lines to individual end users.3A-6 Table 3A-1. Base Year Upstream Emissions in the U.S. GHG Inventory

Emissions Source 2011 Emissions (TgCO 2 e)
CH 4 from Coal Mining 75.2

Underground Mining and Post- Mining 57.4

Surface Mining and Post-Mining 18.0
CH 4 from Natural Gas Systems 172.3

Production 63.6 Processing 23.3 Transmission and Storage 52.1
Distribution 33.2
CO 2 from flaring of natural gas 10.3
Source: 2013 U.S. GHG Inventory (EPA, 2013)

It is important to note that in Table 3A-1, CO 2 -equivalent methane emissions are presented using the Fourth Assessment Report Global Warming Potential (GWP) of 25, whereas CO 2 - equivalent methane emissions in the 1990-2011 U.S. GHG Inventory (EPA 2013) are presented using the Second Assessment Report GWP of 21 . EPA plans to use 25 as the methane GWP starting with the 1990-2013 U.S. GHG Invent ory, to be published in April 2015.

3A.1.4 Compliance Approaches Examined
As is described in the RIA for the proposed EGU GHG Existing Source Emissions Guidelines, the EPA is using the following four building blocks to determine state-specific goal for the proposed EGU GHG Existing Source Emissions Guidelines:

1. Lowering the carbon intensity of generation at individual affected EGUs (e.g., through heat rate improvements) 2. Reducing emissions of the most carbon-inte nsive affected EGUs to the extent that this can be accomplished cost-effectively by shifting generation to less carbon-intensive existing fossil fuel-fired EGUs, including NGCC units that are under construction3A-7 3. Reducing emissions of carbon-emitting EGUs to the extent that this can beaccomplished cost-effectively by expanding the amount of new, lower (or no) carbonintensity generation 4. Reducing emissions of carbon-emitting EGUs to the extent that this can be accomplished cost-effectively by increasing demand-side energy efficiency States will ultimately determine optimal approaches to comply with the goals established in this regulatory action. Each of these goal ap proaches use the four building blocks described above at different levels of stringency. Option 1 involves higher deployment of the four building blocks but allows a longer timeframe to comply (2030) whereas Option 2 has a lower deployment over a shorter timeframe (2025). The RIA depicts illustrative state’s compliance scenarios for the goals set for Options 1 and 2, inclusive of regional and state compliance approaches for each option.

Readers interested in more details on the options and compliance approaches are referred to the RIA for this proposal, which is available in the Docket. This memo presents results for Option 1 and Option 2 and state or regional compliance approaches. As in the RIA, results are reported for the years 2020, 2025 and 2030 for both Option 1 and Option 2 scenarios.

3A.1.5 Activity Drivers
IPM-based activity driver projections from baseline and policy scenarios underlie the estimates of upstream methane emissions. Thes e activity drivers include domestic coal and natural gas production, imports and exports, and natural gas consumption. For a sensitivity analysis described later, regional coal production is used for the Appalachian, Interior, and Western regions. The following Tables 3A-2 to 3A-5 summarize the IPM-based activity driver results from the baseline, Option 1 Regional and State scenarios, and Option 2 Regional and
State scenarios. 3

Under Option 1, the state and regional compliance scenarios result in 24 percent and 22 percent reductions in coal production in 2020, respectively, relative to base case coal production.

3 Uncertainties related to activity drivers are disc ussed in the uncertainties and limitations section. Comment [A4]: If upstream emissions from imports and exports are not included in the calculations, why are they mentioned under the “Activity Drivers”? Comment [A5]: We clarified why imports and exports are included activity factors— please see new footnote in analytical scope section.3A-8 Natural gas production in 2020 increases by 4 percent as a result of the Option 1 State compliance scenario, and increases by 3 percent in the Option 1 Regional compliance scenario
relative to the base case.

Under Option 2, the state and regional compliance scenarios result in 19 percent and 17 percent reductions in coal production in 2020, respectively, relative to base case coal production. Natural gas production in 2020 increases by 3 pe rcent as a result of either the Option 2 state compliance scenario or the Option 2 Regional comp liance scenario, relative to the base case.

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

Coal Production (million short tons) Coal Production Change from Base
Case (million short tons) Coal Production Percent Change
from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030
Base
Case 921 952 951

Option 1
State 695 682 682 -225 -270 -269 -24% -28% -28%

Option 1 Regional 716 698 702 -205 -254 -249 -22% -27% -26%3A-9 Table 3A-3. Projected Coal Production Impacts, Option 2

Coal Production (million short tons) Coal Production Change from Base
Case (million short tons) Coal Production Percent Change
from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030
Base
Case 921 952 951

Option 2
State 747 749 772 -174 -203 -178 -19% -21% -19%

Option 2 Regional 763 764 795 -157 -187 -156 -17% -20% -16%

Table 3A-4. Projected Natural Gas Production Impacts, Option 1
Dry Gas
Production (trillion cubic feet) Dry Gas Production Change

from Base Case (trillion cubic feet) Dry Gas
Production Percent Change from Base
Case
2020 2025 2030 2020 2025 2030 2020 2025 2030
Base
Case 26.6 29.3 31.9

Option 1
State 27.7 29.8 31.7 +1.1 +0.6 -0.2 +4% +2% -1%

Option 1 Regional 27.5 29.7 31.5 +0.9 +0.4 -0.4 +3% +1% -1%3A-10 Table 3A-5. Projected Natural Gas Production Impacts, Option 2
Dry Gas
Production (trillion cubic feet) Dry Gas Production Change

from Base Case (trillion cubic feet) Dry Gas
Production Percent Change from Base
Case
2020 2025 2030 2020 2025 2030 2020 2025 2030
Base
Case 26.6 29.3 31.9

Option 2
State 27.5 29.8 31.6 +0.9 +0.5 -0.3 +3% +2% -1%

Option 2 Regional 27.4 29.7 31.4 +0.8 +0.4 -0.5 +3% +1% -1%

3A.2 Results

This section presents results for the ma in analysis and sensitivity analysis. The detailed methods used to perform the analysis are presented in section 3A.3.

3A.2.1 Primary Results
Both options 1 and 2, in either the state or regional compliance scenarios, result in total net reductions in upstream CH 4 emissions and CO 2 emissions (see Table 3A-6). Under Option 1, the state compliance scenario results in net emissions reduction of 10.6 TgCO 2 e in 2020, while the regional compliance scenario results in 9.9 TgCO 2 e reduction. Under Option 2, the net emissions reduction is smaller: 7.7 and 7.4 TgCO 2 e in the state and regional compliance scenarios in 2020, respectively. These net emissions changes represent the sum of changes in
CH 4 from coal mining, CH 4 from natural gas systems, and CO 2 from flaring in natural gas production.

Under the regional compliance scenario, Option 1 would result in decreases in CH 4 emissions from coal mining of 14.7 TgCO 2 e in 2020, 18.2 TgCO 2 e in 2025, and 17.7 TgCO 2 e in 2030. Under the same scenario, CH 4 from natural gas systems increases relative to the base case Comment [A6]: It should be noted that analytic methods are described in the next section (3A.3). Comment [A7]: We added a sentence in response to this suggestion.3A-11 in 2020 (4.4 TgCO 2 e) and 2025 (2.1 TgCO2e) but decreases relative to the base case in 2030 (by 1.7 TgCO 2 e). Following a si milar pattern, CO 2 from flaring in natural gas production increases by 0.4 TgCO 2 e in 2020 and 0.3 TgCO 2 e in 2025 while decreasing by 0.3 TgCO 2 e in 2030. The total net change under the Option 1 Regional compliance scenario is a reduction in emissions.

Under the regional compliance scenario, Option 2 would result in decreases in CH 4 emissions from coal mining of 11.3 TgCO 2 e in 2020, 13.4 TgCO 2 e in 2025, and 11.1 TgCO 2 e in
2030. CH 4 from natural gas systems increases relative to the base case in 2020 (3.6 TgCO 2 e) and 2025 (1.8 TgCO 2 e), while decreasing in 2030 (by 2.3 TgCO 2 e). CO 2 from flaring in natural gas production increases in 2020 and 2025, and decreases in 2030. Like Option 1, the total net change under the Option 2 Regional compliance scenario is a reduction in emissions. The full
results are listed in Table 3A-6.3A-12 Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios Emissions (TgCO 2 e)
2020 2025 2030 Option 1 State

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

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

CH 4 from Coal Mining -12.4 -14.5 -12.7
CH 4 from Natural Gas Systems +4.4 +2.4 -1.5
CO 2 from NG flaring +0.4 +0.3 -0.3
Total CH 4 + CO 2 -7.7 -11.8 -14.4 Option 2 Regional

CH 4 from Coal Mining -11.3 -13.4 -11.1
CH 4 from Natural Gas Systems +3.6 +1.8 -2.3
CO 2 from NG flaring +0.2 +0.1 -0.4
Total CH 4 + CO 2 -7.4 -11.4 -13.8

3A.2.2 Sensitivity Analysis: Side Case Incorpor ating More Coal Production Geographic Detail
A number of factors could potentially affect the changes in upstream emission as a result of the policy. Here, we present sensitivity analysis examining the possible effect of shifts in regional coal production that might result in a changing the relative share of production originating in underground mines and surface mines. This sensitivity analysis is presented because underground mining generally emits more CH 4 emissions per ton of coal production than surface mining. The compliance scenarios show generally larger reductions in coal3A-13 production in the western region where more of the generally lower-emitting surface mining takes place. This analysis is characterized as a “sensitivity analysis” as the methods that were peer-reviewed as part of the EPA’s contribution to the Climate Action Report, which did not examine alternative coal production scenarios, did not incorporate this geographic component. After incorporating this effect, the net upstream CH 4 emissions reductions that may result from the compliance scenarios are approximately 1 to 2 TgCO 2 e lower than the results found in the main analysis (see Table 3A-7).

Table 3A-7. Upstream Emissions Change s, Baseline to Compliance Scenarios, Side Case Incorporating More Coal Production Geographic Detail Emissions (TgCO 2 e)
2020 2025 2030 Option 1 State

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

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

CH 4 from Coal Mining -11.2 -13.2 -11.5
CH 4 from Natural Gas Systems +4.4 +2.4 -1.5
CO 2 from NG flaring +0.4 +0.3 -0.3
Total CH 4 + CO 2 -6.4 -10.4 -13.2 Option 2 Regional

CH 4 from Coal Mining -10.3 -12.3 -10.0
CH 4 from Natural Gas Systems +3.6 +1.8 -2.3
CO 2 from NG flaring +0.2 +0.1 -0.4
Total CH 4 + CO 2 -6.5 -10.3 -12.83A-14 3A.2.3 Uncertainties and Limitations
Projections of upstream CH 4 emissions and CO 2 emitted from flaring of CH 4 are subject to a range of uncertainties and lim itations. These uncertainties and limitations include estimating the effect of the compliance approach on activity drivers, uncertainty in base year emissions, and uncertainties in changes in emissions factors over relatively long periods of time. For example, EPA’s application of IPM relies on EIA projections for coal imports and exports. Consequently, coal imports and exports are not able to fully respond within the IPM framework to significant fluctuations in power sector coal demand. To the extent international markets may be expected to offset reduced domestic coal demand, changes in U.S. upstream emissions as a result of the policy scenarios would be smaller than what is presented here.

Discussion of uncertainty in historical estimat es of emissions from coal mining and natural gas systems can be found in the 2013 U.S. GHG Inventory. Projected changes in activity drivers and emissions factors are based on a combination of policy, macroeconomic, energy market, and technology factors which are uncertain in both baseline and compliance scenarios. Relatively higher or lower economic growth, or changes in the relative prices or availability of various technologies could result in alternative estimates in the net change in upstream CH 4 emissions
and related CO 2 emissions.

3A.3 Methodologies

3A.3.1 Coal Mining
The scope of CH 4 from coal mining emissions covered in this analysis is discussed above in the “General Approach” section.3A-15 Methodology EPA calculated emissions projections for this source by summing emissions associated with underground mining, post-underground mi ning, surface mining, and post-surface mining.

ݏ݊݋݅ݏݏ݉݅ܧ ௬ ݏ݊݋݅ݏݏ݉݅ܧ ෍	 ൌ ௦,௬

௦ Equation 3
Where:

s = Sources (underground, post-underground, surface, and post-surface mining)
Emissions y,s = Emissions in year y from source s

EPA projected emissions from each source by multiplying an aggregate emissions factor by projected coal production (for underground or surface mining as appropriate). Projected reductions due to recovery and use are then subtracted from potential emissions. 4

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

ൈ ሺ ܪܥ	 െ 1 4 ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋ܴ݁ܿ ௦ ሻ Equation 4
Where:
EF agg,s = Aggregate emissions factor associated with source s InventoryProduction b,s = Coal production associated with source s in the base year from the U.S. GHG Inventory 5

4 Current CH 4 recovery and use projects apply to underground mi ning, but projects related to surface mining could be implemented in the future.3A-16 ProjectedProduction y,s = Projected coal production associated with the emissions source (e.g., either underground or surface mining) in year y
CH 4 RecoveryUseFrac s = Fraction of CH 4 recovered from source s

Emissions Factors To calculate potential emissions from each category, EPA calculated an aggregate CH 4 emissions factor using historical CH 4 emissions and coal production data contained in the most recent U.S. GHG Inventory (EPA 20 13a). For example, historical CH 4 liberated by underground mining was divided by the total underground coal production for the corresponding year. The aggregate emissions factor is the average of this ratio over the most recent five years. Similar calculations were performed for post-undergro und mining emissions, surface mining emissions, and post-surface mining emissions, using either historical underground or surface mining production data as appropriate.

The projection methodology differs from the estimation methodology used in the U.S. GHG Inventory. The inventory does not use emissions factors to calculate CH 4 emissions from underground mines. The U.S. GHG Inventory estimates total CH 4 emitted from underground
mines as the sum of CH 4 liberated from ventilation systems (mine-by-mine measurements) and
CH 4 liberated by means of degasification systems, minus CH 4 recovered and used. EPA estimated surface mining and post-mining CH 4 emissions by multiplying basin-specific coal production, obtained from EIA’s Annual Coal Report (EIA 2012), by basin-specific emissions
factors.

Coal Production Projections For the Sixth U.S. Climate Action Report, EP A projected emissions using projections of underground, surface, and total coal production from the EIA Annual Energy Outlook (AEO) (EIA 2013). For this analysis, projections of total coal production and production by region were drawn from IPM runs performed for the proposal RIA. Projections of underground and surface

5 Because of slight differences between historical and pr ojection datasets, values for production in the base year from each dataset do not cancel3A-17 mining were not available outputs for the various scenarios, so two different approaches were used to estimate underground and surface mining in the baseline and compliance scenarios.

The 2013 AEO projects the total coal production for the United States, as well as the coal production by region, and by various characteris tics including underground and surface mining (see AEO table “Coal Production by Region and Type”). In the primary results presented above, the breakout between underground and surface mining for projection years in both baseline and compliance scenarios is based on the 2013 AEO proportions. This means that the compliance approach is assumed not to affect the relative proportion of underground and surface mining in the main results. Also, note there has been a general trend toward increasing surface mining relative to underground mining (EPA 2013).

