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 wanted 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 

 

************************************** 

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<< E.O. 12866 Chapter_1_Introduction_and_Background_5_15_14.docx >> 

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<< E.O. 12866 Chapter_8_ Net Benefits 051514.docx >> 

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<< E.O. 12866 Appendix_4A_Generating_BPT_estimates_5.15.14.docx >> 

<< EO 12866 Response to Interagency RIA Comments 051514 final.docx >> 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

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 Planning and Standards 

Research Triangle Park, NC 27711 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

2 

CONTACT INFORMATION 

This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, the Office of Atmospheric Programs, and the Office of Policy of the U.S. Environmental 

Protection Agency. Questions related to this document should be addressed to Linda M. 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 Offices including the 

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

Policy. Contributions to this report were also made by RTI International and ICF International. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3 

EPA-452/R-14-002 

 March/2014 

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 Quality Planning and Standards 

Health and Environmental Impacts Division 

Air Economics Group 

Research Triangle Park, North Carolina 27711 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4 

 
***E.O. 12866 Review – Draft – Do Not Cite, 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 Available Control Technology 

BenMAP Benefits Mapping and Analysis Program 

BPT Benefit-per-Ton 

BSER Best System of Emissions Reduction 

Btu British Thermal Units 

C Celsius 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CCR Coal Combustion Residuals 

CCS Carbon Capture and Sequestration or Carbon Capture and Storage 

CCSP Climate Change Science Program 

CFR Code of Federal Regulations 

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 Reliability Coordinating Council 

FUND Framework for Uncertainty, Negotiation, and Distribution Model v 

GDP Gross Domestic Product 

GHG Greenhouse Gas 

GS Geologic Sequestration 

Gt Gigaton 

H2S Hydrogen Sulfide 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

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 Air 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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ACRONYMS (CONTINUED) 

mmBtu Million British Thermal Units 

MW Megawatt 

MWh Megawatt-hour 

N2O Nitrous Oxide 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industry Classification System 

NaOH Sodium Hydroxide 

NATCARB National Carbon Sequestration Database and Geographic Information System 

NEEDS National Electric Energy Data System 

NEMS National Energy Modeling System 

NERC North American Electric Reliability Corporation 

NETL National Energy Technology Laboratory 

NGCC Natural Gas Combined Cycle 

NMMAPS National Morbidity, Mortality Air Pollution Study 

NOAK Next of a Kind or Nth of a Kind 

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 Significant Deterioration 

RCSP Regional Carbon Sequestration Partnerships 

RADS Relative Airways Dysfunction Syndrome 

RES Renewable Electricity Standards 

RFA Regulatory Flexibility Act 

RGGI Regional Greenhouse Gas Initiative 

RIA Regulatory Impact Analysis 

RPS Renewable Portfolio Standards 

SAB-CASAC Science Advisory Board Clean Air Scientific Advisory Committee 

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

Clean Air Compliance 

SAB-EEAC Science Advisory Board Environmental Economics Advisory Committee 

SBA Small Business Administration 

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC Social Cost of Carbon 

SCPC Super Critical Pulverized Coal 

SCR Selective Catalytic Reduction 

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 Change Research Program 

USGS U.S. Geological Survey 

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

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

VCS Voluntary Consensus Standards 

VOC Volatile Organic Compounds 

VOM Variable Operating and Maintenance 

VSL Value of a statistical life 

WTP Willingness to pay 
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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 

sStandards). 

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 (CO2) is the primary greenhouse gas pollutant, accounting 

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

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

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

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

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

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

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

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

ES.2 Summary of Proposed EGU GHG Existing Source Guidelines 

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

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

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

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 increasing demand-side energy efficiency. 

The proposed rule also contains emission guidelines 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 emission reduction opportunities and existing state 

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

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

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

 

1

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

emission reduction achievable through applying the BSER; and the state is authorized to identify the 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 implement 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. 
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ES-3 

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

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

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

emissions. 

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

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

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

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

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

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

those measures to the same degree of stringency that the EPA determines is achievable 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 states in complying with the emission guidelines, 

the benefits, cost and economic impacts reported in this RIA are illustrative of compliance 

actions states may take. 

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 
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ES-4 

the regional approach, states may collaboratively 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. The guidance allows flexibility of 

compliance, including the 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 generation 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 CO2 per 

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

State2

Option 1 Option 2 

Interim Goal 

(2020-2029) 

Final Goal 

(2030 Forward) 

Interim Goal 

(2020-2024) 

Final Goal 

(2025 Forward) 

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

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

Arizona * 735 702 779 763 

Arkansas 968 910 1,083 1,058 

California 556 537 582 571 

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

Connecticut 597 540 651 627 

Delaware 913 841 1,007 983 

Florida 794 740 907 884 

Georgia 891 834 997 964 

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

Idaho 244 228 261 254 

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

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

 

2

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

those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. 

territories at this time. 
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ES-5 

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

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

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

Louisiana 948 883 1,052 1,025 

Maine 393 378 418 410 

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

Massachusetts 655 576 715 683 

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

Minnesota 911 873 1,018 999 

Mississippi 732 692 765 743 

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

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

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

Nevada 697 647 734 713 

New Hampshire 546 486 598 557 

New Jersey 647 531 722 676 

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

New York 635 549 736 697 

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

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

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

Oklahoma 931 895 1,019 986 

Oregon 407 372 450 420 

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

Rhode Island 822 782 855 840 

South Carolina 840 772 930 897 

South Dakota 800 741 888 861 

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

Texas 853 791 957 924 

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

Virginia 884 810 1,016 962 

Washington 264 215 312 284 

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

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

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

Table ES-21 shows the emission reductions associated 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 CO2 emissions 
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ES-6 

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

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

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

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

approaches, respectively. Tables ES-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

CO2 (million 

metric tons) 

SO2 (thousands of 

tons) 

NOX (thousands of 

tons) 

PM2.5 

(thousands of 

tons) 

2020 Regional Compliance Approach 

Base Case 2,161 1,476 1,559 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 Compliance 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. 
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ES-7 

Table ES-23. Summary of Climate and Air Pollutant Emission Reductions Option 2 

CO2 (million 

metric tons) 

SO2 (thousands of 

tons) 

NOX (thousands of 

tons) 

PM2.5 (thousands of 

tons) 

2020 Regional Compliance Approach 

Base Case 2,161 1,476 1,559 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 Compliance 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 Compliance 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 action 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 the 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. 
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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 monitoring, 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 projected additional cost of complying 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 oin 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 efficiency participant costs (paid by electric utility 

consumers). The compliance costs reported in Table ES-43 are not social costs. These costs 

represent the illustrative real resources costs for states to comply with the BSER goals for 

Options 1 and 2. Electric sector Ccompliance costs and monitoring, recordkeeping and reporting 

 

3

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

http://www.epa.gov/powersectormodeling/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. 
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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 Health Co-benefits for Existing EGUs 

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

ancillary human health co-benefits associated with reduced emissions of SO2, NO2, and directly 

emitted PM2.5, which would lead to lower ambient concentrations of PM2.5 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-benefits associated with the proposed emission 

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

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

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

magnitude of these unquantified benefits because previous quantification attempts have been 

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

the impacts are small or unimportant. Table ES-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 CO2 —  SCC TSD 

Climate impacts from ozone and black carbon (directly 

emitted PM) — — Ozone ISA, PM ISA1

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

aerosols, other impacts) — — IPCC1

Improved Human Health (co-benefits) 

Reduced incidence of 

premature mortality 

from exposure to PM2.5

Adult premature mortality based on cohort study estimates

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

Infant mortality (age <1)   PM ISA 

Reduced incidence of 

morbidity from 

exposure to PM2.5

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

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

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

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

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

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

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

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

Lost work days (age 18-65)   PM ISA 

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

Chronic Bronchitis (age >26) — — PM ISA 

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

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

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

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

and populations) 

— — PM ISA2

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

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

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

Reduced incidence of 

mortality from 

exposure to ozone 

Premature mortality based on short-term study estimates (all 

ages)   Ozone ISA 

Premature mortality based on long-term study estimates (age 

30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone 

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

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

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

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

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

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

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

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

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

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

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

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

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and populations) — — NO2 ISA2,3

Table ES-4. continued 
ES-11 

Reduced incidence of 

morbidity from 

exposure to SO2

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

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

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

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

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and populations) — — SO2 ISA1,2

Reduced incidence of 

morbidity from 

exposure to 

methylmercury 

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

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

memory, behavior) — — IRIS; NRC, 20002

Cardiovascular effects — — IRIS; NRC, 20002,3

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

Reduced incidence of 

morbidity from 

exposure to HAP 

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

Improved Environment (co-benefits) 

Reduced visibility 

impairment 

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials 

Household soiling — — PM ISA1,2

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

Reduced PM 

deposition (metals and 

organics) 

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation 

and ecosystem effects 

from exposure to 

ozone 

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

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

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

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

cycles, net primary productivity, leaf-gas exchange, 

community composition) 

— — Ozone ISA2

Reduced effects from 

acid deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

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

Other non-use effects NOx SOx ISA2

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

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial and 

estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

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

Other non-use effects NOx SOx ISA2

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

regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from exposure 

to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure 

to methylmercury 

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

effects) — — Mercury Study RTC2

Commercial, subsistence and recreational fishing — — Mercury Study RTC1

1

 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, 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 suggestive 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 (CO2) 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 CO2 emissions in a given year. It includes a wide range of anticipated 

climate impacts, such as net changes in agricultural productivity and human health, property 

damage from increased flood risk, and changes in energy system costs, such as reduced costs for 

heating and increased costs for air conditioning. It is typically used to assess the avoided 

damages as a result of regulatory actions (i.e., benefits, of rulemakings that have an incremental 

achieve marginal impact on cumulative global reductions in CO2 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 $137 per metric ton of CO2 emissions in the year 2020 (2011$), 

and each estimate increases over time. These SCC estimates are associated with different 

discount rates. The first three estimates are the model average at 5 percent discount rate, 3 

percent, and 2.5 percent, respectively, and the fourth estimate is the 95th percentile at 3 percent. 

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

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Current integrated assessment models do not assign value to all of the important 
ES-13 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature because of a lack of precise information on the nature of damages and because the 

science incorporated into these models understandably lags behind the most recent research. In 

particular, the IPCC Fourth Assessment Report concluded that “It is very likely that [SCC 

estimates] underestimate the damage costs because they cannot include many non-quantifiable 

impacts.” Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 emission reductions to inform the benefitcost analysis. 

ES 5.2 Estimating Air Pollution Health Co-Benefits 

The health co-benefits estimates represent the total monetized human health benefits for 

populations exposed to less PM2.5 and ozone from emission reductions of precursor pollutants 

(e.g., SO2, 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 PM2.5 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 ranging 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 effect coefficients represented by two short-term 

epidemiology studies [Bell et al. (2004) and Levy et al. (2005)]), as well as avoided morbidity 

effects for five non-fatal endpoints ranging in severity from school absence days to hospital 

admissions (U.S. EPA, 2008, 2011). 

Because we were unable to conduct air quality modeling for this RIA, wWe instead used a 

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

estimates for PM2.5, this approach uses an air quality model to convert emissions of PM2.5 

 

4

 We were unable to quantify the co-benefits associated with reducing direct exposure to SO2 and NOx.

5

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

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

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., SO2, NOx,) and directly emitted particles) into changes in ambient PM2.5 

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

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

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

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

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

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

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

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

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

specific location. 

Our estimate of the monetized co-benefits is based on 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 PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5

health co-benefits: 

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA concluded that “many constituents of PM2.5 can be linked with 

multiple health effects, and the evidence is not yet sufficient to allow differentiation 

of those constituents or sources that are more closely related to specific outcomes” 

(U.S. EPA, 2009b). 

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

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

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

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

down to the lowest modeled concentrations. 

3. We assume that there is a “cessation” lag between the change in PM exposures and 

the total realization of changes in mortality effects. Specifically, we assume that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a 
ES-15 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates. 

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

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

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

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

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

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

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

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

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

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

important source of uncertainty to the benefits estimates. 

ES 5.3 Combined Benefits Estimates 

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

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

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

SCC because the literature shows that it is sensitive to assumptions about discount rate and 

because no consensus exists on the appropriate rate to use in an intergenerational context. The 

U.S. government centered its attention on the average SCC at a 3 percent discount rate but 

emphasized the importance of considering all four SCC estimates. 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 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 371 million metric tonnes CO2

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% (95th percentile) $51 $65 to $85 $64 to $81 

In 2025 501 million metric tonnes CO2 

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% (95th percentile) $76 $98 to $130 $96 to $120 

In 2030 545 million metric tonnes CO2

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% (95th percentile) $92 $120 to $150 $110 to $140 

Option 

2 

In 2020 283 million metric tonnes CO2

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% (95th percentile) $39 $51 to $67 $50 to $64 

In 2025 368 million metric tonnes CO2

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% (95th percentile) $56 $72 to $93 $71 to $90 

In 2030 353 million metric tonnes CO2 

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% (95th percentile) $60 $76 to $97 $74 to $93 

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

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

the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and 

reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health 

effects from direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emission 

reductions of directly emitted particles 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 CO2

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% (95th percentile) $52 $68 to $89 $66 to $85 

In 2025 506 million metric tonnes CO2 

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% (95th percentile) $77 $99 to $130 $97 to $120 

In 2030 555 million metric tonnes CO2

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% (95th percentile) $94 $120 to $150 $120 to $150 

Option 

2 

In 2020 295 million metric tonnes CO2

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% (95th percentile) $40 $53 to $69 $51 to $66 

In 2025 376 million metric tonnes CO2

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% (95th percentile) $57 $74 to $95 $72 to $92 

In 2030 373 million metric tonnes CO2 

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% (95th percentile) $63 $80 to $100 $79 to $99 

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

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

the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and 

reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health 

effects from direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emission 

reductions of directly emitted particles are not yet available. 

**Unless otherwise specified, it is the model average. 
ES-18 

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

for Existing Sources 

In this summary, the EPA provides the estimates of the climate benefits, health 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-benefits in 2020 are estimated to be $15 billion to 

$34 billion (2011$) for a 3 percent discount rate and $13 billion to $31 billion (2011$) for a 7 

percent discount rate. The annual compliance costs, including monitoring and reporting costs, are 

approximately $5.4 5 billion (2011$) 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 discount rate (model average). The air pollution health co-benefits 

in 2020 are estimated to be $15 billion to $36 billion for a 3 percent discount rate and $14 billion 

to $33 billion (2011$) for a 7 percent discount rate. The annual compliance costs including 

monitoring and reporting costs, are approximately $7.4 5 billion (2011$) in 2020 [MRR costs to 

be added]. The quantified net benefits (the difference between monetized benefits and costs) are 

$26 billion to $46 billion 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-CO2 greenhouse gases and cobenefits from reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., mercury and 

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

these limitations and uncertainties, it remains clear that the benefits of this proposal are 

substantial and far outweigh the costs. 
ES-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 

95th 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 SO2 and NO2

1.5 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.2 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Option 1 - regional Option 2 – regional 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$4.7 

$17 

$25 

$51 

$3.6 

$13 

$19 

$39 

Air pollution health co-benefits c $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 SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

0.9 tons of Hg 

Ecosystem Effects 

Visibility impairment a

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

b

 The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent of 

total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. 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 SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health 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 

95th 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 SO2 and NO2

2.0 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Option 1 – regional Option 2 – regional 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$7.5 

$25 

$37 

$76 

$5.5 

$18 

$27 

$56 

Air pollution health co-benefits c $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 SO2 and NO2

1.7 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

Visibility impairment a

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

b

 The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent of 

total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. 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 SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health 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 

95th 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 SO2 and NO2

2.1 tons of Hg and 590 tons of HCl 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

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 

95th 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 SO2 and NO2

1.7 tons of Hg and 580 tons of HCl 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

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 reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent of 

total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. 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 SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health estimates based on these additional discount rates. 
ES-22 

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-delivered natural gas prices will increase by 9 to 12 

percent in 2020, with negligible changes by 2030 relative to the base case. Natural gas use for 

electricity generation will increase by as much as 1.2 trillion cubic feet (TCF) in 2020 relative to 

the base case, declining over time. 

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 or 
ES-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 job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts, during periods of 

sustained high unemployment, employment impacts are of particular concern and questions may 

arise about their existence and magnitude. 

States have the responsibility and flexibility to implement policies and practices for 

compliance with Proposed Electric Generating Unit Greenhouse Gas Existing Source Guidelines. 

As such, gGiven 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 approximately 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 CO2 emitted from reconstructed and modified 

EGUs under section 111(b) of the CAA. Based on historical information that has been reported 

to the EPA, the EPA anticipates few, if any, covered units will trigger the reconstruction or 

modification provisions in the period of analysis (through 2025). As a result, we do not 

anticipate any significant costs or benefits associated with this proposal. However, because there 

have been a few units that have notified 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, Technical Support Document: Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working 

Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, 

Council on Environmental Quality, Department of Agriculture, Department of Commerce, 

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

National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 

2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf>. 
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/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>. 

Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International, 49 41–151. 

Krewski D; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes and Y. Shi, et al. 2009. Extended FollowUp and Spatial Analysis of the American Cancer Society Study Linking Particulate Air 

Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: Synthesis Report 

Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC. 

Available at: 

<http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synt

hesis_report.htm>. 

Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspectives, July, 120(7): pp. 965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Meta-regression Analysis.” Epidemiology, 16(4): pp. 458-68. 

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on 

the Toxicological Effects of Methylmercury. Board on Environmental Studies and Toxicology. 

Washington, DC: National Academies Press. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: National Academies Press. 
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 Assessment for Oxides of Nitrogen: Health Criteria (Final 

Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. 

Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/6-ozoneriachapter6.pdf>. 

U.S. EPA. 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur: Ecological 

Criteria National (Final Report). Research Triangle Park, NC: National Center for 

Environmental Assessment. (EPA document number EPA/600/R-08/139, December). 

Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>. 

U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report). Research 

Triangle Park, NC: National Center for Environmental Assessment, RTP Division. (EPA 

document number EPA-600-R-08-139F, December). Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

U.S. EPA. 2010d. Section 3: Re‐analysis of the Benefits of Attaining Alternative Ozone 

Standards to Incorporate Current Methods. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/s3-supplemental_analysis-updated_benefits11-

5.09.pdf >. 

U.S. EPA. 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter. Research Triangle Park, NC: Office of Air 

Quality Planning and Standards, Health and Environmental Impacts Division. (EPA document 

number EPA-452/R-12-003, December). Available at 

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

(Final Report). Washington, DC: U.S. Environmental Protection Agency. (EPA document 

number EPA/600/R-10/076F). Available at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 
ES-27 

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of 

postneonatal infant mortality and particulate air pollution in the United States.” 

Environmental Health Perspectives 105 (6): 608-612. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

1-1 

CHAPTER 1: INTRODUCTION AND BACKGROUND 

1.1 Introduction 

This document presents the expected benefits, costs, and economic impacts of illustrative 

strategies states may implement to comply with the proposed Electric Utility Generating Unit 

(EGU) Existing Source GHG Guidelines in 2020, 2025, and 2030. Furthermore, this document 

provides a separate description of the expected benefits, costs, and economic impacts of 

proposed emission limits for reconstructed and modified EGU sources. This chapter contains 

background information on these rules and an outline of the chapters in the report. 

1.2 Legal, Scientific and Economic Basis for this Rulemaking 

1.2.1 Statutory Requirement 

Section 111 of the Clean Air Act (CAA) requires performance standards for air pollutant 

emissions from categories of stationary sources that may reasonably contribute to endangerment 

of public health or welfare. In April 2007, the Supreme Court ruled in Massachusetts v. the 

Environmental Protection Agency (EPA) that greenhouse gases (GHGs) meet the definition of an 

“air pollutant” under the CAA. This ruling clarified that the authorities and requirements of the 

CAA apply to GHGs. As a result, the EPA must make decisions about whether to regulate GHGs 

under certain provisions of the CAA, based on relevant statutory criteria. The EPA issued a final 

determination that GHG emissions endanger both the public health and the public welfare of 

current and future generations in the Endangerment and Cause or Contribute Findings for 

Greenhouse Gases Under Section 202(a) of the CAA (74 FR 66,496; Dec. 15, 2009). 

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

carbon dioxide (CO2) from new, existing, and modified sources. In January 2014, under the 

authority of CAA section 111(b), the EPA proposed standards for emissions of CO2 from new 

fossil fuel-fired electric steam generating units (utility boilers and integrated gasification 

combined cycle [IGCC] units) and for natural gas-fired stationary combustion turbines. In a 

separate action, EPA is proposing standards to address CO2 emissions from reconstructed and 

modified power plants under the authority of CAA section 111(b). When the EPA establishes 

section 111(b) standards of performance for new sources in a particular source category for a 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

1-2 

pollutant that is not regulated as a criteria pollutant or hazardous air pollutant, the EPA must 

establish requirements for existing sources in that source category for that pollutant under section 

111(d). Under 111(d), the EPA develops “emission guidelines” that the states must develop plans 

to meet. In this action, the EPA is proposing state goals for GHG emissions from existing 

sources under section 111(d) of the CAA. 

1.2.2 Health and Welfare Impacts from Climate Change 

In 2009, the EPA Administrator found that elevated concentrations of greenhouse gases in 

the atmosphere may reasonably be anticipated both 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 designed 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 magnitude and duration over the portions of the U.S. where these events 

have already been observed. If observed warming continues as projected, it will increase heatrelated mortality and illness, especially among the elderly, young, and frail. There is also a 

relationship between climate change and other aspects of air quality. Climate change is expected 

to increase regional ozone pollution compared to the expected ozone levels in the absence of 

climate change. These increases are expected to increase associated risks for respiratory illnesses 

and premature death. 

In addition to impacts on heat-related mortality and air quality, there is also the potential 

for increased deaths, injuries, infectious 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 vector-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 example, changes in the intensity and frequency of 

extreme weather events, such as droughts and heavy storms, have the potential to cause serious 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

1-3 

negative impacts on U.S. agriculture. Additionally, climate change will likely further constrain 

already over-allocated water resources in some regions of the United States, increasing 

competition among agricultural, municipal, industrial, and ecological uses. Furthermore, climate 

change impacts in other regions of the world could raise national security concerns for the 

United States. 

The specific health and welfare impacts of climate change are addressed in more detail in 

Chapter 4 of this Regulatory Impact Assessment (RIA). 

1.2.3 Market Failure 

Many regulations are promulgated to correct market failures, which lead to a suboptimal 

allocation of resources within the free market. Air quality and pollution control regulations 

address “negative externalities” whereby the market does not internalize the full opportunity cost 

of production borne by society as public goods such as air quality are unpriced. 

 GHG emissions impose costs on society, such as negative health and welfare impacts that 

are not reflected in the market price of the goods produced through the polluting process. For this 

regulatory action the good produced is electricity. These social costs associated with the health 

and welfare impacts are referred to as negative externalities. If an electricity producer pollutes 

the atmosphere when it generates electricity, this cost will be borne not by the polluting firm but 

by society as a whole. The market price of electricity will fail to incorporate the full opportunity 

cost to society of generating electricity. In this case, the quantity of electricity generated in a free 

market will not be at the socially optimal level. More electricity will be produced than would 

occur if the power producers had to account for the full opportunity cost of production including 

the negative externality. Consequently, absent a regulation on emissions, the marginal social cost 

of the last unit of electricity produced will exceed its marginal social benefit. 

Clean air is an example of a public good. A public good is one that is available to all and 

its consumption by one does not reduce the availability to others. The characteristics of a public 

good also mean that there may be weak incentives to provide it in a competitive market. 

Regulations are a mechanism to provide and protect public goods. 
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1-4 

1.3 Summary of Regulatory Analysis 

In accordance with Executive Order 12866, Executive Order 13563, and the EPA’s 

“Guidelines for Preparing Economic Analyses,” the EPA prepared this RIA for this “significant 

regulatory action.” This action is an economically significant regulatory action because it is 

expected to have an annual effect on the economy of $100 million or more or adversely affect in 

a material way the economy, a sector of the economy, productivity, competition, jobs, the 

environment, public health or safety, or state, local, or tribal governments or communities. 

This RIA addresses the potential costs, emission reductions, and benefits of the existing 

source emission guidelines that are the focus of this action. Additionally, this RIA includes 

information on potential impacts on electricity markets, employment, and markets outside the 

electricity sector. Chapter 9 of this document provides a separate description of the expected 

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 analysis in this RIA is based proposes GHG 

emission guidelines for states to limit CO2 emissions from certain existing EGUs. The base

caseline for this analysis, which uses the Integrated Planning Model (IPM), includes state rules 

that have been finalized and/or approved by a state’s legislature or environmental agencies, as 

well as final federal rules. The IPM Base Case v.5.13 includes the Clean Air Interstate Rule 

(CAIR),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 limitations 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 μg/m3 or less and annual average of 15 μg/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 Circuit in December 2008 and EPA was required to correct 

legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. CAIR remains in effect 

until replaced by EPA pursuant to the Court’s ruling. CAIR’s provisions were still in effect when EPA Base 

Case v.5.13 was released. 
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1-5 

Additional legally binding and enforceable commitments 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 full 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 CO2 emissions from certain existing EGUs. This rulemaking does not address GHG 

emissions from new, reconstructed, or modified sources. Section 111(b) emission limits for 

reconstructed and modified sources are being proposed in a separate action and are discussed in 

Chapter 9 of this RIA. 

For the purposes of this analysisrule, an affected EGU is any fossil fuel-fired EGU that 

was in operation or had commenced construction as of January 8, 2014, and is therefore an 

“existing source” for purposes of CAA section 111, but in all other respects would meet the 

applicability criteria for coverage under the proposed GHG standards for new fossil fuel-fired 

EGUs published in the Federal Register on that 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) combusts fossil fuel for more than 10 percent of 

its total annual heat input; (3) sells the greater of 219,000 MWh per year or one-third of its 

potential electrical output to a utility distribution system; and (4) was not in operation or under 

construction as of January 8, 2014 (the date the proposed GHG standards of performance for 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 applies 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/powersectormodeling/BaseCasev513.html 
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1-6 

1.4.3 Regulated Pollutant 

This rule sets emission guidelines for CO2 emissions from affected sources. The EPA is 

proposing these guidelines because CO2 is a GHG and fossil fuel-fired power plants are the 

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 (N2O) (and to a lesser extent, 

methane [CH4]) may be emitted from fossil-fuel-fired EGUs, especially from coal-fired 

circulating fluidized bed combustors and from units with selective catalytic reduction and 

selective non-catalytic reduction systems installed for nitrogen oxide (NOX) control. The EPA is 

not proposing separate N2O or CH4 guidelines or an equivalent CO2 emission limit because of a 

lack of available data for these affected sources. Additional information on the quantity and 

significance of emissions and on the availability of cost effective controls would be needed 

before proposing standards for these pollutants. 

1.4.4 Emission Guidelines 

The EPA is proposing emission guidelines for states to use in developing plans to address 

greenhouse gas emissions from existing fossil fuel-fired electric generating units. Specifically, 

the EPA is proposing state-specific rate-based goals for 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 states with options for meeting performance 

standards in a manner that accommodates a diverse range of state approaches. The proposed 

guidelines would also allow states to collaborate and to demonstrate emission performance on a 

multi-state basis, in recognition of the fact that electricity is transmitted across state lines, and 

measures often impact regional EGU CO2 emissions. The illustrative compliance strategies 

presented in this RIA include both regional and state-level compliance scenarios for each of the 

regulatory options presented. 
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1-7 

1.5 Organization of the Regulatory Impact Analysis 

This report presents the EPA’s analysis of the potential benefits, costs, and other economic 

effects of the proposed EGU Existing Source GHG Guidelines to fulfill the requirements of an 

RIA. This RIA includes the following chapters: 

 Chapter 2, Electric Power Sector Profile 

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

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

 Chapter 5, Economic Impacts – Markets Outside the Electricity Sector 

 Chapter 6, Employment 

 Chapter 7, Statutory and Executive Order Analyses 

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

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

Modified and Reconstructed Electric Generating Units 

 
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1-8 

1.6 References 

40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, 

“Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) 

of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 15, 2009 / 

Rules and Regulations. 

U.S. Environmental Protection Agency. Power Sector Modeling Platform v.5.13. November 27, 

2013. Available online at: http://www.epa.gov/powersectormodeling/BaseCasev513.html. 
2-1 

CHAPTER 2: ELECTRIC POWER SECTOR PROFILE 

2.1 Introduction 

This chapter discusses important aspects of the power sector that relate to the proposed 

EGU Existing Source GHG Standards, including the types of power-sector sources affected by 

the proposal, and provides background on the power sector and EGUs. In addition, this chapter 

provides some historical background on the EPA regulation of, and future projections for, the 

power sector. 

2.2 Power Sector Overview 

The production and delivery of electricity to customers consists of three distinct segments: 

generation, transmission, and distribution. 

2.2.1 Generation 

Electricity generation is the first process in the delivery of electricity to consumers. Most 

of the existing capacity for generating electricity does so by creating heat to create high pressure 

steam that is released to rotate turbines which, in turn, create electricity. The existing power 

sector consists of over 18,000 generating units, comprising fossil-fuel-fired units, nuclear units, 

and hydroelectric and other renewable sources dispersed throughout the country (see Table 2-1). 

 
2-2 

Table 2-1. Existing Electricity Generating Capacity by 

Energy Source, 2012

Energy Source 

Number of 

Generators 

Generator 

Nameplate 

Capacity (MW) 

Net Summer 

Capacity 

(MW) 

Net Winter 

Capacity 

(MW) 

Coal 1,309 336,341 309,680 312,293 

Petroleum 3,702 53,789 47,167 51,239 

Natural Gas 5,726 485,957 422,364 455,214 

Other Gases 94 2,253 1,946 1,933 

Nuclear 104 107,938 101,885 104,182 

Hydroelectric Conventional 4,023 78,241 78,738 78,215 

Wind 947 59,629 59,075 59,082 

Solar Thermal and Photovoltaic 553 3,215 3,170 3,053 

Wood and Wood-Derived Fuels 351 8,520 7,508 7,570 

Geothermal 197 3,724 2,592 2,782 

Other Biomass 1,766 5,527 4,811 4,885 

Hydroelectric Pumped Storage 156 20,858 22,368 22,271 

Other Energy Sources 95 2,005 1,729 1,739 

Total 19,023 1,167,995 1,063,033 1,104,459 

Source: Table 4.3, EIA Electric Power Annual, 2013a 

Note: This table presents generation capacity. Actual net generation is presented in Table 2-3. 

In 2012, electric generating sources produced net 3,890 billion kWh to meet electricity 

demand. Roughly 70 percent of this electricity was produced through the combustion of fossil 

fuels, primarily coal and natural gas, with coal accounting for the largest single share. 

 
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 electricity for commercial, industrial, and 

residential uses, each of which consumes roughly a quarter to a third of the total electricity 

produced (see Table 2-3). Some of these uses are highly variable, such as heating and air 

conditioning in residential and commercial buildings, while others are relatively constant, such 

as industrial processes that operate 24 hours a day. 

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

   

Sales/Direct 

Use (Billion 

kWh) 

Share of 

Total End 

Use 

Retail Sales 

Residential 1,375 35.87% 

Commercial 1,327 34.63% 

Industrial 986 25.72% 

Transportation 7.3 0.19% 

Direct Use 138 3.59% 

Total End Use 3,832 100% 

 Source: Table 2.2, EIA Electric Power Annual, 2013a 

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 supplied “base-load” electricity, the portion of 

electricity loads which are continually present, and typically operate throughout the day. Along 

with nuclear generation, these coal units meet the part of demand that is relatively constant. 

Although much of the coal fleet operates as base load, there can be notable differences across 

various facilities (see Table 2-4). For example, coal-fired units less than 100 megawatts (MW) in 

size compose 32 percent of the total number of coal-fired units, but only 4 percent of total 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.5.13 
2-5 

Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a 

generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel 

efficiency. Table is limited to coal-steam units online in 2013 or earlier, and excludes those units with planned 

retirements. 

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

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

Source: National Electric Energy Data System (NEEDS) v.5.13 

Note: This map displays fossil capacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS reflects available 

fossil capacity on-line by the end of 2015. This includes planned new builds and planned retirements. In areas 

with a dense concentration of facilities, some facilities may be obscured. 