In the sensitivity analysis that incorporated more geographical details into coal production, EPA used IPM outputs for coal production from the Appalachian, Interior, and Western regions to estimate the proportion of underground versus surface mining in each scenario. This was done by assuming that the relative proportion of underground versus surface mining in each coal production region would remain constant through the projection period, but that the national proportion of underground and surface production would depend on regional production changes. In general, a larger proportion of coal production in the Appalachian and Interior regions is from underground mining, while a large majority of coal production in western regions is from surface mining. The proportion of underground and surface mining in each region was based on averaging the years 2011 and 2012 histor ical percentages. EIA provides historical regional coal production data broken out between underground and surface mining in its Annual Coal Report (EIA 2012). EPA collated and calculated the proportion of production, underground versus surface, for each year.

CH 4 Mitigation (Recovery and Use) EPA projected coal mine CH 4 mitigation by calculating the historical fraction of methane recovered in relation to generation from underground mines, and applying that fraction to future generation. The historical fraction was averaged over the most recent five years. Future mitigation was estimated by applying the histor ical rate of recovery and use to projected potential emissions generated.3A-18 The U.S. GHG Inventory uses quantitative estimates of CH 4 recovery and use from several sources. Several gassy undergrou nd coal mines in the United States employ ventilation systems
to ensure that CH 4 levels remain within safe concentrati ons. Additionally, some U.S. coal mines supplement ventilation systems with degasification systems, which remove CH 4 from the mine and allow the captured CH 4 to be used as an energy source.

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

௬ୀ௕ Equation 5
Where: CH4RecoveryUse s,y = Recovered emissions from source s in year y
Potential Emissions s, y = Potential emissions from source s in year y b = base year

3A.3.2 Natural Gas Systems
The scope of CH 4 and CO 2 from natural gas systems emissions covered in this analysis are discussed above in the “General Approach” section. Comment [A8]: In	response	to	the following	comment,	we	made	revisions	that improved	the	distinction	between	the	oil and	natural	gas	NSPS	coverage	of new/modified	and	existing	sources: “Methodology and Applicability in Chapter 3 . Throughout the methodology section, the text should be much clearer regarding when rules apply to new vs. modified vs. existing equipment. This distinction is sometimes made in the headers but not in the body text (and sometimes the distinction is not made the headers but is made in the body text). Diligent clarification of these points would help the reader understand why future reductions apply to the projected “annual increase” in emissions. E.g.:

 Bottom of page 23 and top of page 24, clarify in the body text if/when modifications trigger the need for installation of low-bleed devices.

 Top of page 25, clarify in the body text how requirements apply to “new and modified” reciprocating compressors.

 First two-bullets at the bottom of page 26 should begin with “New and Modified.””3A-19 Methodology The methodology for natural gas emissions projections involves the calculation of CH 4 and
CO 2 emissions for over 100 emissions source categorie s across the four natural gas sector stages,
and then the summation of emissions for each sect or stage. The calculation of emissions for each source of emissions in natural gas systems generally occurs in three steps:

1. Calculate potential CH 4 (CH 4 that would be released in the absence of controls) 2. Estimate reductions data associated with voluntary action and regulations 3. Calculate net emissions

EPA calculated projections of potential CH 4 emissions from natural gas systems by summing the projections associated with (1) production, (2) processing, (3) transmission and storage, and (4) distribution. For the 2014 U.S. Climate Action Report in general, activity data were projections of natural gas production and consumption from the Department of Energy’s Energy Information Administration, or EIA (EIA 2013). Additional activity data for projections included liquefied natural gas (LNG) imports, pipeline length, and number of service lines. For this report, activity driver data were taken from IPM outputs for baseline and compliance scenarios consistent with the analysis presented in the RIA. Because the base year inventory emissions explicitly include reductions due to voluntary and regulatory requirements, the projections also include appropriate explicit mi tigation projections as well. Emissions for each source were estimated using the following equation:

ܰܧ ௦,௬ ൌ ܲܧ ௦,௬ െ ܸܴ ௦,௬ െ ܴܴ ௦,௬ Equation 6
Where:
NE s,y = Projected net emissions for source s in year y PE s,y = Projected potential emissions for source s in year y VR s,y, y = Projected voluntary reductions for source s in year y RR s,y, y = Projected regulatory reductions for source s in year y Formatted: Subscript
Formatted: Subscript3A-20 The sections below describe detailed calculations for projections of CH 4 from natural gas systems. This analysis also includes CO 2 emissions that result from flaring in the production

sector. 6 The U.S. Greenhouse Gas Inventory also includ es other sources of venting, fugitive, and flaring CO 2 such as CO 2 processing emissions from acid gas removal units, which are designed
to remove CO 2 from natural gas. This analysis does not include these other non-combustion CO 2 sources. EPA calculated projected flaring CO 2 emissions by scaling emis sions in the base year by the increase in projected natural gas productio n in the IPM scenario outputs consistent with the analysis presented in the RIA

.
Production Stage The production stage includes a total of 35 emissions source categories. Regional emissions were estimated in the base year inventory for the six supply regions (i.e., Northeast, Gulf Coast, Midcontinent, Southwest, Rocky Mountain, and West Coast) for 33 of these sources.

Potential Emissions EPA estimated future year potential emi ssions for the production stage using the following equation.

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

Equation 7
Where:
PE s,y, y = Projected future potential emissions for source s in year y PE s,b, b = Estimated potential emissions for source s in base year b
Gas Production y = Projected natural gas dry production year y Gas Production b = Estimated natural gas dry production for base year b

The natural gas dry production estimates were obtained from the IPM scenario outputs.

Voluntary Reductions

6 The GHG Inventory estimate for CO2 from natural gas flari ng includes some CO2 from flaring of associated gas.3A-21 Projections of voluntary reductions for the pr oduction stage were based on historical data reported by industry to the Natural Gas ST AR program for projects implemented to reduce emissions. Natural Gas STAR tracks projects on an annual basis and assigns a lifetime of limited duration to each reduction project; for purposes of the base year emissions inventory and the future year projections, the reductions associated with each project were either considered to be a “one-year” project or a “permanent” project based on sunset dates provided by the Natural Gas STAR program. Reductions from “one-year” projects were typically from the implementation of new or modified practices, while reductions from “permanent” projects tended to be from equipment installation, replacement, or modification. In the base year emissions inventory and the future year projections, reductions for a “one-year” project were limited to the project’s reported start year, while reductions for a “permanent” project were assigned to the project’s reported start year and every subsequent year thereafter. Thus, the reductions due to “permanent” projects gradually accumulated throughout the inventory time series, while the reductions due to “one-year” projects were replaced every year.

The following production stage voluntary reductions were reported to Natural Gas STAR and applied to individual sources in the emissions inventory:

 Completions for gas wells with hydraulic fract uring (one year)—perform reduced emissions completions (RECs). 7

 Pneumatic device vents (one year)—reduce gas pressure on pneumatic devices; capture/use gas released from gas-operated pneumatic pumps.

 Pneumatic device vents (permanent)—identify and replace high-bleed pneumatic devices; convert pneumatic devices to mechanical controls; convert to instrument air systems; install no-bleed controllers.

 Kimray pumps (permanent)—install/convert gas-driven pumps to electric, mechanical, or solar
pumps.

 Gas engines compressor exhaust (one year)—replace ignition/reduce false starts; turbine fuel use optimization.

7 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically fractured gas well completions and workovers. The upda te uses control/practice-specific emission factors and no longer uses potential methane, or reductions data from Gas STAR or from regulations to calculate emissions, as emissions are directly calculated using the emission factors.3A-22  Gas engines compressor exhaust (permanent)—convert engine starting to Nitrogen- and/or CO2- rich gas; install automated air/fuel ratio controls; install lean burn compressors; replace gas starters with air or N. In addition to these reductions that were applied to specific individual sources in the emissions inventory, there were reductions classifi ed as “Other Production” that were applied to the overall production stage emissions.

It was assumed that the percentage of voluntary reductions relative to potential CH 4 in the most recent base year inventory for the production stage would remain constant in each subsequent future year. 8 In addition, implementation of the Oil and Natural Gas Sector New Source Performance Standards (NSPS)—discussed further below—necessitates the reclassification of certain production reductions from voluntary to regulatory.

Regulatory Reductions As part of the regulatory reductions for the production stage, reductions due to existing Oil
and Natural Gas Sector National Standards for Hazardous Air Pollutants ( NESHAP ) requirements (EPA. 2012a) for dehydrator vents and condensate tanks without control devices were included in the base year inventory. These reductions were carried forward in the future year projections.

In addition, the base year inventory accounted for state-level requirements in Wyoming
and Colorado for RECs. 9 In the base year inventory, a national-level reduction was estimated by

8 The assumption of a constant rate of voluntary reductions re lative to the base year inventory for sources unaffected by regulatory changes is meant to simulate a constant leve l of effort toward voluntary reductions into the future. No enhancements to the voluntary program are assumed. This assumption is a source of uncertainty; due to the voluntary nature of the program, reduction levels can fl uctuate based on participation and investment. Where new regulatory requirements apply to new and modified equipment, voluntary reductions are assumed to continue to apply to existing equipment, but no volun tary reductions are applied to new equipment. As a potential future improvement to these projections, EPA mayplans to develop an alternate methodology to model equipment turnover. However, a revised and reviewed methodology that incorporates equipment turnover is would likely be developed within the context of prepar ing the Agency’s submission to the next Climate Action Report, which will probably be in the 2017 to 2018 timeframe.

9 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically fractured gas well completions and workovers. The upda te uses control/practice-specific emission factors and no Comment [A9]: Please clarify when this methodology change will take place (e.g., is the intention to get this done in time for a final proposed rule?).

Comment [A10]: We added to the footnote explaining the likely timeframe of the potential methodological improvement.

Comment [A11]: “Oil and Natural Gas Sector NESHAP” is not defined anywhere Comment [A12]: Defined and cited the NESHAP in response to suggestion.3A-23 applying a 95 percent REC reduction to the fraction of national emissions occurring in Wyoming and Colorado (i.e., 15.1 percent); this resulted in a national-level reduction of 14.35 percent for gas well completions and workovers with hydraulic fracturing. These reductions were modified
as described below.

The Oil and Natural Gas Sector NSPS for VOCs (EPA 2012a, finalized in 2012) significantly increased the amount of regulatory reductions applicable to the production stage, resulting in substantial CH 4 emissions reductions co-benefits. These reductions are not currently reflected in the 2013 U.S. GHG Inventory for the base year 2011, but are projected for future years as discussed in detail below. The specific Oil and Natural Gas Sector NSPS requirements impact the following production stage sources with regard to VOC (and the associated CH 4 )
emissions:

 Hydraulically fractured natural gas well completions

 Hydraulically refractured natural gas well recompletions

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

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

 New and modified reciprocating and centrifu gal compressors at gathering and boosting stations The impact of these requirements on the future year projections is discussed below. The specific quantitative reductions calculated for th ese projections are based on information from
the Oil and Natural Gas Sector NSPS Background Technical Support Document for the Proposed Standards (EPA 2011) and the Background Supplemental Technical Support Document for the Final New Source Performance Standards (EPA 2012b), referred to collectively in this document as the Oil and Natural Gas Sector NSPS TSD.

Hydraulically Fractured Well Completions The Oil and Natural Gas Sector NSPS requires the use of RECs (or “green completions”) for all new hydraulically - fractured natural gas well completionss . A phase-in period prior to January 1, 2015, also allows for the alternate use of a completion combustion device (i.e., flare),

longer uses potential methane, or reductions data from Ga s STAR or from regulations to calculate emissions, as emissions are directly calculated using the emission factors. Comment [A13]: This requirement only applies to natural gas wells (not oil wells or NGL wells). Please clarify. Comment [A14]: Clarified.3A-24 instead of RECs. In addition, RECs are not re quired for exploratory “wildcat” wells, delineation wells (i.e., used to define the borders of a natural gas reservoir), and low-pressure wells (i.e., completions where well pressure is too low to perform RECs); in these instances, emissions must be reduced using combustion. Based on the Oil and Natural Gas Sector NSPS TSD (EPA 2012b), EPA assumed for the purpose of these projections a 95 percent reduction for both RECs and completion combustion. 10

Although the base year inventory included a national-level reduction of 14.35 percent to account for the required use of RECs in Wyoming and Colorado, there does not appear to be an appreciable difference in emissions reductions resulting from the Oil and Natural Gas Sector NSPS requirements and the state requirements in Wyoming and Colorado. Therefore, for future year projections, the national-level reduction of 14.35 percent was replaced with a 95 percent reduction for new hydraulically fractured well completions.

Hydraulically Refractured Well Workovers The Oil and Natural Gas Sector NSPS also requires the use of RECs for gas wells that are refractured and recompleted. The phase-in period before January 1, 2015, is also applicable. 11 As with completions, a 95 percent reduction was assumed for both RECs and completion combustion. This replaced the national-level reduction of 14.35 percent that was used in the base year inventory.

For both well completions and workovers (or re fractured well completions) with hydraulic fracturing, in conjunction with the Oil and Na tural Gas Sector NSPS, EPA removed REC-related reductions from the projected voluntary reductions from the production stage to avoid double-

10 The Oil and Natural Gas Sector NSPS TSD indicates that 90 percent of flowback gas can be recovered during an REC (based on Natural Gas STAR data) and that any amount of gas that cannot be recovered can be directed to a completion combustion device in order to achieve a minimum 95 percent reduction in emissions. The Oil and Natural Gas Sector NSPS TSD indicates that although industrial flares are required to meet a combustion efficiency of 98 percent, this is not required for completion combustion devices. Completion combustion devices (i.e., exploration and production flares) can be exp ected to achieve 95 percent combustion efficiency.

11 Use of RECs is not considered to be “modified” and woul d not trigger state permitting requirements, while use of flaring or completion combustion would be considered to be “modified.”3A-25 counting. This removal was very straightforward, since these REC-related reductions were calculated separately and then applied to the we ll completions and workov ers source in the base year inventory.

New and Modified High-Bleed, Gas-Driven Pneumatic Controllers The Oil and Natural Gas Sector NSPS also requires the installation of new low-bleed pneumatic devices (i.e., bleed rates less than or equal to 6 standard cubic feet per hour) instead of high-bleed pneumatic devices (i.e., bleed rates gr eater than 6 standard cubic feet per hour) with exceptions where high bleed devices are required for safety reasons. The TSD indicates that a typical production stage high-bleed pneumatic device emits 6.91 tons of CH 4 per year and that replacing the high-bleed device with a typical low-bleed pneumatic device would result in a
reduction of 6.65 tons CH 4 per year; this is a reduction of 96.2 percent. The TSD also indicates that only 51 percent of all pneumatic devices installed are continuous bleed natural gas driven controllers. In addition, it is assumed that 20 percent of the situations where bleed pneumatic devices are installed require a high-bleed device (i .e., instances where a minimal response time is needed, large valves require a high bleed rate to actuate, or a safety isolation valve is involved) (EPA 2011). Based on this information, for the purpose of these projections EPA applied a national-level reduction of 77 percent (i.e., 0.962 × 0.8) to each future year’s annual increase in emissions from pneumatic device vents in the production stage.

In conjunction with the Oil and Natural Gas Sector NSPS, no removal of production stage voluntary reductions was required. The reductions included in the base year inventory already occurred in the past and the associated effects carry forward into the future or were unrelated to the requirements of the Oil and Natural Gas Sector NSPS.

New Storage Tanks The Oil and Natural Gas Sector NSPS also requires that new storage tanks with VOC emissions of 6 tons per year or greater must reduce VOC emissions by at least 95 percent, likely to be accomplished by routing emissions to a combustion device or rerouting emissions into process streams. The Oil and Natural Gas Sector NSPS TSD indicates that approximately 74 percent of the total condensate produced in the United States passes through storage tanks with VOC emissions of 6 tons per year or greater (EPA 2011). Based on this information, for the Formatted: Font: Bold
Formatted: Normal, Space After: 0 pt3A-26 purpose of these projections EPA applied a national-level reduction of 70.3 percent (i.e., 0.95 × 0.74) to each future year’s annual increase in emissions from condensate storage tanks in the production stage.

In conjunction with the Oil and Natural Gas Sector NSPS, no removal of production stage voluntary reductions associated with storage tanks was required. The reductions included in the base year inventory already occurred in the past and the associated effects carry forward into the
future.

New and Modified Reciprocating Compressors The Oil and Natural Gas Sector NSPS requires the replacement of rod packing systems in
new and modified reciprocating compressors at gatheri ng and boosting stations. There are two options for this replacement: every 26,000 hours of operation if operating hours are monitored and documented, or every 36 months if operating hours are not monitored or documented. The Oil and Natural Gas Sector NSPS TSD estimated baseline emissions of 3,773 tons per year of
CH 4 for new reciprocating compressors used in the production stage; the TSD also estimated total reductions from replacing the rod packing for these compressors as 2,384 tons per year of
CH 4 (EPA 2011). Based on this information, for the purpose of these projections EPA applied a national-level reduction of 63.2 percent to each future year’s annual increase in emissions from gathering reciprocating compressors in the production stage.

Processing Stage The processing stage includes a total of 11 emissions source categories. EPA estimated the base year inventory emissions for the processing stage at the national level, instead of at the region level, like the base year inventory emissions for the production stage.

Potential Emissions Because projections of future year processing activity were not available, EPA also used Equation 7 to estimate future year potential emissions for the processing stage by assuming that the quantity of processed natural gas would track closely with the quantity of produced natural

gas.3A-27 As with the production stage, EPA used the natural gas dry production estimates from the IPM scenario outputs.

Voluntary Reductions Projections of voluntary reductions for the pro cessing stage were based on historical data reported by industry to the Natural Gas ST AR program for projects implemented to reduce emissions (EPA 2012c). The following processing stage voluntary reductions were reported to Natural Gas STAR and applied to individu al sources in the emissions inventory:

 Blowdowns/venting (one year)—recover gas from pipeline pigging operations; redesign blowdown/alter ESD practices; reduce emissions when taking compressors offline; use composite wrap repair; use hot taps for in-service pipeline connections; use inert gas and pigs to perform pipeline purges.

 Blowdowns/venting (permanent)—rupture pin shutoff device to reduce venting.

In addition to these reductions that were applied to specific individual sources in the emissions inventory, there were reductions classifi ed as “Other Processing” that were applied to the overall processing stage emissions.

It was assumed that the percentage of voluntary reductions relative to potential CH 4 in the most recent base year inventory for the processing stage would remain constant in each subsequent future year. 12 In addition, implementation of th e Oil and Natural Gas Sector NSPS (discussed further below) necessitates the reclassification of certain processing reductions from voluntary to regulatory.

Regulatory Reductions The only regulatory reductions included in the base year inventory for the processing stage were existing Oil and Natural Gas Sector NESH AP requirements for dehydrator vents (EPA 2013). These reductions were carried forward in the future year projections.

12 This assumption is discussed in Footnote 7.3A-28 The Oil and Natural Gas Sector NSPS signif icantly increased the amount of regulatory reductions applicable to the processing stage re lative to the base year 2011 inventory estimates. The specific Oil and Natural Gas Sector NSPS requirements affect the following processing stage sources with regard to VOC (and the associated CH 4 ) emissions:

 New and modified Rr eciprocating compressors

 New and modified cC entrifugal compressors

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

 New storage tanks (with VOC emissions of at least 6 tons per year)

The impact of these requirements on the fu ture year projections is discussed below.

New and Modified Reciprocating Compressors The Oil and Natural Gas Sector NSPS requires the replacement of rod packing systems in
new and modified reciprocating compressors. There are two options for this replacement: every 26,000 hours of operation if operating hours are monitored and documented, or every 36 months if operating hours are not monitored or documented. The Oil and Natural Gas Sector NSPS TSD estimated baseline emissions of 4,870 tons per year of CH 4 for new reciprocating compressors used in the processing stage; the Oil and Natural Gas Sector NSPS TSD also estimated total reductions from replacing the rod packing for these compressors as 3,892 tons per year of CH 4 (EPA 2011). Based on this information, for the purpose of these projections EPA applied a national-level reduction of 79.9 percent to each future year’s annual increase in emissions from reciprocating compressors in the processing stage.

New and Modified Centrifugal Compressors The Oil and Natural Gas Sector NSPS requires a 95 percent reduction in VOC emissions
from new and modified centrifugal compressors with wet seal systems, which can be accomplished through flaring or by routing captured gas back to a compressor suction or fuel system, or switching to dry seal systems. The Oil and Natural Gas Sector NSPS does not apply to centrifugal compressors with dry seal systems, because they have low VOC emissions. A national-level reduction of 95 percent was applied to each future year’s annual increase in emissions from centrifugal compressors with wet seals in the processing stage.3A-29 In conjunction with the Oil and Natural Gas Sector NSPS, no removal of processing stage voluntary reductions was required. The reductions included in the base year inventory already occurred in the past and the associated effects carry forward into the future or were unrelated to the requirements of the Oil and Natural Gas Sector NSPS.

New and Modified High-Bleed, Gas-Driven Pneumatic Controllers The Oil and Natural Gas Sector NSPS also requires that the VOC emissions limit for continuous-bleed, gas-driven pneumatic controls at gas processing plants be zero. Accordingly, emissions from new pneumatic device vents in the processing stage were set to zero.

New Storage Tanks As described above in the production sector.

Transmission and Storage Stage The transmission and storage stage includes a total of 37 emissions source categories: 25 associated with natural gas transm ission and storage and 12 associated with liquefied natural gas (LNG) transmission and storage. The natural ga s and LNG emissions were estimated at the
national level.

Potential Emissions Future year potential emissions for the natura l gas sources and the six LNG storage sources within the transmission and storage stage we re estimated using the following equation:

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

Equation 8
Where:
PE s,y = Projected future potential emissions for source s in year y PE s,b = Estimated potential emissions for source s in base year b Gas Consumption y = Projected national natural gas consumption in year y Gas Consumption b = Estimated national natural gas consumption in base year b

The national natural gas consum ption estimates were obtain ed from the IPM scenario outputs.3A-30 Future year potential emissions for the six LNG import terminal sources within the transmission and storage stage were es timated using the following equation:

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

Equation 9
Where:
PE s,y = Projected future potential emissions for source s in year y PE s,b = Estimated potential emissions for source s in base year b LNG Imports y = Projected LNG imports in year y LNG Imports b = Estimated LNG imports in base year b

The LNG import estimates were obtained from the table titled “Natural Gas Imports and Exports” of the IPM scenario outputs. The specific estimates used were for the “Liquefied Natural Gas Imports” line item.

Voluntary Reductions Projections of voluntary reductions for the tr ansmission and storage stage were also based on historical data reported by industry to the Natural Gas STAR program for projects implemented to reduce emissions. The followi ng transmission and storage stage voluntary reductions were reported to Natu ral Gas STAR and applied to individual sources in the base year emissions inventory:

 Reciprocating compressors (one year)—replace compressor rod packing systems.

 Reciprocating compressors (permanent)—replace wet seals with dry seals.

 Pipeline venting (one year)—recover gas from pipeline pigging operations; use composite wrap repair; use hot taps for in-service pipeline connections; use inert gas and pigs to perform pipeline purges; use pipeline pump-down techniques to lower gas line pressure.

 Pneumatic devices (permanent)—identify and replace high-bleed pneumatic devices; convert pneumatic devices to mechanical controls ; convert to instrument air systems.3A-31 In addition to these reductions that were applied to specific individual sources in the emissions inventory, there were reductions classi fied as “Other Transmission and Storage” that were applied to the overall transm ission and storage stage emissions.

It was assumed that the percentage of vol untary reductions relative to potential CH 4 in the most recent base year inventory for the transmission and storage stage would remain constant in each subsequent future year. 13 In addition, implementation of the Oil and Natural Gas Sector NSPS (discussed further below) necessitates the reclassification of certain reductions from voluntary to regulatory.

Regulatory Reductions No regulatory reductions were previously included in the inventory for the transmission and storage stage.

The Oil and Natural Gas Sector NSPS includes requirements applicable to the natural gas transmission and storage stage for VOC reductions of at least 95 percent for new storage tanks with VOC emissions of 6 tons per year or more.

The impact of these requirements on the future year projections is discussed below.

New Storage Tanks As described above in the production sector.

Distribution Stage The distribution stage includes a total of 23 emissions source categories consisting of 10 city gate sources, two customer meter sources, three vented sources, and eight pipeline leak sources. For all sources, emissions were estimated at the national level.

Potential Emissions Because future year distribution projections were not available, EPA estimated future year potential emissions for the distribution stage (except for the pipeline leak sources) using

13 This assumption is discussed in Footnote 7.3A-32 Equation 3, assuming that the quantity of distri buted natural gas tracks closely with the quantity of consumed natural gas. 14 The natural gas consumption estimates were obtained from the IPM scenario outputs. Sector-specific consumption es timates were used. For most sources (i.e., all city gate, all vented, and the residential customer meter sources), the “Natural Gas” line item under “Residential Consumption” was used. For the commercial/industry sources, EPA used the summation of the “Natural Gas” line item under “Commercial Consumption” and the “Natural Gas Subtotal” line item (including natural gas, natural gas-to-liquids heat and power, and lease and plant fuel) under “Industrial Consumption.”

Unlike most other sources in the natural gas systems emissions inventory, projected pipeline leak emissions in the distribution stage were not estimated using natural gas production or consumption estimates. Instead , linear extrapolation of historical pipeline miles was used to project leak emissions from distribution mains, while linear extrapolation of the historical number of service lines was used to project leak emissions from services. Linear extrapolation was used because the historical statistics for pi peline miles and number of services show fairly consistent behavioral trends over the entire time series from 1990 to 2011. In particular, the
historical statistics show a distinct trend toward the use of plastic and away from other materials (i.e., cast iron, copper, unprotected steel, and prot ected steel). Historical pipeline length data was drawn from the US GHG Inventory (EPA 2013), which draws pipeline data from a variety of

sources.

Voluntary Reductions Projections of voluntary reductions for the di stribution stage were based on historical data reported by industry to the Natural Gas ST AR program for projects implemented to reduce

14 Many natural gas power plants are connected directly to transmission and distribution pipelines, and thus an increase in consumption of natural gas by power plants might not lead to a proportional change in CH 4 emissions from the distribution segment of the natural gas system. The current projection methodology uses total natural gas consumption as an activity driver to project emissi ons from non-pipeline leak sources in the distribution segment. EPA examined how the results would differ if the distribution segment were excluded. Depending on the scenario and year, the net emissi ons change between the base case and policy cases was reduced by up to 0.9 TgCO 2 e or increased by up to 0.4 TgCO 2 e, assuming the change in power pl ant natural gas consumption had no effect on distribution-segment methane emissions.. Comment [A15]: Footnote added in response to comment provided in separate comment document: Upstream Emissions

. If the purpose is to assess “whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative,” (p. 3A-1) it is not clear why the distribution segment is included in scope. Power plants receive their natural gas directly from transmission pipelines, so a comparative analysis between natural gas-fired power and coal-fired power should exclude the distribution segment (e.g., NETL, 2012). NETL (National Energy Technology Laboratory). 2012. Role of Alternative Energy Sources: Natural Gas Technology Assessment. National Energy Technology Laboratory, U.S. Department of Energy. Available at: http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/DOENETL-2012-1539-NGTechAssess.pdf3A-33 emissions. Unlike the production, processing , and transmission and storage stages, no distribution stage voluntary reductions reported to Natural Gas STAR were applied to individual sources in the emissions inventory. However, there were reductions classified as “Other Distribution” that were applied to the overall distribution stage emissions.

It was assumed that the percentage of voluntary reductions relative to potential CH 4 in the most recent base year inventory for the transmission stage would remain constant in each subsequent future year. 15

Regulatory Reductions There were no requirements in the Oil and Natu ral Gas Sector NSPS that impact emissions from the distribution stage.

15 This assumption is discussed in Footnote 7.3A-34 3A.4 References

IPCC. 2006. “2006 IPCC Guidelines for Nationa l Greenhouse Gas Inventories.” Available at:

< http://www.ipcc-nggip.iges.or.jp/public/2006gl/ >. U. S. Department of State. 2014. “2014 U.S. Climate Action Report to the UNFCCC.” Available
at: < http://www.state.gov/e/oes/rls/rpts/car6/index.htm >. U. S. Energy Information Administration (EIA). 2012. “Annual Coal Report 2011.” Available at:

< http://www.eia.gov/coal/annual/ >. U. S. EIA. 2013. “Annual Energy Outlook 2013.” Available at:

< http://www.eia.gov/forecasts/aeo/ >. U. S. Environmental Protection Agency. 1993. Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress . (EPA document number EPA-430-R-93-003).

U. S. EPA. 2011. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution: Background Technical Support Document for the Proposed Standards.” Available at:

< http://epa.gov/airquality/oilandgas/pdfs/20110728tsd.pdf >.
U. S. EPA. 2012a. “Oil and Natural Gas Sector: New Source Performance Standards and National Emission Standards for Hazardous Air Pollutants Reviews.” 40 CFR Parts 60 and 63 [EPA-HQ-OAR-2010-0505; FRL-9665-1], RIN 2060-AP76. Available at:

< http://www.gpo.gov/fdsys/pkg/FR-2012-08-16/pdf/2012-16806.pdf >.
U. S. EPA. 2012b. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Di stribution: Background Supplemental Technical Support Document for the Final New Source Performance Standards.” Available at:

< http://epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf >. U.S. EPA. 2012c. “Natural Gas STAR Reductions 1990-2011.” Natural Gas STAR Program.