2.2.2 Transmission 

Transmission is the term used to describe the movement of electricity over a network of 

high voltage lines, from electric generators to substations where power is stepped down for local 
2-6 

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 transmission and distribution system is the classic 

example of a natural monopoly, in part because it is not practical to have more than one set of 

lines running from the electricity generating sources to substations or from substations to 

residences and businesses. 

Transmission has generally been developed by the larger vertically integrated utilities that 

typically operate generation and distribution networks. Often distribution is handled by a large 

number of utilities that purchase and sell electricity, but do not generate it. Over the last couple 

of decades, several jurisdictions in the United States began restructuring the power industry to 

separate transmission and distribution from generation, ownership, and operation. As discussed 

below, electricity restructuring has focused primarily on efforts to reorganize the industry to 

encourage competition in the generation segment of the industry, including ensuring open access 

of generation to the transmission and distribution services needed to deliver power to consumers. 

In many states, such efforts have also included separating generation assets from transmission 

and distribution assets to form distinct economic entities. Transmission and distribution remain 

price-regulated throughout the country based on the cost of service. 

 

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, and energy, which were all thought to be natural 

monopolies (prior to 1970) that warranted governmental control of pricing. However, 

deregulation efforts in the power sector were most active during the 1990s. Some of the primary 

drivers for deregulation of electric power included the desire for more efficient investment 

choices, the economic incentive to provide least-cost electric rates through market competition, 

reduced costs of combustion turbine technology that opened the door for more companies to sell 

power with smaller investments, and complexity of monitoring utilities’ cost of service and 

establishing cost-based rates for various customer classes. 

The pace of restructuring in the electric power industry slowed significantly in response to 

market volatility in California and financial turmoil associated with bankruptcy filings of key 

energy companies. By the end of 2001, restructuring had either been delayed or suspended in 

eight states that previously enacted legislation or issued regulatory orders for its implementation 

(shown as “Suspended” in Figure 2-2 below). Eighteen other states that had seriously explored 

the possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA, 

2003) (“Not Active” in Figure 2-2 below). Currently, there are 15 states where price deregulation 

of generation (restructuring) has occurred (“Active” in Figure 2-2 below). Power sector 

restructuring is more or less at a standstill; there have been no recent proposals to the Federal 

Energy Regulatory Commission (FERC) for actions aimed at wider restructuring, and no 

additional states have recently begun retail deregulation activity. 
2-8 

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

Source: EIA “Status of Electricity Restructuring by State” 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 CO2 in 

particular, but also contributes to emissions of sulfur hexafluoride (SF6), CH4, and N2O. In 

20121, the power sector accounted for 323 percent of total nationwide greenhouse gas emissions, 

measured in CO2 equivalent, a slight increase from its 30 percent share in 1990. Table 2-5 and 

Figure 2-3 show the contributions of the power sector relative to other major economic sectors. 

Table 2-6 and Figure 2-4 show the contributions of CO2 and other GHGs from the power sector. 

Table 2-25. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric 

tons of CO2 equivalent) 

Sector/Source 1990 20051995 20002008 20059 20101 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 1,93527 2,0121,862 1,87629 1,852 1,837

Industry 1,5319 1,5581,408 1,372504 1,221416 1,30132 1,298 1,278

Agriculture 458518 511584 501615 517605 547601 613 614

Field Code Changed
2-9 

Commercial 3858 37091 3796 38274 3778 378 353

Residential 345 37167 36586 35871 36057 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 Sinks: 1990–20121. April 20143.

Figure 2-6. Domestic Emissions of Greenhouse Gases, 20121 (million metric tonnes of CO2 

equivalent) 

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–20121. April 20143. 

 Table 2-36. Greenhouse Gas Emissions from the Electricity Sector (Generation, 

Transmission and Distribution), 20121 (million metric tons of CO2 equivalent)

Source Total Emissions 

CO2 from Electricity Generation 2,023 

Coal 1,511 

Natural Gas 492 

Fuel Oil 19 

Geothermal 0.4 

CH4 from Electricity Generation 0.5 

Coal 0.1 

Fuel Oil 0.1 

Other

4,501

67%

Electricity

Generation

2,201

Other 33%

4,459

68%

Electricity

Generation

2,064

32%

Formatted: Complex Script Font: 12 pt

Formatted: Font: Times New Roman, 10 pt
2-10 

Natural Gas 0.4 

Wood + 

N2O from Electricity Generation 18.3 

Coal 9.1 

Fuel Oil 0.3 

Natural Gas 8.7 

Wood 0.1 

SF6 from Electricity Transmission and Distribution 6.0 

Total 2,048 

Source 

Total 

Emissions 

CO2 2,175.5 

CO2 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 

CH4 0.4 

Stationary Combustion* 0.4 

Incineration of Waste + 

N2O 18.3 

Stationary Combustion* 17.9 

Incineration of Waste 0.4 

SF6** 7.0 

Electrical Transmission and Distribution 7.0 

Total 2,201.2 

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–20121. April 20143. 

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

** SF6 is not covered by this rule, which specifically regulates GHG emissions from combustion. 

+ Does not exceed 0.05 Tg CO2 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 CO2 emitted during the combustion of fossil fuels varies according to the 

carbon content and heating value of the fuel used (EIA, 2000) (see Table 2-7). Coal has higher 

carbon content than oil or natural gas and, thus, releases more CO2 during combustion. Coal 

emits around 1.7 times as much carbon per unit of energy when burned as does natural gas (EPA 

2013).

Figure 2-3. Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG 

Emissions (20121) 

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–20121. April 20143. 

 

 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%

0% 4% 0.1%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO2 SF6 N2O CH4

Other

Power Sector
2-12 

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: CO2 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 anticipates that states will pursue a mix of carbonreducing strategies appropriate to each state’s unique situation, developing an effective state plan 

that reflects the composition of the state’s economy, existing state programs and measures, and 

characteristics of the state’s existing electricity power system (e.g., utility regulatory structure, 

generation mix, transmission system and electricity demand). The analysis assumes states will 

develop plans involving four categories (“building blocks”) of demonstrated approaches to 

improve the GHG performance of existing EGUs in the power sector: 

1. 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 carbon-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-specific emission goals, are presented in Chapter 

3. 

The first building block encompasses various improvements at existing EGUs that can 

lower the amount of CO2 they emit when generating power. Heat rate improvements result in any 

changes in equipment, operating procedures or maintenance practices that increase the efficiency 

of converting fuel energy into electricity by an EGU. Such efficiency changes result in more 

electricity being generated by each unit of fuel (e.g., ton of coal or cubic foot of gas), thereby 

lowering the amount of CO2 per kWh of electricity produced as a byproduct of fuel combustion. 

The second building block consists of improvements to lower the electric system’s overall 

carbon intensity by shifting generation among existing EGUs. The nation’s EGUs are connected 

by transmission grids extending over large regions. Through these interconnections, EGU 

owners and/or grid operators prioritize among EGUs when deciding which ones to operate (i.e., 

“dispatch”) to meet electricity demand at any time, subject to various constraints. Opportunities 

exist to lower significantly the electric system’s carbon intensity through redispatch among 

existing EGUs, particularly by shifting generation from coal units to natural gas combined cycle 

(NGCC) units.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 CO2 requirements. Additional redispatch opportunities exist 

to further reduce carbon intensity of the power system, with extent of the additional opportunities 

 

2

We view opportunities to shift generation to existing renewable EGUs such as wind or solar units as limited 

because such units already tend to run when capable of doing so due to their low variable operating costs. 
2-14 

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 existing EGUs with higher carbon intensity. Such 

expansion is consistent with current trends. While not included in the goal setting for building 

block 3, the addition of new NGCC capacity would have a similar impact and is one option states 

may choose to achieve the goal. 

The fourth building block consists of improving the GHG performance of the power sector 

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 that occur across the grid between the 

electricity generation sites and the demand sites. Particularly when integrated into a 

comprehensive approach for addressing GHG emissions, demand-side efficiency improvements 

can improve the carbon profile of the electricity supply system. 

 

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 from 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 CO2 emissions. This program, known as the Regional Greenhouse Gas 

Initiative (RGGI), began in 2009 and sets a regional CO2 cap for participating states. The 

currently participating states include: Connecticut, Delaware, Maine, Maryland, Massachusetts, 

New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO2 emissions from 

all fossil-fired EGUs greater than 25 MW in participating states, and limits total emissions to 91 

million short tons in 2014. This emissions budget is reduced 2.5% annually from 2015 to 2020. 

In September 2006, California Governor Schwarzenegger signed into law Senate Bill 

1368. The law limits long-term investments in baseload generation by the state's utilities to 

power plants that meet an emissions performance standard jointly established by the California 

Energy Commission and the California Public Utilities Commission. The Energy Commission 

has designed regulations that establish a standard for new and existing baseload generation 

owned by, or under long-term contract to publicly owned utilities, of 1,100 lb CO2/MWh. 

In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global 

Warming Solutions Act of 2006. This act includes a multi-sector GHG cap-and-trade program 

which covers approximately 85% of the state GHG emissions. EGUs are 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 goals, and imposed an emission standard that 

applies to any baseload electric generation that commenced operation after June 1, 2008 and is 
2-16 

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 

CO2/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 CO2/MWh, and prohibited utilities from entering into long-term purchase 

agreements for baseload electricity with out-of-state facilities that do not meet that standard. 

Natural gas- and petroleum distillate-fired facilities that are primarily used to serve peak demand 

or to integrate energy from renewable resources are specifically exempted from the performance 

standard. 

In August 2011, New York Governor Cuomo signed the Power NY Act of 2011. This 

regulation establishes CO2 emission standards for new and modified electric generators greater 

than 25 MW. The standards vary based on the type of facility: baseload facilities must meet a 

CO2 standard of 925 lb/MWh or 120 lb/MMBtu, and peaking facilities must meet a CO2 standard 

of 1,450 lbs/MWh or 160 lbs/MMBtu. 

Additionally, the majority of 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 generation. Currently 30 states and the District of 

Columbia have enforceable RPS or other mandatory 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, would need to be regulated as 

well. 

In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public Law 110–

161), the EPA issued the Mandatory Reporting of Greenhouse Gases Rule (74 FR 5620) which 

required reporting of GHG data and other relevant information from fossil fuel suppliers and 

industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy-duty and 

off-road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG 

data to inform future policy decisions. As such, it did not require that sources control greenhouse 

gases, but sources above certain threshold levels must monitor and report emissions. 

In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to 

Deseret Power Electric Cooperative, authorizing it to construct a new waste-coal-fired EGU near 

its existing Bonanza Power Plant, in Bonanza, Utah. The permit did not include emissions 

control requirements for CO2. The EPA acknowledged the Supreme Court decision, but found 

that decision alone did not require PSD permits to include limits on CO2 emissions. Sierra Club 

challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) 

remanded the permit to the EPA to reconsider “whether or not to impose a CO2 BACT (best 

available control technology) limit in light of the ‘subject to regulation’ definition under the 

CAA.” The remand was based in part on EAB’s finding that there was not an established EPA 

interpretation of the regulatory phrase “subject to regulation.” 

In December 2008, the Administrator issued a memo indicating that the PSD Permitting 

Program would apply to pollutants that are subject to either a provision in the CAA or a 

regulation adopted by the EPA under the CAA that requires actual control of emissions of that 

pollutant. The memo further explained that pollutants for which the EPA regulations only require 

monitoring or reporting, such as the provisions for CO2 in the Acid Rain Program, are not subject 

to PSD permitting. Fifteen organizations petitioned the EPA for reconsideration, prompting the 

agency to issue a revised finding in March 2009. After reviewing comments, the EPA affirmed 

the position that PSD permitting is not triggered for a pollutant such as GHGs until a final 

nationwide rule requires actual control of emissions of the pollutant. For GHGs, this meant 

January 2011 when the first national rule limiting GHG emissions for cars and light trucks was 
2-18 

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 — CO2, CH4, N2O, 

hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF6 — 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 homogeneously distributed through the lower level of the 

Earth’s atmosphere (IPCC, 2001). This differentiates them from other greenhouse gases that are 

not homogeneously distributed in the atmosphere. The cause and contribute finding indicated 

that the combined emissions of these well-mixed greenhouse gases from new motor vehicles and 

new motor vehicle engines contribute to the greenhouse gas pollution which threatens public 

health and welfare. Both findings were published in the Federal Register on December 15, 2009 

(Docket ID EPA-HQ-OAR-2009-0171). These findings did not themselves impose any 

requirements on any industry or other entities, but allowed the EPA to regulate greenhouse gases 

under the CAA (see preamble section II.E for regulatory background). This action was a 

prerequisite to implementing the EPA's proposed greenhouse gas emission standards for lightduty vehicles, which was finalized in January 2010. Once a pollutant is regulated under the 

CAA, it is subject to permitting requirements under the PSD and Title V programs. The 2009 

Endangerment Finding and a denial of reconsideration were challenged in a lawsuit; on June 26, 

2012, the DC Circuit Court upheld the Endangerment Finding and the Reconsideration Denial, 

ruling that the Finding was neither arbitrary nor capricious, was consistent with Massachusetts v. 

EPA, and was adequately supported by the administrative record. The Court found that the EPA 

had based its decision on “substantial scientific evidence,” noted that the EPA’s reliance on 

assessments was consistent with the methods decision-makers often use to make a science-based 

judgment, and stated that “EPA’s interpretation of the governing CAA provisions is 

unambiguously correct.” 

In May 2010, the EPA issued the final Tailoring Rule which set thresholds for GHG 

emissions that define when permits under the New Source Review and Title V Operating Permit 

programs are required for new and existing industrial facilities. Facilities responsible for nearly 
2-19 

70 percent of the national GHG emissions from stationary sources, including EGUs, were subject 

to permitting requirements under the rule. This rule was upheld by the D.C. Circuit in 2012. 

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 consideration 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 are 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-fired electric utility generating units. This action 

proposes to establish separate standards for fossil fuel-fired electric steam generating units 

(utility boilers and Integrated Gasification Combined Cycle (IGCC) units) and for natural gasfired stationary combustion turbines. These proposed standards reflect separate determinations of 

the best system of emission reduction (BSER) adequately demonstrated for utility boilers and 

IGCC units and for natural gas-fired stationary combustion turbines. This action proposes a 

standard of performance for utility boilers and IGCC units based on partial implementation of 

carbon capture and storage (CCS) as the BSER. The proposed emission limit for those sources is 

1,100 lb CO2/MWh. This action also proposes standards of performance for natural gas-fired 

stationary combustion turbines based on modern, efficient natural gas combined cycle (NGCC) 

technology as the BSER. The proposed emission limits for those sources are 1,000 lb CO2/MWh 

for larger units and 1,100 lb CO2/MWh for smaller units. 

 

5

 http://www.epa.gov/airquality/ghgsettlement.html 
2-20 

2.7 Revenues, Expenses, and Prices 

Due to lower retail electricity sales, total utility operating revenues declined in 2012 to 

$27181 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 consumption (utility revenues), but have also 

reduced the price and cost of fossil fuels and other expenses. Electricity sales and revenues 

associated with the generation, transmission, and distribution of electricity are expected to 

rebound and increase modestly by 2015, when revenues are projected to be roughly $359 billion 

(see Table 2-9). 

Table 2-8 shows that investor-owned utilities (IOUs) earned income of about 1311.9

percent compared to total revenues in 20121. 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 20121, 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 change rapidly and do not display the variability of 

other energy or commodity prices, although the frequency at which these prices change varies 

across different types of customers. Retail rate regulation has largely insulated consumers from 

the rising and falling wholesale electricity price signals whose variation in the marketplace on an 

hourly, daily, and seasonal basis is critical for driving lowest-cost matching of supply and 

demand. In fact, the real price of electricity today is lower than it was in the early 1960s and 

1980s (see Figure 2-5). 
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 2010

cents/KWh (2011$)
2-22 

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

Utilities for 2011 ($millions)

20109 20110 20121

Utility Operating Revenues 285,512276,124 280,520285,512 280,520270,912

Electric Utility 260,119249,303 255,573260,119 255,573249,166

Other Utility 25,39326,822 24,94625,393 24,94621,745

Utility Operating Expenses 253,022244,243 247,118253,022 247,118235,694

Electric Utility 234,173219,544 228,873234,173 228,873220,722

Operation 166,922154,925 161,460166,922 161,460152,379

Production 128,831118,816 122,520128,831 122,520111,714

Cost of Fuel 44,13840,242 42,77944,138 42,77938,998

Purchased Power 67,28467,630 61,44767,284 61,44754,570

Other 17,40910,970 18,29417,409 18,29418,146

Transmission 6,9486,742 6,8766,948 6,8767,183

Distribution 4,0073,947 4,0444,007 4,0444,181

Customer Accounts 5,0915,203 5,1805,091 5,1805,086

Customer Service 4,7413,857 5,3114,741 5,3115,640

Sales 185178 185185 185221

Admin. and 

General 17,12015,991 17,34317,120 17,34318,353

Maintenance 14,95714,092 15,77214,957 15,77215,489

Depreciation 20,95120,095 22,55520,951 22,55523,677

Taxes and Other 31,34329,081 29,08631,343 29,08629,177

Other Utility 18,84924,698 18,24518,849 18,24514,972

Net Utility Operating Income 32,49031,881 33,40232,490 33,40235,218

Source: Table 8.3, EIA Electric Power Annual, 2013a1

Note: This data does not include information for public utilities. 
2-23 

Table 2-9. Projected Revenues by Service Category in 2015 for Public Power and InvestorOwned Utilities (billions) 

Generation $207 

Transmission $40 

Distribution $111 

Total $359 

Source: EIA 2013b

Note: Data are derived by taking either total electricity use (for generation) or sales (transmission and distribution) 

and multiplying by forecasted prices by service category from Table 8 of EIA AEO 2013 (Electricity Supply, 

Disposition, Prices, and Emissions). 

On a state-by-state basis, retail electricity prices vary considerably. The Northeast and 

California have average retail prices that can be as much as double those of other states (see 

Figure 2-6). 

 
2-24 

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

Source: EIA Annual Energy Outlook, 2012a

2.8 Natural Gas Market 

The natural gas market in the United States has historically experienced significant price 

volatility from year to year, between seasons within a year, and can undergo major price swings 

during short-lived weather events (such as cold snaps leading to short-run spikes in heating 

demand). Over the last decade, gas prices (both Henry Hub prices and delivered prices to the 

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 considerable revision as new 

sources of gas have been discovered and have come to market, although there continues to be 

some uncertainty surrounding the precise quantity of the resource base. 

Current and projected natural gas prices are considerably lower than the prices observed 

over the past decade, largely due to advances in hydraulic fracturing and horizontal drilling 

techniques that have opened up new shale gas resources and substantially increased the supply of 

economically recoverable natural gas. According to an EIA “Energy in Brief” AEO 2012 (EIA 

2012b): 

Shale gas refers to natural gas that is trapped within shale formations. Shales are finegrained sedimentary rocks that can be rich sources of petroleum and natural gas. Over the past 

decade, the combination of horizontal drilling and hydraulic fracturing has allowed access to 

large volumes of shale gas that were previously uneconomical to produce. The production of 

natural gas from shale formations has rejuvenated the natural gas industry in the United States. 

The U.S. Energy Information Administration's Annual Energy Outlook 2012 (Early 

Release) estimates that the United States possessed 2,214 trillion cubic feet (Tcf) of technically 

recoverable natural gas resources as of January 1, 2010. Natural gas from proven and unproven 

shale resources accounts for 542 Tcf of this resource estimate. Many shale formations, especially 

the Marcellus, are so large that only small portions of the entire formations have been intensively 

production-tested. Consequently, the estimate of technically recoverable resources is highly 

uncertain, and is regularly updated as more information is gained through drilling and 

production. At the 2010 rate of U.S. consumption (about 24.1 Tcf per year), 2,214 Tcf of natural 

gas is enough to supply over 90 years of use. Although the estimate of the shale gas resource 

base is lower than in the prior edition of the Outlook, shale gas production estimates increased 

between the 2011 and 2012 Outlooks, driven by lower drilling costs and continued drilling in 

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 20143

from the AEO 20132, and EIA is still forecasting abundant reserves consistent with the above 

findings. Recent historical data reported to EIA is also consistent with these trends, with 20132

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 20132, 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, technological 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 2040

2011 dollars per million Btu

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 2012a, EIA 2013b, EIA 2014

2.9 Electricity Demand and Demand Response 

Electricity performs a vital and high-value function in the economy. Historically, growth in 

electricity consumption has been closely aligned with economic growth. Overall, the U.S. 

economy has become more efficient over time, producing more output (gross domestic product – 

GDP) per unit of energy input, with per capita energy use fairly constant over the past 30 years 

(EIA, 2010d). The growth rate of electricity demanded has also been in overall decline for the 

past sixty years (see Figure 2-87), with several key drivers that are worth noting. First, there has 

been a significant structural shift in the U.S. economy towards less energy-intensive sectors, like 

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

2011 dollars per million Btu

Natural Gas Spot Price, Annual Average (Henry

Hub)

EIA Historical

EIA Projected (AEO 2012)

EIA Projected (AEO 2013)

EIA Projected (AEO 2014)
2-29 

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 reduce 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 altered the outlook for future electricity growth.

 

10 EIA 2013b

‐2%

0%

2%

4%

6%

8%

10%

12%

14%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040
2-30 

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

Annual Energy Outlook 20143

Source: EIA 2009, EIA 20143

Energy efficiency initiatives have become more common, and investments in energy 

efficiency are projected to continue to increase for 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 the 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/downloads/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/ipcc_tar/?src=/climate/ipcc_tar/wg1/218.htm. 

International Energy Agency (IEA).2013. “Tracking Clean Energy Progress 2013.” Input to the 

Clean Energy Ministerial. 2013. Available online at: http://www.iea.org/etp/tracking/. 

National Energy Technology Laboratory (NETL). 2008. “Reducing CO2 Emissions by 

Improving the Efficiency of Existing Coal-fired 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 CO2 Emissions with Next Generation CO2-Enhanced Oil Recovery (CO2-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 Storage 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/environment/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/page/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/archive/aeo10/index.html>. 

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

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

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

<http://www.eia.gov/forecasts/archive/aeo11/>. 

U.S. EIA. 2012a. “Annual Energy Outlook 2012 (Early Release).” Available online at: 

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

U.S. EIA. 2012b. “Energy in Brief: What is Shale Gas and Why is it Important?” Available 

online at: <http://www.eia.gov/energy_in_brief/about_shale_gas.cfm>.

U.S. EIA. 2012ac. “Today in Energy: Most states have Renewable Portfolio Standards.” 

Available online at: <http://www.eia.gov/todayinenergy/detail.cfm?id=4850>.

U.S. EIA. 2013a. “Electric Power Annual 20112.”Available online at: 

<http://www.eia.gov/electricity/annual/>. 

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

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

U.S. EIA.2014. “Annual Energy Outlook 2014.” Available online at: 

<http://www.eia.gov/forecasts/aeo/>. 
2-33 

U.S. Environmental Protection Agency. 20143. “Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990–20121,” April. Available online at: 

<http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.htmlhttp://www.epa.gov

/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2013-Main-Text.pdf>. 

U.S. Geological Survey Geologic (USGS). 2013. “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 Cite, Quote or Release During Review*** 

3-A-11-1

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 CO2, SO2, NOX, Hg, HCl, and other air 

pollutants throughout the United States for the entire power system. The IPM analysis is 

complemented by an analysis of the cost and scope of reductions in electricity demand that can 

be achieved through energy efficiency programs. 

3.1 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 use in developing plans to attain the state-specific 

goals. This rule, as proposed, would set in motion actions to lower the carbon intensity of power 

generation in the United States. 

Over the last decade, EPA has conducted extensive analyses of regulatory actions 

affecting the power sector. These efforts support the Agency’s understanding of key variables 

that influence the effects of a policy and provide the framework for how the Agency estimates 

the costs and benefits associated with its actions. 

The estimated annual costs of the proposed action are between $5.4 and $7.4 billion in 

2020 and between $7.3 and $8.8 billion in 2030 for the primary Option (Option 1). The 

alternative option (Option 2) has annual estimated costs of between $4.2 and $5.4 billion in 2020 

and between $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. 

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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]

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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 Cite, Quote or Release During Review*** 

3-A-21-1

3.2 3.3 Power Sector Modelling Framework 

IPM, developed by ICF Consulting, is a state-of-the-art, peer-reviewed, dynamic linear 

programming model that can be used to project power sector behavior under future business-asusual conditions and examine prospective air pollution control policies throughout the United 

States for the entire electric power system. EPA used IPM, inclusive of the electricity demand 

reductions achieved through the energy efficiency scenario included in the proposed rule, to 

project likely future electricity market conditions with and without the proposed rule. The level 

of energy efficiency-driven reductions in electricity demand and the associated costs are reported 

in Section 3.6. 

The Integrated Planning Model (IPM) is a multi-regional, dynamic, deterministic linear 

programming model of the U.S. electric power sector. It provides forecasts of least cost capacity 

expansion, electricity dispatch, and emission control strategies while meeting energy demand 

and environmental, transmission, dispatch, and reliability constraints. EPA has used IPM for 

over two decades to better understand power sector behavior under future business-as-usual 

conditions and evaluate the economic and emission impacts of prospective environmental 

policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses 

the best available information from utilities, industry experts, gas and coal market experts, 

financial institutions, and government statistics as the basis for the detailed power sector 

modeling in IPM. The model documentation provides additional information on the assumptions 

discussed here as well as all other model assumptions and inputs.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 emissions from biomass were not included in EPA Base Case 

v.5.13. EPA is currently developing methods to estimate 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. 

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

3-A-31-1

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 model includes an endogenous representation of the 

North American natural gas supply system through a natural gas module that reflects a partial 

supply/demand equilibrium of the North American gas market accounting for varying levels of 

potential power sector gas demand and corresponding gas production and price levels.4 This 

module consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas regasification facility locations that are tied together by a series of linkages (i.e., pipelines) that 

represent the North American natural gas transmission and distribution network. 

IPM also endogenously models the partial equilibrium of coal supply and EGU coal 

demand levels throughout the continental U.S., taking into account assumed non-power sector 

demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal 

transport network, which consists of over four thousand linkages representing rail, barge, and 

truck and conveyer linkages. The coal supply curves in IPM, which are publicly available, were 

developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices 

and associated supply costs that power plants would face if selecting that coal over the modeling 

time horizon. The IPM documentation outlines the methods and data used to quantify the 

economically recoverable coal reserves, characterize their cost, and build the 36 coal regions 

curves. The coal supply curves were developed in consultation with Wood Mackenzie, one of the 

leading energy consulting firms and specialists in coal supply. These curves have been 

independently reviewed by industry experts and have been made available for public review 

prior to this rulemaking process.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 using 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 Cite, Quote or Release During Review*** 

3-A-41-1

EPA has used IPM extensively over the past two decades to analyze options for reducing 

power sector emissions. Previously, the model has been used to forecast the costs, emission 

changes, and power sector impacts for the Clean Air Interstate Rule, Cross-State Air Pollution 

Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon 

Pollution Standards for New Power Plants. 

The model and EPA's assumptions input into the model undergo periodic formal peer 

review. The rulemaking process also provides opportunity for expert review and comment by a 

variety of stakeholders, including owners and operators of capacity in the electricity sector that is 

represented by the model, public interest groups, and other developers of U.S. electricity sector 

models. The feedback that the Agency receives provides a highly detailed review of key input 

assumptions, model representation, and modeling results. IPM has received extensive review by 

energy and environmental modeling experts in a variety of contexts. For example, in the late 

1990’s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 

prospective studies that are periodically conducted. The model has also undergone considerable 

interagency scrutiny when it has been used to conduct over a dozen legislative analyses 

(performed at Congressional request) over the past decade. The Agency has also used the model 

in a number of comparative modeling exercises sponsored by Stanford University’s Energy 

Modeling Forum over the past 15 years. 

IPM has also been employed by states (e.g., for RGGI, the Western Regional Air 

Partnership, Ozone Transport Assessment Group), other Federal and state agencies, 

environmental groups, and industry, all of whom subject the model to their own review 

procedures. 

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 architecture. This is the fifth major iteration of EPA’s 

base case using IPM, and calibrates certain information and data from the Energy Information 

Agency's (EIA) Annual Energy Outlook (AEO), in this case AEO 2013 (hence the platform 

name of v5.13). 

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3-A-51-1

The current base case represents a projection of electricity sector activity that takes into 

account only those Federal and state laws and regulations whose provisions were either in effect 

or enacted and clearly delineated at the time the base case was finalized in August 2013. The 

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 construct the "model" plants that represent existing and 

committed7

 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 plant cost and performance, pollution control costs 

and performance, emission rate assignments, and state rules and enforcement actions. The update 

also included further refinement to the modeled regions to reflect more recent power market 

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 μg/m3 or less and annual average of 15 μg/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 Circuit in December 2008 and EPA was required to correct 

legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. CAIR remains in effect 

until replaced by EPA pursuant to the Court’s ruling. CAIR’s provisions were still in effect when EPA Base 

Case v.5.13 was released.

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/powersectormodeling/BaseCasev513.html>. 

Comment [A5]: Need to fix font in footnote 

EPA: Thank you. The font is now corrected. 
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3.3 3.4 State Goals in this Proposal 

In this action, the EPA is proposing state-specific rate-based goals to guide states in the 

development of their plans. The agency is proposing one option (Option 1) for state-specific 

goals and requesting comment on a second set of state-specific goals and compliance period 

(Option 2). 

Table 3-15. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO2 Per 

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

State9

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 District of Columbia because current information indicates 

those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. 

territories at this time. 

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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 1Table 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 capacity, fossil units included in Indian Country, the 

treatment of cogeneration in historical data, and the assumed applicability for some units. The 

resulting changes to the state goals are minor, and any differences in results obtained by 

analyzing these new goals would be negligible. Furthermore, these small changes in the goals 

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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 individual state (these scenarios are referred to as 

“state”), and another set of scenarios where groups of states are assumed to collaborate and 

achieve compliance across larger regions (referred to as “regional”). Estimates of the benefits, 

costs, and economic impacts of this proposed action are presented for both state and regional 

compliance.10 These illustrative compliance scenarios are designed to reflect, to the extent 

possible, the scope and nature of the proposed guidelines. However, there is considerable 

uncertainty with regards to the precise measures that states will adopt to meet the proposed 

requirements, since there are considerable flexibilities afforded to the states in developing their 

state plans. Nonetheless, the analysis of the benefits, costs, and relevant impacts of the proposed 

rule attempts to encapsulate some of those flexibilities in order to inform states and stakeholders 

of the potential overall impacts of the proposal. The relevant impacts, costs, and benefits are 

provided for 2020, 2025, and 2030 for Options 1 and 2 (with both state and regional 

compliance). 

It is also important to note that the analysis does not specify any particular CO2 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. 

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affected by the illustrative state compliance scenarios 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 sources14 ) must be less than or equal to the applicable state goal, on 

average, over the entire compliance period, but not in any particular year. 

The illustrative compliance scenarios further assume that the states adopt intertemporal 

averaging in the initial compliance period for both Option 1 and Option 2. That is, the average 

emissions rate at covered sources in each state must be less than or equal to the applicable state 

goal over the compliance period. The initial compliance period for Option 1 is 2020 to 2029 and 

for Option 2 it is 2020 to 20245. After the initial compliance period the average emission rate of 

 

11 As discussed in the preamble, for compliance purposes states may be able to include sources of generation in the 

calculation of the state’s emission rate other than those sources of generation considered in constructing the 

building blocks for setting the state goals. However, this illustrative analysis does not include those options. 

12 For the illustrative scenarios renewable generation includes generation from wind, solar, geothermal, and biomass 

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

13 See Technical Support Document – GHG Abatement Measures

14 Ttotal emissions from covered sources divided by the sum of total generation from covered sources plus demandside energy savings
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the affected sources in each year must be less than or equal to the state goal in the illustrative 

compliance scenarios. 