U.S. EPA. 2013a. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2011. (EPA document number EPA-430-R-13-001). Available at:

< http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html >. U.S. EPA. 2013b. “Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO 2 and Non-Energy CO 2 Sources.” Available at:

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

4A-1 

### APPENDIX 4A: GENERATING REGIONAL BENEFIT-PER-TON ESTIMATES FOR ELECTRIC GENERATING UNITS

The purpose of this appendix is to provide additional detail regarding the generation of the benefit-per-ton estimates applied in Chapter 4 of this Regulatory Impact Analysis (RIA). Specifically, this appendix describes the methods for generating benefit-per-ton estimates by region for PM 2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector.
4A.1 Overview of Benefit-per-Ton Estimates

As described in the Technical Support Document: Es timating the Benefit per Ton of Reducing PM 2.5 Precursors from 17 Sectors (U.S. EPA, 2013a) (hereafter “BPT TSD”), the procedure for calculating average benefit-per-ton coefficients generally follows three steps,shown graphically in Figure 4A-1. As an example, in order to calculate national average benefitper-ton estimates for each ambient PM 2.5 precursor emitted from EGU sources, we:

1. Use air quality modeling to predict ambient concentrations of primary PM 2.5 , nitrate and sulfate across the contiguous U.S. that are attributable to the EGU sector.

2. Estimate the health impacts, and the economic value of these impacts, associated with the attributable ambient concentrations of primary PM 2.5 , sulfate and nitrate PM 2.5 using the environmental Benefits Mapping and Analysis Program (BenMAP v4.0.66) 1 (Abt Associates, Inc, 2012).

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

1 In this stage we estimate the PM 2.5 -related impacts associated with changes in directly emitted PM 2.5 , nitrate and sulfate separately, so that we may ultimately calculate the benefit - per - ton reduced of the corresponding PM 2.5 precursor, or directly emitted PM 2.5 , in step 3. When estimating these imp acts we apply effect coefficients that relate changes in total PM 2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 specie.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-2 Figure 4A-1

. Conceptual Overview of Benefit-per- ton Ton Calculation

4A.2 Underlying Source Apportionment Air Quality Modeling
EPA performed a national-scale air quality modeling analysis using the Comprehensive Air Quality Model with Ex tensions (CAMx) model 2 to estimate PM 2.5 and ozone concentrations attributable to 17 industrial sectors, including E GUs. In this section, we provide a short summary of the air quality modeling used to generate these benefit-per-ton estimates. Readers interested in a full discussion of the air quality modeling may consult the Air Quality Modeling Technical Support Document: Source Sector Assessments (U.S. EPA, 2011a). The source apportionment modeling is also discussed in Fann, Baker and Fulcher (2012).
CAMx simulates the numerous physical a nd chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. Emissions of precursor species are injected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. Source appor tionment techniques track the formation and transport of ozone and PM from specific emissi ons sources and calculate the contribution of sources and precursors to ozone and PM 2.5 at individual receptor locations. In contrast to “zero-

2 Environ International Corporation. 2010. Comprehensive Air Quality Model with Extensions, Version 5.3. User’s Guide. Novato, CA. December. Available at <http://www.camx.com>. 

## PM 2.5 

## air quality

change for a given sector Human health benefits Benefit-per- t on calculation***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-3 out” modeling techniques, the tracking of emissions and resulting pollution in source apportionment modeling does not affect the transp ort, chemical transformation, or atmospheric chemical relationships within the modeling. More details on the implementation of source apportionment in CAMx can be found in the CAMx user’s guide.
The modeling analyses were performed for a domain covering the continental U.S. This domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over the eastern and western U.S. The base year of data used to construct the modeling platform includes emissions and meteorology for 2005. Specifically, the starting point for the emission projections in this modeling was the 2005 v4.3 emissions platform (U.S. EPA, 2005). EGU emission estimates for 2016 are from the Integrated Planning Model (IPM), and the projections include
emission reductions related to the NOx State Im plementation Plan Call (U.S. EPA, 1998) and the proposed Transport Rule (U.S. EPA, 2010c). 3

4A.3 Regional PM 2.5 Benefit-per- ton Ton Estimates for EGUs
The approach for generating regional benefit - per-ton estimates for PM 2.5 from EGU emissions is a small modification of the approach described above from the BPT TSD. The regional benefit-per-ton esti mates reflect the ambient PM 2.5 attributable to the EGU sector in the source apportionment modeling (“sector modeling”) from Fann, Baker, and Fulcher (2012), as illustrated in Figures 4A-2 and 4A-3 for 2016. After estimating the PM 2.5 benefits for each of the analysis years applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated the benefits results regionally (i.e., East, West, and California) rather than nationally, as shown in Figure 4A-4. 4

Due to the low emissions of SO 2 , NO X , and directly emitted particles from EGUs in Californiaand the high population density, we separated out California in order not to bias the benefit-perton estimates for the rest of the Western U.S. In order to calculate the benefit-per-ton estimates,

3 The 2016 air quality modeling simulations underlying the benefit-per-ton estimates do not reflect emission reductions anticipated from the EGU sector as a result of the recently promulgated MATS rule, and so are likely to overstate the total PM 2.5 from this sector and do not account for the vacatur of the CSAPR rule . (Fann, Fulcher, and Baker ([ 2013 )). ]). The EGU contribution on a per-ton basis would be similar. Because the emission reductions in this RIA are calculated from an IPM base case that includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits estimates.

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

4A-4 we divided the regional benefits estimates by the corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the benefit-per-ton estimates for a currency year of 2011$. 5

Figure 4A-2. Modeled PM 2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 ) (Source: BPT TSD)
This method provides estimates of the regional average benefit-per-ton for each ambient
PM 2.5 precursor emitted from EGU sources. For some pr ecursor emissions, such as NOx, there is generally a non-linear relationship between emissions and formation of PM 2.5 . This means that each ton of NOx reduced would have a different impact on ambient PM 2.5 depending on the initial level of emissions and potentially on the levels of emissions of other pollutants. In contrast, SO 2 is generally linear in forming PM 2.5 . For non-linear pollutants like NOx, a marginal benefit-per-ton approach would better approximate the specific benefits associated with an emissions reduction scenario for a given set of baseline base case emissions, but we do not have sufficient air quality modeling data to calculate a marginal benefit-per-ton estimates for the EGU

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

4A-5sector. Therefore, using an average benefit-per-ton estimate for NOx adds uncertainty to the cobenefits estimated in this RIA. Because most of the estimated co-benefits for the proposed guidelines are attributable to reductions in SO 2 emissions, the added uncertainty is likely to be
small.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-6Figure 4A-3. Modeled PM2.5Concentrations Attributable to EGUs byPrecursor in 2016 (Annual Mean, in μg/m3)a)Sulfateb)Nitratec)ElementalCarbonandOrganicCarbond)CrustalPM2.5***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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

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

Pollutant National EGU emissions Regional EGU Emissions East West California SO 2 3,793,362 3,520,296 273,070 4,886 Directly emitted PM 2.5 (EC+OC) 30,078 26,172 3,892 1,523 Directly emitted PM 2.5 (crustal) 243,497 214,219 28,438 840 NOx (all year) 1,826,582 1,425,148 401,584 13,485 NOx (ozone season) 931,189 728,402 195,748 7,039

In this RIA, we estimate emission reductions from EGUs using IPM. 6 IPM outputs provide endogenously projected unit level emissions of SO 2 , NOx, CO 2 , Hg, HCl from EGUs, but CO, VOC, NH 3 and total directly emitted PM 2.5 and PM 10 emissions are post-calculated. 7 In addition, directly emitted particle emissions calculated from IPM outputs do not include

6 See Chapter 3 of this RIA for additional information regarding IPM.

7 Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/ docs/v513/FlatFile_Methodology.pdf Formatted Table Formatted: Centered

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4A-8 speciation, i.e. they are only the total emissions. In order to estimate the benefits associated with reduced emissions of directly emitted particle s, we must determine the fraction of total PM 2.5 emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 8 Figure 4A-5 illustrates the relative breakdown of dir ectly emitted PM 2.5 components from EGUs in the modeling by Fann, Baker, and Fulcher (2012). In this modeling, the national average
EC+OC fraction of emitted PM 2.5 is 10% with a range of 5% to 63% in different states due to the different proportion of fuels. The national average is similar to the averages for the east and west regions at 10% and 7%, respectively. Only five states had EC+OC fractions greater than 30%. For crustal emissions, the national average fraction of emitted PM 2.5 from EGUs is 78% with a range of 26% to 83%. The national average is similar to the averages for the east and west regions at 78% and 81%, respectively. Only four states had crustal fractions less than 50%.

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

There are several uncertainties associated with estimating the benefits associated with reducing emissions of EC+OC and crustal PM 2.5 from EGUs. As previously mentioned, IPM
does not estimate PM 2.5 emissions by component, and total PM 2.5 is estimated as an IPM post-

8 Crustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represents particles not classified as one of the other species (e.g., organic car bon, elemental carbon, nitrate, sulfate, chloride, etc.). ‐ 5,000 10,000 15,000 20,000 25,000TexasPennsylvaniaOhioIndianaFloridaWestVirginiaGeorgiaKentuckyAlabamaNorthCarolinaSouthCarolinaIllinoisMinnesotaMissouriWyomingArizonaTennesseeVirginiaMichiganWisconsinNorthDakotaUtahIowaNewMexicoKansasOklahomaColoradoMarylandNewYorkArkansasLouisianaNevadaCaliforniaNebraskaMontanaNewJerseyMississippiMassachusettsConnecticutWashingtonSouthDakotaNewHampshireOregonDelawareRhodeIslandMaineIdahoEmissionsinSectorModeling(Tons) PNO3 PSO4 POC PEC PMFINE (crustal)

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4A-9 processing step using emission factors. In order to conduct air quality modeling, PM 2.5 from EGUs is speciated into components during the emissions modeling process based on emission profiles for EGUs by source classification code. Even though these speciation profiles are not unit-specific, an emission profile based on the s ource classification code is highly sophisticated and reflects the fuel and the unit configuration. In addition, the air quality model output has been adjusted to match an interpolated surface of speciated PM 2.5 measurements, using the Model Attainment Test Software (Abt Associates, In c, 2010). Uncertainties in some emissions sectors can sometimes be large enough that air quality es timates from well-character ized sectors such as EGUs may be changed to match observed monitor data. While these changes are usually minimal, in this instance, uncertainties in crustal emissions from other sectors impact the relationship between EGU crustal emissions and resulting air quality impacts. Because the cobenefits associated with directly emitted particle s for the proposed guidelines are small relative to the co-benefits of reducing SO 2 emissions, these uncertainties would not substantially affect
the estimate of total monetized health co-benefits. 9 In calculating the PM 2.5 co-benefits in this RIA, we estimate the emission reductions of EC+OC and crustal emissions by applying the national average fractions (i.e., 78% crustal and 10% EC+OC) to the emission reductions of all directly emitted particles from EGUs. Because the benefit-per-ton estimates for reducing emissions of EC+OC are larger than the benefit-per-ton estimate for crustal emissions, this assumption underestimates the monetized PM 2.5 co-benefits in certain states with higher EC+OC fractions, such as California and North Dakota. We further underestimate the co-benefits by not quantifying or monetizi ng the co-benefits associated with reducing directly emitted particulate
nitrate and sulfate.
Although it is possible to calculate 95 th percentile confidence intervals using the approach described in this appendix (e.g., U.S. EPA, 2011b), we generally do not calculate confidence intervals for benefit-per-ton estimates. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 2012a) for an indication of the combined random sampling error in the health impact and economic valuation functions using Monte Carlo methods. In general, the 95 th percentile confidence interval for the total monetized PM 2.5 benefits ranges from approximately90% to +180% of the central estimates based on concentration-response functions from Krewski

9 See Figure 5-2 of this RIA for the relative proportion of he alth co-benefits from directly emitted particles relative

to co-benefits from SO 2 emissions. Formatted: Space After: 3 pt
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4A-10 et al. (2009) and Lepeule et al. (2012). The 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30% for mortality incidence based on Krewski et al. (2009) and ±46% based on Lepeule et al. (2012). These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of th e effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Tables 4A-2 through 4A-4 provide the national and regional benefit-per-ton estimates forthe EGU sector at discount rates of 3% and 7% in 2020, 2025, and 2030 respectively. Tables 4A5 through 4A-7 provide the incidence per ton estimates for the EGU sector in 2020, 2025, and 2030 respectively.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-11Table 4A-2. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2020 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $360,0007% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000Directly emittedPM2.5(EC+OC)3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $640,0007% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000Directly emittedPM2.5(Crustal)3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $240,0007% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000NOx (as PM2.5)3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $38,0007% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, but the benefit perton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects fromdirect exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Table 4A-3. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2025 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,0007% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000Directly emittedPM2.5(EC+OC)3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,0007% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000Directly emittedPM2.5(Crustal)3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,0007% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000NOx (as PM2.5)3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,0007% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, but the benefit perton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects fromdirect exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Formatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:Font: Not BoldFormatted:paragraph under tablesFormatted:paragraph under tablesFormatted:paragraph under tables, Indent:Before: 0", First line: 0"***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-12Table 4A-4. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,0007% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000Directly emittedPM2.5(EC+OC)3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,0007% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000Directly emittedPM2.5(Crustal)3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,0007% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000NOx (as PM2.5)3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,0007% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, but the benefit perton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects fromdirect exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Formatted:paragraph under tables***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-13Table 4A-5. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2020*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature MortalityKrewski et al. (2009) – adult 0.004400 0.000730 0.015000 0.002000 0.000860 0.000130 0.006100 0.001200 0.017000 0.001800 0.031000 0.012000Lepeule et al. (2012) – adult 0.009900 0.001700 0.035000 0.004500 0.001900 0.000290 0.014000 0.002800 0.039000 0.004100 0.070000 0.026000Woodruff et al. (1997) – infants 0.000010 0.000002 0.000033 0.000004 0.000002 0.000000 0.000016 0.000003 0.000036 0.000004 0.000067 0.000025MorbidityEmergency department visits for asthma 0.002300 0.000410 0.008700 0.001000 0.000380 0.000055 0.002800 0.000520 0.008600 0.001000 0.016000 0.005900Acute bronchitis 0.006300 0.001100 0.022000 0.002800 0.001600 0.000330 0.011000 0.002300 0.031000 0.003400 0.057000 0.021000Lower respiratory symptoms 0.081000 0.014000 0.290000 0.036000 0.021000 0.004200 0.150000 0.029000 0.390000 0.044000 0.720000 0.270000Upper respiratory symptoms 0.120000 0.020000 0.410000 0.051000 0.030000 0.006000 0.210000 0.042000 0.560000 0.062000 1.000000 0.390000Minor restricted-activity days 3.200000 0.540000 11.000000 1.400000 0.780000 0.130000 5.400000 1.000000 16.00000 1.700000 29.00000 11.00000Lost work days 0.540000 0.090000 1.900000 0.240000 0.130000 0.023000 0.910000 0.180000 2.700000 0.290000 4.900000 1.800000Asthma exacerbation 0.290000 0.020000 0.420000 0.053000 0.074000 0.006200 0.220000 0.043000 1.400000 0.064000 1.100000 0.400000Hospital Admissions, Respiratory 0.001300 0.000220 0.004500 0.000580 0.000190 0.000026 0.001400 0.000270 0.004100 0.000440 0.007400 0.002800Hospital Admissions, Cardiovascular 0.001600 0.000270 0.005600 0.000720 0.000220 0.000032 0.001800 0.000340 0.005000 0.000520 0.009000 0.003400Non-fatal Heart Attacks (Peters) 0.005000 0.000860 0.018000 0.002300 0.000810 0.000110 0.006000 0.001200 0.018000 0.001900 0.032000 0.012000Non-fatal Heart Attacks (All others) 0.000550 0.000093 0.001900 0.000250 0.000087 0.000012 0.000640 0.000130 0.001900 0.000210 0.003400 0.001300* All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.Formatted:paragraph under tablesFormatted:Subscript***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-14Table 4A-6. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2025*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature MortalityKrewski et al. (2009) – adult 0.004700 0.000770 0.016000 0.002100 0.000940 0.000140 0.006800 0.001400 0.019000 0.002000 0.034000 0.013000Lepeule et al. (2012) – adult 0.011000 0.001700 0.037000 0.004800 0.002100 0.000310 0.015000 0.003100 0.043000 0.004500 0.077000 0.029000Woodruff et al. (1997) – infants 0.000009 0.000001 0.000031 0.000004 0.000002 0.000000 0.000016 0.000003 0.000034 0.000004 0.000063 0.000024MorbidityEmergency department visits for asthma 0.002400 0.000420 0.009000 0.001100 0.000410 0.000059 0.003000 0.000560 0.009000 0.001000 0.016000 0.006100Acute bronchitis 0.006600 0.001100 0.023000 0.002900 0.001700 0.000350 0.012000 0.002400 0.032000 0.003600 0.059000 0.022000Lower respiratory symptoms 0.084000 0.014000 0.300000 0.037000 0.022000 0.004400 0.150000 0.031000 0.410000 0.046000 0.760000 0.290000Upper respiratory symptoms 0.120000 0.020000 0.430000 0.053000 0.032000 0.006300 0.220000 0.044000 0.590000 0.065000 1.100000 0.410000Minor restricted-activity days 3.200000 0.540000 12.000000 1.400000 0.820000 0.140000 5.700000 1.100000 16.00000 1.70000 30.00000 11.00000Lost work days 0.550000 0.091000 2.000000 0.240000 0.140000 0.024000 0.970000 0.190000 2.700000 0.300000 5.000000 1.800000Asthma exacerbation 0.300000 0.021000 0.440000 0.055000 0.078000 0.006500 0.230000 0.046000 1.400000 0.067000 1.100000 0.420000Hospital Admissions, Respiratory 0.001400 0.000240 0.005000 0.000640 0.000220 0.000030 0.001600 0.000320 0.004800 0.000500 0.008500 0.003200Hospital Admissions, Cardiovascular 0.001800 0.000290 0.006100 0.000780 0.000250 0.000036 0.002000 0.000390 0.005600 0.000580 0.0100000.003800Non-fatal Heart Attacks (Peters) 0.005500 0.000930 0.019000 0.002500 0.000910 0.000120 0.006800 0.001300 0.020000 0.002100 0.036000 0.013000Non-fatal Heart Attacks (All others) 0.000600 0.000100 0.002100 0.000270 0.000098 0.000013 0.000730 0.000140 0.002200 0.000230 0.003900 0.001500* All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.Formatted:paragraph under tables***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-15Table 4A-7. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2030*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature Mortality 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000Krewski et al. (2009) – adult 0.011000 0.001800 0.040000 0.005100 0.002400 0.000340 0.017000 0.003400 0.048000 0.005000 0.086000 0.032000Lepeule et al. (2012) – adult 0.000009 0.000001 0.000030 0.000004 0.000002 0.000000 0.000015 0.000003 0.000032 0.000004 0.000060 0.000023Woodruff et al. (1997) – infantsMorbidity 0.002500 0.000430 0.009300 0.001100 0.000430 0.000062 0.003200 0.000600 0.009400 0.001100 0.017000 0.006400Emergency department visits for asthma 0.006800 0.001100 0.024000 0.003000 0.001800 0.000370 0.013000 0.002600 0.033000 0.003700 0.061000 0.023000Acute bronchitis 0.087000 0.014000 0.310000 0.038000 0.023000 0.004700 0.170000 0.033000 0.420000 0.047000 0.780000 0.300000Lower respiratory symptoms 0.130000 0.021000 0.440000 0.055000 0.034000 0.006700 0.240000 0.047000 0.610000 0.067000 1.100000 0.420000Upper respiratory symptoms 3.300000 0.540000 12.000000 1.500000 0.860000 0.150000 6.000000 1.200000 17.00000 1.800000 30.00000 11.00000Minor restricted-activity days 0.560000 0.092000 2.000000 0.250000 0.150000 0.025000 1.000000 0.200000 2.800000 0.300000 5.100000 1.900000Lost work days 0.310000 0.021000 0.460000 0.057000 0.082000 0.006900 0.240000 0.048000 1.500000 0.069000 1.200000 0.430000Asthma exacerbation 0.001600 0.000260 0.005500 0.000710 0.000250 0.000034 0.001800 0.000360 0.005500 0.000580 0.009900 0.003700Hospital Admissions, Respiratory 0.001900 0.000320 0.006700 0.000850 0.000280 0.000041 0.002300 0.000440 0.006400 0.000660 0.012000 0.004300Hospital Admissions, Cardiovascular 0.006000 0.001000 0.021000 0.002700 0.001000 0.000140 0.007700 0.001500 0.023000 0.002400 0.0410000.015000Non-fatal Heart Attacks (Peters) 0.000660 0.000110 0.002300 0.000290 0.000110 0.000015 0.000830 0.000160 0.002500 0.000260 0.004400 0.001700Non-fatal Heart Attacks (All others) 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000* All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.Formatted:paragraph under tables***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-16 4A.4 Regional Ozone Benefit-per- ton Ton Estimates
The process for generating the regional ozone benefit-per-ton estimates is consistent with the process for PM 2.5 . The key difference is substituting the ozone impacts from EGUs in the sector modeling for the PM 2.5 impacts. We have not historically generated ozone benefit-per-ton estimates due to the complex non-linearity in th e atmospheric response to changes in ozone precursor pollutants from different sectors in different geographic areas. However, for EGUs, we have increased confidence in the magnitude and location of ozone-season NOx emissions and
ambient ozone concentrations attr ibutable to these emissions. In addition, the sector modeling provides information regarding the total EGU contribution to ozone formation, which provides greater transferability than an air quality modeling scenario for a regulation that might include
both increases and decreases in emissions in di fferent locations. Figure 4A-6 provides the average ambient ozone concentrations from the EGU sector in the sector modeling for 2016. 10

10 The 2016 air quality modeling simulations do not reflect emission reductions anticipated from the EGU sector as a result of the recently promulgated MATS rule, and so ma y overstate the total ozone contribution from this sector and do not account for the vacatur of the CSAPR rule . (Fann, Fulcher, and Baker (2013). However, the EGU contribution on a per-ton basis would likely be similar. Because the emission reductions in this RIA are calculated from an IPM base case that includes MATS (s ee Chapter 3), there is no double-counting concern with the resulting co-benefits estimates. Formatted: Indent: Before: 0", Hanging: 0.2"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-17 Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb)
We assume that all of the ozone impacts fr om EGUs are attributable to NOx emissions. VOC emissions, which are also a precursor to ambient ozone formation, are insignificant from
the EGU sector relative to NOx emissions from EGUs and the total VOC emissions inventory.Therefore, we believe that our assumption that EGU-attributable ozone formation at the regionallevel is due to NOx alone is reasonable.

Similar to PM 2.5 , this method provides estimates of the regional average benefit-per-ton. Due to the non-linear chemistry between NOx emissions and ambient ozone, using an average benefit-per-ton estimate for NOx adds uncertain ty to the ozone co-benefits estimated for the proposed guidelines. Because most of the estimated co-benefits for the proposed guidelines are attributable to changes in ambient PM 2.5 , the added uncertainty is likely to be small.

In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates calculated using NO X emissions from U.S. EGUs during the ozone-season only (May to September). As shown in Table 4A-1, ozone-season NOx emissions from EGUs are approximately half of all-year NO X emissions. Because we estimate ozone health impacts from May to September only, this approach underestimat es ozone co-benefits in areas with a longer***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-18 ozone seasons such as southern California and Texas. When the underestimated benefit-per-ton estimate is multiplied by ozone-season only NOx emission reductions, this results in an
underestimate of the monetized ozone co-benefit s. For illustrative purposes, Tables 4A-8 through 4A-10 provide the ozone benefit-per-ton estimate s using both all-year NOx emissions and ozoneseason only NOx for 2020, 2025, and 2030, respectively. Tables 4A-11 through 4A-13 provide the ozone season incidence-per-ton estimates for 2020, 2025, and 2030, respectively.
Table 4A-8. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2020 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NOx $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000

All-year NOx $1,900 to $8,000 $2,300 to $9,600 $450 to $2,000 $3,800 to $16,000 * The range of estimates reflects the range of epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e the conversion from NOx precursor emissions to ambient ozone.

Table 4A-9. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2025 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NOx $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000

All-year NOx $2,500 to $11,000 $3,000 to $13,000 $650 to $2,800 $5,200 to $22,000 * The range of estimates reflects the range of epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e the conversion from NOx precursor emissions to ambient ozone.

Table 4A-10. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2030 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NOx $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000

All-year NOx $2,700 to $12,000 $3,200 to $14,000 $730 to $3,200 $5,700 to $25,000 * The range of estimates reflects the range of epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e the conversion from NOx precursor emissions to ambient ozone. Formatted: paragraph under tables

Formatted: paragraph under tables

Formatted: paragraph under tables***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-19 Table 4A-11. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2020* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000260 0.000053 0.000420 Levy et al. (2005) 0.001200 0.000250 0.001900 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.001600 0.000250 0.002200 Hospital Admissions, Respiratory (ages < 2) 0.000710 0.000220 0.001200 Emergency Room Visits, Respiratory 0.000840 0.000150 0.001300 Acute Respiratory Symptoms 1.500000 0.380000 3.000000 School Loss Days 0.510000 0.140000 1.000000 * All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambien t ozone. These estimates reflect ozone-season NOx emissions.

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2025* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000530 0.000110 0.000890 Levy et al. (2005) 0.002400 0.000520 0.004100 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.003500 0.000590 0.004900 Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000440 0.002300 Emergency Room Visits, Respiratory 0.001700 0.000300 0.002700 Acute Respiratory Symptoms 3.000000 0.770000 5.900000 School Loss Days 1.000000 0.290000 2.100000 * All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambien t ozone. These estimates reflect ozone-season NOx emissions.

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2030* Health Endpoint East West California Premature Mortality – adult

Bell et al. (2004) 0.000570 0.000130 0.001000 Levy et al. (2005) 0.002600 0.000580 0.004500 Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004000 0.000690 0.005900 Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000460 0.002400 Emergency Room Visits, Respiratory 0.001800 0.000320 0.002800 Acute Respiratory Symptoms 3.000000 0.810000 6.000000 School Loss Days 1.100000 0.310000 2.200000 * All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambien t ozone. These estimates reflect ozone-season NOx emissions.

4A.5 Evaluating the Benefit-per- ton Ton Estimates
In this section, we provide information that can be used to evaluate the regional benefitper-ton estimates and provide some characterization of the uncertainty inherent in these estimates Formatted: paragraph under tables

Formatted: paragraph under tables

Formatted: paragraph under tables***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-20 relative to benefits estimated using scenario-sp ecific air quality modeling. First, we evaluated how well the spatial distribution of emissions underlying the regional benefit-per-ton estimates match the spatial distribution of the emissions fo r the illustrative compliance scenarios for the proposed guidelines. Second, we evaluated how well the national and regional benefit-per-ton estimates predicted modeled benefits estimated for MATS (U.S. EPA, 2011b) and CSAPR (U.S. EPA, 2011c).
For the first evaluation, we provide graphs of the EGU emissions in each state in the sector modeling base case used to generate the regional benefit-per-ton estimates and the
emissions in the IPM base case for this RIA, as shown in Figures 4A-7 through 4A-9. It is important to note that these graphs do not show the emission reductions anticipated from implementation of the proposed guidelines. Rather, these graphs illustrate the similarities and differences in the spatial distribution of the em issions from the EGU sector from the different base cases in order to evaluate the potential uncer tainty associated with using the benefit-per-ton estimates to estimate the health co-benefits of the proposed guidelines. As noted in section 4A-2 above, the sector modeling base case does not reflect emission reductions anticipated from the EGU sector as a result of the recently promulgated MATS rule, 11 but the IPM base case does reflect those emission reductions. Even though the sector modeling has substantially more SO 2 emissions from EGUs (3.8 million tons in 2 016 vs. 1.5 million tons in 2020), the spatial distribution is relatively similar across states and thus the resulting benefit-per-ton would likely
be similar. Therefore, we would not expect this difference to have a substantial effect on the resulting co-benefits estimated for the proposed guidelines. Baseline Base case emissions for annual NOx and ozone-season NOx are also reasonably similar.