For EE, the megawatt-hour (MWh) savings and associated costs are specified 

exogenously for Option 1 and Option 2 compliance scenarios consistent with the EE "best 

practice" performance levels informing calculation of state goals under each option. EPA has 

determined that these performance levels are achievable at costs that are generally less than 

avoided power system costs. 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 the 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 generation 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 choose to cooperate in order to achieve more costeffective outcomes, since some states 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 Transmission Organizations (RTOs). These regions 

do not always follow state borders, however, so certain states that fall into more than one region 

were grouped in regions where there was a majority of geographic territory (area) or generation. 

While Florida and Texas each have unique NERC regions unto themselves, for purposes of this 

compliance analysis, those states were each grouped with other neighboring states. 

 

15 The generating units assumed to be affected by the regulation in the illustrative strategies are described in the 

preamble. 
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 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. 

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 Figure 3-25. NERC Assessment 

Areas 

 Figure 3-36. Regional Compliance Zones Used in this Analysis 

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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 region17 must be less than or equal to the weighted average18 of the state goals in that 

region, over the compliance period. . Furthermore, as in the “State” scenarios, the regions are 

assumed to adopt intertemporal averaging in the initial compliance period for each Option. 

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

policy mechanism to achieve the state goals. While IPM 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.

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 Savings19

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 energy efficiency savings projections, refer to the Greenhouse 

Gas Abatement Measures TSD.

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in what leading states have already accomplished or have requirements in place to accomplish.20

For Option 1, adjustments were made to each state’s annual incremental reduction in electricity 

consumption by ramping up from an historical basis21 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 incremental electricity demand reduction, 

which we refer to as the “savings rate.” The pace of improvement from the state’s historical 

value is assumed to be 0.2% per year, beginning in 2017 until the target rate of reduction from 

baseline electricity demand is achieved. States already at or above the 1.5% annual incremental 

savings rate are assumed to have a 1.5% rate beginning in 2017 and sustain that rate. For Option 

2, the annual incremental savings rate is ramped up from an historical basis to a target rate of 

1.0% and at a pace of improvement of 0.15% per year, beginning in 2017, until the target rate is 

achieved. States already at or above the 1.0% annual incremental savings rate are assumed to 

sustain a 1.0% rate beginning in 2017. Twenty leading states have either achieved, or have 

established requirements that will lead them to achieve, this rate of savings. The incremental 

savings rate for each state, for each year, is then used to derive cumulative annual energy savings 

based upon information/assumptions about the average life of EE measures and the distribution 

of measure lives within a state’s full portfolio of EE programs. The cumulative annual energy 

savings derived for Option 1 and Option 2 using this methodology are used consistently to set 

goals and to conduct power sector compliance modeling for each option.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 pathway for additional EE resulting from accelerated use of 

energy efficiency policies, in all states, consistent with a level of performance that has already been 

demonstrated or required by policies (e.g., energy efficiency resource standards) of leading energy efficiency 

implementing states, and consistent with a demonstrated annual pace of performance improvement from current 

levels. It does not represent an estimate of the full potential for end-use EE. 

21 The historical basis of the percentage of reduced electricity consumption differs for each state and is drawn from 

the data reported in Energy Information Administration (EIA) Form 861, 2012, available at 

http://www.eia.gov/electricity/data/eia861/. 

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. 
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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 borders, 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 compliance modeling for each option, the cumulative savings rate is applied 

to 2012 electricity consumption in the calculation of state goals whereas compliance modeling applies that rate 

to forecasted base case demand. Thus, for a given rate of demand reduction, the quantity of electricity demand 

reduced is greater in the compliance modeling than in setting the state goals, consistent with forecasted base case 

demand growth from 2012 to 2020 and beyond. 

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. 
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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 2TABLE 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% 
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Source: See Greenhouse Gas Abatement Measures TSD 

1.5.2 3.6.2 Demand-Side Energy Efficiency Total Costs25

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 reached 0.5% (a 20% additional cost escalation is 

applied to subsequent investments) and a 1.0% (a 40% additional cost escalation is applied to 

subsequent investments). 

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 multiplied by the cumulative annual savings 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 increasing amounts of demand-side energy efficiency over 

 

25 For a more detailed discussion of the demand-side energy 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 costs in this document assume a 3% discount rate. 

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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. 
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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 subsequent 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-side energy efficiency programs (to cover program costs) 

is typically collected through a standard per kWh surcharge to the rate-payer; the regional retail 

price impacts analyzed from this RIA’s compliance scenarios assumes 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 into 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 CO2 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 assumption of fixed demand. However, the modeling also does 

not capture the effect of higher costs of producing electricity, which is attributable to other methods of 

complying with the state goals, on electricity demand for the same reason (i.e., fixed demand in IPM). For 

further discussion of these issues see the Limitations section at the end of this chapter. 

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. 

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emissions31), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005 

emissions). For Option 2, EPA projects annual CO2 reductions between 13% and 14% in 2020 

(reaching 23% below 2005 emissions) and 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 target in the base case to offset reductions that 

other states would otherwise have made. 

 Table 3-37. Projected CO2 Emission Impacts 

 CO2 Emissions 

(MM Tonnes) 

CO2 Emissions 

Reductions from 

Reference Base Case 

(MM Tonnes) 

CO2 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 SO2, 22% to 24% reduction of NOX, 

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 SO2, 17% to 18% reduction in NOX, and an 11% to 14% reduction in mercury. The 

projected non-CO2 reductions presented in Table 3-4Table 3-4 below demonstrate similar trends 

in 2025 and 2030. 

 Table 3-48. Projected Non-CO2 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 CO2 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. 
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2020 

SO2

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

NOX

(thousand tons) 1,559 1,213 1,191 1,290 1,277 -22.2% -23.6% -17.2% -18.0% 

Hg 

(tons) 8.3 7.0 6.8 7.3 7.1 -15.3% -18.1% -11.3% -14.0% 

PM2.5

(thousand tons) 

2025 

SO2

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

NOX

(thousand tons) 1,587 1,166 1,151 1,279 1,271 -26.5% -27.5% -19.4% -19.9% 

Hg 

(tons) 8.7 7.0 6.7 7.4 7.1 -19.5% -23.2% -14.9% -19.3% 

PM2.5

(thousand tons) 

2030 

SO2

(thousand tons) 1,530 1,106 1,059 1,243 1,209 -27.7% -30.8% -18.8% -21.0% 

NOX

(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% 

PM2.5

(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 production is likely to be positive or negative and 

also assessed the potential magnitude of changes relative to CO2 emissions reductions anticipated 

at power plants. This assessment included CO2 emissions from the flaring of methane, but did 

not evaluate potential changes in other combustion-related CO2 emissions, such as emissions 

associated with drilling, mining, processing, and transportation in the natural gas and coal 

production sectors. This analysis found that the net upstream CH4 emissions from natural gas 

systems and coal mines and CO2 emissions from flaring of methane will likely decrease under 

the proposed guidelines. Furthermore, the changes in upstream methane emissions are small 
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relative to the changes in direct emissions from power plants. The technical details supporting 

this analysis can be found in the Appendix to this chapter. 

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 changes in equilibrium fuel prices on the expenditures of the 

electricity sector to serve demand. 

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

http://www.epa.gov/powersectormodeling/BaseCasev513.html 

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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. 
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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 expected in the base case to expend over $359 billion in 

2020 to generate, transmit, and distribute electricity to end-use consumers. Therefore, the 

projected costs of compliance with the proposed rule amount to a one to two percent increase in 

the cost to meet electricity demand, while securing public health and welfare benefits that are 

several times more valuable (as described in Chapters 4 and 8). 

The annual compliance costs presented in Table 3-5 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 EGU to generate the same amount of electricity 

using less fuel. The decrease in required fossil fuel results in a lower output-based CO2 emissions 

rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling 

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. 

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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 CO2-intensive generation to less CO2-intensive generation. 

Compared to the Base Case, existing coal steam capacity is projected to operate at a lower 

capacity factor for both Option 1 and Option 2, on average, although projected average capacity 

factors for existing coal steam boilers remains around 75% across scenarios. Existing natural gas 

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 compliance scenarios, reflecting the fact that states 

have the flexibility to choose among alternative CO2 reduction strategies that were part of BSER, 

instead of employing re-dispatch to the maximum extent. In addition, future electric demand is 

considerably lower than the Base Case. 

Table 3-711. 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]
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Option 2 State 75% 73% 76% 57% 56% 49% 

 Source: Integrated Planning Model run by EPA, 2014 

Demand-Side Energy Efficiency. Another approach 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 credited 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 emissions rate at covered sources 

(which exclude new NGCC) over the 2020-2030 compliance period provides an incentive to 

shift generation within the covered sources to less carbon-intensive generation. in 2020 for 

Option 1. In 2020, for Option 1, total generation declines approximately three percent under 

Option 1 as a result of the reduction in total demand attributable to the demand-side energy 

savings 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 result in an 11% reduction in total generation 

relative to the base case. The coal-fired fleet in 2030 generates between 25% and 27% less than 

in the base case, while natural-gas-fired generation from existing combined cycles increases 18% 

to 19% relative to the base case. Gas-fired generation from new combined cycle capacity 

increases in 2020, as projected new natural gas combined cycle capacity replaces retired coal 

capacity. By 2030, generation from newly built natural gas combined cycle decreases between 

36% and 40% relative to the base case, consistent with the decrease in new capacity (see 

 

35 Because fossil fuel-fired EGUs typically have higher variable costs than other EGUs (such as nuclear and 

renewable EGUs), they are typically the first to be displaced when demand is reduced.

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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 total 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.
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Table 3-8912. 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 

CO2 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. 
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0.000.501.001.502.002.503.003.504.004.505.00

Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State

OtherOilNon‐hydroRenewablesHydroelectricNatural GasNuclearCoal

2020 2025 2030

Option 1 Option 2 Option 1 Option 2 Option 1 Option 2
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0.000.501.001.502.002.503.003.504.004.505.00

Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State

OtherOilNon‐hydroRenewablesHydroelectricNatural GasNuclearCoal

2020 2025 2030

Option 1 Option 2 Option 1 Option 2 Option 1 Option 2
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0.000.501.001.502.002.503.003.504.004.505.00

Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State

OtherOilNon‐hydroRenewablesHydroelectricNatural GasNuclearCoal

2020 2025 2030

Option 1 Option 2 Option 1 Option 2 Option 1 Option 2
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Figure 3-47. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh)Source: Integrated Planning Model run by EPA, 2014 

0.000.501.001.502.002.503.003.504.004.505.00

Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State

OtherOilNon‐hydroRenewablesHydroelectricNatural GasNuclearCoal

2020 2025 2030

Option 1 Option 2 Option 1 Option 2 Option 1 Option 2
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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 transmission system. Within each model region, 

IPM assumes that adequate transmission capacity is available to deliver any resources located in, 

or transferred to, the region.36

EPA examined the implications of each of the scenarios for regional resource adequacy 

and for potential concerns over on grid reliability. To conduct this analysis, EPA examined key 

parameters from the IPM projections to assess whether concerns over regional resource adequacy 

would be likely to arise or whether changes in generation and flow pattern impacts would raise 

issues for reliability management. The key parameters analyzed were: 

Operating Reserve Margins. The IPM model ensures that target reserve margins from NERC 

will be met, by maintaining existing capacity or by building additional new capacity if needed. 

Capacity that is otherwise uneconomic to operate will be retired only when planned reserve 

margins can be met from within the region or by transfers from other regions. 

Operational Capacity. Since IPM ensures that NERC target margins will be met, EPA analyzed 

the remaining operating capacity for 2020 through 2030 to determine what types of changes in 

the generation fleet were projected to occur through retirements, additional generation and 

energy efficiency. Although there were changes from the base case to the policy cases, none of 

 

36 For more detail on IPM’s electric load modeling and power system operation, please see IPM documentation 

(http://www.epa.gov/powersector modeling). 

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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. 
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the policy cases were found to raise concerns over regional resource adequacy. Moreover, the 

time horizon for compliance with this rule will permit environmental and reliability planners to 

coordinate these changes and address potential concerns before they arise. 

Interregional Power Flows and Capacity Transfers. IPM constrains both interregional energy 

flows and interregional transfers of capacity to meet reserve requirements. These limits are based 

on grid operator data from ISOs, RTOs and other planning entities. Although these limits keep 

projected flows within expected bounds, EPA further examined how the policy options impacted 

these flows and transfers to identify any large shifts from the base case. None of the interregional 

changes in the policy cases suggested that there would be increases in flows that would raise 

significant concerns about grid congestion or grid management. 

The increased energy efficiency anticipated with the proposed rule also contributes to 

meeting resource adequacy, by lowering the regional load and thereby lowering operating 

capacity needed to meet planning reserve margins. 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 concludes that the proposed rule will not raise significant concerns 

over regional resource adequacy or raise the potential for interregional grid problems. EPA 

believes any remaining local issues can be managed through standard reliability planning 

processes. 

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. 
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 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 

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under the proposed rule, EPA projects less new natural gas combined cycle capacity built under 

the proposed rule than is built in the base case over the time horizon presented in this 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” 
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 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 for 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” 

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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

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IPM modeling of natural gas prices uses both short- and long-term price signals to 

balance supply and demand for the fuel across the modeled time horizon. As such, it should be 

understood that the pattern of IPM natural gas price projections over time is not a forecast of 

natural gas prices incurred by end-use consumers at any particular point in time. The natural gas 

market in the United States has historically experienced some degree of price volatility from year 

to year, between seasons within a year, and during short-lived weather events (such as cold snaps 

leading to short-run spikes in heating demand). These short-term price signals are fundamental 

for allowing the market to successfully align immediate supply and demand needs. However, 

end-use consumers are typically shielded from experiencing these rapid fluctuations in natural 

gas prices by retail rate regulation and by hedging through longer-term fuel supply contracts by 

the power sector. IPM assumes these longer-term price arrangements take place “outside of the 

model” and on top of the “real-time” shorter-term price variation necessary to align supply and 

demand. Therefore, the model’s natural gas price projections should not be mistaken for 

traditionally experienced consumer price impacts related to natural gas, but a reflection of 

expected average price changes over the period of time represented by the modeling horizon. 

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. 
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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 costs to calculate changes to regional retail 

prices. There are many factors influencing the estimated retail electricity price impacts, namely 

projected changes in generation mix, fuel prices, and development of new generating capacity. 

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These projected changes vary regionally under each compliance option in response to the goals, 

and can also vary depending upon retail electricity 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 (cents/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 (cents/kWh) Percent Change from ReferenceBase Case
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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 3TABLE 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 (cents/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. 
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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. 

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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 bill 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 improvements. The IPM-projected annualized estimates 

of private compliance costs provided in this analysis are meant to show the increase in 

production (generating) costs to the power sector in response to the final rule. To estimate these 

annualized costs, EPA uses a conventional and widely-accepted approach that applies a capital 

recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental 

operating expenses. The CRF is derived from estimates of the cost of capital (private discount 

rate), the amount of insurance coverage required, local property taxes, and the life of capital.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 using IPM (v5.13) documentation, available at: 

http://www.epa.gov/powersectormodeling/BaseCasev513.html. 

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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 degree, a continuation of the impacts of state 

demand-side energy efficiency policies but does not explicitly represent many of the existing 

state policies in this area (e.g., energy efficiency resource standards). To some degree the 

implicit representation of state policies in the EPA’s base case alters the impacts assessment, but 

the direction of change is not known with certainty. This issue is discussed in more detail in the 

Greenhouse Gas Abatement Measures TSD.

Cost estimates for the proposed rule are based on rigorous power sector modeling using 

ICF’s Integrated Planning Model.38 IPM assumes “perfect foresight” of market conditions over 

the time horizon modeled; to the extent that utilities and/or energy regulators misjudge future 

conditions affecting the economics of pollution control, costs may be understated as well. 

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>. 

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Comment [A44]: It should be clarified that 

this is incremental to base case number of 

retirements. 

EPA: Clarification added. 
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3-A-131-1

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 recordkeeping costs for both state entities and affected 

EGUs for the compliance years 2020, 2025 and 2030. In calculating the costs for state entities, 

EPA estimated personnel costs to oversee compliance, and review and report annually to EPA on 

program progress relative to meeting the state’s reduction goal. To calculate the national costs, 

EPA estimated that 49 states and 1,228 facilities would be affected. 

In calculating the cost for affected EGUs to comply, EPA assumed that the state plan 

would utilize a rate-based emission limit. The EPA estimated that the majority of the cost to 

EGUs would be in calculating net energy output. Since the majority of EGUs do have some 

energy usage meters or other equipment available to them, EPA believes a new system for 

calculating net energy output is not needed. However, an affected EGU will still need to be 

modify their equipment to comply with a rate-based limit as described in the emission 

guidelines. The EPA estimates that it would take 3 working months for a technician to retrofit 

any existing energy meters to meet the requirements set in the state plan. Additionally EPA 

believes that 1 hour will be needed for each EGU operator to read the rule and understand how 

the facility will comply with the rule. Also, after all modifications are made at a facility to 

measure net energy output, each EGUs Data Acquisition System (DAS) would need to be 

upgraded to supply the rate-based emissions value to either the state or EPA’s Emissions 

Collection and Monitoring Plan System (ECMPS). 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 

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3-A-141-1

possible contingency measures, state plan revisions and the subsequent public meetings if 

revisions are indeed needed, download data from the ECMPS for their annual reporting and 

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 Requirements (billions of 2011$) 

 2020 2025 2030 

Option 1 Regional 5.5 4.6 7.3 

Option 1 State 7.5 5.5 8.8 

Option 2 Regional 4.3 4.5 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 consumed as a result of reallocating some resources 

towards pollution mitigation. Estimates of social costs may be compared to the social benefits 

expected as a result of a regulation to assess its net impact on society. The social costs of a 

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regulatory action will not necessarily be equivalent to the expenditures associated with 

compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section 

provides a qualitative discussion of the relationship between social costs and compliance cost 

estimates presented in this chapter. 

For the illustrative compliance scenarios cost estimates presented in this Chapter are the sum of 

expenditures on end-use energy efficiency programs and the change in expenditures required by 

the electricity sector to comply with the proposed guidelines. These two components are 

estimated separately. The expenditures required to achieve the assumed demand reductions 

through end-use energy efficiency programs are estimated using historical data and expert 

judgment. The change in the expenditures required by the electricity sector to meet demand and 

maintain compliance are estimated by IPM and reflect both the reduction in electricity 

production costs due to the reduction in demand caused by the end-use energy efficiency 

programs and the increase in electricity production costs required to achieve the additional 

emission reductions necessary to comply with the state goals.39 

As described in section 3.6.1, the illustrative scenarios generally assume that, in achieving their 

goals, states adopt energy efficiency programs which lead to net demand reductions in each year 

equivalent to those applied in the calculation of their respective goals. The estimated 

expenditures required to achieve those net demand reductions through end-use energy efficiency 

programs are presented in this chapter and detailed in the GHG Abatement Measures TSD 

chapter on end-use energy efficiency. The social cost of achieving these energy savings comes in 

the form of increased expenditures on technologies and/or services that are required to lower 

end-user’s electricity consumption beyond the business as usual. Under the assumption of 

complete and well-functioning markets the expenditures required to reduce electricity 

consumption on the margin will represent society’s opportunity cost of the resources required to 

produce the energy savings. 

 

39 As described in section 3.5, IPM provides the least-cost solution to attaining the constraints representing the 

regulation required used to achieve the state goals but for the component of the state goals that is achieved by 

end use energy efficiency programs. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3-A-161-1

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 scenarios includes this limited share of the program 

costs that is expended on end-users who would have reduced their demand in the absence of the 

program. Thus, with respect to this particular issue, the social cost of the illustrative scenarios 

would be less than the estimated compliance cost reported in Table 3-9 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 increases or by producers through reductions in their 

profits. While the allocation of these expenditures between consumers and producers is 

important for understanding the distributional impact of potential compliance strategies, it does 

not necessarily affect the opportunity cost required for the production of the energy savings from 

a social perspective. However, specific design elements of demand-side or end-use energy 

 

40 For example if a rebate program is offered for energy efficient appliances, some participants that claim the rebate 

may have purchased the more energy efficient appliances even without the rebate and so the rebate and 

investment made by these end-users is not a social cost of the program. 

41 The demand reductions assumed in the illustrative compliance scenarios only include those additional demand 

reductions motivated by the program costs. That is, the demand reductions assumed for the illustrative 

compliance scenarios already account for the potential that some program costs do not lead to additional energy 

efficiency investments by end-users. For more information see the GHG Abatement Measures TSD chapter on 

end-use energy efficiency. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3-A-171-1

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 savings, 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 electricity price. In addition to heterogeneity in 

product services and consumer preferences, there are other explanations for the energy paradox, 

falling both within and outside the neoclassical rational expectations paradigm that is used in 

benefit/cost analysis. The end-use energy efficiency chapter of the GHG Abatement Measures 

TSD discusses the energy paradox in detail and provides additional hypothesis for why 

consumers may not make energy efficiency investments that ostensibly seem to be in their own 

interest. The TSD discussion also provides details on how the presence of additional market 

failures can lead to levels of energy efficiency investment that may be too low from society’s 

 

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

improvements at a capital cost that is less than the anticipated fuel expenditure savings. 
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3-A-181-1

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 and energy use and those that would receive the 

benefits associated with reduced energy use through lower electricity bills. 

The other component of compliance cost reported in this chapter is the change in resource cost 

(i.e., expenditures) required by the electricity sector to fulfill the remaining demand while 

making additional CO2 emissions intensity reductions necessary to comply with the state goals. 

Included in the estimate of these compliance costs, developed using IPM, are the cost savings 

associated with the reduction in required electricity generation due to the demand reductions 

from end-use energy efficiency programs and improvements in heat rate. By shifting the demand 

curve for electricity, energy efficiency programs reduce the production cost in the sector. The 

resource cost estimates from IPM therefore account for the increased cost of providing electricity 

at a lower average emissions rate net of the reduction in production costs due to lower demand 

from end-use energy efficiency programs. 

Under the assumption that impacts outside the electricity market and markets providing inputs to 

the electricity sector do not meaningfully affect the prices in those markets, and the assumption 

of fixed electricity demand, then the social costs associated with the regulatory action would be 

equal to the resource costs measured by IPM (net of tax and subsidy payments and recovery of 

sunk costs). Under these assumptions the change in compliance cost will equal the reduction in 

producer43 and consumer surplus44 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 measure representing the monetized value of the benefit consumers receive from 

consumption of purchased goods or services beyond their opportunity cost as defined by the market price. 
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3-A-191-1

theoretically consistent method for estimating partial-equilibrium changes in welfare where 

prices and outputs change in multiple markets see Chapter 9 of Just et al. (2004). 

3.12 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

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9-0 
Page 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 elements 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 harness 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 market 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 electric demand, new RE, etc, 

lower levels of redispatch are projected (compared to the BSER level of 70%). Regional 

scenarios result in slightly fewer 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 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.” 

EPA: Revised to “base case.” 
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4-1 

CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN 

HEALTH CO-BENEFITS 

4.1 Introduction 

Implementing the proposed Emission Guidelines for Greenhouse Gas (GHG) Emissions 

from Existing Stationary Sources: Electric Utility Generating Units (EGUs) (hereafter, EGU 

GHG Existing Source Guidelines) is expected to reduce emissions of carbon dioxide (CO2) and 

have ancillary human health benefits (i.e., co-benefits) associated with lower ambient 

concentrations 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 (PM2.5) and ozone by reducing emissions of 

precursor pollutants (i.e., sulfur dioxide (SO2), nitrogen dioxide (NO2), and directly emitted 

PM2.5). Data, resource, and methodological limitations prevent EPA from monetizing the 

benefits from several important co-benefit categories, including reducing direct exposure to SO2, 

NO2, and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. 

We qualitatively discuss these unquantified benefits in this chapter. 

This chapter provides estimates of the monetized climate benefits and air pollution health 

co-benefits associated with emission reductions for two options with two illustrative compliance 

scenarios across several analysis years and discount rates. The estimated benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

the Mercury and Air Toxics Standards (MATS).

4.2 Estimated Climate Benefits from CO2

The primary goal of the proposed guidelines is to reduce emissions of CO2. In this section, 

we provide an overview of the public health and public welfare effects associated with climate 

change. We also provide information regarding the economic valuation of CO2 using the Social 

Cost of Carbon (SCC), a metric that estimates the monetary value of impacts associated with 

marginal changes in CO2 emissions in a given year. Table 4-1 summarizes the quantified and 

unquantified climate benefits in this analysis. 

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4-2 

 Table 4-1. Climate Effects 

Benefits 

Category 

Specific Effect Effect Has Been 

Quantified 

Effect Has Been 

Monetized 

More 

Information 

Improved Environment 

Reduced 

climate effects 

Global climate impacts from CO2 —  SCC TSD 

Climate impacts from ozone and black 

carbon (directly emitted PM) 

— — Ozone ISA, PM 

ISA*

Other climate impacts (e.g., other GHGs 

such as methane, aerosols, other impacts) 

— — IPCC*

* We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 

4.2.1 Public Health Effects from Climate Change 

Climate change threatens public health in a number of ways: direct temperature effects, the 

effect of higher atmospheric CO2 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 Finding’s conclusions for each of these effects in 

turn.1

Heat is already the leading cause of weather-related deaths in the United States, and heat 

waves are associated with marked short-term increases 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 and 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 morbidity 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 have also been found to be sensitive to hot 

temperatures are the mentally ill, those lacking 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/

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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 rates. While a single study is not conclusive, this 

study finds that increases in heat-related mortality, 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 and forest growth, and the composition of plant 

and animal species populations, pasture and forest 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 populous 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 modeling 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. 
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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 resulting 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 increased 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, infectious diseases, toxic contamination, and mental 

health problems. 

Climate change is expected to lead to increases tropical cyclone intensity (hurricanes and 

tropical storms). Increases in tropical cyclone intensity are linked to increases in the risk of 

deaths, injuries, waterborne and food borne diseases, as well as post-traumatic stress disorders. 

Storm surge is the leading cause of death in coastal 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 environmental 

conditions 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 CO2 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, distribution, dispersion, and allergenicity of 

aeroallergens. These changes in aeroallergens and subsequent human exposures could affect the 

prevalence and severity of allergy symptoms. However, the scientific literature does not provide 

definitive data or conclusions on how climate change might impact aeroallergens and 
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subsequently the prevalence of allergenic illnesses in the U.S. The IPCC concluded that pollens 

are likely to increase with elevated temperature and CO2 (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 settlements; and ecosystems and wildlife. Impacts 

from climate change will also occur outside of the United States. This section summarizes the 

2009 Endangerment Finding’s conclusions for each of these effects. 

As noted above, elevated CO2 concentrations and modest temperature increases may 

stimulate plants to increase photosynthesis, biomass, 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 precipitation 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 temperatures 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. 
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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 has very likely increased the size and number of 

wildfires, insect outbreaks, and increased tree mortality 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 strains 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 States, 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 
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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 over-allocated water resources in some regions 

of the United States, increasing competition among agricultural, municipal, industrial, and 

ecological uses. Increased incidence of extreme 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 difficulty 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 disruptive 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 development and pollution related to development. 

Although increases in mean sea level over the 21st 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 
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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 situated 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 intensity and frequency of extreme weather events than 

gradual changes in temperature. Extreme weather events could threaten U.S. energy 

infrastructure (transmission and distribution), transportation infrastructure (roads, bridges, 

airports and seaports), water infrastructure, and other built aspects of human settlements. 

Moreover, soil subsidence caused by the melting of permafrost in the Arctic region is a risk to 

gas and oil pipelines, electrical transmission 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. 
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4-9 

Climate change and ocean acidification will likely impair a wide range of planktonic and other 

marine calcifiers such as corals. Even without ocean 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 sea ice will drastically shrink marine habitat for 

polar bears, ice-inhabiting seals, 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 response to climate change can increase ecosystem vulnerability to 

other disturbances, including wildfires and biological invasion. Disturbances 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 functioning. 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). 
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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 potential threat multiplier with regard to national 

security issues. While some of these international risks do not readily lend 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 CO2 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 CO2 emissions in a 

given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that have an incremental achieve marginal impact on cumulative 

global reductions in CO2 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 Technical Support Document (SCC TSD) provides a 

 

3

 The federal government concluded that a global measure of the benefits from reducing U.S. GHG emissions should 

be the standard practice when conducting regulatory impact analysis in support of federal rule makings. See 

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 (2013) and 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 (2010). 
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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, improvements in the way damages are modeled are 

confined to those that have been incorporated into the latest versions of the models by the 

developers themselves and used in peer-reviewed publications. The model updates that are 

relevant to the SCC estimates include: an explicit representation of sea level rise damages in the 

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 impacts, the agricultural sector, and reduced space 

heating requirements, as well as changes to the transient response of temperature to the buildup 

of GHG concentrations and the inclusion of indirect effects of methane emissions in the Climate 

Framework for Uncertainty, Negotiation, and Distribution (FUND) model. The 2013 SCC TSD 

provides complete details. 

When attempting to assess the incremental economic impacts of carbon dioxide emissions, 

the analyst faces a number of serious challenges. A report from the National Academies of 

 

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 Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available 

at: http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf

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, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, 

Revised November 2013). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf. 
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4-12 

Science (NRC, 2009) points out that any assessment will suffer from uncertainty, speculation, 

and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past 

and future emissions on the climate system, (3) the impact of changes in climate on the physical 

and biological environment, and (4) the translation of these environmental impacts into 

economic damages.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 the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Current integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

incorporated into these models understandably lags behind the most recent research. The limited 

amount of research linking climate impacts to economic damages makes the modeling exercise 

even more difficult. These individual limitations do not all work in the same direction in terms of 

their influence on the SCC estimates, though taken together they suggest that the SCC estimates 

are likely conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that 

“It is very likely that [SCC estimates] underestimate the damage costs because they cannot 

include many non-quantifiable impacts.” 

Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 reductions to inform benefit-cost analysis. 

The new versions of the models used to estimate the values presented below offer some 

improvements in these areas, although further work remains warranted. Accordingly, the EPA 

and other agencies continue to engage in research on modeling and valuation of climate impacts 

with the goal to improve these estimates. Additional details are provided in the SCC TSDs. 

 

6

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

Use. National Academies Press. See docket ID EPA-HQ-OAR-2009-0472-11486. 
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The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per 

metric ton of CO2 emissions in the year 2020 (2011$).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 95th percentile of the SCC from all three models at 

a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SCC distribution (representing less likely, but 

potentially catastrophic, outcomes). 

Table 4-2 presents the updated global SCC estimates for the years 2015 to 2050. In order 

to calculate the dollar value for emission reductions, the SCC estimate for each emissions year 

would be applied to changes in CO2 emissions for that year, and then discounted back to the 

analysis year using the same discount rate used to estimate the SCC.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 considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. Furthermore, the U.S. Interagency 

Working Group on the Social Cost of Carbon has so far only considered estimates for the social cost of CO2. 

While CO2 is the dominant GHG emitted by the sector, we recognize the representative facilities within these 

comparisons may also have different emission rates for other climate forcers that will serve a minor role in 

determining the overall social cost of generation. 
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4-14 

 

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

Year 

Discount Rate and Statistic 

5% Average 3% Average 2.5% Average 3% (95th percentile) 

2015 $12 $39 $61 $116

2020 $13 $46 $68 $137

2025 $15 $50 $74 $153

2030 $17 $55 $80 $170

2035 $20 $60 $85 $187

2040 $22 $65 $92 $204

2045 $26 $70 $98 $220

2050 $28 $76 $104 $235

* The SCC values vary depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for 

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

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for 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 CO2 reduced 383 371 295 283 

5% (average) $4.9 $4.7 $3.8 $3.6 

3% (average) $18 $17 $14 $13 

2.5% (average) $26 $25 $20 $19 

3% (95th percentile) $52 $51 $40 $39 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

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

Existing Source Guidelines in 2025 (billions of 2011$)* 

Discount Rate and Statistic Option 1 – 

state 

Option 1 - 

regional 

Option 2 – 

state 

Option 2 - 

regional 

Million metric tonnes of CO2 reduced 506 501 376 368 

5% (average) $7.6 $7.5 $5.6 $5.5 

3% (average) $25 $25 $19 $18 

2.5% (average) $37 $37 $28 $27 

3% (95th percentile) $77 $76 $57 $56 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

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

Existing Source Guidelines in 2030 (billions of 2011$)* 

Discount Rate and Statistic Option 1 – 

state 

Option 1 - 

regional 

Option 2 – 

state 

Option 2 - 

regional 

Million metric tonnes of CO2 reduced 555 545 373 353 

5% (average) $9.5 $9.3 $6.4 $6.0 

3% (average) $31 $30 $21 $20 

2.5% (average) $44 $44 $30 $28 

3% (95th 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 benefits presented above are associated with changes 

in CO2 emissions only. Implementing these guidelines, however, will have an impact on the 

emissions of other pollutants that will affect the 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 carbon or ozone precursors would lead to further 

cooling, but reductions in the other aerosol species and precursors would lead to warming. 

Therefore, changes in non-CO2 pollutants could potentially augment or offset the climate 

benefits calculated here. These pollutants can act in different ways and on different timescales 

than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) 

incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. 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 accelerating melting. In terms of lifetime, while 

carbon dioxide emissions can increase concentrations in the atmosphere for hundreds or 

thousands of years, many of these other pollutants are short lived and remain in the atmosphere 

 

9

 The federal government’s SCC estimates are designed to assess the climate benefits associated with changes in 

CO2 emissions only. 
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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 potential magnitude of changes relative to CO2

emissions reductions anticipated at power plants. This assessment included CO2 emissions from 

the flaring of methane, but did not evaluate potential changes in other combustion-related CO2

emissions, such as emissions associated with drilling, mining, processing, and transportation in 

the natural gas and coal production sectors. This analysis found that the net upstream CH4

emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane 

will likely decrease under the proposed guidelines. Furthermore, the changes in upstream 

methane emissions are small relative to the changes in direct emissions from power plants. 

4.3 Estimated Human Health Co-Benefits 

In addition to CO2, implementing these proposed guidelines is expected to reduce 

emissions of SO2 and NOX, which are precursors to formation of ambient PM2.5, as well as 

directly emitted fine particles.10 Therefore, reducing these emissions would also reduce human 

exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In addition, in the 

presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to form 

ozone. Reducing NOX emissions would also reduce human exposure to ozone and the incidence 

of ozone-related health effects. Although we do not have sufficient data to quantify these impacts 

in this analysis, reducing emissions of SO2 and NOx would also reduce ambient exposure to SO2

and NO2, respectively. In this section, we provide an overview of the monetized PM2.5 and 

 

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 represents particles not classified as one of the other species 

(e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).
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ozone-related co-benefits estimated for the proposed guidelines. The estimated co-benefits 

associated with these emission reductions are beyond those achieved by previous EPA 

rulemakings, including MATS. A full description of the underlying data, studies, and 

assumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA 

(U.S. EPA, 2008b, 2010d). 

There are several important considerations in assessing the air pollution-related health cobenefits for a climate-focused rulemaking. First, these estimated health co-benefits do not 

account for any climate-related air quality changes (e.g., increased ambient ozone associated 

with higher temperatures) but rather changes in precursor emissions affected by this rulemaking. 

Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM2.5-related health co-benefits would be impacted by excluding 

climate-related air quality changes since the science is unclear as to how climate change may 

affect PM2.5 exposure. Second, the estimated health co-benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, 

the estimated climate benefits reported in this RIA reflect global benefits, while the estimated 

health co-benefits are calculated for the contiguous U.S. only. Excluding temperature 

modification of air pollution risks and international air pollution-related health benefits implies 

that the quantified health co-benefits likely lead to underestimation. 

Implementing these guidelines may lead to reductions in ambient PM2.5 concentrations 

below the National Ambient Air Quality Standards (NAAQS) for PM and ozone in some areas 

and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 

2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences 

worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate 

the potential costs and benefits of attaining a revised air quality standard nationwide based on an 

array of emission reduction strategies for different sources including known and unknown 

controls, incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction 

strategies that States may choose to enact when implementing a revised NAAQS. The setting of 

a NAAQS does not directly result in costs or benefits, and as such, EPA’s NAAQS RIAs are 

merely illustrative and the estimated costs and benefits are not intended to be added to the costs 
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and benefits of other regulations that result in specific costs of control and emission reductions. 

However, it is possible that some costs and benefits estimated in this RIA may account for the 

same air quality improvements as estimated in the illustrative NAAQS RIAs. 

Similar to NAAQS RIAs, the emission reduction scenarios estimated for the proposed 

guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the 

illustrative compliance scenarios would occur in one well-characterized sector (i.e., the EGU 

sector). In general, EPA is more confident in the magnitude and location of the emission 

reductions for implementation rules, which typically require specific emission reductions in a 

specific sector. As such, emission reductions achieved under promulgated implementation rules 

will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the 

incremental costs and benefits associated with attaining revised future NAAQS. EPA does not 

re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline 

to account for implementation rules promulgated after an RIA was completed. For more 

information on the relationship between illustrative analyses, such as for the NAAQS and this 

proposal, and implementation rules, please see Section 1.3 of the PM NAAQS RIA (U.S. EPA, 

2012a). 

4.3.1 Health Impact Assessment for PM2.5 and Ozone 

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) 

identified the human health effects associated with ambient PM2.5 exposure, which include 

premature morality and a variety of morbidity effects associated with acute and chronic 

exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical 

Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with 

ambient ozone exposure, which include premature morality and a variety of morbidity effects 

associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified 

co-benefit categories captured in EPA’s health co-benefits estimates for reduced exposure to 

ambient PM2.5 and ozone. Although the table below does not list unquantified health effects such 

as exposure to SO2, NO2, and mercury nor welfare effects such as acidification and nutrient 

enrichment, these effects are described in detail in Chapters 5 and 6 of the PM NAAQS RIA 

(U.S. EPA, 2012a) and summarized later in this chapter. It is important to emphasize that the list 

of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete. 
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4-19 

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

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

Improved Human Health 

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 or 

age >30) 

  PM ISA 

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

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

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

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

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

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

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

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

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

Lost work days (age 18-65)   PM ISA

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

Chronic Bronchitis (age >26) — — PM ISA

Emergency room visits for cardiovascular effects (all 

ages) — — PM ISA1

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

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

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations) 

— — PM ISA2

Reproductive and developmental effects (e.g., low 

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

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

Reduced incidence of 

mortality from 

exposure to ozone 

Premature mortality based on short-term study 

estimates (all ages)   Ozone ISA 

Premature mortality based on long-term study 

estimates (age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone 

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

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

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

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

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

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

Other respiratory effects (e.g., premature aging of 

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

1

 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have 

quantified them in sensitivity analyses for other analyses. 

2

 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 

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3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. 

We follow a “damage-function” approach in calculating benefits, which estimates changes 

in individual health endpoints (specific effects that can be associated with changes in air quality) 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Because EPA rarely has the time or resources to perform new research to measure 

directly, either health outcomes or their values for regulatory analyses, our estimates are based 

on the best available methods of benefits transfer, which is the science and art of adapting 

primary research from similar contexts to estimate benefits for the environmental quality change 

under analysis. Because we did not conduct scenario-specific air quality modeling for the 

proposed guidelines, wWe use a “benefit-per-ton” approach to estimate the PM2.5 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) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone. We use the 

environmental Benefits Mapping and Analysis Program (BenMAP) (version 4.0.66) to 

systematize health impact analyses by applying a database of key input parameters, including 

population projections, health impact functions, and valuation functions (Abt Associates, 2012). 

For this assessment, the HIA is limited to those health effects that are directly linked to ambient 

PM2.5 and ozone concentrations. There may be other indirect health impacts associated with 

reducing emissions, such as occupational health exposures. Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect for a given increment of air 

pollution (often per 10 μg/m3

 for PM2.5 or ppb for ozone). These relative risks can be used to 

develop risk coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a 

health effect. We refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone 

NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the epidemiology 

studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature 

mortality below. The size of the mortality effect estimates from epidemiological studies, the 
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serious nature of the effect itself, and the high monetary value ascribed to prolonging life make 

mortality risk reduction the most significant health endpoint quantified in this analysis. 

4.3.1.1 Mortality Concentration-Response Functions for PM2.5

Considering a substantial body of published scientific literature and reflecting thousands of 

epidemiology, toxicology, and clinical studies, the PM ISA documents the association between 

elevated PM2.5 concentrations and adverse health effects, including increased premature mortality 

(U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific 

Advisory Committee of EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 

2009c), concluded that there is a causal relationship between mortality and both long-term and 

short-term exposure to PM2.5 based on the entire body of scientific evidence. The PM ISA also 

concluded that the scientific literature consistently finds that a no-threshold 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 effect coefficients from the most recent 

epidemiology studies examining two large population cohorts: the American Cancer Society 

cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al., 2012). The PM 

ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts provide the strongest 

evidence of the association between long-term PM2.5 exposure and premature mortality with 

support from a number of additional cohort studies. The SAB’s Health Effects Subcommittee 

(SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions 

(U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths 

and weaknesses, we present PM2.5 co-benefits estimates using relative risk estimates from both 

these cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

As a characterization of uncertainty regarding the adult PM2.5-mortality relationship, EPA 

graphically presents the PM2.5 co-benefits derived from EPA’s expert elicitation study (Roman et 

al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health 

experts probabilistic distributions describing uncertainty in estimates of the reduction in 
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4-22 

mortality among the adult U.S. population resulting from reductions in ambient annual average 

PM2.5 concentrations. In that study, twelve experts provided independent opinions regarding the 

PM2.5-mortality concentration-response function. Because the experts relied upon the ACS and 

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 

PM2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports 

the conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that newer scientific literature could revise the experts’ quantitative responses if elicited 

again, we believe that these general conclusions are unlikely to change. 

4.3.1.2 Mortality Concentration-Response Functions for Ozone 

In 2008, the National Academies of Science (NRC, 2008) issued a series of 

recommendations to the EPA regarding the quantification and valuation of ozone-related shortterm mortality. Chief among these was that “...short-term exposure to ambient ozone is likely to 

contribute to premature deaths” and the committee recommended that “ozone-related mortality 

be included in future estimates of the health benefits of reducing ozone exposures...” The NAS 

also recommended that “...the greatest emphasis be placed on the multicity and NMMAPS 

[National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the 

co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS 

analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as 

effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. 

(2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. 

EPA, 2008b, 2010d).11 For simplicity, we report the ozone mortality estimates in this RIA as a 

 

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

Health Risk and Exposure Assessment (U.S. EPA, 2014a). Therefore, the ozone mortality studies applied in this 

analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most updated 

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range from Bell et al. (2004) to Levy et al. (2005) to represent the lowest and the highest cobenefits estimates based on these six ozone mortality studies. In addition, we graphically present 

estimated co-benefits derived from all six studies mentioned above as a characterization of 

uncertainty regarding the ozone -mortality relationship in Figure 4-1. 

4.3.2 Economic Valuation for Health Co-benefits 

After quantifying the change in adverse health impacts, we estimate the economic value of 

these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the 

risk of future adverse health effects by a small amount for a large population. Therefore, the 

appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. 

For some health effects, such as hospital admissions, WTP estimates are generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect. The unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 

2012a). 

Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and 

over 90 percent of monetized ozone-related co-benefits. The economics literature concerning the 

appropriate method for valuing reductions in premature mortality risk is still developing. The 

adoption of a value for the projected reduction in the risk of premature mortality is the subject of 

continuing discussion within the economics and public policy analysis community. Following 

the advice of the SAB’s Environmental Economics Advisory Committee (SAB-EEAC), the EPA 

currently uses the value of statistical life (VSL) approach in calculating estimates of mortality 

benefits, because we believe this calculation provides the most reasonable single estimate of an 

individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 

2000). The VSL approach is a summary measure for the value of small changes in mortality risk 

experienced by a large number of people. 

 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. 
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4-24 

EPA continues work to update its guidance on valuing mortality risk reductions, and the 

Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is 

available, the Agency determined that a single, peer-reviewed estimate applied consistently, best 

reflects the SAB-EEAC advice it has received. Therefore, EPA has decided to apply the VSL 

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses 

(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 analysis year. Specifically, the VSLs applied in this 

analysis in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million 

for 2025 and 2030.14 

The Agency is committed to using scientifically sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). EPA is taking SAB’s recommendations under advisement. 

In valuing PM2.5-related premature mortality, we discount the value of premature mortality 

occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that 

there is a “cessation” lag between changes in PM exposures and the total realization of changes 

in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the 

 

12 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future. 

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. 
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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 PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions 

do not change the total number of estimated deaths but rather the timing of those deaths. Because 

short-term ozone-related premature mortality occurs within the analysis year, the estimated 

ozone-related co-benefits are identical for all discount rates. 

4.3.3 Benefit-per-ton Estimates for PM2.5

Due to analytical limitations, it was not possible to conduct scenario-specific air quality 

modeling for this proposal. Instead, wWe used a “benefit-per-ton” approach to estimate the 

PM2.5 co-benefits in this RIA. EPA has applied this approach in several previous RIAs (e.g., U.S. 

EPA, 2011b, 2011c, 2012b). These benefit-per-ton estimates provide the total monetized human 

health co-benefits (the sum of premature mortality and premature morbidity), of reducing one ton 

of PM2.5 (or PM2.5 precursor such as NOX or SO2) from a specified source. Specifically, in this 

analysis, we multiplied the estimates for the EGU sector by the corresponding emission 

reductions based on regional (i.e., East, West, and California) benefit-per-ton estimates. 

The method used to calculate the regional benefit-per-ton estimates is a slight modification 

of the national benefit-per-ton estimates described in the TSD: Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013a). The national estimates were 

derived using the approach published in Fann et al. (2012), but they have since been updated to 

reflect the epidemiology studies and Census population data first applied in the final PM 

NAAQS RIA (U.S. EPA, 2012a). The approach in Fann et al. (2012) is similar to the work 

previously published by Fann et al. (2009), but the newer study includes improvements that 

provide more refined estimates of PM2.5-related health benefits for emissions reductions in the 

various sectors. Specifically, the air quality modeling data reflect industrial sectors that are more 

narrowly defined. In addition, the updated air quality modeling data reflects more recent 

emissions data (2005 rather than 2001) and has higher spatial resolution (12km rather than 36 km 

grid cells).15 For this rulemaking, to generate the regional benefit-per-ton estimates we simply 

 

15 Although the modeling underlying the benefit-per-ton estimates does not reflect emission reductions anticipated 

from MATS, the EGU contribution to ambient PM2.5 and ozone on a per-ton basis would be similar. (Fann, 
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aggregated the EGU impacts in BenMAP to the region (i.e., East, West, and California) rather 

than aggregating to the nation as was done in Fann et al. (2012). We then divided the regional 

benefits by the regional emissions rather than the national emissions. The appendix to this 

chapter provides additional detail regarding these calculations. 

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution 

of the modeled sector emissions, which may not match the emission reductions anticipated by 

the proposed guidelines, and they may not reflect local variability in population density, 

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

location. In addition, these estimates reflect the regional average benefit-per-ton for each ambient 

PM2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission 

reductions. The regional benefit-per-ton estimates, although less subject to these types of 

uncertainties than national estimates, still should be interpreted with caution. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary 

between precursors depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. 

4.3.4 Benefit-per-ton Estimates for Ozone 

Similar to PM2.5, we used a “benefit-per-ton” approach in this RIA to estimate the ozone 

co-benefits, which represent the total monetized human health co-benefits (the sum of premature 

mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also 

consistent with the PM2.5 estimates, we generated regional benefit-per-ton estimates for ozone for 

the EGU sector using the air quality modeling data described in Fann et al. (2012) and using the 

updated Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012a). In 

contrast to the PM2.5 estimates, the ozone estimates are not based on changes to annual 

emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NOX

emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate 

 Fulcher, and Baker, 2013) Because the emission reductions in this RIA are calculated from an IPM base case 

that includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits 

estimates.

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ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with a longer ozone seasons such as southern California and Texas. These 

estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx 

alone. Because EGUs emit little VOC relative to NOX emissions, it is unlikely that VOCs 

emitted by EGUs would contribute substantially to regional ozone formation. All benefit-per-ton 

estimates have inherent limitations and should be interpreted with caution. We provide more 

detailed information regarding the generation of these estimates in the appendix to this chapter. 

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 PM2.5 and ozone-related co-benefits using concentrationresponse functions from different studies and expert opinion for the options evaluated in 2020 as 

an illustrative analysis year. Figures 4-2 and 4-3 provide a breakdown of the monetized health 

co-benefits for each of the options evaluated in 2020 as an illustrative analysis year by precursor 

pollutant and region, respectively. 

 

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. 

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

(2011$)* 

Pollutant Discount 

Rate National Regional 

East West California 

SO2

3% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $320,000 

7% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $570,000 

7% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000 

Directly emitted 

PM2.5 (crustal) 

3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $220,000 

7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000 

NOX (as PM2.5) 3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $34,000 

7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000 

NOX (as Ozone) N/A $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 

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

2025 (2011$)* 

Pollutant Discount 

Rate National Regional 

East West California 

SO2

3% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,000 

7% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,000 

7% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000 

Directly emitted 

PM2.5 (crustal) 

3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000 

7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000 

NOX (as PM2.5) 3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000 

7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000 

NOX (as Ozone) N/A $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).
***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 

SO2

3% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,000 

7% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,000 

7% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000 

Directly emitted 

PM2.5 (crustal) 

3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000 

7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000 

NOX (as PM2.5) 3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000 

7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000 

NOX (as Ozone) N/A $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 

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

Existing Source Guidelines in 2020 (thousands of short tons) 

Region SO2 All-year NOx Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)* 

Directly emitted 

PM2.5 (crustal)* 

Option 1 - State 

East 311 315 135 

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 SO2

All-year 

NOx 

Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)* 

Directly emitted 

PM2.5 (crustal)* 

Option 1 - State 

East 395 378 164 

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 SO2 All-year NOx Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)* 

Directly emitted 

PM2.5 (crustal)* 

Option 1 - State 

East 441 376 163 

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 

SO2 $13,000 to $29,000 $11,000 to $26,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $2,200 to $4,900 $2,000 to $4,400 

NOx (as Ozone) $640 to $2,700 $640 to $2,700 

Total $15,000 to $36,000 $14,000 to $33,000

Option 1 - Regional 

SO2 $12,000 to $26,000 $11,000 to $24,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500 

NOx (as Ozone) $630 to $2,700 $630 to $2,700 

Total $15,000 to $34,000 $13,000 to $31,000

Option 2 - State 

SO2 $10,000 to $23,000 $9,100 to $21,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $1,700 to $3,800 $1,500 to $3,400 

NOx (as Ozone) $480 to $2,100 $480 to $2,100 

Total $12,000 to $29,000 $11,000 to $26,000

Option 2 - Regional 

SO2 $9,800 to $22,000 $8,900 to $20,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $1,700 to $3,900 $1,600 to $3,500 

NOx (as Ozone) $470 to $2,000 $470 to $2,000 

Total $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 NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

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 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles 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 

SO2 $18,000 to $40,000 $16,000 to $36,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $2,900 to $6,500 $2,600 to $5,800 

NOx (as Ozone) $1,000 to $4,400 $1,000 to $4,400 

Total $22,000 to $51,000 $20,000 to $46,000

Option 1 - Regional 

SO2 $17,000 to $38,000 $15,000 to $35,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $3,000 to $6,800 $2,700 to $6,100 

NOx (as Ozone) $1,000 to $4,300 $1,000 to $4,300 

Total $21,000 to $50,000 $19,000 to $45,000

Option 2 - State 

SO2 $14,000 to $30,000 $12,000 to $27,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $2,100 to $4,700 $1,900 to $4,200 

NOx (as Ozone) $720 to $3,100 $720 to $3,100 

Total $16,000 to $38,000 $15,000 to $35,000 

Option 2 - Regional 

SO2 $13,000 to $29,000 $12,000 to $26,000 

Directly emitted PM2.5 (EC+OC) #NUM! to #NUM! #NUM! to #NUM!

Directly emitted PM2.5 (crustal) #NUM! to #NUM! #NUM! to #NUM!

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500 

NOx (as Ozone) $730 to $3,100 $730 to $3,100 

Total $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 NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

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 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles 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 

SO2 $21,000 to $47,000 $19,000 to $43,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $2,900 to $6,600 $2,600 to $6,000 

NOx (as Ozone) $1,100 to $4,600 $1,100 to $4,600 

Total $25,000 to $58,000 $23,000 to $53,000

Option 1 - Regional 

SO2 $20,000 to $44,000 $18,000 to $40,000 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $3,000 to $6,700 $2,700 to $6,100 

NOx (as Ozone) $1,100 to $4,500 $1,100 to $4,500 

Total $24,000 to $55,000 $21,000 to $50,000

Option 2 - State 

SO2 $14,000 to $32,000 $13,000 to $29,000

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5) $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

SO2 $13,000 to $30,000 $12,000 to $27,000

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5) $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 NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

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 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles 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 PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 

2020* 

Option 1 - 

state 

Option 1 - 

regional 

Option 2 - 

state 

Option 2 - 

regional 

PM2.5-related Health Incidences 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 1,600 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 PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

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 PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 

2025* 

Option 1 - 

state 

Option 1 - 

regional 

Option 2 - 

state 

Option 2 - 

regional 

PM2.5-related Health Incidences 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 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 PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

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 PM2.5-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 

PM2.5-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 PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

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 PM2.5 graphs show the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone 

mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005), 

and Ito et al. (2005). Ozone co-benefits occur in the analysis year, so they are the same for all discount rates. These 

estimates do not include climate benefits. The monetized co-benefits do not include climate benefits or reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. Results would be 

similar if the state compliance scenario was shown.

PM2.5       Ozone

Formatted: Normal, Space After: 0 pt, Don't

keep with next

Formatted: Normal, Don't keep with next
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. 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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 0 pt
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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 Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 

includes many data sources as inputs, including emission inventories, air quality data from 

models (with their associated parameters and 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 the world (i.e., regulations, technology, and human 

behavior). Each of these inputs may be uncertain and would affect the estimate of co-benefits. 

When the uncertainties from each stage of the analysis are compounded, even small uncertainties 

can have large effects on the total quantified benefits. Therefore, the estimates of co-benefits in 

each analysis year should be viewed as representative of the general magnitude of co-benefits of 

the illustrative compliance scenarios, rather than the actual co-benefits anticipated from 

implementing the proposed guidelines. 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008b) because we lack 

the necessary air quality modeling input and/or monitoring data to run the benefits model. 

However, the results of the quantitative and qualitative uncertainty analyses presented in the PM 

NAAQS RIA and Ozone NAAQS RIAs can provide some information regarding the uncertainty 

inherent in the estimated co-benefits results presented in this analysis. For example, sensitivity 

analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions 

could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by 

between 10 percent and –27 percent and that alternate income growth adjustments could change 

the PM2.5-related mortality co-benefits by between 33 percent and −14 percent. Although we 

generally do not calculate confidence intervals for benefit-per-ton estimates and they can provide 

an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA 

can provide an indication of the random sampling error in the health impact and economic 

valuation functions using Monte Carlo methods. In general, the 95th percentile confidence interval 

for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the 

central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95th percentile 

confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

Unlike RIAs for which EPA conducts air quality modeling, we do not have data 

information on the specific location of the air quality changes associated with the proposed 

guidelines. As such, it is not feasible to estimate the proportion of co-benefits occurring in 

different locations, such as designated nonattainment areas. Instead, we applied benefit-per-ton 

estimates, which reflect specific geographic patterns of emissions reductions and specific air 
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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-estimate or under-estimate of the actual co-benefits of 

controlling PM and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits 

may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct 

air quality modeling. Great care should be taken in applying these estimates to emission 

reductions occurring in any specific location, as these are all based on broad emission reduction 

scenarios and therefore represent average benefits-per-ton over the entire region. The benefitper-ton for emission reductions in specific locations may be very different than the estimates 

presented here. To the extent that the geographic distribution of the emissions reductions 

achieved by implementing the proposed guidelines is different than the 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 regarding the benefit-per-ton estimates applied in this RIA, including an 

evaluation of the similarities and differences in the spatial distribution of EGU emissions in the 

sector modeling and the IPM base case discussed in Chapter 3 of this RIA.

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

available scientific literature and methods and supported by the SAB-HES and the National 

Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for 

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

co-benefits. 

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA concluded that “many constituents of PM2.5 can be linked with 

multiple health effects, and the evidence is not yet sufficient to allow differentiation 

of those constituents or sources that are more closely related to specific outcomes” 

(U.S. EPA, 2009b). 
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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 PM2.5, including both 

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

down to the lowest modeled concentrations. 

3. We assume that there is a “cessation” lag between the change in PM exposures and 

the total realization of changes in mortality effects. Specifically, we assume that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. 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 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], 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 PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s 

Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in 

identifying any particular point at which our confidence in reported associations becomes 

appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA 

does not view these concentration benchmarks as a concentration threshold below which we 

would not quantify health co-benefits of air quality improvements.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 PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b). 

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4-44 

relationship between long-term PM2.5 exposures and mortality is causal and that overall the 

studies support the use of a no-threshold log-linear model to estimate the current body of 

scientific literature indicates that a no-threshold 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 PM2.5 concentration. However, we believe that it 

is still important to characterize the distribution of exposure to baseline concentrations. As a 

surrogate measure of mortality impacts, we provide the percentage of the population exposed at 

each PM2.5 level in the baseline of the source apportionment modeling used to calculate the 

benefit-per-ton estimates for this sector.18 It is important to note that baseline exposure is only 

one parameter in the health impact function, along with baseline incidence rates population and 

change in air quality. In other words, the percentage of the population exposed to air pollution 

below the LML is not the same as the percentage of the population experiencing health impacts 

as a result of a specific emission reduction policy. The most important aspect, which we are 

unable to quantify without rule-specific air quality modeling, is the shift in exposure anticipated 

by implementing the proposed guidelines. Therefore, caution is warranted when interpreting the 

LML assessment in this RIA because these results are not consistent with results from RIAs that 

had air quality modeling. 

Table 4-19 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and 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. (Fann, Fulcher, and Baker, 2013). Therefore, the baseline PM2.5 concentrations in 

the LML assessment are higher than would be expected if MATS was reflected.

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

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 10th and 25th

percentile. For Krewski, the LML is 5.8 μg/m3

 and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3

 and we do not have the data for one standard deviation below the mean. It is 

important to emphasize that although we have lower levels of confidence in levels below the LML for each study, 

the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5

do not occur. 

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

the Baseline Sector Modeling 

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

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

Percentage of PopulationExposed

Baseline Annual MeanPM2.5 Level (μg/m3)

LML of Lepeule et al.

(2012)study

LML of Krewski et

al. (2009) study
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4-46 

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

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 CO2 and reduced exposure to PM2.5 and ozone, but we were unable to 

monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, 

carbon monoxide, SO2, and NO2, as well as ecosystem effects and visibility impairment. 

Specifically, we estimated the combined climate benefits at discount rates of 5 percent, 3 

percent, 2.5 percent, and 3 percent (95th percentile) (as estimated by the interagency working 

group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by 

EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e) and OMB’s Circular A4 [OMB, 2003]). 

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

Cumulative percentage of populationexposed

Baseline AnnualMeanPM2.5 Level(μg/m3)

LML of Krewski et

al. (2009) study

LML of Lepeule et

al. (2012) study
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4-47 

Different discount rates are applied to SCC than to the health co-benefit estimates 

because CO2 emissions are long-lived and subsequent damages occur over many years. 

Moreover, several rates are applied to SCC because the literature shows that it is sensitive to 

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

an intergenerational context. The SCC interagency group centered its attention on the 3 percent 

discount rate but emphasized the importance of considering all four SCC estimates.19 The EPA 

has evaluated the range of potential impacts by combining all SCC values with health co-benefits 

values at the 3 percent and 7 percent discount rates. To be consistent with concepts of 

intergenerational discounting, values for health benefits, which occur within a generation, would 

only be combined with SCC values using a lower discount rate (e.g., the 7 percent health benefit 

estimates would be combined with 5 percent or lower SCC values, but the 3 percent health 

benefit would not be combined with the 5 percent SCC value). While the 5 percent SCC and 3 

percent health benefit estimate falls within the range of values we analyze, this individual 

estimate should not be used independently in an analysis, as it is represents a combination of 

discount rates that is unlikely to occur. Combining the 3 percent SCC values with the 3 percent 

health benefit values assumes that there is no difference in discount rates between 

intragenerational and intergenerational impacts. 

Tables 4-20 through 4-22 provide the combined climate and health benefits for each 

option evaluated for 2020, 2025, and 2030. Figure 4-6 shows the breakdown of the monetized 

benefits by pollutant for each option evaluated in 2020 as an illustrative analysis year using a 3 

percent discount rate. 

 

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

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
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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 CO2

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% (95th percentile) $52 $68 to $89 $66 to $85 

Option 1 - regional 371 million metric tonnes CO2

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% (95th percentile) $51 $65 to $85 $64 to $81 

Option 2 - state 295 million metric tonnes CO2

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% (95th percentile) $40 $53 to $69 $51 to $66 

Option 2 - regional 283 million metric tonnes CO2

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% (95th percentile) $39 $51 to $67 $50 to $64 

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

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP; 

ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles 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 CO2

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% (95th percentile) $77 $99 to $130 $97 to $120 

Option 1 - regional 501 million metric tonnes CO2

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% (95th percentile) $76 $98 to $130 $96 to $120 

Option 2 - state 376 million metric tonnes CO2

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% (95th percentile) $57 $74 to $95 $72 to $92 

Option 2 - regional 368 million metric tonnes CO2

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% (95th percentile) $56 $72 to $93 $71 to $90 

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

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles 

are not yet available. 
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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 CO2

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% (95th percentile) $94 $120 to $150 $120 to $150 

Option 1 - regional 545 million metric tonnes CO2

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% (95th percentile) $92 $120 to $150 $110 to $140 

Option 2 - state 373 million metric tonnes CO2

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% (95th percentile) $63 $120 to $150 $110 to $140 

Option 2 - regional 353 million metric tonnes CO2

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% (95th percentile) $60 $76 to $97 $74 to $93 

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

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 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 Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

4.5 Unquantified Co-benefits 

The monetized co-benefits estimated in this RIA only reflect a subset of co-benefits 

attributable to the health effect reductions associated with ambient fine particles and ozone. Data, 

time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the 

co-benefits from several important benefit categories, 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, SO2carbon monoxide, SO2 , Nand NO2, 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 NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

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

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

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

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — NO2 ISA2,3

Reduced incidence of 

morbidity from 

exposure to SO2

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

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

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

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

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — SO2 ISA1,2

Reduced incidence of 

morbidity from 

exposure to CO 

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced incidence of 

morbidity from 

exposure to 

methylmercury 

Neurologic effects—IQ loss — — IRIS; NRC, 

20001

Other neurologic effects (e.g., developmental 

delays, memory, behavior) — — IRIS; NRC, 

20002

Cardiovascular effects — — IRIS; NRC, 

20002,3

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

20002,3

Reduced incidence of 

morbidity from exposure 

to HAP 

Effects associated with exposure to hydrogen 

chloride — — ATSDR, IRIS1,2

Improved Environment 

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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 ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials 

Household soiling — — PM ISA1,2

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

Reduced effects from 

PM deposition (metals 

and organics) 

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation 

and ecosystem effects 

from exposure to ozone 

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition) 

— — Ozone ISA2

Reduced effects from 

acid deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

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

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles, 

fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure 

to methylmercury 

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

reproductive effects) — — Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — — Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA. 2

We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. 

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4.5.1 HAP Co-benefits 

Due to methodology and resource limitations, we were unable to estimate the co-benefits 

associated with reducing emissions of the hazardous air pollutants in this analysis. EPA’s SABHES concluded that “the challenges for assessing progress in health improvement as a result of 

reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of 

exposure-response functions, uncertainties in emissions inventories and background levels, the 

difficulty of extrapolating risk estimates to low doses and the challenges of tracking health 

progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 

2009, EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated 

with HAP emitted by EGUs. Below we describe the health effects associated with the two HAP 

for which we were able to quantify emission reductions for the proposed guidelines: mercury and 

hydrogen chloride. 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 transformed into a more toxic form, methylmercury 

(MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially 

the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS 

Study was issued which provides a thorough review of the effects of MeHg on human health 

(NRC, 2000).

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 Internet at <http://www.epa.gov/hg/report.htm>. 

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obtain other related and more recent publications to complement the material summarized by the 

NRC in 2000. 