11 See Figure 5C-1 from the MATS RIA (U.S. EPA, 2011b) for more information regarding the state-level SO 2

emission reductions anticipated from MATS. Formatted: Space After: 3 pt***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-21 ‐ 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000AlabamaArizonaArkansasCaliforniaColoradoConnecticutDelawareDistrictofColumbiaFloridaGeorgiaIdahoIllinoisIndianaIowaKansasKentuckyLouisianaMaineMarylandMassachusettsMichiganMinnesotaMississippiMissouriMontanaNebraskaNevadaNewHampshireNewJerseyNewMexicoNewYorkNorthCarolinaNorthDakotaOhioOklahomaOregonPennsylvaniaRhodeIslandSouthCarolinaSouthDakotaTennesseeTexasUtahVermontVirginiaWashingtonWestVirginiaWisconsinWyomingTons EGU sector modeling
IPM Base Formatted: Complex Script Font: 12 pt***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-22Figure 4A-7. Comparison of Baseline CaseSO2Emissions in 2020‐50,000100,000150,000200,000250,000300,000350,000400,000450,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIAFormatted:Left: 0.7", Right: 0.7", Top: 1",Bottom: 1", Width: 11", Height: 8.5"***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-23‐20,00040,00060,00080,000100,000120,000140,000160,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIAFormatted:Complex Script Font: 12 pt***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-24Figure 4A-8. Comparison of BaselineCaseAnnual NOx Emissions in 2020‐25,00050,00075,000100,000125,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

TonsEGUsectormodelingIPMBaseFormatted:Complex Script Font: 12 pt***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-25‐10,00020,00030,00040,00050,00060,00070,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

TonsEGUsectormodelingIPMBaseFormatted:Font: Not Bold***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-26Figure 4A-9. Comparison of Baseline CaseOzone-season NOx Emissions in 2020‐10,00020,00030,00040,00050,00060,00070,00080,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIAFormatted:Space After: 0 pt, Keep with nextComment [A1]:OMB comment: It would begreat to have a similar graph for GHG emissions as aresult of this rule – baseline v. rule.EPA response: We have clarified that these graphsdo not show emission reductions from the proposedguidelines in the legend, figure titles, and text.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-27 For the second evaluation, we used the national and regional benefit-per-ton estimates to
estimate the modeled sulfate benefits estimated for MATS and CSAPR. 12 We focused on sulfate because the vast majority of PM 2.5 benefits for both rules were from reductions in ambient sulfate. We found that the national benefit-pe r-ton estimates overestimated the modeled CSAPR benefits by 7 percent and the MATS benefits by 33 percent. The regional benefit-per-ton estimates overestimated the CSAPR benefits by 14 percent and MATS benefits by 29 percent.
This evaluation shows that the national and regional benefit-per-ton estimates perform reasonably well, with a slight overestimate, but th is does not mean that the co-benefits estimated for the proposed guidelines would necessarily be within a similar range.
We also evaluated whether available modeling information could be used to generate reliable state-level benefit-per-ton estimates for PM 2.5 . 13 In general, we would expect state-level estimates to better reflect the sp atial differences in emission reductions and associated health impacts across scenarios than regional estimates, which implicitly assume a constant percentage of emission reductions across the region. Varying percentage reductions across states are further magnified by spatial differences in population density, base line case health incidence rates, air quality response, and intersta te pollution transport.
We tested several methods for generating state-level benefit-per-ton estimates. The method that performed best used state-by-state contribution modeling in the Eastern U.S. conducted for CSAPR (U.S. EPA, 2011a) to adjust the regional benefit-per-ton estimates for the EGU sector. Although the CSAPR modeling is th e best available for estimating state-level benefit-per-ton estimates, it zeroed-out all anthropogenic emission sources within a state rather than just zeroing-out the emissions from the EGU sector alone . Currently, we do not have air quality modeling that provides information regarding the PM 2.5 response to EGU only emissions for each state separately. We evaluated the performance of the state-level estimates by comparing the modeled benefits for two previous rulemakings (i.e., CSAPR and MATS) to the benefits estimated using state-level benefit-per-t on. When we evaluated the state-level estimates in the same manner as the national and regional estimates, we found that the state-level estimates

12 The benefits analyses for both the MATS and CSAPR RIAs relied on benefit-per-ton estimates generated from

similar policy-specific air quality modeling conducted speci fically for those rulemakings. For this evaluation, we compared the benefits estimated from the air quality modeli ng, not the benefits estimated for the final rules in the RIA generated using the benefit-per-ton estimates for those rulemakings.

13 We did not attempt to generate stat e-level ozone benefit-per-ton estimates due to inter-state transport, complex

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

4A-28 performed similar ly , in general, to the regional estimates for estimating total national benefits but were unreliable in estimating the benefits that would accrue to each state. In addition, because the spatial differences in emission reductions between the options evaluated in this RIA are small, using the state-level estimates did not make a difference in the relative co-benefits across options.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-29***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-30 4A.6 References
Abt Associates, Inc. 2010. “User’s Guide: Mode led Attainment Test So ftware.” Available at: <http://www.epa.gov/scram001/modelingapps_mats.htm>. Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Prot ection Agency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987- 2000.” Journal of the American Medical Association, 292(19): pp. 2372-8. Bell, M.L.; F. Dominici and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the Na tional Morbidity, Mortality, and Air Pollution Study.” Epidemiology . 16(4): pp. 436-45. Fann, N.; C.M. Fulcher and B.J. Hubbell. 2009. “The Influence of Location, Source, and Emission Type in Estimates of the Human Health Benefits of Reducing a Ton of Air Pollution.” Air Quality and Atmospheric Health, 2:169–176. Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 -Related Health Benefits of Emission Reductions for 17 Indust rial, Area and Mobile Emission Sectors Across the U.S.” Environment International, 49 41–151. Fann, N.; K.R. Baker and C.M. Fulcher. 2013. “The Recent and Future Health Burden of Air Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technol ogy, 47 (8) (2013), pp. 3580–3589. Huang Y.; F. Dominici and M. Bell. 2005. “Bay esian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics , 16, 547-562. Ito, K.; S.F. De Leon and M. Lippmann. 2005. “Associations Between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology , 16(4): p. 446-57. Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer So ciety Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspective, Jul; 120(7):965-70. Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis.” Epidemiology, 16(4): pp. 458-68. Roman, H.A.; K. D. Walker; T. L. Walsh; L. Co nner; H. M. Richmond; B. J. Hubbell and P. L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environmental Scientific Technology , 42(7):2268-2274. Schwartz, J. 2005. “How Sensitive is the Asso ciation Between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respiratory and Critical Care Medicine, Vol. 171 (6): 627-31.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-31 U.S. Environmental Protection Agency. 1998. Regulatory Impact Analysis for the NOx SIP Call, FIP, and Section 126 Petitions. (EPA document number EPA-452/R-98-003). Available at: <http://http://www.epa.gov/ttn /oarpg/otag/sipriav2.zip>. U.S. EPA. 2005. “2005 National Emissions Inventory Data & Documentation

. ” Available at: <http://www.epa.gov/ttnchie1/net/2005inventory.html>.
U.S. EPA. 2010. Regulatory Impact Analysis for the Proposed Federal Transport Rule. (EPA document number EPA-HQ-OAR-2009-0491). Research Triangle Park, NC. Available at: <http://www.epa.gov/ttn/ecas/regdata /RIAs/proposaltrria_final.pdf>. U.S. EPA. 2011a. “Air Quality Modeling Final Rule Technical Support Document [Cross-State Rule].” Research Triangle Park, NC: Office of Air Quality Planning and Standards, Air Quality Assessment Division, June. Available on the Internet at: <http://www.epa.gov/crossstate rule/pdfs/AQModeling.pdf >.
U.S. EPA. 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards

. Research Triangle Park, NC: Office of Air Qu ality Planning and Standards, Health and Environmental Impacts Division. (EPA docu ment number EPA-452/R-11-011, December). Available at <http://www.epa.gov/ttn/ ecas/regdata/RIAs/matsriafinal.pdf>.
U.S. EPA. 2011c . “ Regulatory Impact Analysis (RIA) for the Final Transport Rule.” Research Triangle Park: Office of Air and Radiation, July. Available at: <http://www.epa.gov/airtrans port/pdfs/FinalRIA.pdf>. U.S. EPA. 2011d. “Air Quality Modeling Technical Support Document: Source Sector Assessments.” Research Triangle Park, NC. Available at: <http://www.epa.gov/scram001/reports/EPA454_R11_006.pdf>. U.S. EPA. 2012a. “Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter.” Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and En vironmental Impacts Division. (EPA document number EPA-452/R-12-003, December). Available at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. EPA. 2013a. “Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5 Precursors from 17 Sectors.” Research Triangle Park, NC: Office of Air Quality Planning and Standards, February. Available at: <http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13 .pdf>.
U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). Washington, DC: U.S. Environmental Protection Agency. (EPA document number EPA/600/R-10/076F). Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>.

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## Preamble These are additional comments received on the preamble since the 1 st 

### set of comments.



## Building Block 4 Limitations of EIA 861 Data

. As is the case with energy efficiency data more generally, information reported by utilities on schedule 6 of EIA 861 is likely to reflect the use of different definitions and reporting protocols by different utilities in different states. Unlike terms such as kWh sold, kWh generated, or revenue by customer class, that have clear and commonly understood definitions, the terminology surrounding savings achieved through efficiency programs is not very uniform. It is essential to note that estimates of reductions in electricity demand attributable to energy efficiency programs are just that

– estimates, not metered or otherwise precisely measured values. Moreover, these estimates are subject to a high degree of uncertainty due to such factors as:

 Lack of standardization in methods of determining the counterfactual baseline.

 Absence of agreement on and standardization of basic terms, definitions, and methods.

 Lack of consistent, generally accepted evaluation, measurement & verification (EM&V) procedures.

 Variations in State requirements and rules for operating energy efficiency programs.

These factors make it difficult to evaluate the quality, degree of uncertainty, and utility of energy efficiency data beyond indicating in broad strokes the impact on electricity demand. A study prepared for EIA found that “the high variability of energy efficiency programs, the lack of lengthy track records, and the difficulty of measuring their benefits make analyzing these programs challenging, particularly in comparing them across states or across energy service areas.” ( Analysis of Energy Efficiency Program Evaluation, Measurement, and Verification Reports: Residential Space Cooling and Commercial Lighting Measures , prepared by Leidos Engineering, LLC for the U.S. EIA, March 2014, http://www.eia.gov/analysis/studi es/emv/coollight/pdf/emvrpt.pdf)

Although EIA reviews data submissions based on historical values for a specific respondent and industry

‐ wide
ranges, EIA in no sense conducts an independent audit of the submissions. Moreover, the annual data reported
to EIA (usually in the April following the end of a calendar year) has typically not been through an EM&V
process. This is because the EM&V process can take months or years.

EIA has received over the years comments and concerns related to the quality and coverage of the EIA

‐ 861
energy efficiency data. These comments have covered, for example:

 Whether EIA should collect gross or net energy savings.

 An argument that EIA should require the reporting of life

‐ cycle savings for energy efficiency
programs.

 That the agency needs to improve its quality review of the reported data.

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2 of 15  EIA’s energy efficiency terms are not well defined and are not consistent with industry and state programs; but as noted above, the lack of consistent terminology is a chronic problem across this field.

All EIA surveys require periodic Office of Management and Budget (OMB) re
‐ approval under provisions of the Paperwork Reduction Act. As a part of the public notice and comment process surrounding the re
‐ approval of the Form EIA

‐ 861, EIA has proposed improvements to the demand side management schedule and the definitions of terms used for collecting this data and solicited input on issues and potential modifications to the
current data collection. Pending OMB approval the revised survey will go into the field in 2014 to collect CY2013 data. The “Supporting Statement” EIA has prepared for OMB summarizes the survey changes planned by EIA and public comments (at the following link see pages 15, 16, and 57 to 63: http://www.eia.gov/survey/cha nges/electricity/part_a.pdf ).



## EPA Response: EPA Response: Thank you for this detailed information and sources regarding the EIA

‐ 861 data. EPA is aware of the history of the form, the improvements in data collection breadth and detail over time, and the existing limitations. These are presented in the TSD. In addition, we have added a footnote in the preamble section on BB4 where EIA 861 is first referenced which summarizes the data limitations and references further discussion in TSD.



## RIA

1. DSM and State Differences

. For any given level of overall demand reduction selected for Building Block 4, another issue for consideration is how, if at all, to reflect or address differences in the composition of energy demand across states. There are significant differences across states in the residential, commercial, and industrial shares of overall electricity demand. Given the possibility that EE savings potential and program activity may vary across sectors, it might be useful to explicitly address this matter in the EE TSD.


## EPA Response: The TSD on end

‐ use energy efficiency addresses this by presenting results of EE potential studies that show large potential across all regions of the country regardless of sectoral mix, electricity prices, climate, demographic trends, level of past EE policy efforts, etc. EPA does not believe that sectoral variation between states leads to substantial variation in achievable EE potential.



## 2. Baseline

. What constitutes the business

‐ as
‐ usual baseline? Additionally, how are planned retirements being treated

– are they part of the baseline.



## EPA Response: There is a summary of what the base case includes in Chapter 3, Section 3.3.1of the RIA and

more detailed information can be found in the IPM documentation: http://www.epa.gov/airmarkt/progsregs/epa

‐ ipm/BaseCasev513.html Planned retirements are included in the baseline for purposes of the IPM modeling.

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## 3. Uncertainty Analysis

. Has EPA conducted uncertainty analysis on gas price sensitivity and effectiveness of demand

‐ side management?



## EPA Response: EPA is working to supplement certain elements and provide more info on key variables such

as natural gas, but will be unable to address sensitivity analyses of demand

‐ side management effectiveness for this proposal. EPA has not conducted uncertainty analysis of “effectiveness” of DSM. It is not clear to EPA exactly what this commenter has in mind as this could imply a variety of different types of analysis addressing different aspects of the proposal (e.g., goal setting, impacts assessment, and state plan requirements). EPA would be interested in clarifying the question and exploring further prior to the final rule.



## 4. Fuel Price Changes

. Please discuss what EPA is assuming about fuel price changes over time. 

## EPA Response: All assumptions underpinning fuels in IPM can be found in the IPM documentation, as indicated and summarized in chapter 3 of the RIA. http://www.epa.gov/airmarkt/progsregs/epa

‐ ipm/BaseCasev513.html



## 5. Impacts at the State Level

. Can EPA provide the marginal costs associated with each state goal? In the RIA?


## EPA Response:

EPA did not base state goals on marginal cost. The preamble and accompanying TSDs explain why the costs of each individual BSER block are reasonable and the IPM modeling of the policy demonstrates that the overall costs of the combination of the four blocks is reasonable.



## 6. PM Benefits Per Ton

. Can EPA expand the discussion of uncertainties with using benefits per ton estimates for PM reductions.


## EPA Response: Section 4.3.6 of the RIA is the uncertainty characterization for the estimated health co
‐ benefits, which includes a discussion of the uncertainties associated with using benefit

‐ per
‐ ton estimates. We added a pointer to the more detailed evaluation of the benefit

‐ per
‐ ton estimates in the appendix (section 4A.5).



## 7. Air Quality Modeling

. Can EPA commit in the RIA to doing air quality modeling and resulting PM benefits (rather than using a BPT approach) for the final rule? This is one of the most expensive rules that EPA has proposed, and EPA’s analysis suggests there are substantial non
‐ CO2 PM benefits. For a rule of this magnitude, we think it is important to have the most accurate and robust analysis of the air quality benefits.


## EPA Response : Given the structure of 111(d) and the focus on climate benefits, we believe that the benefit

‐ per
‐ ton analysis that we have conducted to estimate the air pollution health co
‐ benefits is appropriate. However, we recognize that more precise air quality modeling can be informative.



## 8. Public Health Effects from Climate Change

. 

### We would like to discuss this section and make

sure we have consistent and agreed upon language, similar to what we’ve done with the PM benefits

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4 of 15 language for this package and others going forward. In particular, many of the statements seem to lack citations, and it’s unclear if the reader is to presume they can find that statement, as presented, in the Endangerment Finding

. 

## EPA Response: EPA is happy to discuss further. Please note that we have updated the parallel section in the preamble to include, for example, a brief discussion of peer
‐ reviewed assessments published after the 2009 Endangerment Finding. We anticipate carrying these edits to the relevant section of RIA Section 4.2.1 (benefits chapter). In addition, the current RIA references the Endangerment Finding. The first paragraph in the climate change impacts section, 4.2.1. Public Health Effects from Climate Change , states “This section summarizes the 2009 Endangerment Finding’s conclusions for each of these effects in turn,” and includes a web address for the 2009 Endangerment Finding.



## 9. Projected Impacts on the Entire Economy

. EPA projects that average nationwide retail electricity prices are projected to increase roughly 6 to percent in 2020, and roughly 3 percent by 2030 in the contiguous United States (see RIA, p.ES

‐ 21). These compliance costs and increased electricity prices may negatively impact U.S. industries that are import

‐ competing or exporting, particularly those industries that

use production methods or intermediate goods that are energy intensive. EPA acknowledges these

concerns in its discussion of secondary market impacts (see RIA, Chapter 5), but does not provide an estimate of such impacts, stating that serious technical challenges prevent it at this time. While it is unlikely that any model could fully and accurately capture all the impacts of the proposed rule, it is possible to provide credible upper and lower bounds to the economy
‐ wide impacts of the proposed rule by making reasonable assumptions about the range of potential impacts to energy prices and other variables. Can EPA provide credible upper and lower bound estimates? If not for the proposed rule, can EPA commit to providing this for the final rule RIA?