In its review of the literature, the NAS found neurodevelopmental effects to be the most 

sensitive and best documented endpoints and appropriate for establishing a reference dose (RfD) 

(NRC, 2000); in particular NAS supported the use of results from neurobehavioral or 

neuropsychological tests. The NAS report noted that studies in animals reported sensory effects 

as well as effects on brain development and memory functions and support the conclusions based 

on epidemiology studies. The NAS noted that their recommended endpoints for an RfD are 

associated with the ability of children to learn and to succeed in school. They concluded the 

following: “The population at highest risk is the children of women who consumed large 

amounts of fish and seafood during pregnancy. The committee concludes that the risk to that 

population is likely to be sufficient to result in an increase in the number of children who have to 

struggle to keep up in school.” 

The NAS summarized data on cardiovascular effects available up to 2000. Based on these 

and other studies, the NRC concluded that “Although the data base is not as extensive for 

cardiovascular effects as it is for other end points (i.e., neurologic effects), the cardiovascular 

system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated 

that “additional studies are needed to better characterize the effect of methylmercury exposure on 

blood pressure and cardiovascular function at various stages of life.” 

Additional cardiovascular studies have been published since 2000. EPA did not to develop 

a quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists on the dose-response functions for these 

effects. In addition, there is inconsistency among available studies as to the association between 

MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the 

exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet 

received the review and scrutiny of the more well-established neurotoxicity data base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier 
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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 between 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 from dietary, and chronic MeHg exposures similar 

to and above those seen in the Faroes and Seychelles populations. 

Although exposure to some forms of Hg can result in a decrease in immune activity or an 

autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is limited 

(NRC, 2000)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 tenuous. Multiple human epidemiological studies 

have found no significant association between Hg exposure and overall cancer incidence, 

although a few studies have shown an association between Hg exposure and specific types of 

cancer incidence (e.g., acute leukemia and liver cancer) (NRC, 2000). 

There is also some evidence of reproductive and renal toxicity in humans from MeHg 

exposure. However, overall, human data regarding reproductive, renal, and hematological 

toxicity from MeHg are very limited and are based on either studies of the two high-dose 

 

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 methyl 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 Assessment. Office of Research and Development. Available 

at http://www.epa.gov/iris/subst/0073.htm. 
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poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of 

chronic exposures at the levels of interest in this analysis. 

4.5.1.2 Hydrogen Chloride 

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

membranes of the nose, throat, and respiratory 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 concentrations can rapidly lead to swelling and 

spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of 

rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can 

lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irritant-induced type 

of asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects.27

4.5.2 Additional NO2 Health Co-Benefits 

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also associated 

with a variety of adverse health effects associated with direct exposure. Unfortunately, we were 

unable to estimate the health co-benefits associated with reduced NO2 exposure in this analysis. 

Therefore, this analysis only quantified and monetized the PM2.5 and ozone co-benefits 

associated with the reductions in NO2 emissions. 

Following a comprehensive review of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) 

(U.S. EPA, 2008c) concluded that there is a likely causal relationship between respiratory health 

effects and short-term exposure to NO2. The NOx ISA concluded that the evidence “is sufficient 

 

26 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen 

Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at 

http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02. 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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to infer a likely causal relationship between short-term NO2 exposure and adverse effects on the 

respiratory system.” These epidemiologic and experimental studies encompass a number of 

endpoints including emergency department visits and hospitalizations, respiratory symptoms, 

airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also 

concluded that the relationship between short-term NO2 exposure and premature mortality was 

“suggestive but not sufficient to infer a causal relationship,” because it is difficult to attribute the 

mortality risk effects to NO2 alone. Although the NOx ISA stated that studies consistently 

reported a relationship between NO2 exposure and mortality, the effect was generally smaller 

than that for other pollutants such as PM. 

4.5.3 Additional SO2 Health Co-Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of adverse health effects associated with direct exposure. Unfortunately, we were unable to 

estimate the health co-benefits associated with reduced SO2 in this analysis because we do not 

have air quality modeling data available. Therefore, this analysis only quantifies and monetizes 

the PM2.5 co-benefits associated with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008a). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

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SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for pollutants. We did not quantify these co-benefits due to data 

constraints. 

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits 

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute to a 

variety of adverse welfare effects, including acidic deposition, visibility impairment, and nutrient 

enrichment. Deposition of nitrogen causes acidification, which can cause a loss of biodiversity of 

fishes, zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in 

sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in 

terrestrial ecosystems. In the northeastern U.S., the surface waters affected by acidification are a 

source of food for some recreational and subsistence fishermen and for other consumers and 

support several cultural services, including aesthetic and educational services and recreational 

fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to 

aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant 

to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of these 

plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to 

increased mortality of canopy trees. Terrestrial acidification affects several important ecological 

services, including declines in habitat for threatened and endangered species (cultural), declines 

in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in 

forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d) 

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

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also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 

2008d) 

4.5.5 Ozone Welfare Co-Benefits 

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2013b). Sensitivity to ozone is highly variable 

across species, with over 65 plan species identified as “ozone-sensitive”, many of which occur in 

state and national parks and forests. These effects include those that damage or impair the 

intended use of the plant or ecosystem. Such effects are considered adverse to the public welfare 

and can include reduced growth and/or biomass production in sensitive plant species, including 

forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased 

susceptibility to harsh weather, disease, insect pest infestation, and competition), species 

composition shift, and changes in ecosystems and associated ecosystem services. 

4.5.6 Carbon Monoxide Co-Benefits 

CO in ambient air is formed primarily by the incomplete combustion of carbon-containing 

fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these 

reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, 

such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses 

through the respiratory system to the blood, which can cause hypoxia (reduced oxygen 

availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that are dependent upon concentration and duration of exposure. The Integrated Science 

Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO 

is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individuals 

with coronary heart disease. Epidemiologic studies associate short-term CO exposure with 

increased risk of emergency department visits and hospital admissions. Coronary heart disease 

includes those who have angina pectoris (cardiac chest pain), as well as those who have 

experienced a heart attack. Other subpopulations potentially at risk include individuals with 

diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and 

individuals in very early or late life stages, such as older adults or the developing young. The 

evidence is suggestive of a causal relationship between short-term exposure to CO and 

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respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for 

birth outcomes and developmental effects following long-term exposure to CO, and for central 

nervous system effects linked to short- and long-term exposure to CO. 

4.5.7 Visibility Impairment Co-Benefits 

Reducing secondary formation of PM2.5 would improve levels visibility in the U.S. because 

suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 

2009b). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to 

people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility 

increases the quality of life where individuals live and work, and where they engage in 

recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern 

U.S. and particulate nitrate is an important contributor to light extinction in California and the 

upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. 

EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. 

Without air quality modeling, we are unable to estimate visibility related benefits, and we are 

also unable to determine whether the emission reductions associated with the proposed 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas. 

 

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National Research Council. 2010. Advancing the Science of Climate Change. National 

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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>. 
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Developing Best Estimates of Dose-Response Functions An SAB Workshop Report of an 

EPA/SAB Workshop (Final Report). EPA-SAB-EC-WKSHP-02-001. January. Available at: 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005B

F6F6/$File/ecwkshp02001%2Bappa-g.pdf>. 

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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>. 

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SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction.

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<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A70051

6498/$File/eeacf013.pdf>. 

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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>. 

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Benefits of Reducing Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. 

July. Available at: 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$Fi

le/EPA-COUNCIL-08-001-unsigned.pdf>. 

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Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available at: 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73

ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. 
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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>. 

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Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the 

Clean Air Act. EPA-COUNCIL-10-001. June. Available at: < 

http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA

39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>. 

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Review of Valuing Mortality Risk Reductions 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>. 

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United States. Thomas R. Karl, Jerry M. Melillo, and Thomas C. Peterson, (eds.). Cambridge 

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postneonatal infant mortality and particulate 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 electricity, natural gas, and coal resulting from the 

proposed Electric Utility Generating Unit (EGU) Existing Source Greenhouse Gas (GHG) 

Guidelines. This chapter addresses the impact of these changes on other markets and discusses 

some of the determinants of the magnitude of these impacts. 

Under the proposed EGU Existing Source GHG Guidelines states are not required to use 

any of the measures that the EPA determines constitute BSER, or use those measures to the same 

degree of stringency that the EPA determines is achievable 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 illustrative of compliance actions states may take. The 

compliance approaches adopted by the states will ultimately drive the magnitude and timing of 

impacts on the prices of electricity, natural gas, and coal and in turn other markets that use these 

commodities in the production process. The flexibility afforded to states by the Clean Air Act 

also allows them to adopt programs which include design elements that may mitigate or promote 

particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas 

Initiative use the revenues from allowance auctions to support direct bill assistance for retail 

consumers, fund investments in clean energy and electricity demand reduction for business 

consumers, and support employment in the development of clean and renewable energy 

technologies. In its recent regulations to limit GHG emissions, California’s Air Resources Board 

designated a portion of allowances to be allocated to electric distribution companies for the 

purpose of mitigating electricity rate increases and their associated impacts. Other states may 

choose to comply with particularly robust deployment of renewables, energy efficiency, or 

natural gas in order to promote manufacturing demand or employment in those sectors. 

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To support the Transport Rule proposed in the summer of 2010, among other rulemakings, 

the EPA used the Economic Model for Policy Analysis (EMPAX) to estimate the effect of 

impacts projected by the Integrated Planning Model (IPM) on markets outside the electricity 

sector. EMPAX is a dynamic computable general equilibrium (CGE) model that forecasts a new 

equilibrium for the entire economy after a policy intervention. While the external Council on 

Clean Air Compliance Analysis (Council) peer review of this study stated that inclusion of 

benefits in an economy-wide model, specifically adapted for use in that study, “represent[ed] a 

significant step forward in benefit-cost analysis,” EPA recognizes that serious technical 

challenges remain when attempting to evaluate the benefits and costs of potential regulatory 

actions using economy-wide models. Consistent with the Council’s advice regarding the 

importance of including benefit-side effects demonstrated by the 1990 to 2020 Clean Air Act 

study, and the lack of available multi-year air quality projections needed to include these benefitside effects, EPA has not conducted CGE modeling for this proposal. 

However, the EPA recognizes that serious technical challenges remain when attempting to 

evaluate the impacts of potential regulatory actions using economy-wide models. The EPA is 

therefore establishing a new Science Advisory Board (SAB) panel on economy-wide modeling 

to consider the technical merits and challenges of using this analytical tool to evaluate costs, 

benefits, and economic impacts in regulatory development. The EPA will use the 

recommendations and advice of this panel as an input into its process for improving benefit-cost 

and economic impact analyses that are used to inform decision-making at the agency. The panel 

will also be asked to identify potential paths forward for improvements that could address the 

challenges posed when economy-wide models are used to evaluate the effects of regulations. 

The advice from the Science Advisory Board (SAB) panel formed specifically to address 

the subject of economy-wide modeling will not be available in time for this proposal. Given the 

ongoing SAB panel on economy-wide modeling, the uncertain nature of the ultimate energy 

price impacts due to the compliance flexibility for states, and the ongoing challenges accurately 

representing costs, energy efficiency savings and economic benefits in economy-wide modeling, 

this Chapter proceeds by presenting the energy impacts associated with the illustrative scenarios 
analyzed in Chapter 3. This Chapter then presents a qualitative discussion of the factors that will, 

in part, determine the timing and magnitude of effects in other markets. 

5.2 Summary of Secondary Market Impacts of Energy Price Changes 

Electricity, natural gas, and coal are important inputs to the production of other goods and 

services. Therefore, changes in the price of these commodities will shift the production costs for 

sectors that use electricity, natural gas, and coal in the production of other goods and services. 

Such changes in production costs may lead to changes in the quantities and/or prices of the goods 

or services produced and changes in imports and exports. We refer to these changes as secondary 

market impacts. 

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on the 

illustrative compliance approaches modeled for this proposal. IPM is a multi-regional, dynamic, 

deterministic linear programming model of the U.S. electric power sector that is described in 

more detail in Chapter 3. The prices are average prices weighted by the amount used. Table 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.

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

Regulatory Option 

2020 2025 2030 

Option 1 Regional Compliance:

Electricity Price Change: 5.9% 2.7% 2.7% 

Delivered Natural Gas Price Change: 9.3% -3.3% -0.9% 

Delivered Coal Price Change: -16.3% -18.3% -18.1% 

Option 2 Regional Compliance 2020 2025 2030

Electricity Price Change: 3.6% 2.4% 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. Conversely, in options and years when prices of these 

inputs decreased, one would expect increases in production and decreases in market prices within 

these sectors. Smaller changes in input price changes are assumed to lead to smaller impacts 

within secondary markets. However, a number of factors in addition to the magnitude and sign of 

the energy price changes, influence the magnitude of the impact on production and market prices 

for sectors using electricity, natural gas, or coal as inputs to production. These factors are 

discussed below. 

5.3.1 Share of Total Production Costs 

The impact of energy price changes in a particular sector depends, in part, on the share of 

total production costs attributable to those 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 production costs, the impact will be smaller than for 

sectors in which these inputs make up a greater portion of total production costs. Therefore more 

energy-intensive sectors would potentially experience greater cost increases when electricity, 

natural gas, or coal prices increase, but would also experience greater savings when these input 

prices decrease.1

5.3.2 Ability to Substitute between Inputs to the Production Process 

The ease with which producers are able to substitute other inputs for electricity, natural 

gas, or coal, or even amongst those commodities, influences the impact of price changes for 

these inputs. Those sectors with a greater ability to substitute across energy inputs or to other 

inputs will be able to, at least partially, offset the increased cost of these inputs resulting in 

smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some 

sectors may choose to use more of these inputs in place of other more costly substitutes. 

5.3.3 Availability of Substitute Goods and Services 

The ability of producers in sectors experiencing changes in their input prices to pass along 

the increased costs to their customers in the form of higher prices for their products depends, in 

part, on the availability of substitutes for the sectors’ products. Substitutes may either be other 

domestic products or foreign imports. If close substitutes exist, the demand for the product will 

in general be more elastic and the producers will be less able to pass on the added cost through a 

price increase. 

Such substitution can also take place between foreign and domestic goods within the same 

sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of 

goods imported or exported from sectors using these inputs. When the cost of domestic 

production increases due to more expensive inputs, imports may increase as consumers substitute 

towards relatively less costly foreign-produced goods. If imports increase as a result of a 

regulation and those imports come from countries with fewer emission controls, this can result in 

foreign emission increases that offset some portion of domestic decreases, an effect commonly 

 

1

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

electricity price also depends on the expenditures the sector makes to reduce its demand for electricity under any 

energy efficiency program that was adopted to achieve a state goal. 

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 (CO2) and other GHG emissions. Unlike most criteria pollutants and hazardous 

air pollutants, the impacts of CO2 emissions are not affected by the location from which those 

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 transmission does not 

lend itself to significant imports or exports of electricity.

5.4 Conclusions 

Changes in the price of electricity, natural gas, and coal can impact markets for goods and 

services produced by sectors that use these energy inputs in the production process. The direction 

and magnitude of these impacts is influenced by a number of factors. Changes in cost of 

production may lead to changes in price, quantity produced, and profitability of firms within 

secondary markets. If regulation results in changes in domestic markets that lead to an increase 

in imports, increases in production in countries with more energy-intensive production may lead 

to emissions leakage. 

Comment [A3]: Would EPA provide the 

page numbers in Chapter 3 that leads to this 

conclusion? 
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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 job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts,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 Electric Generating Unit Greenhouse Gas (EGU GHG) 

Existing Source Guidelines for the electric power industry, coal and natural gas production, and 

demand-side energy efficiency. 

Section 6.2 presents the overview of the guidelines and a general description of the 

associated building block framework. Section 6.3 describes the theoretical framework used to 

analyze regulation-induced employment impacts, discussing how economic theory alone cannot 

predict whether such impacts are positive or negative. Section 6.4 presents an overview of the 

peer-reviewed literature relevant to evaluating the effect of environmental regulation on 

employment. Section 6.5 provides background regarding recent employment trends in the 

electricity generation, coal and natural gas extraction, renewable energy, and demand-side 

energy efficiency-related sectors. Section 6.6 presents the EPA’s quantitative projections of 

potential employment impacts in these sectors. These projections are based in part on a detailed 

model of the electricity production sectors used for this regulatory analysis. Additionally, this 

section discusses projected employment impacts due to demand-side energy efficiency activities. 

Section 6.7 offers several conclusions. 

6.2 Overview of the Proposed EGU GHG Existing Source Guidelines 

The EPA is proposing emission guidelines for states to use in developing plans to address 

greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, EPA is proposing 

 

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 developing plans to attain the state-specific goals. The 

guidelines, as proposed, will lower carbon intensity of power generation in the United States. 

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

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

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

such reductions and non-air quality health and environmental impact and energy requirements, 

the Administrator determines has been adequately demonstrated. Consistent with CAA section 

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

emission reductions that a state can achieve through the cost-effective application of BSER. The 

EPA is using four building blocks as a basis to determine state-specific goals. 

6.2.1 Determining State Goals Utilizing the Four Building Blocks 

In proposing state goals, EPA is applying the following four building blocks. Each 

represents a demonstrated approach to improving the GHG performance of existing EGUs in the 

power sector: 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. 
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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, etc.) over time. In light of these difficulties, economic theory provides a 

constructive framework for analysis. 

Microeconomic theory describes how firms adjust their use of inputs in response to 

changes in economic conditions.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 effects and substitution effects.7

 Regulation affects the profit-

 

3

 See Layard and Walters (1978), a standard microeconomic 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 quantity 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 regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer 

and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand 

effect; 2) a cost effect; and 3) a factor-shift effect. 
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6-4 

maximizing quantity of output by changing the marginal cost of production. If regulation causes 

marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in 

demand, and resulting in a decrease in production. The output effect describes how, holding 

labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by 

Berman and Bui, although many assume that regulation increases marginal cost, it need not be 

the case. A regulation could induce a firm to upgrade to less polluting, and more efficient 

equipment that lowers marginal production costs. In such a case, output could increase. For 

example, in the context of the current rule, improving the heat rate of utility boiler increases fuel 

efficiency, lowering marginal production costs, and thereby potentially increasing the boiler’s 

generation. An unregulated profit-maximizing firm may not have chosen to install such an 

efficiency-improving technology if the investment cost were too high.8

The substitution effect describes how, holding output constant, regulation affects laborintensity of production. Although increased environmental regulation may increase use of 

pollution control equipment and energy to operate that equipment, the impact on labor demand is 

ambiguous. For example, equipment inspection requirements, specialized waste handling, or 

pollution technologies that alter the production process may affect the number of workers 

necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the 

effect of regulation on pollution control equipment and expenditures required by the regulation 

and the corresponding change in labor-intensity of production. 

In summary, as output and substitution effects may be positive or negative, theory alone 

cannot predict the direction of the net effect of regulation on labor demand at the level of the 

regulated firm. Operating within the bounds of standard economic theory, however, empirical 

estimation of net employment effects on regulated firms is possible when data and methods of 

sufficient detail and quality are available. The literature, however, illustrates difficulties with 

empirical estimation, For example, studies sometimes rely on confidential plant-level 

employment data from the U.S. Census Bureau, possibly combined with pollution abatement 

expenditure data that are too dated to be reliably informative. In addition, the most commonly 

used empirical methods do not permit estimation of net effects. 

 

8

 An unregulated profit-maximizing firm may not have chosen to install such an efficiency-improving technology if 

the investment cost were too high. 
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The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. The approach must be modified when 

applied at the industry level. 

At the industry-level, labor demand is more responsive if: (1) the price elasticity of 

demand for the product is high, (2) other factors of production can be easily substituted for labor, 

(3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total 

production costs.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 individual firm, theory cannot determine the sign or 

magnitude of industry-level regulatory effects on labor demand. Determining these signs and 

magnitudes requires additional sector-specific empirical study. For environmental rules, much of 

the data needed for these empirical studies 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. 
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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 environmental data to assess these impacts. 

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

 

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 unemployment 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 later paragraphs of this section. 

13 E.g. Graff Zivin and Neidell (2012). 
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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 either negative or positive. Estimation of net 

employment effects for regulated sectors is possible when data of sufficient detail and quality are 

available. Finally, economic theory suggests that labor supply effects are also possible. In the 

next section, we discuss the empirical literature. 

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

The labor economics literature contains an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the theoretical framework discussed 

in the preceding section.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 environmental 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 increases in demand for specialized labor within the

environmental protection sector have been prepared for several EPA regulations in the past, 

including the Mercury and Air Toxics Standards (MATS).16 Overall, the peer-reviewed literature 

does not contain evidence that environmental regulation has a large impact on net employment 

(either negative or positive) in the long run across the whole economy. 

 

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

15 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 

16 U.S. EPA (2011b). 
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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 regulations. Using SCAQMD’s local air quality 

regulations, Berman and Bui identify the effect of environmental regulations on net employment 

in regulated manufacturing industries relative to other plants in the same 4-digit 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 regulation “probably increased labor demand slightly” 

but that “the employment effects of both compliance and increased stringency are fairly precisely 

estimated zeros, even when exit and dissuaded entry effects are included” (Berman and Bui, 

2001, p. 269).18 

The few studies in peer-reviewed journals evaluating employment impacts of policies 

that reduce CO2 emissions in the electric power generation sector are in the European context. In 

a sample of 419 German firms, 13 percent of which were in the electricity sector, Anger and 

Oberndorfer (2008) find that the initial allocation of emission permits did not significantly affect 

employment growth in the first year of the 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 CO2 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), 

tThey do not estimate the number of jobs created 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)
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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, controlling for electricity prices and labor regulation 

(right to work laws). Their methodology identifies employment impacts by focusing on 

neighboring counties with different air quality regulations. They find limited evidence that 

environmental regulations may cause employment to be lower within “county-border-pairs.” 

This result suggests that regulation may cause an effective relocation of labor across a county 

border, but since one county’s loss is another’s gain, such shifts cannot be transformed into an 

estimate of a national net effect on employment. Moreover this result is sensitive to model 

specification choices. 

6.4.2 Labor Supply Impacts 

The empirical literature on environmental regulatory employment impacts focuses 

primarily on labor demand. However, there is a nascent literature focusing on regulation-induced 

effects on labor supply. 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>.
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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 variation in monitored ozone levels on productivity. They 

find “ozone levels well below federal air quality standards have a significant impact on 

productivity: a 10 parts per billion (ppb) decreases in ozone concentrations increases worker 

productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 

This section has outlined the challenges associated with estimating regulatory effects on 

both labor demand and supply for specific sectors. These challenges make it difficult to estimate 

net national employment estimates that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the macro-economy. 

Quantitative estimates are further complicated by the fact that macroeconomic models often have 

little sectoral detail and usually assume that the economy is at full employment. The EPA is 

currently seeking input from an independent expert panel on modeling economy-wide regulatory 

impacts, including employment effects.21

6.5 Recent Employment Trends 

The U.S. electricity system includes employees that support electric power generation, 

transmission and distribution, the extraction of fossil fuels, renewable energy generation and 

supply-side and demand-side energy efficiency. This section describes recent employment trends 

in the electricity system. 

6.5.1 Electric Power Generation 

In 2013 the electric power generation, transmission and distribution sector (NAICS 2211) 

employed 394,000 workers in the U. S.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-panel-on-the-role-of-economy-wide-modeling>.

22 U.S. Bureau of Labor Statistics. “Current Employment Survey Seasonally Adjusted Employment for Electric 

Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Data 

extracted on: February 19, 2014. Available at: <http://www.bls.gov/ces/data.htm>.
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6-11 

accounted for the largest share of workers (30 percent).23 These categories include inspection, 

testing, repairing and maintaining of electrical equipment and/or installation and repair of cables 

used in electrical power and distribution systems. Other major occupation categories include 

office and administrative support (17 percent), production occupations (15 percent), architecture 

and engineering (11 percent), business and financial operations (7 percent) and management (6 

percent). 

As shown in Figure 6.1, employment in the Electric Power Industry averaged 435,000 

workers in the early 2000s, declining to an average of 400,000 workers later in the decade, and to 

394,000 workers in 2013. 

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, Electric Power Generation, Transmission, and Distribution 

(NAICS 2211). Available at: <http://www.bls.gov/oes/current/naics4_221100.htm>. 350 400 450 500

2000 2005 2010 2015

Year

Electric Power Industry national employment (in thousands)
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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 employment, 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 Title 30 Employment Data – selected contract employment 

is included, p. 2. Available at: 

<http://www.msha.gov/Stats/Part50/WQ/MasterFiles/MIWQ%20Master_20133.pdf>. U.S. Bureau of Labor 

Statistics, Current Employment Statistics – contract employment not included. Available at: 

<http://www.bls.gov/ces/idcf/forme_sp.pdf>. 

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>. 
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6-13 

Figure 6.2. Coal Production Employment 

6.5.2.2 Oil and Gas Extraction 

In 2013, there were 198,000 employees in the oil and gas extraction sector (NAICS 

211).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/iag211.htm Accessed Feb. 19>. 20 40 60 80 100

2000 2005 2010 2015

Year

BLS - employees MSHA - employees

MSHA - contractors

Coal mining employment - national (in thousands)
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Figure 6.3. Oil and Gas Production Employment 

6.5.3 Clean Energy Employment Trends 

Clean energy resources, such as energy efficiency and renewable energy, are used to 

meet energy demand, reduce peak electricity system loads, and reduce reliance on the most 

carbon-intensive sources of electricity. However, there is not a single clean energy sector in 

standard national accounts classifications. Renewable generation is not reported to the Bureau of 

Labor and Statistics separately from other electric power generation. Similarly, manufacturers of 

energy efficient appliances are not reported separately from conventional appliance 

manufacturers and green building design is not separate from the construction sector. Instead, 

clean energy technology and services are supported by industries throughout the economy. 

Without a specific industrial classification, it is difficult to quantify the exact number of 

clean energy-related jobs or document the trends. Employees engaged in clean energy can span 

many job classifications, such as experts required to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians who install service or operate 100 150 200 250

2000 2005 2010 2015

Year

Oil and Gas Extraction national employment (in thousands)
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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 less 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>. 
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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 economy in 2010, rising 7.4 percent 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 trends in this sector, we therefore, rely on a 

Brookings Institution study, Muro et al. (2011). This study built a national database of “clean 

economy” jobs from the bottom up, verifying each company individually.34 They include a list of 

 

31 BLS has identified 325 detailed industries (6-digit NAICS) as potential producers of green goods and services. 

Available at: <http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, last modified date: March 

19, 2013. 

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>.

33U.S. Department of Labor, U.S. Bureau of Labor Statistics. 2010. National Occupational Employment and Wage 

Estimates, United States. Available at: <http://www.bls.gov/oes/2010/may/oes_nat.htm>, May. National 

Occupational Employment and Wage Estimates, United States http://www.bls.gov/oes/2010/may/oes_nat.htm. 

34 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15. 
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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 Electricity Generation-Related Employment 

Sector Jobs, 2010 

2003-2010 Average Annual 

Growth Rate 

Biofuels/Biomass 20,680 8.9 

Geothermal 2,720 6.7 

Hydropower 55,467 -3.6 

Renewable Energy Services 1,981 6.3 

Solar Photovoltaic 24,152 10.7 

Solar Thermal 5,379 18.4 

Waste-to-Energy 3,320 3.7 

Wave/Ocean Power 371 20.9 

Wind 24,294 14.9 

Total 138,364 3.1 

 Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A.

6.5.3.3 Employment Trends in Demand-Side Energy Efficiency Activities 

U.S. government data used for calculating the historical trends in the demand-side energy 

efficiency sector come from the BLS green goods and services surveys. BLS reports an energy 

efficiency category, finding 1.49 million private sector energy efficiency jobs in 2010 and 1.64 

million in 2011. 

For a longer term trend the Brookings Institution study (Muro et al. 2011) built a national 

database of “clean economy” jobs from the bottom up, verifying each company individually.35

 

35 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15 
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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/Series/resources/0713_clean_economy.pdf, Appendix A 

In addition, other research institutes and industry groups have clean economy or clean 

energy employment databases. While definitions and timeframes vary, all show positive 

employment trends of 1.9 percent or more growth in clean energy-related jobs annually. 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. 
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Specifically, the direct employment effects of supply-side initiatives include changes in labor 

demand for manufacturing, installing, and operating higher efficiency or renewable energy 

electricity generating assets supported by the initiative while reducing the demand for labor that 

would have been used by less efficient or higher emitting generating assets. Once implemented, 

increases in operating efficiency would impact the power sector’s demand for fuel and plans for 

EGU retirement and new construction. 

In addition, EPA expects state compliance plans to also include demand-side energy 

efficiency policies and programs that typically change energy consumption patterns of business 

and residential consumers by reducing the quantity of energy required for a given level of 

production or service. Demand-side initiatives generally aim to increase the use of cost-effective 

energy efficiency technologies (e.g., including more efficient appliances and air conditioning 

systems, more efficient lighting devices, more efficient design and construction of new homes 

and businesses), and advance efficiency improvements in motor systems and other industrial 

processes. Demand-side initiatives can also directly reduce energy consumption, such as through 

programs encouraging changing the thermostat during the hours a building is unoccupied or 

motion-detecting room light switches. Such demand-side energy efficiency initiatives directly 

affect employment by encouraging firms and consumers to shift to more efficient products and 

processes than would otherwise be the case. Employment in the sectors that provide these more 

efficient devices and services would be expected to increase, while employment in the sectors 

that produce less efficient devices would be expected to contract. 

This analysis uses the cost projections from the engineering-based Integrated Planning 

Model (IPM) to project labor demand impacts of the proposed guidelines for the electricity 

generation sector (fossil, renewable, and nuclear), and the fuel production sector (coal and 

natural gas). These projections include effects attributable to heat rate improvements, 

construction of new EGUs, changes in fuel use, and reductions in electricity generation due to 

demand side energy efficiency activities. To project labor requirements for demand side energy 

efficiency activities, the analysis uses a different approach that combines data on historic 

changes in employment and expenditures in the energy efficiency sector with projected changes 

in expenditures in the sector arising from state implementation of the proposed guidelines. 
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We project labor impacts for two options for establishing the “best system of emission 

reduction” (BSER) for GHG emissions from existing EGUs. The EPA is proposing a BSER goal 

approach referred to as Option 1 and taking comment on a second approach referred to as Option 

2. 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 Electricity Generation and Fossil Fuel Extraction 

The analytical approach used in this analysis is a bottom-up engineering method 

combining EPA’s cost analysis of the proposed guidelines with data on labor productivity, 

engineering estimates of the amount and types of labor needed to manufacture, construct, and 

operate different types of generating units, and prevailing 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 employment impacts throughout the U.S. economy, the 

engineering based analysis focuses on the direct impact on labor demand in industries closely 

involved with electricity generation. The engineering approach projects labor changes measured 

as the change in each analysis year in job-years36 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 2020 may represent 20 full-time jobs or 40 half-time jobs 

in that year. 
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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 maintaining generating units, as well as labor needed to mine coal 

and natural gas) are the same as we used in MATS (with updated data where available), the 

methods used to estimate the labor associated with installing new 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 2020 a significantly larger amount of renewable 

and CC capacity will be built to implement the GHG guidelines under all of the option scenarios. 

For example, in the base case IPM estimates that 11 GW of non-hydro renewable capacity will 

be built between 2016 and 2020, 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 being equal, a lower heat rate is more efficient because 

more electricity is generated from each ton of coal. 
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assumes38 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 impacts in later years from avoiding having to build 

additional new capacity. Because of the demand-side energy efficiency programs, the total 

amount of electricity needed by 2030 is substantially lower with all of the Options than in the 

base case. For example, in Option 1 with regional compliance only half the total new 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 CO2 and precursor pollutants from fossil fuel 

generation. The avoided needed to build new capacity, however, also has significant labor 

impacts. A portion of the employment that would have been used to build the new capacity in the 

base case will not occur with the implementation of the GHG guidelines. Similarly, less labor 

involved with operating and providing fuel for new units will be needed with the GHG 

guidelines than in the base case. 