The National Export Initiative (NEI) Executive Order directs the Federal government to “improve conditions that directly affect the private sector’s ability to export.” The private sector’s ability to export is driven largely by the relative cost competitiveness of U.S. production compared to its foreign competition. Regulations that raise U.S. costs of production can adversely impact competitiveness. Higher compliance costs of domestic regulations can lead to lower output and employment of import competing and exporting industries. Therefore, in order to meet the policy goals of the NEI, it is important to take into account the potential international trade impacts of rules proposed by the Federal government.

To take these effects properly into account, commenters ask if EPA has considered any of the following, and if so, what are the impacts?

 Does the rule place U.S. producers at a competitive disadvantage vis a vis their international competition?

 Which are the industries whose exports would be most affected by the rule?

 How much market share will the U.S. industries affected by the rule lose in the short run
as a result of the loss in competitiveness?

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5 of 15  How much output (in dollar terms) and how many jobs does that loss in market share represent?

 What is the long run effect on output and employment as investors shift production locations in response to the relative change in rates of return on investment?


## EPA Response: EPA will explore providing quantitative impacts for the final rule.



## 10. IPM Model Output. Can EPA provide the interagency reviewers with the IPM model runs?



## EPA Response: EPA will be providing the IPM modeling data on Friday.



## 11. Biomass

. USDA and other federal and state agencies, including more than 29 state Renewable Portfolio Standards (RPS), consider biomass a clean and renewable alternative to fossil fuels and is a key component of meeting renewable energy objectives as well as meeting targets for reducing the accumulation of greenhouse gas emissions in the atmosphere. EPA includes lifecycle emissions from other sectors in this analysis (see for example the treatment of upstream methane emissions from reduced coal demand). While EPA acknowledges that they have yet to determine what the appropriate emission factors might be for categories of biomass, by selecting to weight biomass in a similar manner to coal (195 lbs/MMBtu), EPA has severely limited the least

‐ cost optimization of the IPM model and introduced an artificial barrier to allowing the model to select co
‐ firing of renewable biofuels as well as a full transition to biofuels. This ‘introduced bias’ projection constrains the model and limits biomass use in the electricity sector and limit the cost

‐ effective approaches available through co
‐ firing and dedicated biomass generation. EPA has been working on a framework for determining biogenic carbon emissions for several years now with the intention of completing that analysis prior to summer 2014. It seems that the remaining issues with that analysis are policy

‐ related and not technical. Furthermore, those policy judgments will affect certain sectors integral to this rulemaking, namely those within renewable energy building block. Those policy decisions should be made in coordination with OMB and other federal agencies (DOI and CEQ in particular) prior to this proposed rule being published.

Furthermore, EPA has not provided interagency reviewers the data from IPM runs, model constraints, and the associated parameters; so that the potential for that bias cannot be adequately framed or judged. In the least, EPA should conduct some sensitivity analysis on this parameter to judge how sensitive the IPM model is to a more realistic biomass emission factor of 0 lbs/MMBtu. Renewable biomass carbon capture and storage (Bio

‐ CCS) is also a viable option for removing carbon from the atmosphere. In order to understand the IPM Model’s sensitivity to this technology, USDA also recommends running the model with a biomass emission factor of ‐ 100 lbs/MMBtu.

Most state RPS targets count biomass, including biomass co
‐ fired with fossil fuels, as an eligible fuel. Implicitly, biomass is considered as being equivalent to other forms of renewable energy. IPM modeling runs prepared for EPA for past regulatory analyses and EIA’s projections using NEMS both assume no net CO 2 

### emissions from biomass

‐ fired generation. However, it appears that in the draft RIA analysis for the 111(d) rule, EPA assumed a CO 2 

### emissions factor for biomass of 195 lbs per Million Btu (Table 2

‐ 4, page 2

‐ 11, RIA), close to the carbon dioxide emissions factor for coal, which ranges from 205 to 217 lbs per Million Btu.

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6 of 15 o Given that biomass is considered to have an emission rate between fuel oil and coal for purposes of the draft RIA analysis, is there a need to make conforming adjustments in the TSD procedures used to generate best

‐ practice goals based on existing RPS that include biomass as a (clean) RPS

‐ eligible fuel?


## EPA Response: EPA is not estimating any lifecycle emissions in this proposal, and the modeling reflects CO 2 

### emissions from combustion for all sources (including biomass). There is an ongoing Agency process pertaining to biomass emissions explained in the proposal that will help clarify the potential role of biomass in the 111(d) program.



## 12. Scope (Chapter 3A). This needs much more clarity on why EPA is including some sources and not others as part of the analysis. EPA clearly states what it includes and what it doesn’t but there is often no justification for why some sources are not included in the calculations. For example, why focus only on upstream methane emissions, as opposed to all upstream greenhouse gas emissions? Energy

‐ related CO2 emissions from NG systems are non
‐ trivial (e.g., roughly 9% of U.S. natural gas end

‐ use occurs during its production, processing and transmission; EIA). Also, non
‐ combustion CO2 emissions (e.g., vented during acid

‐ gas removal) are not included in this analysis.



## EPA Response: We have added the following discussion to page 3A

‐ 3 in section 3A1.1 explaining the scope of analysis:

EPA defined the boundaries of this assessment in order to provide targeted insights into the potential net change in methane emissions from natural gas systems and coal production activities specifically, but not to provide a comprehensive assessment of life cycle GHG emissions. CO 2 

### emissions from flared methane are included because regulatory and voluntary programs influence the rate of methane flaring over time, but the CO 2 

### remaining after flaring is still, from the perspective of this assessment, a methane

‐ related GHG. Because of the multiple building blocks associated with the compliance strategies presented in the RIA, a comprehensive life

‐ cycle assessment would require an examination of the broader power sector and related input markets, and their potential changes in response to the rule. This analysis would be complex and likely subject to data limitations and substantial uncertainties. For these reasons, EPA chose to limit the scope of the upstream analysis to the important and well

‐ defined issue of methane emissions from natural gas systems and coal production.



## 13. Upstream Emissions

. If the purpose is to assess “whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative,” (p. 3A

‐ 1) it is not clear why the distribution segment is included in scope. Power plants receive their natural gas directly from transmission pipelines, so a comparative analysis between natural gas
‐ fired power and coal

‐ fired power should exclude the distribution segment (e.g., NETL, 2012).

NETL (National Energy Technology Laboratory). 2012. Role of Alternative Energy Sources: Natural Gas Technology Assessment. National Energy Technology Laboratory, U.S. Department of Energy. Available at: http://www.netl.doe.gov/File%20Library/Res earch/Energy%20Analysis/Publications/DOE

‐ NETL

‐ 2012

‐ 1539

‐ NGTechAssess.pdf

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## EPA Response: The following footnote was added to page 3A

‐ 32 in section 3A.3.2.

Many natural gas power plants are connected directly to transmission and distribution pipelines, and thus an increase in consumption of natural gas by power plants might not lead to a proportional change in CH 4 

### emissions from the distribution segment of the natural gas system. The current projection methodology uses total natural gas consumption as an activity driver to project emissions from non
‐ pipeline leak sources in the distribution segment. EPA examined how the results would differ if the distribution segment were excluded. Depending on the scenario and year, the net emissions change between the base case and policy cases was reduced by up to 0.9 TgCO 2 

### e or increased by up to 0.4 TgCO 2 

### e, assuming the change in power plant natural gas consumption had

no effect on distribution

‐ segment methane emissions.



## 14. Methodology and Applicability in Chapter 3

. Throughout the methodology section, the
text should be much clearer regarding when rules apply to new vs. modified vs. existing equipment. This distinction is sometimes made in the headers but not in the body text (and sometimes the distinction is not made the headers but is made in the body text). Diligent clarification of these points would help the reader understand why future reductions apply to the projected “annual increase” in emissions. E.g.:

 Bottom of page 23 and top of page 24, clarify in the body text if/when modifications trigger the need for installation of low

‐ bleed devices.

 Top of page 25, clarify in the body text how requirements apply to “new and modified” reciprocating compressors.

 First two

‐ bullets at the bottom of page 26 should begin with “New and Modified.”



## EPA Response: In response to these comments, EPA made revisions to Appendix 3A that improved the distinction between the oil and natural gas NSPS coverage of new/modified and existing sources.


## 15. Retirements and Heat Rate Improvements. Taken together, the retirements from MATS and the draft 111(d) rule as estimated in the draft RIA reflect the removal of over 33 percent of the 2012 capacity (336 GW according to Table 2.1 on page 2

‐ 2 of the draft RIA) that was considered in the TSD analysis of HRI. Given the significant change in the fleet, and the likelihood that weaker performing units would be disproportionately represented among retirements, explicit analysis of the applicability of the TSD analysis of HRI opportunities (both performance and cost) in the 336 GW fleet of 2012 to the projected fleet of 195 to 198 GW that remain in service in 2020 in the RIA analysis of Option 1 could be useful.


## EPA Response: This issue is addressed in section 2.5.11 of the GHG Abatement Measures TSD, which states: “The EPA’s research found 233 coal

‐ fired, non
‐ cogeneration EGUs that have announced they will retire before 2016. A sensitivity analysis was applied to the EGUs in the study population that plan to operate through 2015. The results are identical to the full population

– both achieve a heat rate reduction of 4% under the 30 percent difference option described in best practices.”



## 16. Heat Rate Improvements in IPM

. As noted on page 3A

‐ 2

‐ 19 the draft RIA, for purposes of the modeling conducted for the compliance scenarios, it is assumed that coal units over 25 MW have the choice

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8 of 15 to improve heat rates by 6% under option 1 and 4% under option 2 at a capital cost of $100 per kW of capacity in both options. As stated on page 3A

‐ 2

‐ 19, heat rate improvement (HRI) is virtually automatic in the compliance analysis, with roughly 99% of units over 25 MW pursuing HRI in the modeling. Information regarding the following would be helpful:

 Is the assumption about HRI technology cost and performance new to the version of the IPM model used for this RIA, or was it available in previous versions of IPM used for other EPA analyses (such as the MATS RIA) or in the commercial version of IPM?

 Are HRI technology cost and performance actually modeled as a single cost and performance point, as suggested in the draft RIA, or is there a distribution of cost and performance parameters across the fleet?


## EPA Response: HRI was not a core component of previous regulatory elements and was not incorporated into past versions of EPA’s application of IPM. The HRI is modeled as single cost and performance retrofit option.



## 17. Dispatch Changes

. There are significant differences between recent EIA analyses of plant dispatch and the analysis in Chapter 3 of the draft TSD that underpins the selection of 70% NGCC utilization as a re
‐ dispatch building block. A related matter involves choice of the capacity concept

– nameplate or seasonal capacity

– used to calculate capacity factors.

1. The draft rule assumes that 70% is an attainable fleet

‐ wide best

‐ practice performance indicator for the NGCC plant capacity factor in each state. TSD Chapter 3 Table 3

‐ 4 (page 20) shows national average NGCC dispatch factors in 2020 increasing steadily from 52% to 75% as carbon dioxide fee levels rise from

zero to $50 per metric ton of emissions.

a. EIA has also run side cases to the Annual Energy Outlook 2014 (AEO2014) Reference case 1 

### that lead to re
‐ dispatch in response to CO 2 

### fees of $10 or $25 per ton of CO2 starting in 2015, with fees escalating at a 5 percent real rate for the duration of the projection. Thus, by 2020, fees in the two cases are $13 per ton and $32 per ton, respectively, rising to $21 per ton and $52 per ton respectively by 2030. EIA’s NGCC utilization rates are not steadily increasing with the level of the fee

– as fees get high enough, NGCCs start to lose out to other types of generation.

b. EIA’s fee cases do not reach a 70% national average NGCC capacity factor. In EIA’s two lower fee
case, the fleet

‐ wide NGCC capacity factor reaches a maximum of 58% in 2025, while in the higher fee case the maximum capacity factor is 67% in 2018. In the AEO2014 reference case, the U.S.

‐ wide capacity factor for NGCC reaches a maximum of 54% in 2025. The figure below shows the fleet

‐ wide capacity factors for NGCC by year in the AEO2014 reference case and carbon fee cases.

1 

### Similar side cases, with similar results were also run for AEO2013

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9 of 15 c. In some regions, including SERC

‐ S (southeast), RFC

‐ W (western PJM) and SWPP

‐ S (southern SPP), the average NGCC capacity factor exceeds 70% for five or more consecutive years during the projection (14 consecutive years in the southeast). In other words, there is nothing in the NEMS model that prevents NGCC plants from running at a 70% capacity factor (or higher), if warranted by the economic signals in the scenario being modeled. 2. Additional clarification on the method used to calculate capacity factors in the draft RIA would be helpful. Chapter 3 of the TSD, at page 7, suggests the use of capacity factors based on nameplate capacity. 2 

### According to Table 2

‐ 2 of the RIA, the difference between Nameplate Capacity and the
average of Net Summer and Net Winter Capacity, which may be a better indicator of usable capacity, is

on the order of 7 to 10 percent. Thus, an assumption that 70% is an attainable fleet

‐ wide capacity factor (based on nameplate capacity) is actually assuming the upper limit is closer to 80% (when considering actual seasonal capability).

a. This is an important issue for considering comparisons with EIA’s AEO capacity factors, which are calculated based on summer capacity, which is typically more than 10 percent lower than nameplate capacity.



## EPA Response: The goals of the two different analyses are very different. EIA is focused on finding the most cost effective emission reductions. EPA’s analysis is focused on determining whether the reductions assuming a 70% capacity factor can be achieved at a reasonable cost. The EIA's analysis does, to a certain extent, address elements essential to BSER determination, such as feasibility, and takes into account certain impacts on natural gas prices. However, it does not address state

‐ by

‐ state utilization opportunities, which EPA's analysis

2 

### Excerpt from TSD, Chapter 3, page 7: While some units may model actual weather adjusted capacity by the hour/minute, this data is not reported for the fleet. Therefore, the EPA used the reported nameplate capacity reported for units. The net generation was divided into the nameplate generation capacity of a unit multiplied by the number of hours in a year . This calculation of capacity factor provides an indication of how much net generation a unit is providing as a percent of its total generating capacity. 0% 10% 20% 30% 40% 50% 60% 70% 80%

2011 2016 2021 2026 2031 2036 

## NGCC fleet

‐ wide capacity factor in three cases

AEO2014 Reference Case AEO2014 $10 CO2 Fee case AEO2014 $25 CO2 Fee case

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10 of 15 must account for. EPA analysis arrives at 70% utilization on the basis of several different factors supporting BSER.



## 18. Redispatch and Reliability. Regarding the reliability impacts of redispatch, a metric that could provide some initial insights into reliability is to compare, across the base and policy cases, the share of generation that is provided by dispatchable capacity with on
‐ site fuel stockpiles in each region.