Overall, the impact of much more rapid construction of new renewable and fossil 

generation capacity in the early years of implementing the GHG guidelines, followed by the need 

to build substantially less capacity in later years as the demand-side energy efficiency programs 

reduce the overall electricity demand relative to the base case, creates an important but complex 

temporal dynamic to the supply side labor analysis. In the early years of implementation there 

will be a sizable net increase in the amount of labor needed to construct and operate the new 

generating capacity. However in later years the reduced need for additional newly built capacity 

results in a sizable net decrease in the amount of labor needed relative to the base case. 

The changes in the timing and overall need for new capacity 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 labor needs. In addition, there are similar labor 

impacts from the loss of operating and fuel related jobs arising from the retirement of existing 

coal generating capacity. 

 

38 Table 4.7, IPM 5.13 Documentation. 

Formatted: Subscript
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A critical component of the overall labor impacts of implementing the GHG guidelines is 

the impact of the labor associated with the demand-side energy efficiency activities. The 

demand side labor impacts are presented in Section 6.6.2. All of the labor impacts of the 

demand-side energy efficiency activities are increases in labor needs, which more than offset the 

loss of supply-side jobs associated with the decreasing demand for electricity Section 6.6.2 

projects additional labor required for 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 expenditures, and apportionment of total labor cost to 

various labor categories. Hourly labor expenditures (including wages, fringe benefits, and 

employer-paid costs including taxes, insurance and administrative costs) for each category are 

used to estimate the labor quantity (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 
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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 distributed throughout the interval. Tables 6-4 and 

6-5 reflect this assumption by presenting the average labor utilization per year during each of the 

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-related labor impacts occur in the first interval (2017 

to 2020), and are assumed to be occur evenly throughout that four year interval. Therefore, the 

HRI-related labor estimates in Tables 6-4 and 6-5 are the annual average labor impacts for of the 

4 years. There are no HRI improvements made after 2020. 

The labor estimates for operating and maintaining generating units annually are based on 

IPMs estimates of Fixed Operating and Maintenance (FOM) Costs. IPM estimates FOM for 

each year individually, so the net changes in O&M-related labor estimates in Tables 6-4 and 6-5 

are single year estimates for 2020, 2025 and 2030. These O&M labor estimates are not the 

average annual averages labor needs throughout each multi-year interval. There are O&M labor 

changes occurring in the all years throughout the entire period 2017-2030, but labor impact 

changes each year. The 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 occur with each alternative option and compliance 

alternative. These labor impacts include not only the impacts of Building Blocks 1 through 3, but 

also the changes in total generation needed that result from the demand-side energy efficiency 

activities in Building Block 4. The additional upstream labor impacts from the demand side 

activities are presented below in Section 6.2.2. 
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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 guidelines will result in the amount of some types 

of additional new generating capacity will increase 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 renewable generation capacity (primarily wind) will 
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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 (with 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 initial labor impacts of changes 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 changes 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 maintenance 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 the amount of fuel needed by any changes in additional capacity. 

 

39 Table 4.7, IPM 5.13 Documentation. 
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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. 

 
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Table 6-4. Engineering-Baseda Changes in Labor Demand Utilization, in 2020, 2025 and 

2030, Regional Compliance Approach - (Number of Job-Yearsb 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 

17,0500 -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 

25,9400 -72,000 -79,900 26,72,600 -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 impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Figures in table are average job-years during each of 

the years in each range. Negative job-year estimates when additional generating capacity must be built in the base 

case, but is avoided in the Guideline implementation scenarios due to HRI or Demand-side energy efficiency 

programs. 

**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance 

activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, 

while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.

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Table 6-5. Engineering-Baseda

 Changes in Labor Utilization, State Compliance Approach - 

(Number of Job-Years of Employment in Year)

Category Option 1 Option 2 

Construction-related (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 impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Figures in table are average job-years during each of 

the years in each range. Negative job-year estimates when additional generating capacity must be built in the base 

case, but is avoided in the Guideline implementation scenarios due to HRI or Demand-side energy efficiency 

programs. 

**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance 

activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, 

while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.
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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 energy 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 quantify 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 conditioning during periods of high electricity demand). 

Employment impacts of demand-side energy efficiency programs have not been 

extensively studied in the peer-reviewed, published economics literature. Instead, most research 

has focused on consumer response to and amount of energy savings achieved by these programs 

(e.g., Allcott (2011a, 2011b), Arimura et. al (2012)). Results suggest that demand-side energy 

efficiency programs reduce energy use and generate small increases in consumer welfare. These 

policy impacts are due to 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. 
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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 more than $90 billion for strategic clean energy 

investments intended to promote job creation and the deployment of low-carbon technologies” 

(p. 137), with nearly $20 billion for energy efficiency investments. The Council of Economic 

Advisors (CEA) (2011) estimated higher economic activity and employment than would have 

otherwise occurred without the American Recovery and Reinvestment Act. Using CEA’s 

methods to quantify job creation for the Recovery Act, Aldy uses the share of stimulus funds for 

clean energy investments to estimate job-years supported by the Recovery Act. The largest 

sources of job creation in clean energy are those that received the largest shares of stimulus 

funds: renewable energy, energy efficiency, and transit. Aldy’s estimates, while informative, are 

not directly applicable for employment analysis in this rulemaking as there are important 

differences in expected employment impacts from a historically large fiscal stimulus specifically 

targeting job creation during a period of exceptionally high unemployment versus environmental 

regulations taking effect several years from now. 

Yi (2013) analyzes clean energy policies and employment for U.S. metropolitan areas in 

2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing 

an additional state clean energy policy tool (renewable energy policies, 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% more clean energy employment within that MSA. 

These estimates are not transferable to this rulemaking since states are likely to change intensity 

as well as number of clean energy programs. 

Outside of the peer-reviewed, published economics literature, analysts have estimated 

employment effects of existing and proposed state 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 analyses 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 insights as to how states analyze demand-side energy 

efficiency activities. Appendix A6.C.1 Other Approaches to Estimating Employment Effects of 
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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 the historical change in expenditure in the sector 

and multiply this fraction by projected expenditure in the sector undertaken in response to these 

proposed guidelines. 

Data used for calculating the numerator of the fraction comes from the “energy 

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 total revenues by NAICS by the fraction of green revenues reported by BLS 

to obtain green revenues. The only U.S. Government data source containing this revenue 

information for all NAICS sectors is the U.S. Economic Census. This Census is conducted at 5-

year intervals (the latest available year is 2007), however, making it unsuitable for identifying 

the change in revenues from 2010 to 2011. Instead, we use data from the Annual Survey of 

Manufacturers. The disadvantage of this data source is that the manufacturing sector makes up 

only 50 percent of the 132 NAICS codes belonging to the energy efficiency sector as defined by 

the BLS Green Goods and Services surveys, with the remainder in the construction or service 

sectors. Thus, this analysis implicitly assumes that the same number of jobs per dollar are 

supported in construction and service sectors as in manufacturing. Using this approach we obtain 

a factor of 2.56 additional demand side energy efficiency jobs per additional million 2011 dollars 

of expenditure. 

Having calculated the fraction of additional jobs per additional dollar of energy efficiency 

expenditure, we use energy sector model projections of the first-year costs required for states to 

 

41For 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.
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attain the goal of demand side efficiency improvements set by building block four.43 

Multiplying this dollar expenditure by the jobs per additional dollar figure results in projected 

employment impacts for demand-side energy efficiency activities of 78,800 in 2020, 112,000 in 

2025, and 111,800 in 2030 for both the proposed Option 1 regional and state compliance 

approaches. The estimates for Option 2 are shown on Table 6-6 below. 

 Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts 

 For Option 1 and Option 2 for Both Regional and State Compliance Approaches 

 Employment iImpact (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 absence 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 CO2 Abatement Measure for Existing Power Plants”.*Since these figures represent number of employees 

(full or part time) they should not be added to the full-time equivalent job-years reported in Table 6-5. 

Although this approach has the advantage of illuminatcalculating the change in jobs for 

an incremental change in expenditures, this approach is limited by its focus on manufacturing 

sectors and. direction of bias (oOverestimation or underestimation) cannot be determined at this 

time. The EPA is requesting comment on this method, data, identification of related studies and 

peer reviewed articles and other methods. 

There is more uncertainty involved in this approach than the standard bottom-up 

engineering analysis used to estimate electricity generation and fuel production employment 

impacts of this rulemaking. For those, the EPA was able to identify a limited set of activities 

(e.g., constructing a new NGCC power plant), and study associated labor requirements. Demandside energy efficiency improvements, in contrast, encompass a wide array of activities (subsidies 

for efficient appliances, “smart meters,” etc.). In addition, there is considerable uncertainty 

regarding which activities a state will choose. Thus, the validity of the jobs per dollar approach 

 

43 See Greenhouse Gas Abatement Measures TSD, Appendix 5-4.Section XX.YY

Comment [A2]: Footnote moved to caveat

section.

Formatted: Left
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used here relies on the assumption that states will use a mix of activities similar to the 2011 

composition of energy efficiency sectors identified by BLS. 

In addition, the EPA does not have access to bottom-up information regarding labor 

requirements for these activities. Use of a constant job per dollar fraction is at best a crude 

approximation of these labor requirements. The EPA has identified several other limitations of 

this approach, outlined below. 

Job Reclassification. Job numbers in this chapter represent gross changes in the affected sector. 

As such they may over-estimate impacts to the extent that jobs created displace workers 

employed elsewhere in the economy. For demand-side efficiency activities this potential overstatement is may be higher than in other sectors. 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 toward the purchase of imported goods this 

projection will overstate the U.S. employment impact of those expenditures.

Fixed Coefficient. Implicit in this approach is the assumption that employment impacts can be 

projected decades into the future on the basis of a single calculation from 2010-2011 data. The 

labor intensity of demand side energy efficiency will likely change with technological innovation 

in the sector. In addition, even absent technological change, labor intensity of expenditures will 

likely change over time as states alter their portfolio of efficiency activities (e.g., by moving to 

higher cost activities after exhausting opportunities for low cost efficiency gains). 

Non-additional Activities. Here we assume that all activities financed by demand side energy 

efficiency expenditures are additional to what would have been undertaken in the absence of 

these programs. If utilities finance some actions customers would have undertaken in the absence 

of these programs (e.g., if a customer receives a rebate for an energy efficient appliance that 

would have been purchased without the rebate), these numbers would over-estimate employment 

impacts of the proposed guidelines. Comment [A3]: New "additionality" caveat
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6.7 Conclusion 

This chapter presents qualitative and quantitative discussions of potential employment 

impacts of the proposed guidelines for electricity generation, fuel production, and demand-side 

energy efficiency sectors. The qualitative discussion identifies challenges associated with 

estimating net employment effects and discusses anticipated impacts related to the rule. It 

includes an in-depth discussion of economic theory underlying analysis of employment impacts. 

Labor demand impacts for regulated firms can be decomposed into output and substitution 

effects, both of which may be positive or negative. Consequently, theory alone cannot predict the 

direction or magnitude of a regulation’s employment impact. It is possible to combine theory 

with empirical study specific to the regulated firms and other relevant sectors if data and 

methods of sufficient detail and quality are available. Finally, economic theory suggests that 

labor supply effects are also possible. 

We examine the peer-reviewed economics literature analyzing various aspects of labor 

demand, relying on the above theoretical framework. Determining the direction of employment 

effects in regulated industries is challenging because of the complexity of the output and 

substitution effects. Complying with a new or more stringent regulation may require additional 

inputs, including labor, and may alter the relative proportions of labor and capital used by 

regulated firms (and firms in other relevant industries) in their production processes. The 

available literature illustrates some of the difficulties for empirical estimation: studies sometimes 

rely on confidential plant-level employment data from the U.S. Census Bureau, possibly 

combined with pollution abatement expenditure data that are too dated to be reliably informative. 

In addition, the methods do not permit estimation of net economy-wide effects. Empirical 

analysis at the industry level requires estimates of product demand elasticity; production factor 

substitutability; supply elasticity of production factors; and the share of total costs contributed by 

wages, by industry, and perhaps even by facility. For environmental rules, many of these data 

items are not publicly available, would require significant time and resources in order to access 

confidential U.S. Census data for research, and also would not be necessary for other 

components of a typical RIA. Econometric studies of environmental rules converge on the 

finding that employment effects, whether positive or negative, have been small in regulated 

sectors. 
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Quantitative analysis in this chapter projects a subset of potential employment impacts in 

the electricity generation, fuel production, and demand-side energy efficiency sectors. States 

have the responsibility and flexibility to implement policies and practices for compliance with 

Proposed Electric Generating Unit Greenhouse Gas Existing Source Guidelines. As such, given 

the wide range of approaches that may be used, quantifying the associated employment impacts 

is difficult. EPA’s employment analysis includes projected employment impacts associated with 

these guidelines for the electric power industry, coal and natural gas production, and demandside energy efficiency activities. These projections are derived, in part, from a detailed model of 

the electricity production sector used for this regulatory analysis, and U.S government data on 

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 illustrative 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
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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 absence 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 CO2 Abatement Measure for Existing Power Plants”. 
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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 absence 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 CO2 Abatement Measure for Existing Power Plants”. 

Category 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).
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 Appendix 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 generating units, as well as labor needed to mine 

coal and natural gas) are the same as was used in MATS (with updated data where available). 

The labor analysis in the MATS RIA was primarily concerned with the labor needs of 

retrofitting pollution control equipment. The EGU GHG Existing Source guidelines labor 

analysis, however, involves the quantity and timing of the labor needs of building new renewable 

and natural gas, as well as making heat rate improvements at existing coal fired EGUs. These 

construction-related compliance activities in the EGU GHG Existing Source guidelines required 

developing additional appropriate analytical methods that were not needed for the MATS 

analysis. The newly developed analytical methods for the construction-related activities are 

similar in structure and overall approach to the methods used in MATS, but required additional 

data and engineering information not needed in the MATS RIA. 

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. 
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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 make 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 opportunities due to reductions in the total amount 

of new generating capacity needed to be built in the later years because of reduced 

overall demand for electricity because of demand-side energy efficiency activities; 

 lost operating and maintenance employment opportunities due to increased retirements of 

coal and small oil/gas units; 

 changes (both positive and negative) in coal mining and natural gas extraction 

employment due to the aggregate net changes in fuel demands arising from all the 

activities occurring due to compliance with the proposed guidelines. 

 

Some of the changes are one-time labor effects which are associated with the building (or 

avoiding building) new generating capacity and installing HRI. This type of employment effects 

involves project-specific labor that is used for 2 to 4 years to complete a specific construction 

and installation type of project. There are other labor effects, however, which continue year after 

year. For example, bringing new generating capacity online creates an ongoing need for labor to 

operate and maintain the new generating capacity throughout the expected service life of the 

unit. New generating capacity also creates a need for additional employment to provide the fuel 
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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 capacity, as well as similar lost labor 

opportunities from decisions to reduce a portion of the amount of additional capacity needed in 

the base case. 

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 culminating in 2020. Hence, the per-year cost of 

heat rate was calculated to be $25/kW, and this cost was used in the next step. 

This cost was then allocated to four categories based on the estimates provided by 

Andover Technology Partners (ATP), which were adapted from proxy projects involving 

installation of combustion control retrofits, such as those installed under the Best Available 

Retrofit Technology (BART) submissions from coal-fired power plants located in Wyoming and 

Arizona. For more details, refer to the Staudt (2014) report.[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 labor – boilermakers and general construction, 

engineering and management support labor, labor required to produce the equipment in upstream 

sectors, and labor required to supply the materials (assumed to be primarily steel) in upstream 

sectors. More details about these estimates are provided in the Staudt (2014) report. 

Based on the cost allocated in each categories and output per worker figures for 

respective industries in 2020, the employment gains for heat rate improvement were 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 Growth Rate due to Heat Rate Improvement 
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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 sector, and steel manufacturing (NAICS 33121) was 

used for materials. Use of machinery manufacturing for equipment and steel for materials was 

based on an analysis of the types of materials and equipment needed for these projects, and what 

EPA determined to be the most appropriate industry sectors for those. For more details, refer to 

the Staudt (2014) report. 

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 biomass, geothermal, landfill gas, onshore wind, and solar). 

For each category, the analysis estimated the impacts due to both the construction and 

operating labor requirements for corresponding capacity changes. The construction labor was 

estimated using information on the capital costs, while the operating labor was estimated using 

the FOM costs. 

Because IPM outputs provide annualized capital costs ($MM), EPA first converted the 

annualized capital costs to changes in the total capital investment using the corresponding capital 
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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 phase 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 assumed to be primarily steel), installation labor, and 

support labor in engineering and management. The percentage breakdowns shown in Table 6A-3 

were estimated using information provided by Staudt (2014), based primarily on published 

budgets for new unit assembled in a study for the National Energy Technology Laboratory 

(NETL). For more details, refer to the Staudt (2014) report. Annual capital costs for each 

generation type provided by the IPM output were allocated according to this breakdown. 

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

 Equipment Material Labor Eng. and Const. 

Mgt 

Renewables 54% 6% 31% 9% 
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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 and 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 the general power plant 

construction phase, was matched with the power industry specific construction sector. 

Engineering/management was matched to the engineering services sector to determine their 

respective output per worker. For materials, EPA assumed steel to be the proxy and used the 

steel manufacturing sector for this productivity. Equipment was assumed to primarily come from 

machinery manufacturing sector (such as turbines, engines and fans). 
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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/gas units for the relevant year and the estimated 

changes in total FOM costs due to those retiring units. Thus, the basic assumption in this 

analysis was that increased retirements (over the base case) will lead to reduced FOM 

expenditures at those plants which were assumed to lead to direct job losses for plant workers. 

In order to estimate the total FOM changes due to retirements, EPA first estimated the 

average FOM costs ($/kW) for existing coal-fired and oil/gas-fired units in the base case, as 

shown in Table 6A-5 below. It was assumed that the average FOM cost of existing units in the 

base case can be used as a proxy for the lost economic output due to fossil retirements. Thus, 

changes in the FOM costs for these retiring units were derived by taking the product of the 

incremental change in capacity and the average FOM costs. These values were converted to lost 

employment using data from the Economic Census and BLS on the output/worker estimates for 

the utility sector. [8] 

Table 6A-5 Average FOM Cost Assumptions 
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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 losses in upstream sectors that supply other inputs 

to the EGU sector (except fuel related job losses, which are estimated separately and discussed in 

the next section). 

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 coal employment analyses under Title IV of the 

Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for 

various coal supplying regions were taken from EPA’s base and policy case runs for the 

proposed EGU GHG NSPS. These changes were converted to job-years using 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 
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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 combined oil and gas extraction sector. This section thus used an 

adjusted labor productivity estimate for the combined oil and gas sector that accounts for the 

relative contributions of oil and natural gas in the total sector output (in terms of the value of 

energy output in MMBtu). This estimate of labor productivity was then used with the 

incremental natural gas demand for the respective IPM runs to estimate the job-years for the 

specific year (converting the TCF of gas used projected by IPM into MMBtu using the 

appropriate conversion factors). In addition, the pipeline construction costs were estimated using 

endogenously determined gas market model parameters in IPM used by EPA for the MATS rule 

(using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for 

additional pipeline would be proportionate to those projected for the MATS rule and were hence 

extrapolated from those estimates.[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. 
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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 output. 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 higher degree of variability in recent years than the 

manufacturing sectors, which would have introduced a high degree of uncertainty in forecasting 

productivity growth rates for future years. 

[11] See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 

2015”. Technical Support Document, March 2011. 
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7-11 

 
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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 investments 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 approaches 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 distribution or operations and maintenance of energy 

efficiency equipment. They examined five different 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. 
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Appendix 6B-13 

2008 employment levels using self-reported data from energy efficiency-related providers, 11 

state case studies, nearly 300 interviews with trade 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 state and sector-specific employment data. California 

programs enabled California households to redirect their expenditures toward other goods and 

services, creating about 1.5 million 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 additional 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. 
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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 cumulatively 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 CO2 emissions 

and was established in 2009.52 Approximately 90 percent of CO2 allowances are auctioned by the 

RGGI states53 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 Authority. 2013. “System Benefits Charge Program SBC3 

Post-Program Annual Report,” June 4. 

51 New York State Energy Research and Development Authority. 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 economic 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, Massachusetts, 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. 
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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 and 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 chapter 6 above, other research institutes and 

numerous industry groups have developed databases of employment in the clean economy or in 

clean energy. While the definitions 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 energy, 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 Greenhouse Gas Initiative, “Regional Investments of RGGI 

CO2 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), 
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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 covers jobs related to solar installation, 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, electric 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=8287977D43FA05E237DD5FEF58B00B3A?docName=cleanjobsarchiv

es. Environmental Entrepreneurs. (2012). What Clean Energy Jobs? These Clean Energy Jobs: Third Quarter 2012 

Clean Energy Jobs Roundup. 
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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

 
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7-1 

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER ANALYSIS 

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

Improving Regulation and Regulatory Review 

Under Section 3(f) (1) of Executive Order 12866 (58 FR 51735, October 4, 1993), this 

action is an "economically significant regulatory action” because it is likely to have an annual 

effect on the economy of $100 million or more or to adversely affect in a material way the 

economy, a sector of the economy, productivity, competition, jobs, the environment, public 

health or safety, or state, local, or tribal governments or communities. The $100 million 

threshold can be triggered by either costs or benefits, or a combination of them. 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 CO2 emissions from the electric power industry. Actions taken to comply with 

the proposed guidelines will also reduce the emissions of directly emitted PM2.5, sulfur dioxide 

(SO2) and nitrogen oxides (NOX). The benefits associated with these PM, SO2 and NOX

reductions are referred to as co-benefits, as these reductions are not the primary objective of this 

rule. 

The EPA has used the U.S. government’s social cost of carbon (USG SCC) estimates 

(i.e., the monetary value of impacts associated with a marginal change in CO2 emissions in a 

given year), to analyze CO2 climate impacts of this rulemaking. The four USG SCC estimates 

are associated with different discount rates (model average at 2.5 percent discount rate, 3 

percent, and 5 percent; 95th percentile at 3 percent), and each increases over time. In this 

summary, EPA provides the estimate of climate benefits associated with the SCC value deemed 

to be central by the USG (the model average at 3% discount rate). The EPA estimates that in 

2020, the Option 1 regional compliance approach will yield monetized climate benefits of 

approximately $17 billion with a 3 percent model average (2011$). The air pollution health cobenefits in 2020 are estimated to be $15 billion to $34 billion for a 3 percent discount rate, and 

Comment [A1]: This chapter was revised to 

reflect edits made to the preamble. 
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7-2 

$13 billion to $31 billion (2011$) for a 7 percent discount rate. The annual illustrative 

compliance costs estimated by IPM, inclusive of demand side energy efficiency program and 

participant costs and MRR costs, are approximately $5.5 billion (2011$) in 2020. The quantified 

net benefits (the difference between monetized benefits and costs) in 2020, are estimated to be 

$26 billion to $45 billion 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 $18 billion (2011$). 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, and $14 billion to $33 billion (2011$) for a 7 percent discount rate. 

The annual illustrative compliance costs estimated by IPM, inclusive of demand side energy 

efficiency program and participant costs and MRR costs, are approximately $7.5 billion (2011$) 

in 2020. The quantified net benefits in 2020, are estimated to be $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 approximately $30 billion (3 percent, model average, 2011$). 

The air pollution health co-benefits in 2030 are estimated to be $24 billion to $55 billion for a 3 

percent discount rate, and $21 billion to $50 billion (2011$) for a 7 percent discount rate. The 

annual illustrative compliance costs estimated using IPM, inclusive of demand side energy 

efficiency program and participant costs and MRR costs, are approximately $7.3 billion (2011$) 

in 2030. The quantified net benefits (the difference between monetized benefits and costs) in 

2030, are estimated to be $47 billion to $78 billion 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 foregoing discussion, it remains clear that the benefits of this 

proposal are substantial, and far exceed the costs. Additional benefit, cost and net benefit 

estimates for Option 1 and Option 2 are provided in Chapter 8 of this report. 
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7.2 Paperwork Reduction Act 

The information collection requirements in this proposed rule have been submitted for 

approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act, 

44 U.S.C. 3501 et seq. The Information Collection Request (ICR) document prepared by the 

EPA has been assigned the EPA ICR number [insert #]. 

The information collection requirements are based on the recordkeeping and reporting 

burden associated with developing, implementing, and enforcing a state plan to limit CO2

emissions from existing sources in the power sector. These recordkeeping and reporting 

requirements are specifically authorized by CAA section 114 (42 U.S.C. 7414). All information 

submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a 

claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR 

part 2, subpart B. 

The annual burden for this collection of information for the states (averaged over the first 

3 years following promulgation of this proposed action) is estimated to be a range of 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 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 total annual burden for the federal government (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 
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maintaining information, and disclosing and providing information; adjust the existing ways to 

comply with any previously applicable instructions and requirements; train personnel to be able 

to respond to a collection of information; search data sources; complete and review the collection 

of information; and transmit or otherwise disclose the information. 

An agency may not conduct or sponsor, and a person is not required to respond to a 

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) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule, subject to notice and comment rulemaking 

requirements, under the Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities. Small entities include small businesses, small organizations, and small 

governmental jurisdictions. 

For purposes of assessing the impacts of this rule on small entities, small entity is defined 

as: 

1. A small business is defined by the SBA’s regulations at 13 CFR 121.201 (for the 

electric power generation industry, the small business size standard is an ultimate 

parent entity with less than 750 employees). The NAICS codes for the affected 

industry are in Table 14 below. 

2. A small governmental jurisdiction that is a government of a city, county, town, 

school district or special district with a population of less than 50,000. 

A small organization that is any not-for-profit enterprise that is independently 

owned and operated and is not dominant in its field. 
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Table 7-1 Potentially Regulated Categories and Entitiesa 

Category NAICS Code Examples of Potentially 

Regulated Entities 

Industry 221112 Fossil fuel electric power 

generating units. 

State/Local Government 221112b Fossil fuel electric power 

generating units owned by 

municipalities. 

a

 Include NAICS categories for source categories that own and operate electric power generating units (includes 

boilers and stationary combined cycle combustion turbines). 

b

 State or local government-owned and operated establishments are classified according to the activity in which they 

are engaged. 

After considering the economic impacts of this proposed rule on small entities, EPA 

certifies that this action will not have a significant economic impact on a substantial number of 

small entities. 

The proposed rule will not impose any requirements on small entities. Specifically, 

emission guidelines established under CAA section 111(d) do not impose any requirements on 

regulated entities and, thus, will not have a significant economic impact upon a substantial 

number of small entities. After emission guidelines are promulgated, states establish standards on 

existing sources and it is those state requirements that could potentially impact small 

entities. Our analysis here is consistent with the analysis of the analogous situation arising when 

the EPA establishes NAAQS, which do not impose any requirements on regulated entities. As 

here, any impact on small entities would only arise when states take subsequent action to 

maintain and/or achieve the NAAQS through their state implementation plans. See American 

Trucking Assoc. v. EPA, 175 F.3d 1029, 1043-45 (D.C. Cir. 1999) national standards for 

allowable concentrations of particulate matter in ambient air as required by section 109 of the 

CAA. See also American Trucking Associations v. EPA. 175 F.3d at 1044–45 (NAAQS do not 
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have significant impacts upon small entities because NAAQS themselves impose no regulations 

upon small entities). 

Nevertheless, the EPA is aware that there is substantial interest in the proposed rule 

among small entities (municipal and rural electric cooperatives). As detailed in Section III.A.of 

the preamble, the EPA has conducted an unprecedented amount of stakeholder outreach on 

setting emission guidelines for existing EGUs. While formulating the provisions of the proposed 

rule, the EPA considered the input provided over the course of the stakeholder outreach. Section 

III.B. of the preamble describes the key messages from stakeholders. In addition, as described in 

the RFA section of the preamble to the proposed standards of performance for GHG emissions 

from new EGUs (79 FR 1499-1500, January 8, 2014), the EPA conducted outreach to 

representatives of small entities while formulating the provisions of the proposed standards. 

Although only new EGUs would be affected by those proposed standards, the outreach regarded 

planned actions for new and existing sources. We invite comments on all aspects of the proposal 

and its impacts, including potential impacts on small entities. 

7.4 Unfunded Mandates Reform Act (UMRA) 

This proposed action does not contain a federal mandate that may result in expenditures 

of $100 million or more for State, local, and tribal governments, in the aggregate, or the private 

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 EPA initiated consultations with 

governmental entities while formulating the provisions of the proposed standards for new EGUs. 

Although only new EGUs would be affected by those proposed standards, the outreach regarded 

planned actions for new and existing sources. As described in the UMRA discussion in the 

preamble to the proposed standards of performance for GHG emissions from new EGUs (79 FR 

1500-1501, January 8, 2014), the EPA consulted with the following 10 national organizations 

representing state and local elected officials: 
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 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 proposed emission guidelines, the EPA also 

considered the input provided over the course of the extensive stakeholder outreach conducted 

by the EPA. 

7.5 Executive Order 13132, Federalism 

Under Executive Order 13132, EPA may not issue an action that has federalism 

implications, that imposes substantial direct compliance costs, and that is not required by statute, 

unless the Federal government provides the funds necessary to pay the direct compliance costs 

incurred by State and local governments, or the EPA consults with state and local officials early 

in the process of developing the proposed action. 

The EPA has concluded that this action may have federalism implications, because it may 

impose substantial direct compliance costs on State or local governments, and the Federal 

government will not provide the funds necessary to pay those costs. As discussed in the 

Supporting Statement found in the docket for this rulemaking, the development of state plans 

will entail many hours of staff time to develop and coordinate programs for compliance with the 

proposed rule, as well as time to work with state legislatures as appropriate, and develop a plan 

submittal. 
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The EPA consulted with state and local officials early in the process of developing the 

proposed action to permit them to have meaningful and timely input into its development. As 

described in the Federalism discussion in the preamble to the proposed standards of performance 

for GHG emissions from new EGUs (79 FR 1501, January 8, 2014), the EPA consulted with 

state and local officials in the process of developing the proposed standards for newly 

constructed EGUs. This outreach regarded planned actions for new, reconstructed, modified and 

existing sources. The EPA invited the following 10 national organizations representing state and 

local elected officials to a meeting on April 12, 2011, in Washington DC: (1) National Governors 

Association; (2) National Conference of State Legislatures, (3) Council of State Governments, 

(4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of 

Counties, (7) International City/County Management Association, (8) National Association of 

Towns and Townships, (9) County Executives of America, and (10) Environmental Council of 

States. These 10 organizations representing elected state and local officials have been identified 

by the EPA as the “Big 10” organizations appropriate to contact for purpose of consultation with 

elected officials. On February 26, 2014, the EPA re-engaged with those governmental entities to 

provide a pre-proposal update on the emission guidelines for existing EGUs and emission 

standards for modified and reconstructed EGUs. In addition, extensive stakeholder outreach 

conducted by the EPA allowed state leaders, including governors, environmental commissioners, 

energy officers, public utility commissioners, and air directors, opportunities to engage with EPA 

officials and provide input regarding reducing carbon pollution from power plants. 

A detailed Federalism Summary Impact Statement (FSIS) describing the most pressing 

issues raised in pre-proposal and post-proposal comments will be forthcoming with the final rule, 

as required by section 6(b) of Executive Order 13132. In the spirit of Executive Order 13132, 

and consistent with EPA policy to promote communications between EPA and State and local 

governments, EPA specifically solicits comment on this proposed action from State and local 

officials. 

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

Safety 

The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those 

regulatory actions that concern health or safety risks, such that the analysis required under 
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section 5-501 of the Order has the potential to influence the regulation. This action is not subject 

to EO 13045 because it does not involve decisions on environmental health or safety risks that 

may disproportionately affect children. The EPA believes that the CO2 emission reductions 

resulting from implementation of the proposed guidelines, as well as substantial ozone and PM2.5 

emission reductions as a co-benefit, would further improve children’s health. 

7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect 

 Energy Supply, Distribution, or Use 

Executive Order 13211 (66 FR 28355; May 22, 2001) requires the EPA to prepare and 

submit a Statement of Energy Effects to the Administrator of the Office of Information and 

Regulatory Affairs, OMB, for actions identified as ‘‘significant energy actions.’’ This action, 

which is a significant regulatory action under EO 12866, is likely to have a significant effect on 

the supply, distribution, or use of energy. We have prepared a Statement of Energy Effects for 

this action as follows. We estimate a 4 to 7 percent increase in retail electricity prices, on 

average, across the contiguous U.S. in 2020, and a 16 to 22 percent reduction in coal-fired 

electricity generation as a result of this rule. The EPA projects that electric power sector 

delivered natural gas prices will increase by about 8 to 12 percent in 2020 and reflect no change 

to a decrease of approximately 3 percent in 2030. 

 Additional information is available in Chapter 3 of this RIA.

7.8 National Technology Transfer and Advancement Act (NTTAA) 

Section 12(d) of the NTTAA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) 

directs the EPA to use Voluntary Census Standards in their regulatory and procurement activities 

unless to do so would be inconsistent with applicable law or otherwise impractical. Voluntary 

consensus standards are technical standards (e.g., materials specifications, test methods, 

sampling procedures, business practices) developed or adopted by one or more voluntary 

consensus bodies. The NTTAA directs the EPA to provide Congress, through annual reports to 

the OMB, with explanations when an agency does not use available and applicable VCS. This 

proposed rulemaking does not involve technical standards. 
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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 environmental justice part of their mission by 

identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of their programs, policies and activities on minority populations and lowincome populations in the U.S. 

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, finding that certain parts of the population may be 

especially vulnerable based on their circumstances. These include the poor, the elderly, the very 

young, those already in poor health, the disabled, those living alone, and/or indigenous 

populations dependent on one or a few resources. The Administrator placed weight on the fact 

that certain groups, including children, the elderly, and the poor, are most vulnerable to climaterelated health effects. 

Strong scientific evidence that the potential impacts of climate change raise 

environmental justice issues is found in the major assessment reports by the U.S. Global Change 

Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IPCC), and the 

National Research Council (NRC) of the National Academies, summarized in the record for the 

Endangerment Finding. Their conclusions include that poor communities can be especially 

vulnerable to climate change impacts because they tend to have more limited adaptive capacities 

and are more dependent on climate-sensitive resources such as local water and food supplies. In 

 

1 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air 

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”). 
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7-11 

addition, Native American tribal communities possess unique vulnerabilities to climate change, 

particularly those on established reservations that are restricted to reservation boundaries and 

therefore have limited relocation options. Tribal communities whose health, economic wellbeing, and cultural traditions depend upon the natural environment will likely be affected by the 

degradation of ecosystem goods and services associated with climate change. Southwest native 

cultures are especially vulnerable to water quality and availability impacts. Native Alaskan 

communities are likely to experience disruptive impacts, including shifts in the range or 

abundance of wild species crucial to their livelihoods and well-being. The most recent 

assessments continue to strengthen scientific understanding of climate change risks to minority 

and low-income populations. 

This proposed rule limits GHG emissions by establishing CO2 emission guidelines for 

existing fossil fuel-fired EGUs. In addition to reducing CO2 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 energy efficiency. These emission reductions will 

include SO2 and NOx, which form ambient PM2.5 and ozone in the atmosphere, and hazardous air 

pollutants (HAP), such as mercury. In the final rule revising the annual PM2.5 standard2

, the EPA 

identified persons with lower socioeconomic status as an at-risk population for experiencing 

adverse health effects related to PM exposures. Persons with lower socioeconomic status have 

been generally found to have a higher prevalence of pre-existing diseases, limited access to 

medical treatment, and increased nutritional deficiencies, which can increase this population’s 

risk to PM-related and ozone-related effects.3

 Therefore, in areas where this rulemaking reduces 

exposure to PM2.5, ozone, and methylmercury persons with low socioeconomic status would also 

benefit. Chapter 4 of this RIA provides additional information regarding 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 Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. 

December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
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While there will be many locations with improved air quality for PM2.5, ozone, and 

methylmercury, there may also be EGUs whose emissions of one or more of these pollutants or 

their precursors increase as a result of the proposed emission guidelines for existing fossil 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 steps to avoid increased utilization of particular 

EGUs and thus to avoid any significant increases in emissions including emissions of other 

regulated pollutants whose environmental effects would be more localized around the affected 

EGU. To the extent that states take this path, there would be no new environmental justice 

concerns in the areas near such EGUs. For any EGUs that make modifications that do trigger 

NSR permitting, the applicable local, state, or federal permitting program will ensure that there 

are no new NAAQS violations and that no existing NAAQS violations are made worse. For 

those EGUs in a permitting situation for which the EPA is the permit reviewing authority, the 

EPA will consider environmental justice issues as required by Executive Order 12898. 

In addition to some EGUs possibly being required by a state to make modifications for 

increased energy efficiency, another effect of the proposed CO2 emission guidelines for existing 

fossil fuel-fired EGUs would be increased utilization of other, unmodified EGUs with relatively 

low GHG emissions per unit of electrical output, in particular high efficiency gas-fired EGUs. 

Because such EGUs would not have been modified physically nor changed their method of 

operation, they would not be subject to review in the NSR permitting program. Such plants 

would have more hours in the year in which they operate and emit pollutants, including 

pollutants whose environmental effects if any would be localized rather than global as is the case 

with GHG emissions. Changes in utilization already occur now as demands for and sources of 

electrical energy evolve, but the proposed CO2 emission guidelines for existing fossil fuel-fired 

EGUs can be expected to cause more such changes. Because gas-fired EGUs emit essentially no 
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mercury, increased utilization would not increase methylmercury concentrations in their 

vicinities. Increased utilization generally would not cause higher peak concentrations of PM2.5, 

NOx, or ozone around such EGUs than is already occurring because peak hourly or daily 

emissions generally would not change, but increased utilization may make periods of relatively 

high concentrations more frequent. It should be noted that the gas-fired sources that are likely to 

be dispatched more frequently than at present have very low emissions of primary particulate 

matter, SO2 and hazardous air pollutants per unit of electrical output, such that local (or regional) 

air quality for these pollutants is likely to be affected very little. For natural gas-fired EGUS, the 

EPA found that regulation of HAP emissions “is not appropriate or necessary because the 

impacts due to HAP emissions from such units are negligible based on the results of the study 

documented in the utility RTC” 65 FR 79831. In studies done by DOE/NETL comparing cost 

and performance of coal- and NG-fired generation, they assumed SO2, PM (and Hg) emissions to 

be “negligible.” Their studies predict NOx emissions from a NGCC unit to be approximately 10 

times lower than a subcritical or supercritical coal-fired boiler. Many are also very well 

controlled for emission of NOx through the application of after combustion controls such as 

selective catalytic reduction, although not all gas-fired sources are so equipped. Depending on 

the specificity of the state CAA section 111(d) plan, the state may be able to predict which EGUs 

and communities may be in this type of situation and to address any concerns about localized 

NO2 concentrations in the design of the CAA section 111(d) program, or separately from the 

CAA section 111(d) program but before its implementation. In any case, existing tracking 

systems will allow states and the EPA to be aware of the EGUs whose utilization has increased 

most significantly, and thus to be able to prioritize our efforts to assess whether air quality has 

changed in the communities in the vicinity of such EGUs. There are multiple mechanisms in the 

CAA to address situations in which air quality has degraded significantly. 

In conclusion, this proposed rule will result 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 pollutants with potential for effects on localized communities than 

would be experienced in the absence of the proposed rule. The EPA cannot exactly predict how 

emissions from specific EGUs would change as an outcome of the proposed rule due to the stateled implementation. Therefore, the EPA has concluded that it is not practicable to determine 
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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 identify 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-fired EGU, but provides guidance to any tribe with 

delegated authority to address CO2 emissions from EGU sources found subject to section 111(d) 

of the CAA. Thus, Executive Order 13175 does not apply to this action. 

The EPA conducted outreach to tribal environmental staff and offered consultation with 

tribal officials in developing this action. Because the EPA is aware of tribal interest in this 

proposed rule, prior to the April 13, 2012 proposal (77 FR 22392-22441), the EPA offered 
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consultation with tribal officials early in the process of developing the proposed regulation to 

permit them to have meaningful and timely input into its development. The EPA's consultation 

regarded planned actions for new and existing sources. In addition, on April 15, 2014, prior to 

proposal, the EPA met with Navajo Energy Development Group officials. For this proposed 

action for existing EGUs, a tribe that has one or more affected EGUs located in its area of Indian 

Country4 would have the opportunity, but not the obligation, to establish a CO2 performance 

standard and a CAA section 111(d) plan for its area of Indian country. 

Consultation letters were sent to 584 tribal leaders. The letters provided information 

regarding the EPA's development of both the NSPS and emission guidelines for fossil fuel-fired 

EGUs and offered consultation. No tribes have requested consultation. Tribes were invited to 

participate in the national informational webinar held August 27, 2013. In addition, 

consultation/outreach meeting was held on September 9, 2013, with tribal representatives from 

some of the 584 tribes. The EPA also met with tribal environmental staff via National Tribal Air 

Association teleconferences on July 25, 2013, and December 19, 2013. In those teleconferences, 

the EPA provided background information on the GHG emission guidelines to be developed and 

a summary of issues being explored by the agency. Tribes have expressed varied points of view. 

Some tribes raised concerns about the impacts of the regulations on EGUs and the subsequent 

impact on jobs and revenue for their tribes. Other tribes expressed concern about the impact the 

regulations would have on the cost of water to their communities as a result of increased costs to 

the EGU that provide energy to transport the water to the tribes. Other tribes raised concerns 

about the impacts of climate change on their communities, resources, life ways and hunting and 

treaty rights. The tribes were also interested in the scope of the guidelines being considered by 

the agency (e.g., over what time period, relationship to state and multi-state plans) and how 

tribes will participate in these planning activities. In addition, the EPA held a series of listening 

sessions prior to development of this proposed action. In 2013, tribes participated in a session 

with the state agencies, as well as a separate session with tribes. 

 

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. 
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During the public comment period for this proposal, the EPA will hold meetings with 

tribal environmental staff to inform them of the content of this proposal, as well as offer further 

consultation with tribal elected officials where it is appropriate. We specifically solicit comment 

from tribal officials on this proposed rule. 
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CHAPTER 8: COMPARISON OF BENEFITS AND COSTS 

8.1 Comparison of Benefits and Costs 

 The illustrative benefits, costs, and net benefits 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 CO2 goals that reflect application 

of cost-effective measures from all four building blocks discussed. Options 1 and 2, presented in 

this RIA, reflect possible compliance approaches with state-specific CO2 goals. These Options 

are a reflection of what compliance could look like for Options 1 and 2, assuming states comply 

with the CO2 goals on an individual state basis (referred to as state approach) or a number of 

states comply collectively on a regional basis (referred to as regional approach). EPA is 

proposing Option 1 and taking comment on Option 2. The guidelines allow flexibility of 

compliance, and EPA recognizes that actual compliance may differ from the illustrative 

approaches 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 CO2 emissions in a 

given year), to analyze CO2 climate impacts of this rulemaking. The four USG SCC estimates 

are associated with different discount rates (model average at 2.5 percent discount rate, 3 

percent, and 5 percent; 95th percentile at 3 percent), and each increases over time. In this 

comparison of benefits and costs, the 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). 

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 percent 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 Cite, 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 are 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 estimates for the Options 1 and 2 regional and state 

compliance approaches for 2025. For the regional compliance approach to the proposal Option 1 

in 2030, the EPA estimates this 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 estimated to be $24 billion to $55 billion for a 3 

percent discount rate and $21 billion to $50 billion (2011$) for a 7 percent discount rate. The 

annual illustrative compliance costs estimated using IPM, inclusive of demand side energy 

efficiency program and participant costs and MRR costs [MRR costs to be added], are 

approximately $7.3 billion (2011$) in 2030. The quantified 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 rate, and $45 to $75 billion assuming a 7 percent 

discount rate (2011$) for the Option 1 state compliance approach. This table also reflects benefit, 

cost, and net benefit estimates in 2030 for Option 2 regional and state compliance approaches. 

 Based upon the foregoing discussion, it remains clear that the 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 categories of impacts. Unquantified impacts include 

those associated with changes in emissions of other pollutants that affect the climate, such as 

methane. In addition, the analysis does not quantify co-benefits from reducing exposure to SO2, 

NOx, and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem 

effects and visibility impairment.
***E.O. 12866 Review – Draft – Do Not Cite, 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 

95th 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 SO2 and NO2

1.5 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.2 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Option 1 – regional Option 2 – regional 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$4.7 

$17 

$25 

$51 

$3.6 

$13 

$19 

$39 

Air pollution health co-benefits c $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 SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

0.9 tons of Hg 

Ecosystem Effects 

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

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, 

regardless of their chemical composition, are equally potent in causing premature mortality because the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle type. 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.

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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 

95th 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 SO2 and NO2

2.0 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Option 1 – regional Option 2 – regional 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$7.5 

$25 

$37 

$76 

$5.5 

$18 

$27 

$56 

Air pollution health co-benefits c $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 SO2 and NO2

1.7 tons of Hg 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem Effects 

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

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, 

regardless of their chemical composition, are equally potent in causing premature mortality because the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle type. 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. 

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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 

95th 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 SO2 and NO2

2.1 tons of Hg and 590 tons of HCl 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

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 

95th 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 SO2 and NO2

1.7 tons of Hg and 580 tons of HCl 

Ecosystem Effects 

Visibility impairment 

Direct exposure to SO2 and NO2

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 CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, 

regardless of their chemical composition, are equally potent in causing premature mortality because the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle type. 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]. 

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8-6 

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. 
10-1 

CHAPTER 9: BENEFITS, COSTS, AND ECONOMIC IMPACTS OF STANDARDS OF 

PERFORMANCE FOR RECONSTRUCTED AND MODIFIED ELECTRIC UTILITY 

GENERATING UNITS

9.1 This chapter will be transmitted with the 111(b) rulemaking package. 
3A-1 

APPENDIX 3A: ANALYSIS OF POTENTIAL UPSTREAM METHANE EMISSIONS 

CHANGES IN NATURAL GAS SYSTEMS AND COAL MINING

The purpose of this appendix is to describe the methodology for estimating upstream 

methane (CH4) emissions related to natural gas systems and coal mining sectors that may result 

from the compliance approaches examined in the Regulatory Impact Analysis (RIA). The US 

Environmental Protection Agency (EPA) assessed whether the net change in upstream methane 

emissions from natural gas and coal production is likely to be positive or negative and also 

assessed the potential magnitude of these upstream changes relative to CO2 emissions reductions 

anticipated at power plants from the compliance strategies examined in the RIA. In addition to 

estimating changes in upstream methane emissions, tThis assessment included estimating CO2

emissions from vented, fugitive and flared emissions associated with fuel production, processing, 

transmission, storage, and distribution of fuels prior 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 CH4 and CO2 projections were calculated for coal mining and natural gas 

systems. Finally, Section 3A.4 contains a bibliography of cited resources. 

3A.1 General Approach

3A.1.1 Analytical Scope 

Upstream CH4 and CO2 emissions associated with coal mining and natural gas systems 

were estimated for 2020 through 2030 using methodologies developed for the 2014 U.S. Climate 

Action Report (U.S. Department of State 2014). The base year for the projections is 2011, as 

reported in the 2013 U.S. GHG Inventory (EPA 2013a). The projection methodologies use 

activity driver data outputs such as coal and natural gas production from the 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. 

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3A-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-CO2 source projections in that report, see “Methodologies for U.S. 

Greenhouse Gas Emissions Projections: Non-CO2 and Non-Energy CO2 Sources” (EPA, 2013b). 

Uncertainties and limitations are discussed, including a side case which incorporates additional 

geographic information for estimating CH4 from coal mining. 

The term “upstream emissions” in this memo refers to vented, fugitive and flared 

emissions associated with fuel production, processing, transmission, storage, and distribution of 

fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on 

upstream CH4 from the natural gas systems and coal mining sectors. In addition, the analysis 

included CO2 resulting from flaring in natural gas production. This analysis does not assess other 

upstream GHG emissions changes, such as CO2 emissions from the combustion of fuel used in 

natural gas and coal production activities or other non-combustion CO2 emissions from natural 

gas systems, such as vented CO2 and CO2 emitted from acid-gas removal processes.. 

Also, the EPA assessed potential upstream methane emissions from natural gas systems 

and coal mining sectors within the domestic 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.1activities 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 estimate domestic methane emissions related to trade of 

natural gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural gas 

within the US (or transporting natural gas within the US while en route for export)..

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3A-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 emissions 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. CO2 emissions from flared methane are included because regulatory and 

voluntary programs influence the rate of methane flaring over time, but the CO2 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 CH4 emissions from coal mining 

(including pre-mining drainage) and post-mining activities (i.e., coal handling), including both 

underground and surface mining. Emissions from abandoned mines are not included. Energyrelated CO2 emissions, such as emissions from mining equipment and vehicles transporting coal 

are not included. CH4, which is contained within coal seams and the surrounding rock strata, is 

released into the atmosphere when mining operations reduce the pressure above and/or 

surrounding the coal bed. The quantity of CH4 emitted from these operations is a function of two 

primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of the 

coal, with higher coal ranks corresponding to higher carbon content and generally higher CH4

content. Pressure increases with depth and prevents CH4 from migrating to the surface; as a 

result, underground mining operations typically emit more CH4 than surface mining. In addition 

to emissions from underground and surface mines, post-mining processing of coal and 

abandoned mines also release CH4. Post-mining emissions refer to CH4 retained in the coal that 

is released during processing, storage, and transport of the coal. 

3A.1.3 Natural Gas Systems Source Description 

Within natural gas systems, this analysis covers vented and fugitive CH4 emissions from 

the production, processing, transmission and storage, and distribution segments of the natural gas 

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 all 

upstream 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 CO2 from flaring of natural gas. Not included are vented and fugitive 

CO2 emissions from natural gas systems, such as vented CO2 emissions removed during natural 

gas processing, or energy-related CO2 such as emissions from stationary or mobile combustion. 

The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds of 

processing facilities, and over a million miles of transmission and distribution pipelines. CH4 and 

non-combustion2

 CO2 emissions from natural gas systems are generally process-related, with 

normal operations, routine maintenance, and system upsets being the primary contributors. There 

are four primary stages of the natural gas system which are briefly described below. 

Production: In this initial stage, wells are used to withdraw raw gas from underground 

formations. Emissions arise from the wells themselves, gathering pipelines, and well-site gas 

treatment facilities (e.g., dehydrators, separators). Major emissions source categories within the 

production stage include pneumatic devices, gas wells with liquids unloading, and gas well 

completions and re-completions (i.e., workovers) with hydraulic fracturing (EPA 2013). Flaring 

emissions account for the majority of the non-combustion CO2 emissions within the production 

stage. 

Processing: In this stage, natural gas liquids and various other constituents from the raw gas are 

removed, resulting in “pipeline-quality” gas, which is then injected into the transmission system. 

Fugitive CH4 emissions from compressors, including compressor seals, are the primary 

emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion 

CO2 emissions in the processing stage come from acid gas removal units, which are designed to 

remove CO2 from natural gas. 

Transmission and Storage: Natural gas transmission involves high-pressure, large-diameter 

pipelines that transport gas long distances from field production and processing areas to 

distribution systems or large-volume customers such as power plants or chemical plants. 

Compressor station facilities, which contain large reciprocating and turbine compressors, are 

 

2

 In this document, consistent with IPCC accounting terminology, the term “combustion emissions” refers to the 

emissions associated with the combustion of fuel for useful heat and work, while “non-combustion emissions” 

refers to emissions resulting from other activities, including flaring and CO2 removed from raw natural gas. 
3A-5 

used to move the gas throughout the U.S. transmission system. Fugitive CH4 emissions from 

these compressor stations and from metering and regulating stations account for the majority of 

the emissions from this stage. Pneumatic devices and non-combusted engine exhaust are also 

sources of CH4 emissions from transmission facilities. Natural gas is also injected and stored in 

underground formations, or liquefied and stored in above-ground tanks, during periods of lower 

demand (e.g., summer), and withdrawn, processed, and distributed during periods of higher 

demand (e.g., winter). Compressors and dehydrators are the primary contributors to emissions 

from these storage facilities. Emissions from LNG import terminals are included within the 

transportation and storage stage. 

Distribution: Distribution pipelines take the high-pressure gas from the transmission system at 

“city gate” stations, reduce the pressure, and then distribute the gas through primarily 

underground mains and service lines to individual end users. 

 
3A-6 

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

Emissions Source 

2011 Emissions 

(TgCO2e) 

CH4 from Coal Mining 75.2 

Underground Mining and Post-

 Mining 

57.4 

Surface Mining and Post-Mining 18.0 

CH4 from Natural Gas Systems 172.3 

Production 63.6 

Processing 23.3 

Transmission and Storage 52.1 

Distribution 33.2 

CO2 from flaring of natural gas 10.3 

 Source: 2013 U.S. GHG Inventory (EPA, 2013) 

It is important to note that in Table 3A-1, CO2-equivalent methane emissions are presented 

using the Fourth Assessment Report Global Warming Potential (GWP) of 25, whereas CO2-

equivalent methane emissions in the 1990-2011 U.S. GHG Inventory (EPA 2013) are presented 

using the Second Assessment Report GWP of 21. EPA plans to use 25 as the methane GWP 

starting with the 1990-2013 U.S. GHG Inventory, to be published in April 2015. 

3A.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-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 NGCC units that are under construction 
3A-7 

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 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 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). 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. These activity drivers include domestic coal and 

natural gas production, imports and exports, and natural gas consumption. For a sensitivity 

analysis described later, regional coal production is used for the Appalachian, Interior, and 

Western regions. The following Tables 3A-2 to 3A-5 summarize the IPM-based activity driver 

results from the baseline, Option 1 Regional and State scenarios, and Option 2 Regional and 

State scenarios.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 discussed 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 percent as a result of either the Option 2 state 

compliance scenario or the Option 2 Regional compliance 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 main analysis and sensitivity analysis. The detailed methods 

used to perform the analysis are presented in section 3A.3.

3A.2.1 Primary Results 

 Both options 1 and 2, in either the state or regional compliance scenarios, result in total 

net reductions in upstream CH4 emissions and CO2 emissions (see Table 3A-6). Under Option 1, 

the state compliance scenario results in net emissions reduction of 10.6 TgCO2e in 2020, while 

the regional compliance scenario results in 9.9 TgCO2e reduction. Under Option 2, the net 

emissions reduction is smaller: 7.7 and 7.4 TgCO2e in the state and regional compliance 

scenarios in 2020, respectively. These net emissions changes represent the sum of changes in 

CH4 from coal mining, CH4 from natural gas systems, and CO2 from flaring in natural gas 

production. 

 Under the regional compliance scenario, Option 1 would result in decreases in CH4

emissions from coal mining of 14.7 TgCO2e in 2020, 18.2 TgCO2e in 2025, and 17.7 TgCO2e in 

2030. Under the same scenario, CH4 from natural gas systems increases relative to the base case 

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 TgCO2e) and 2025 (2.1 TgCO2e) but decreases relative to the base case in 2030 (by 

1.7 TgCO2e). Following a similar pattern, CO2 from flaring in natural gas production increases 

by 0.4 TgCO2e in 2020 and 0.3 TgCO2e in 2025 while decreasing by 0.3 TgCO2e in 2030. The 

total net change under the Option 1 Regional compliance scenario is a reduction in emissions. 

 Under the regional compliance scenario, Option 2 would result in decreases in CH4

emissions from coal mining of 11.3 TgCO2e in 2020, 13.4 TgCO2e in 2025, and 11.1 TgCO2e in 

2030. CH4 from natural gas systems increases relative to the base case in 2020 (3.6 TgCO2e) and 

2025 (1.8 TgCO2e), while decreasing in 2030 (by 2.3 TgCO2e). CO2 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 (TgCO2e) 

 2020 2025 2030 

Option 1 State 

CH4 from Coal Mining -16.1 -19.3 -19.1 

CH4 from Natural Gas Systems +5.1 +2.6 -1.0 

CO2 from NG flaring +0.4 +0.2 -0.3 

Total CH4 + CO2 -10.6 -16.4 -20.5 

Option 1 Regional 

CH4 from Coal Mining -14.7 -18.2 -17.7 

CH4 from Natural Gas Systems +4.4 +2.1 -1.7 

CO2 from NG flaring +0.4 +0.1 -0.5 

Total CH4 + CO2 -9.9 -16.0 -19.8 

Option 2 State 

CH4 from Coal Mining -12.4 -14.5 -12.7 

CH4 from Natural Gas Systems +4.4 +2.4 -1.5 

CO2 from NG flaring +0.4 +0.3 -0.3 

Total CH4 + CO2 -7.7 -11.8 -14.4 

Option 2 Regional 

CH4 from Coal Mining -11.3 -13.4 -11.1 

CH4 from Natural Gas Systems +3.6 +1.8 -2.3 

CO2 from NG flaring +0.2 +0.1 -0.4 

Total CH4 + CO2 -7.4 -11.4 -13.8 

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

A number of factors could potentially affect the changes in upstream emission as a result 

of the policy. Here, we present sensitivity analysis examining the possible effect of shifts in 

regional coal production that might result in a changing the relative share of production 

originating in underground mines and surface mines. This sensitivity analysis is presented 

because underground mining generally emits more CH4 emissions per ton of coal production 

than surface mining. The compliance scenarios show generally larger reductions in coal 
3A-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 CH4 emissions reductions that may result from 

the compliance scenarios are approximately 1 to 2 TgCO2e lower than the results found in the 

main analysis (see Table 3A-7). 

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

Side Case Incorporating More Coal Production Geographic Detail 

 Emissions (TgCO2e) 

 2020 2025 2030

Option 1 State 

CH4 from Coal Mining -14.1 -17.4 -17.8

CH4 from Natural Gas Systems +5.1 +2.6 -1.0

CO2 from NG flaring +0.4 +0.2 -0.3

Total CH4 + CO2 -8.6 -14.6 -19.2

Option 1 Regional 

CH4 from Coal Mining -13.1 -16.5 -16.3

CH4 from Natural Gas Systems +4.4 +2.1 -1.7

CO2 from NG flaring +0.4 +0.1 -0.5

Total CH4 + CO2 -8.3 -14.3 -18.5

Option 2 State 

CH4 from Coal Mining -11.2 -13.2 -11.5

CH4 from Natural Gas Systems +4.4 +2.4 -1.5

CO2 from NG flaring +0.4 +0.3 -0.3

Total CH4 + CO2 -6.4 -10.4 -13.2

Option 2 Regional 

CH4 from Coal Mining -10.3 -12.3 -10.0

CH4 from Natural Gas Systems +3.6 +1.8 -2.3

CO2 from NG flaring +0.2 +0.1 -0.4

Total CH4 + CO2 -6.5 -10.3 -12.8
3A-14 

3A.2.3 Uncertainties and Limitations 

Projections of upstream CH4 emissions and CO2 emitted from flaring of CH4 are subject to 

a range of uncertainties and limitations. These uncertainties and limitations include estimating 

the effect of the compliance approach on activity drivers, uncertainty in base year emissions, and 

uncertainties in changes in emissions factors over relatively long periods of time. For example, 

EPA’s application of IPM relies on EIA projections for coal imports and exports. Consequently, 

coal imports and exports are not able to fully respond within the IPM framework to significant 

fluctuations in power sector coal demand. To the extent international markets may be expected 

to offset reduced domestic coal demand, changes in U.S. upstream emissions as a result of the 

policy scenarios would be smaller than what is presented here. 

Discussion of uncertainty in historical estimates of emissions from coal mining and natural 

gas systems can be found in the 2013 U.S. GHG Inventory. Projected changes in activity drivers 

and emissions factors are based on a combination of policy, macroeconomic, energy market, and 

technology factors which are uncertain in both baseline and compliance scenarios. Relatively 

higher or lower economic growth, or changes in the relative prices or availability of various 

technologies could result in alternative estimates in the net change in upstream CH4 emissions 

and related CO2 emissions. 

3A.3 Methodologies 

3A.3.1 Coal Mining 

The scope of CH4 from coal mining emissions covered in this analysis is discussed above 

in the “General Approach” section. 

 
3A-15 

Methodology 

EPA calculated emissions projections for this source by summing emissions associated 

with underground mining, post-underground mining, surface mining, and post-surface mining. 

௦,௬ݏ݊݋݅ݏݏ݉݅ܧ ෍	 ൌ ௬ݏ݊݋݅ݏݏ݉݅ܧ

௦

Equation 3 

Where: 

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

mining) 

Emissionsy,s = Emissions in year y from source s

EPA projected emissions from each source by multiplying an aggregate emissions factor 

by projected coal production (for underground or surface mining as appropriate). Projected 

reductions due to recovery and use are then subtracted from potential emissions.4

௦,௬݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲቆ ൈ ௦,௕݊݋݅ݐܿݑ݀݋ݎܲݕݎ݋ݐ݁݊ݒ݊ܫ ൈ ௦,௔௚௚ܨܧ ൌ ௦,௬ݏ݊݋݅ݏݏ݉݅ܧ

௦ቇ,௕݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲ

௦ሻܽܿݎܨ݁ݏܷݕݎ݁ݒ݋4ܴ݁ܿܪܥ	 െ 1ሺ ൈ

Equation 4 

Where: 

EFagg,s = Aggregate emissions factor associated with source s

InventoryProductionb,s = Coal production associated with source s in the base 

year from the U.S. GHG Inventory5

 

4

 Current CH4 recovery and use projects apply to underground mining, but projects related to surface mining could 

be implemented in the future. 
3A-16 

ProjectedProductiony,s = Projected coal production associated with the 

emissions source (e.g., either underground or surface 

mining) in year y 

CH4RecoveryUseFracs = Fraction of CH4 recovered from source s 

Emissions Factors 

To calculate potential emissions from each category, EPA calculated an aggregate CH4

emissions factor using historical CH4 emissions and coal production data contained in the most 

recent U.S. GHG Inventory (EPA 2013a). For example, historical CH4 liberated by underground 

mining was divided by the total underground coal production for the corresponding year. The 

aggregate emissions factor is the average of this ratio over the most recent five years. Similar 

calculations were performed for post-underground mining emissions, surface mining emissions, 

and post-surface mining emissions, using either historical underground or surface mining 

production data as appropriate. 

The projection methodology differs from the estimation methodology used in the U.S. 

GHG Inventory. The inventory does not use emissions factors to calculate CH4 emissions from 

underground mines. The U.S. GHG Inventory estimates total CH4 emitted from underground 

mines as the sum of CH4 liberated from ventilation systems (mine-by-mine measurements) and 

CH4 liberated by means of degasification systems, minus CH4 recovered and used. EPA 

estimated surface mining and post-mining CH4 emissions by multiplying basin-specific coal 

production, obtained from EIA’s Annual Coal Report (EIA 2012), by basin-specific emissions 

factors. 

Coal Production Projections 

For the Sixth U.S. Climate Action Report, EPA projected emissions using projections of 

underground, surface, and total coal production from the EIA Annual Energy Outlook (AEO) 

(EIA 2013). For this analysis, projections of total coal production and production by region were 

drawn from IPM runs performed for the proposal RIA. Projections of underground and surface 

 

5

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

from each dataset do not cancel 
3A-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 characteristics including underground and surface mining 

(see AEO table “Coal Production by Region and Type”). In the primary results presented above, 

the breakout between underground and surface mining for projection years in both baseline and 

compliance scenarios is based on the 2013 AEO proportions. This means that the compliance 

approach is assumed not to affect the relative proportion of underground and surface mining in 

the main results. Also, note there has been a general trend toward increasing surface mining 

relative to underground mining (EPA 2013). 

In the sensitivity analysis that incorporated more geographical details into coal production, 

EPA used IPM outputs for coal production from the Appalachian, Interior, and Western regions 

to estimate the proportion of underground versus surface mining in each scenario. This was done 

by assuming that the relative proportion of underground versus surface mining in each coal 

production region would remain constant through the projection period, but that the national 

proportion of underground and surface production would depend on regional production 

changes. In general, a larger proportion of coal production in the Appalachian and Interior 

regions is from underground mining, while a large majority of coal production in western regions 

is from surface mining. The proportion of underground and surface mining in each region was 

based on averaging the years 2011 and 2012 historical percentages. EIA provides historical 

regional coal production data broken out between underground and surface mining in its Annual 

Coal Report (EIA 2012). EPA collated and calculated the proportion of production, underground 

versus surface, for each year. 