Consideration of the same metric across regions could also be useful in addressing concerns about potential reliability impacts in regions where, despite declining, the share of generation provided by dispatchable capacity with on
‐ site fuel stockpiles remains above levels that have proven to be workable and reliable in other regions.



## EPA Response: EPA has addressed reliability in the resource adequacy TSD. We will consider this idea for the final rule.



## 19. Renewables Estimates and Treatment in RIA

. The calculation of renewable shares in the draft rule appears to be based upon a best practice share of total generation that non
‐ hydro renewables can provide by 2029, based on current RPS targets in states that have existing RPS requirements. The list of
current state targets is similar to what EIA uses to develop its regional RPS targets, but there are a few differences.

 EIA performs analysis to revise or adjust targets based on the specific state laws, and often several targets for different customers are included in the legislation, so this is not a straight

‐ forward calculation. The biggest differences in state goals are in Massachusetts (EIA

– 15%, EPA

– 33%) and Maine (EIA 33%, EPA

– 40%).

 EPA’s proposal is based on non
‐ hydropower renewable generation, which is how most of the state programs are structured, but not all. In particular, New York has a 29% RPS target and according to EIA modeling, hydro generation counts toward this cap (and provides over 20% of state generation currently). EPA’s best practice for New York seems to assume they can reach the 29% without hydro generation.

o EIA also assumes that end

‐ use generation can be used to meet the RPS standards, while the EPA assumption seems to be that only the power sector will be used to meet these goals.

The draft RIA shows very modest incremental additions to renewables as a compliance strategy. Table 3

‐ 15 in the draft RIA (page 3A

‐ 5) reports that cumulative additions to non
‐ hydro renewable in through 2030 are 41 to 42 GW under Option 1, only 8 to 9 GW more than the 33 GW expected in the RIA base case. In considering the RIA result, it would be useful to know what assumptions have been incorporated regarding future federal policies that subsidize renewable, such as the Production Tax Credit and the Investment Tax Credit. Also consider adding discussion of these policies to the preamble.



## EPA Response: This information is documented in the IPM documentation, along with the specific state RPS
programs: http://www.epa.gov/airmarkt/progsregs/epa

‐ ipm/BaseCasev513.html

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11 of 15 

## 20. Energy Efficiency

. The methodology for adjustments to the AEO2013 electricity sales baseline outlined in Chapter 5 of the draft GHG Abatement TSD appears to subtract efficiency improvements that are already reflected in EIA’s Reference case sales projection. Subtraction of EE savings that are already included in the baseline, rather than just those that are not in the baseline, would result in both an overstatement of EE savings and an understatement of projected load. Getting this right is important, because seemingly modest changes in the assumptions can result in potentially very significant implications for the draft RIA. This is because the EE savings as estimated in the draft TSD are exogenously subtracted from projected electricity demand in the draft RIA. In other words, in addition to their use to develop state goals, the TSD calculations feed directly into estimates of electricity demand, and therefore generation, presented in the draft RIA.

 The process for adjusting the baseline to reflect state

‐ level efficiency goals is outlined beginning on page 37, Chapter 5, of the draft TSD. According to step 1, business as usual (BAU) sales for each state are calculated starting with the 2012 data value, which is then adjusted by the regional growth rate from 2012 to 2040. (Note: a possible alternative that EPA might wish to consider is to use the year
‐ by ‐ year growth rates, or growth rates for 2012 to 2030 period to match the implementation horizon.) There is a wide range of regional growth rates in the AEO2013 baselines, which reflect differences in demographic trends, industrial levels and trends, income growth, etc., across regions. Importantly, continuation of existing efficiency programs is also implicitly reflected the AEO2013 regional demand growth estimates.

 Specifically, EIA’s projections of energy demand are developed using the end use modules of the National Energy Modeling System. For the buildings sectors, which dominate electric power demand, there are two modules: the Residential Demand Module (RDM) and the Commercial Demand Module (CDM). In both modules the general procedure is the same. First, estimates of building stock are developed. Then equipment in buildings is either estimated using consumption survey data or is selected for new buildings on a cost

‐ effective basis for all end uses. Finally, the usage and thus energy consumption of each end use is estimated at the Census division level. The calculation of this estimate of energy consumption starts with historical data for each Census division. To the extent that this historical data includes the effects of energy efficiency program activities, the NEMS projection of energy consumption maintains the historic relationships among the input variables. This is equivalent to assuming the maintenance of historic trends in energy efficiency program activity for each Census division throughout the projection period. The procedures for the Residential and Commercial sectors

are described in more detail in the Assumptions to the Annual Energy Outlook , with the RDM addressed

on page 31 (PDF page 34) and the CDM on page 45 (PDF page 48).

 Given the above, the procedure described in step 2 on page 37, which does not appear to take account of the reflection of existing trends in EE savings in the Reference case baseline, seems problematical. Consider, for example, a hypothetical state with a long and successful history of energy efficiency programs that is already achieving the 1.5 percent per year savings that are used to assemble the building block. If those savings are fully reflected in the baseline under the procedures outlined above, maintaining this result should not produce ANY incremental savings that can be applied to the Step 1 BAU estimates in Table 5

‐ 13. In that case, all of the percentages in Table 5

‐ 14 would be zero, and all of the corresponding entries in the first row of Table 5

‐ 15 would also be zero. Table 5

‐ 16 would also not be relevant, since the expiration pattern of existing EE savings would also be reflected in the data used to estimate growth in sales that already reflects historic trends, including the expiration of EE savings

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12 of 15 over time. With both EE savings and their expiration already reflected in the baseline, all entries in Table 5

‐ 17 would also be zero. Further discussion of the actual analytic procedure employed by EPA may resolve this issue, as it is described in several disparate parts of the draft documents provided for review.

Clearly, the example cited above is a polar case that is provided for illustrative purposes only. In cases where the AEO2013 regional projections reflect a lower level of EE savings than used to construct the building block, the application of the procedures outlined from pages 37 to 45 for the increment of EE savings over and above what is already included in the AEO2013 regional projections may be more appropriate.

 In this regard, it is noteworthy that while there are significant differences across states (and the regions they constitute) in the level of EE savings reported to EIA in 2012, as reported in Table 5

‐ 4 of the draft TSD, the average reported EE savings level is 0.58 percent for the United States as a whole, roughly 40 percent of the 1.5 percent level targeted in option 1 after complete phase in. If it is reasonable to assume that that continuation of the average 2012 level of EE savings is built into the AEO2013 baseline projection, then only the incremental 0.92 percent (1.5 percent ‐ 0.58 percent) would seem to be appropriate to deduct from the AEO Reference case, on average, given the assumption that all states can or will implement a 1.5 percent total annual EE demand reduction. Over the time frame of the RIA, the annual difference between deducting 0.92 percent vs. 1.5 percent accumulates to a large difference in the amount of energy savings.

 Another matter, and one that is very hard to discern, concerns the impact of trends in EE savings

over history, not just the 2012 levels, in the AEO2013 electricity sales baseline. To the extent that a rising trend over time in annual EE savings is being picked up in the equations used to estimate regional electricity demand growth, that trend may already be reflected to some extent in the AEO2013 projections. In other words, the increasing effect of EE in the period used for calibration might imply not just continuing, but increasing, EE in EIA’s Reference case.

 Variation across regions in projected electricity sales growth in the AEO2013 baseline, when considered in the context of proposed levels of EE savings to be subtracted from that baseline per the draft Chapter 5 TSD methodology that is directly imported in the draft RIA, results in many areas of the Northeast and Midwest having an absolute decline in electricity sales over the 2017 to 2030 period. Based on recent experience with weak load conditions, this raises the possibility of significant electricity market issues that may merit further discussion and consideration in both the draft TSD and draft RIA.

a. Chapter 5 of the draft TSD shows the projected cumulative savings as a percent of total sales from 2017 through 2029 under option 1, while Table 5

‐ 22 provides summary statistics for savings in 2030. There is relatively modest variation across states, with an identified range of savings from 9.9% to 12.5% over the 2017 to 2030 across states according to Table 5

‐ 22.

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13 of 15 b. Applying the information in Tables 5

‐ 21 and 5

‐ 22 of the draft TSD in conjunction with the regional AEO2013 Reference case sales growth table below suggests that “subtraction” of the Retail electricity sales savings implied by the State

‐ level EE best practice goals would result in significant declines in load over the 2017 to 2030 period in some regions of the country. It bears noting that since many states are assumed to phase in EE savings, the market impacts of low demand would be even greater after 2030, which a cumulative demand reduction from baseline of 15 percent from 2030 to 2040 if the annual EE targets were maintained.

c. As shown in the table below, there is significant regional variation in electricity sales growth projected across the United States in the AEO2013 Reference case. While growth averages 11.2% across the United States from 2017 to 2030, regional demand growth rates range from under 3% to nearly 20%. As a general rule, sales in the Northeast and Midwest are projected to grow much more slowly than sales in the South and West.

AEO2013 REFERENCE CA SE ELECTRICITY SALES GROWTH, 2017 to 2030 % GROWTH
Texas Regional Entity 12.3% Florida Reliability Coor dinating Council 18.8% Midwest Reliability Council / East 4.7% Midwest Reliability Council / West 7.9% Northeast Power Coordinating Council / New England 2.4%Northeast Power Coordinating Council / NYCWestchester 4.4% Northeast Power Coordinating Council / Long Island 3.9% Northeast Power Coordinating Council / Upstate New York 3.2% Reliability First Corpor ation / East 7.8% Reliability First Corporat ion / Michigan 4.8% Reliability First Corpor ation / West 5.5% SERC Reliability Corporation / Delta 11.7% SERC Reliability Corporation / Gateway 7.3% SERC Reliability Corporation / Southeastern 15.2% SERC Reliability Corporation / Central 12.4% SERC Reliability Corporation / Virginia-Carolina 16.8% Southwest Power Pool / North 9.5% Southwest Power Pool / South 12.1% Western Electricity Coordinati ng Council / Southwest 19.7% Western Electricity Coordinati ng Council / California 13.4% Western Electricity Coordinating Council / Northwest Power Pool Area 13.7% Western Electricity Coordinating Council / Rockies 18.0%

United States 11.2%

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14 of 15 d. Given that aspects of the evaluation of the building blocks 1, 2, and 3 may be sensitive to whether regional demand is growing or shrinking, it might be useful for the analysis in the draft TSD and RIA to either explain why there is no significant sensitivity or to address its implications.



## EPA Response:

As we understand them, these comments address two issues and their representation within the Abatement Measures TSD (Chapter 5) and the RIA, both.

This comment raise two separate issues 1) the degree to which a continuation, or even growth, of state EE policies and associated impacts on demand are implicitly reflected in the AEO2013 electricity demand that EPA’s IPM reference case is calibrated to Our understanding of the interagency comment is that the commenter is not offering an estimate of this effect or whether it is an affect that is growing, level, or declining over time, but suggesting that it be acknowledged. EPA has added discussion to the Abatement Measures TSD of this issue and potential impacts on goal setting and impacts assessment. We have also acknowledged the issue in revision to the RIA, referencing the TSD discussion. EPA has evaluated this issue and finds that this has no impact on goal setting because the goal setting approach uses cumulative % impacts, rather than absolute MWh impacts of this building block. EPA finds that there would be some effect on our impacts assessment but because that assessment is driven by the relative change between reference and base scenarios, any impact would be small and of indeterminate direction (i.e., not clear whether this would increase or reduce costs). Before the rule is finalized, we are interested in considering any additional information that quantifies the magnitude of this effect

over time.
Regarding the second issue

– implications of increasing or declining regional load growth under the policy case scenario

– we have added discussion of this in Chapter 5 of the abatement measures TSD.


## 21. Electricity Demand Response

. It is our understanding that EPA’s implementation of the IPM model does not allow electricity demand to respond to prices in its base case. However IPM is capable of modelling electricity demand responses. By exogenously imposing that electricity demand is reduced by EE/DSM programs, compliance costs estimated within the IPM model are artificially understated. The RIA compensates for this understatement by adding back in an estimate of the EE costs. From the materials provided in the RIA, it appears that the estimated average cost of EE/DSM reductions is very large, larger than the overall average compliance cost. If so, an internally consistent model response in which electricity consumption was permitted to decline in response to higher prices, or other modeled changes, would equate marginal costs across the different components and result in lower average costs.

Given that, can EPA provide the follow?

- IPM run(s) with endogenous demand response in IPM. This demand response would capture historical efficiency adopted in response to prices, although it would miss efficiency achieved through non
‐ price mechanisms. This IPM run would equate marginal costs of different abatement technologies.

- Within the current cost calculation method, that marginal compliance costs be approximately equated

across the different modes of compliance. This strategy is consistent with the premise of the proposal that the BSER provides targets and the SIP provides flexible compliance. This could entail some

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15 of 15 approximate or iterative method in which the modeled EE improvements are dialed back so that their marginal cost is equated to model

‐ based marginal costs.


## EPA Response: While IPM is capable of modeling price

‐ drive demand response, EPA has employed a different methodology in keeping with the core elements of BSER, and end

‐ use energy efficiency in particular. The inclusion of end

‐ use EE into the analysis is based on a broader policy framework that seeks to recognize the important role end

‐ use EE has in reducing EGU emissions, and is premised on policy interventions that seek to harness this important resource, which is widely acknowledged as hampered by market failures and other barriers. EPA would not expect a market simulation model to successfully simulate this potential precisely, because policies to circumvent market failures are necessary to realize these energy savings in practice.



## 22. UMRA and Federalism

. Why wouldn’t this rule cost $100M for UMRA? $2 million/state to implement? Same thing for Federalism?


## EPA Response: EPA has completed an analysis of the information collection costs associated with this rulemaking will submit an ICR for Interagency review. The information collection requirements are based on the recordkeeping and reporting burden associated with developing, implementing, and enforcing a state plan to limit CO 2 

### emissions from existing sources in the power sector. The annual burden for this collection of information for the states (averaged over the first 3 years following promulgation of this proposed action) is estimated to be a range of 410,220 hours at a total annual labor cost of $28,531,605, to 822,040 hours at a total annual labor cost of $57,174,454. The lower estimate reflects the assumption that some states already have energy efficiency programs in place. The higher estimate reflects the assumption that no states have energy efficiency programs in place. The total annual burden for the federal government (averaged over the first 3 years following promulgation of this proposed action) is estimated to be 53,091 hours at a total annual labor cost of $3,109,057. Based upon these annual burden estimates, EPA concludes that this rule will exceed the Federalism annual threshold of $25 million, but not exceed the UMRA threshold of $100 million. In addition to the ICR analysis, EPA analyzed the monitoring, reporting and recordkeeping (MRR) costs associated with this rulemaking. In Chapter 3, Section 3.10 of the RIA, EPA discusses the MRR costs in 2020, 2025 and 2030. EPA estimates that costs to states and EGU sources for additional MRR are approximately $64.3 million in 2020, and $8.9 million in 2025 and 2030, respectively. Only a subset of these MRR costs would be applicable to state reporting and recordkeeping costs.