CH4 Mitigation (Recovery and Use) 

EPA projected coal mine CH4 mitigation by calculating the historical fraction of methane 

recovered in relation to generation from underground mines, and applying that fraction to future 

generation. The historical fraction was averaged over the most recent five years. Future 

mitigation was estimated by applying the historical rate of recovery and use to projected 

potential emissions generated. 
3A-18 

The U.S. GHG Inventory uses quantitative estimates of CH4 recovery and use from several 

sources. Several gassy underground coal mines in the United States employ ventilation systems 

to ensure that CH4 levels remain within safe concentrations. Additionally, some U.S. coal mines 

supplement ventilation systems with degasification systems, which remove CH4 from the mine 

and allow the captured CH4 to be used as an energy source. 

௬,௦݁ݏܷݕݎ݁ݒ݋4ܴ݁ܿܪܥ ෍	ൌ ௦ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋4ܴ݁ܿܪܥ

௬,௦ݏ݊݋݅ݏݏ݉݅ܧ݈݅ܽݐ݁݊ݐ݋ܲ

ൗ5

௕ିସ

௬ୀ௕

Equation 5 

Where: 

CH4RecoveryUses,y = Recovered emissions from source s in year y 

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

b = base year 

3A.3.2 Natural Gas Systems 

The scope of CH4 and CO2 from natural gas systems emissions covered in this analysis are 

discussed above in the “General Approach” section. 

 

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 CH4 and 

CO2 emissions for over 100 emissions source categories across the four natural gas sector stages, 

and then the summation of emissions for each sector stage. The calculation of emissions for each 

source of emissions in natural gas systems generally occurs in three steps: 

1. Calculate potential CH4 (CH4 that would be released in the absence of controls) 

2. Estimate reductions data associated with voluntary action and regulations 

3. Calculate net emissions 

EPA calculated projections of potential CH4 emissions from natural gas systems by 

summing the projections associated with (1) production, (2) processing, (3) transmission and 

storage, and (4) distribution. For the 2014 U.S. Climate Action Report in general, activity data 

were projections of natural gas production and consumption from the Department of Energy’s 

Energy Information Administration, or EIA (EIA 2013). Additional activity data for projections 

included liquefied natural gas (LNG) imports, pipeline length, and number of service lines. For 

this report, activity driver data were taken from IPM outputs for baseline and compliance 

scenarios consistent with the analysis presented in the RIA. Because the base year inventory 

emissions explicitly include reductions due to voluntary and regulatory requirements, the 

projections also include appropriate explicit mitigation projections as well. Emissions for each 

source were estimated using the following equation: 

ܰܧ௦,௬ ൌ ܲܧ௦,௬ െ ܸܴ௦,௬ െ ܴܴ௦,௬

 Equation 6 

Where: 

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

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

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

RRs,y, y = Projected regulatory reductions for source s in year y

Formatted: Subscript

Formatted: Subscript
3A-20 

The sections below describe detailed calculations for projections of CH4 from natural gas 

systems. This analysis also includes CO2 emissions that result from flaring in the production 

sector.6

 The U.S. Greenhouse Gas Inventory also includes other sources of venting, fugitive, and 

flaring CO2 such as CO2 processing emissions from acid gas removal units, which are designed 

to remove CO2 from natural gas. This analysis does not include these other non-combustion CO2

sources. EPA calculated projected flaring CO2 emissions by scaling emissions in the base year 

by the increase in projected natural gas production in the IPM scenario outputs consistent with 

the analysis presented in the RIA.

Production Stage 

The production stage includes a total of 35 emissions source categories. Regional 

emissions were estimated in the base year inventory for the six supply regions (i.e., Northeast, 

Gulf Coast, Midcontinent, Southwest, Rocky Mountain, and West Coast) for 33 of these sources. 

Potential Emissions 

EPA estimated future year potential emissions for the production stage using the 

following equation. 

ܲܧ௦,௬ ൌ ܲܧ௦,௕ ൈ ቀ

ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡೤

ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡್

ቁ 

Equation 7 

Where: 

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

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

Gas Productiony = Projected natural gas dry production year y

Gas Productionb = Estimated natural gas dry production for base year b

The natural gas dry production estimates were obtained from the IPM scenario outputs. 

Voluntary Reductions 

 

6

 The GHG Inventory estimate for CO2 from natural gas flaring includes some CO2 from flaring of associated gas. 
3A-21 

Projections of voluntary reductions for the production stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

emissions. Natural Gas STAR tracks projects on an annual basis and assigns a lifetime of limited 

duration to each reduction project; for purposes of the base year emissions inventory and the 

future year projections, the reductions associated with each project were either considered to be a 

“one-year” project or a “permanent” project based on sunset dates provided by the Natural Gas 

STAR program. Reductions from “one-year” projects were typically from the implementation of 

new or modified practices, while reductions from “permanent” projects tended to be from 

equipment installation, replacement, or modification. In the base year emissions inventory and 

the future year projections, reductions for a “one-year” project were limited to the project’s 

reported start year, while reductions for a “permanent” project were assigned to the project’s 

reported start year and every subsequent year thereafter. Thus, the reductions due to “permanent” 

projects gradually accumulated throughout the inventory time series, while the reductions due to 

“one-year” projects were replaced every year. 

The following production stage voluntary reductions were reported to Natural Gas STAR 

and applied to individual sources in the emissions inventory: 

 Completions for gas wells with hydraulic fracturing (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 update uses control/practice-specific emission factors and no 

longer uses potential methane, or reductions data from Gas STAR or from regulations to calculate emissions, as 

emissions are directly calculated using the emission factors. 
3A-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 classified as “Other Production” that were applied to 

the overall production stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the production stage would remain constant in each 

subsequent future year.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 relative to the base year inventory for sources unaffected 

by regulatory changes is meant to simulate a constant level of effort toward voluntary reductions into the future. 

No enhancements to the voluntary program are assumed. This assumption is a source of uncertainty; due to the 

voluntary nature of the program, reduction levels can fluctuate based on participation and investment. Where 

new regulatory requirements apply to new and modified equipment, voluntary reductions are assumed to 

continue to apply to existing equipment, but no voluntary reductions are applied to new equipment. As a 

potential 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 preparing 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 update 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 CH4 emissions reductions co-benefits. These reductions are not currently 

reflected in the 2013 U.S. GHG Inventory for the base year 2011, but are projected for future 

years as discussed in detail below. The specific Oil and Natural Gas Sector NSPS requirements 

impact the following production stage sources with regard to VOC (and the associated CH4) 

emissions: 

 Hydraulically fractured natural gas well completions 

 Hydraulically refractured natural gas well recompletions 

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

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

 New and modified reciprocating and centrifugal compressors at gathering and boosting 

stations 

The impact of these requirements on the future year projections is discussed below. The 

specific quantitative reductions calculated for these projections are based on information from 

the Oil and Natural Gas Sector NSPS Background Technical Support Document for the 

Proposed Standards (EPA 2011) and the Background Supplemental Technical Support 

Document for the Final New Source Performance Standards (EPA 2012b), referred to 

collectively in this document as the Oil and Natural Gas Sector NSPS TSD. 

Hydraulically Fractured Well Completions 

The Oil and Natural Gas Sector NSPS requires the use of RECs (or “green completions”) 

for all new hydraulically- fractured natural gas well 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 Gas 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 required for exploratory “wildcat” wells, delineation 

wells (i.e., used to define the borders of a natural gas reservoir), and low-pressure wells (i.e., 

completions where well pressure is too low to perform RECs); in these instances, emissions must 

be reduced using combustion. Based on the Oil and Natural Gas Sector NSPS TSD (EPA 

2012b), EPA assumed for the purpose of these projections a 95 percent reduction for both RECs 

and completion combustion.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 refractured well completions) with hydraulic 

fracturing, in conjunction with the Oil and Natural Gas Sector NSPS, EPA removed REC-related 

reductions from the projected voluntary reductions from the production stage to avoid double-

 

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 expected to achieve 95 percent combustion efficiency. 

11 Use of RECs is not considered to be “modified” and would not trigger state permitting requirements, while use of 

flaring or completion combustion would be considered to be “modified.” 
3A-25 

counting. This removal was very straightforward, since these REC-related reductions were 

calculated separately and then applied to the well completions and workovers source in the base 

year inventory. 

New and Modified High-Bleed, Gas-Driven Pneumatic Controllers

The Oil and Natural Gas Sector NSPS also requires the installation of new low-bleed 

pneumatic devices (i.e., bleed rates less than or equal to 6 standard cubic feet per hour) instead of 

high-bleed pneumatic devices (i.e., bleed rates greater than 6 standard cubic feet per hour) with 

exceptions where high bleed devices are required for safety reasons. The TSD indicates that a 

typical production stage high-bleed pneumatic device emits 6.91 tons of CH4 per year and that 

replacing the high-bleed device with a typical low-bleed pneumatic device would result in a 

reduction of 6.65 tons CH4 per year; this is a reduction of 96.2 percent. The TSD also indicates 

that only 51 percent of all pneumatic devices installed are continuous bleed natural gas driven 

controllers. In addition, it is assumed that 20 percent of the situations where bleed pneumatic 

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

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3A-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 gathering and boosting stations. There are two 

options for this replacement: every 26,000 hours of operation if operating hours are monitored 

and documented, or every 36 months if operating hours are not monitored or documented. The 

Oil and Natural Gas Sector NSPS TSD estimated baseline emissions of 3,773 tons per year of 

CH4 for new reciprocating compressors used in the production stage; the TSD also estimated 

total reductions from replacing the rod packing for these compressors as 2,384 tons per year of 

CH4 (EPA 2011). Based on this information, for the purpose of these projections EPA applied a 

national-level reduction of 63.2 percent to each future year’s annual increase in emissions from 

gathering reciprocating compressors in the production stage. 

Processing Stage 

The processing stage includes a total of 11 emissions source categories. EPA estimated the 

base year inventory emissions for the processing stage at the national level, instead of at the 

region level, like the base year inventory emissions for the production stage. 

Potential Emissions 

Because projections of future year processing activity were not available, EPA also used 

Equation 7 to estimate future year potential emissions for the processing stage by assuming that 

the quantity of processed natural gas would track closely with the quantity of produced natural 

gas. 
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 processing stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

emissions (EPA 2012c). The following processing stage voluntary reductions were reported to 

Natural Gas STAR and applied to individual sources in the emissions inventory: 

 Blowdowns/venting (one year)—recover gas from pipeline pigging operations; redesign 

blowdown/alter ESD practices; reduce emissions when taking compressors offline; use composite 

wrap repair; use hot taps for in-service pipeline connections; use inert gas and pigs to perform 

pipeline purges. 

 Blowdowns/venting (permanent)—rupture pin shutoff device to reduce venting. 

In addition to these reductions that were applied to specific individual sources in the 

emissions inventory, there were reductions classified as “Other Processing” that were applied to 

the overall processing stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the processing stage would remain constant in each 

subsequent future year.12 In addition, implementation of the Oil and Natural Gas Sector NSPS 

(discussed further below) necessitates the reclassification of certain processing reductions from 

voluntary to regulatory. 

Regulatory Reductions 

The only regulatory reductions included in the base year inventory for the processing stage 

were existing Oil and Natural Gas Sector NESHAP requirements for dehydrator vents (EPA 

2013). These reductions were carried forward in the future year projections. 

 

12 This assumption is discussed in Footnote 7. 
3A-28 

The Oil and Natural Gas Sector NSPS significantly increased the amount of regulatory 

reductions applicable to the processing stage relative to the base year 2011 inventory estimates. 

The specific Oil and Natural Gas Sector NSPS requirements affect the following processing 

stage sources with regard to VOC (and the associated CH4) emissions: 

 New and modified Rreciprocating compressors 

 New and modified cCentrifugal compressors 

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

 New storage tanks (with VOC emissions of at least 6 tons per year) 

The impact of these requirements on the future year projections is discussed below. 

New and Modified Reciprocating Compressors 

The Oil and Natural Gas Sector NSPS requires the replacement of rod packing systems in 

new and modified reciprocating compressors. There are two options for this replacement: every 

26,000 hours of operation if operating hours are monitored and documented, or every 36 months 

if operating hours are not monitored or documented. The Oil and Natural Gas Sector NSPS TSD 

estimated baseline emissions of 4,870 tons per year of CH4 for new reciprocating compressors 

used in the processing stage; the Oil and Natural Gas Sector NSPS TSD also estimated total 

reductions from replacing the rod packing for these compressors as 3,892 tons per year of CH4

(EPA 2011). Based on this information, for the purpose of these projections EPA applied a 

national-level reduction of 79.9 percent to each future year’s annual increase in emissions from 

reciprocating compressors in the processing stage. 

New and Modified Centrifugal Compressors 

The Oil and Natural Gas Sector NSPS requires a 95 percent reduction in VOC emissions 

from new and modified centrifugal compressors with wet seal systems, which can be 

accomplished through flaring or by routing captured gas back to a compressor suction or fuel 

system, or switching to dry seal systems. The Oil and Natural Gas Sector NSPS does not apply to 

centrifugal compressors with dry seal systems, because they have low VOC emissions. A 

national-level reduction of 95 percent was applied to each future year’s annual increase in 

emissions from centrifugal compressors with wet seals in the processing stage. 
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 transmission and storage and 12 associated with liquefied natural gas 

(LNG) transmission and storage. The natural gas and LNG emissions were estimated at the 

national level. 

Potential Emissions 

Future year potential emissions for the natural gas sources and the six LNG storage sources 

within the transmission and storage stage were estimated using the following equation: 

ܲܧ௦,௬ ൌ ܲܧ௦,௕ ൈ ቀ

ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡೤

ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡್

ቁ 

Equation 8 

Where: 

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

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

Gas Consumptiony = Projected national natural gas consumption in year y

Gas Consumptionb = Estimated national natural gas consumption in base year b

The national natural gas consumption estimates were obtained from the IPM scenario 

outputs. 
3A-30 

Future year potential emissions for the six LNG import terminal sources within the 

transmission and storage stage were estimated using the following equation: 

ܲܧ௦,௬ ൌ ܲܧ௦,௕ ൈ ቀ

௅ேீ	ூ௠௣௢௥௧௦೤

௅ேீ	ூ௠௣௢௥௧௦್

ቁ 

Equation 9 

Where: 

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

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

LNG Importsy = Projected LNG imports in year y

LNG Importsb = Estimated LNG imports in base year b

The LNG import estimates were obtained from the table titled “Natural Gas Imports and 

Exports” of the IPM scenario outputs. The specific estimates used were for the “Liquefied 

Natural Gas Imports” line item. 

Voluntary Reductions 

Projections of voluntary reductions for the transmission and storage stage were also based 

on historical data reported by industry to the Natural Gas STAR program for projects 

implemented to reduce emissions. The following transmission and storage stage voluntary 

reductions were reported to Natural Gas STAR and applied to individual sources in the base year 

emissions inventory: 

 Reciprocating compressors (one year)—replace compressor rod packing systems. 

 Reciprocating compressors (permanent)—replace wet seals with dry seals. 

 Pipeline venting (one year)—recover gas from pipeline pigging operations; use composite wrap 

repair; use hot taps for in-service pipeline connections; use inert gas and pigs to perform pipeline 

purges; use pipeline pump-down techniques to lower gas line pressure. 

 Pneumatic devices (permanent)—identify and replace high-bleed pneumatic devices; convert 

pneumatic devices to mechanical controls; convert to instrument air systems. 
3A-31 

In addition to these reductions that were applied to specific individual sources in the 

emissions inventory, there were reductions classified as “Other Transmission and Storage” that 

were applied to the overall transmission and storage stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the transmission and storage stage would remain constant in 

each subsequent future year.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 distributed 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 estimates 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 pipeline miles and number of services show fairly 

consistent behavioral trends over the entire time series from 1990 to 2011. In particular, the 

historical statistics show a distinct trend toward the use of plastic and away from other materials 

(i.e., cast iron, copper, unprotected steel, and protected steel). Historical pipeline length data was 

drawn from the US GHG Inventory (EPA 2013), which draws pipeline data from a variety of 

sources. 

Voluntary Reductions 

Projections of voluntary reductions for the distribution stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

 

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 CH4 emissions 

from the distribution segment of the natural gas system. The current projection methodology uses total natural 

gas consumption as an activity driver to project emissions from non-pipeline leak sources in the distribution 

segment. EPA examined how the results would differ if the distribution segment were excluded. Depending on 

the scenario and year, the net emissions change between the base case and policy cases was reduced by up to 0.9 

TgCO2e or increased by up to 0.4 TgCO2e, assuming the change in power plant natural gas consumption had no 

effect on distribution-segment methane emissions..

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/Rese

arch/Energy%20Analysis/Publications/DOENETL-2012-1539-NGTechAssess.pdf
3A-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 CH4 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 Natural 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 National Greenhouse Gas Inventories.” Available at: 

<http://www.ipcc-nggip.iges.or.jp/public/2006gl/>. 

U. S. Department of State. 2014. “2014 U.S. Climate Action Report to the UNFCCC.” Available 

at: < http://www.state.gov/e/oes/rls/rpts/car6/index.htm>. 

U. S. Energy Information Administration (EIA). 2012. “Annual Coal Report 2011.” Available at: 

<http://www.eia.gov/coal/annual/>. 

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

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

U. S. Environmental Protection Agency. 1993. Anthropogenic Methane Emissions in the United 

States, Estimates for 1990: Report to Congress. (EPA document number EPA-430-R-93-003). 

U. S. EPA. 2011. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and 

Natural Gas Production, Transmission, and Distribution: Background Technical Support 

Document for the Proposed Standards.” Available at: 

<http://epa.gov/airquality/oilandgas/pdfs/20110728tsd.pdf>. 

U. S. EPA. 2012a. “Oil and Natural Gas Sector: New Source Performance Standards and 

National Emission Standards for Hazardous Air Pollutants Reviews.” 40 CFR Parts 60 and 63 

[EPA-HQ-OAR-2010-0505; FRL-9665-1], RIN 2060-AP76. Available at: 

<http://www.gpo.gov/fdsys/pkg/FR-2012-08-16/pdf/2012-16806.pdf>. 

U. S. EPA. 2012b. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and 

Natural Gas Production, Transmission, and Distribution: Background Supplemental Technical 

Support Document for the Final New Source Performance Standards.” Available at: 

<http://epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf>. 

U.S. EPA. 2012c. “Natural Gas STAR Reductions 1990-2011.” Natural Gas STAR Program. 

U.S. EPA. 2013a. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2011. (EPA 

document number EPA-430-R-13-001). Available at: 

<http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>. 

U.S. EPA. 2013b. “Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO2

and Non-Energy CO2 Sources.” Available at: 

<http://www.state.gov/documents/organization/219472.pdf> 
***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 PM2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector. 

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

As described in the Technical Support Document: Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013a) (hereafter “BPT TSD”), the 

procedure for calculating average benefit-per-ton coefficients generally follows three steps, 

shown graphically in Figure 4A-1. As an example, in order to calculate national average benefitper-ton estimates for each ambient PM2.5 precursor emitted from EGU sources, we: 

1. Use air quality modeling to predict ambient concentrations of primary PM2.5, nitrate and 

sulfate across the contiguous U.S. that are attributable to the EGU sector. 

2. Estimate the health impacts, and the economic value of these impacts, associated with the 

attributable ambient concentrations of primary PM2.5, sulfate and nitrate PM2.5 using the 

environmental Benefits Mapping and Analysis Program (BenMAP v4.0.66)1

 (Abt 

Associates, Inc, 2012). 

3. Divide the PM2.5-related health impacts attributable to each type of PM2.5, and the 

monetary value of these impacts, by the level of associated precursor emissions. That is, 

primary PM2.5 benefits are divided by direct PM2.5 emissions, sulfate benefits are divided 

by SO2 emissions, and nitrate benefits are divided by NOx emissions. 

 

1

 In this stage we estimate the PM2.5-related impacts associated with changes in directly emitted PM2.5, nitrate and 

sulfate separately, so that we may ultimately calculate the benefit- per -ton reduced of the corresponding PM2.5

precursor, or directly emitted PM2.5, in step 3. When estimating these impacts we apply effect coefficients that 

relate changes in total PM2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that 

are differentiated by PM2.5 specie. 
***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 Extensions (CAMx) model2

 to estimate PM2.5 and ozone concentrations 

attributable to 17 industrial sectors, including EGUs. In this section, we provide a short summary 

of the air quality modeling used to generate these benefit-per-ton estimates. Readers interested in 

a full discussion of the air quality modeling may consult the Air Quality Modeling Technical 

Support Document: Source Sector Assessments (U.S. EPA, 2011a). The source apportionment 

modeling is also discussed in Fann, Baker and Fulcher (2012). 

CAMx simulates the numerous physical and chemical processes involved in the 

formation, transport, and destruction of ozone, particulate matter and air toxics. Emissions of 

precursor species are injected into the model where they react to form secondary species such as 

ozone and then transport around the modeling domain before ultimately being removed by 

deposition or chemical reaction. Source apportionment techniques track the formation and 

transport of ozone and PM from specific emissions sources and calculate the contribution of 

sources and precursors to ozone and PM2.5 at individual receptor locations. In contrast to “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-ton 

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 transport, chemical transformation, or atmospheric 

chemical relationships within the modeling. More details on the implementation of source 

apportionment in CAMx can be found in the CAMx user’s guide. 

The modeling analyses were performed for a domain covering the continental U.S. This 

domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over the eastern 

and western U.S. The base year of data used to construct the modeling platform includes 

emissions and meteorology for 2005. Specifically, the starting point for the emission projections 

in this modeling was the 2005 v4.3 emissions platform (U.S. EPA, 2005). EGU emission 

estimates for 2016 are from the Integrated Planning Model (IPM), and the projections include 

emission reductions related to the NOx State Implementation Plan Call (U.S. EPA, 1998) and the 

proposed Transport Rule (U.S. EPA, 2010c).3

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

The approach for generating regional benefit- per-ton estimates for PM2.5 from EGU 

emissions is a small modification of the approach described above from the BPT TSD. The 

regional benefit-per-ton estimates reflect the ambient PM2.5 attributable to the EGU sector in the 

source apportionment modeling (“sector modeling”) from Fann, Baker, and Fulcher (2012), as 

illustrated in Figures 4A-2 and 4A-3 for 2016. After estimating the PM2.5 benefits for each of the 

analysis years applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated the benefits results 

regionally (i.e., East, West, and California) rather than nationally, as shown in Figure 4A-4. 4

Due to the low emissions of SO2, NOX, and directly emitted particles from EGUs in California 

and the high population density, we separated out California in order not to bias the benefit-perton estimates for the rest of the Western U.S. In order to calculate the benefit-per-ton estimates, 

 

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 PM2.5 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 PM2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, 

in μg/m3

) (Source: BPT TSD) 

This method provides estimates of the regional average benefit-per-ton for each ambient 

PM2.5 precursor emitted from EGU sources. For some precursor emissions, such as NOx, there is 

generally a non-linear relationship between emissions and formation of PM2.5. This means that 

each ton of NOx reduced would have a different impact on ambient PM2.5 depending on the 

initial level of emissions and potentially on the levels of emissions of other pollutants. In 

contrast, SO2 is generally linear in forming PM2.5. For non-linear pollutants like NOx, a marginal 

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-5

sector. 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 SO2 emissions, the added uncertainty is likely to be 

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

4A-6

Figure 4A-3. Modeled PM2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m3) 

a) Sulfate b) Nitrate

c) Elemental Carbon and Organic Carbon d) Crustal PM2.5
***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 

SO2 3,793,362 3,520,296 273,070 4,886 

Directly emitted PM2.5 (EC+OC) 30,078 26,172 3,892 1,523 

Directly emitted PM2.5 (crustal) 243,497 214,219 28,438 840 

NOx (all year) 1,826,582 1,425,148 401,584 13,485 

NOx (ozone season) 931,189 728,402 195,748 7,039 

In this RIA, we estimate emission reductions from EGUs using IPM.6 IPM outputs 

provide endogenously projected unit level emissions of SO2, NOx, CO2, Hg, HCl from EGUs, 

but CO, VOC, NH3 and total directly emitted PM2.5 and PM10 emissions are post-calculated.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

Formatted: Centered

Formatted: Centered

Formatted: Centered

Formatted: Centered

Formatted: Centered

Formatted: Not Superscript/ Subscript
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-8

speciation, i.e. they are only the total emissions. In order to estimate the benefits associated with 

reduced emissions of directly emitted particles, we must determine the fraction of total PM2.5 

emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions.8

Figure 4A-5 illustrates the relative breakdown of directly emitted PM2.5 components from EGUs 

in the modeling by Fann, Baker, and Fulcher (2012). In this modeling, the national average 

EC+OC fraction of emitted PM2.5 is 10% with a range of 5% to 63% in different states due to the 

different proportion of fuels. The national average is similar to the averages for the east and west 

regions at 10% and 7%, respectively. Only five states had EC+OC fractions greater than 30%. 

For crustal emissions, the national average fraction of emitted PM2.5 from EGUs is 78% with a 

range of 26% to 83%. The national average is similar to the averages for the east and west 

regions at 78% and 81%, respectively. Only four states had crustal fractions less than 50%. 

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

Modeling 

There are several uncertainties associated with estimating the benefits associated with 

reducing emissions of EC+OC and crustal PM2.5 from EGUs. As previously mentioned, IPM 

does not estimate PM2.5 emissions by component, and total PM2.5 is estimated as an IPM 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 carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

‐

5,000

10,000

15,000

20,000

25,000

Texas

Pennsylvania

Ohio

Indiana

Florida

West Virginia

Georgia

Kentucky

Alabama

North Carolina

South Carolina

Illinois

Minnesota

Missouri

Wyoming

Arizona

Tennessee

Virginia

Michigan

Wisconsin

North Dakota

UtahIowa

New Mexico

Kansas

Oklahoma

Colorado

MarylandNew York

Arkansas

Louisiana

Nevada

California

Nebraska

Montana

NewJersey

Mississippi

Massachusetts

Connecticut

Washington

South Dakota

New Hampshire

Oregon

Delaware

Rhode Island

Maine

Idaho

Emissionsin SectorModeling (Tons)

PNO3 PSO4

POC PEC

PMFINE (crustal)

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

4A-9

processing step using emission factors. In order to conduct air quality modeling, PM2.5 from 

EGUs is speciated into components during the emissions modeling process based on emission 

profiles for EGUs by source classification code. Even though these speciation profiles are not 

unit-specific, an emission profile based on the source classification code is highly sophisticated 

and reflects the fuel and the unit configuration. In addition, the air quality model output has been 

adjusted to match an interpolated surface of speciated PM2.5 measurements, using the Model 

Attainment Test Software (Abt Associates, Inc, 2010). Uncertainties in some emissions sectors 

can sometimes be large enough that air quality estimates from well-characterized sectors such as 

EGUs may be changed to match observed monitor data. While these changes are usually 

minimal, in this instance, uncertainties in crustal emissions from other sectors impact the 

relationship between EGU crustal emissions and resulting air quality impacts. Because the cobenefits associated with directly emitted particles for the proposed guidelines are small relative 

to the co-benefits of reducing SO2 emissions, these uncertainties would not substantially affect 

the estimate of total monetized health co-benefits.9

 In calculating the PM2.5 co-benefits in this 

RIA, we estimate the emission reductions of EC+OC and crustal emissions by applying the 

national average fractions (i.e., 78% crustal and 10% EC+OC) to the emission reductions of all 

directly emitted particles from EGUs. Because the benefit-per-ton estimates for reducing 

emissions of EC+OC are larger than the benefit-per-ton estimate for crustal emissions, this 

assumption underestimates the monetized PM2.5 co-benefits in certain states with higher EC+OC 

fractions, such as California and North Dakota. We further underestimate the co-benefits by not 

quantifying or monetizing the co-benefits associated with reducing directly emitted particulate 

nitrate and sulfate. 

Although it is possible to calculate 95th percentile confidence intervals using the approach 

described in this appendix (e.g., U.S. EPA, 2011b), we generally do not calculate confidence 

intervals for benefit-per-ton estimates. Instead, we refer the reader to Chapter 5 of PM NAAQS 

RIA (U.S. EPA, 2012a) for an indication of the combined random sampling error in the health 

impact and economic valuation functions using Monte Carlo methods. In general, the 95th

percentile confidence interval for the total monetized PM2.5 benefits ranges from approximately90% to +180% of the central estimates based on concentration-response functions from Krewski 

 

9 See Figure 5-2 of this RIA for the relative proportion of health co-benefits from directly emitted particles relative 

to co-benefits from SO2 emissions. 

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4A-10

et al. (2009) and Lepeule et al. (2012). The 95th percentile confidence interval for the health 

impact function alone ranges from approximately ±30% for mortality incidence based on 

Krewski et al. (2009) and ±46% based on Lepeule et al. (2012). These confidence intervals do 

not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence 

rates, populations exposed, and transferability of the effect estimate to diverse locations. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture about 

the overall uncertainty in the benefits estimates. 

Tables 4A-2 through 4A-4 provide the national and regional benefit-per-ton estimates for 

the EGU sector at discount rates of 3% and 7% in 2020, 2025, and 2030 respectively. Tables 4A5 through 4A-7 provide the incidence per ton estimates for the EGU sector in 2020, 2025, and 

2030 respectively. 
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Table 4A-2. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2020 (2011$)* 

Pollutant Discount Rate National Region East West California 

SO2 3% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $360,000 7% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $640,000 

7% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000 

Directly emitted 

PM2.5 (Crustal) 

3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $240,000 

7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000 

NOx (as PM2.5) 3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $38,000 7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 

Table 4A-3. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2025 (2011$)* 

Pollutant Discount Rate National Region East West California 

SO2 3% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,000 7% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,000 7% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000 

Directly emitted 

PM2.5 (Crustal) 

3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000 7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000 

NOx (as PM2.5) 3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000 7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
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Table 4A-4. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2030 (2011$)* 

Pollutant Discount Rate National Region East West California 

SO2 3% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,000 7% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000 

Directly emitted PM2.5 (EC+OC) 

3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,000 7% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000 

Directly emitted PM2.5 (Crustal) 

3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000 7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000 

NOx (as PM2.5) 3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000 7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
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Table 4A-5. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2020* 

Health Endpoint 

East West California 

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal 

Premature Mortality 

Krewski et al. (2009) – adult 0.004400 0.000730 0.015000 0.002000 0.000860 0.000130 0.006100 0.001200 0.017000 0.001800 0.031000 0.012000Lepeule 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 0.026000

Woodruff et al. (1997) – infants 0.000010 0.000002 0.000033 0.000004 0.000002 0.000000 0.000016 0.000003 0.000036 0.000004 0.000067 0.000025

Morbidity 

Emergency department visits for asthma 0.002300 0.000410 0.008700 0.001000 0.000380 0.000055 0.002800 0.000520 0.008600 0.001000 0.016000 0.005900Acute 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 fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from precursor emissions to ambient fine particles. 
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Table 4A-6. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2025* 

Health Endpoint 

East West California 

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal 

Premature Mortality 

Krewski et al. (2009) – adult 0.004700 0.000770 0.016000 0.002100 0.000940 0.000140 0.006800 0.001400 0.019000 0.002000 0.034000 0.013000Lepeule 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 0.029000

Woodruff et al. (1997) – infants 0.000009 0.000001 0.000031 0.000004 0.000002 0.000000 0.000016 0.000003 0.000034 0.000004 0.000063 0.000024

Morbidity 

Emergency department visits for asthma 0.002400 0.000420 0.009000 0.001100 0.000410 0.000059 0.003000 0.000560 0.009000 0.001000 0.016000 0.006100Acute 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 0.010000 0.003800Non-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 fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
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Table 4A-7. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2030* 

Health Endpoint 

East West California 

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal 

Premature Mortality 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000

Krewski et al. (2009) – adult 0.011000 0.001800 0.040000 0.005100 0.002400 0.000340 0.017000 0.003400 0.048000 0.005000 0.086000 0.032000Lepeule 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 0.000023

Woodruff et al. (1997) – infants 

Morbidity 0.002500 0.000430 0.009300 0.001100 0.000430 0.000062 0.003200 0.000600 0.009400 0.001100 0.017000 0.006400

Emergency department visits for asthma 0.006800 0.001100 0.024000 0.003000 0.001800 0.000370 0.013000 0.002600 0.033000 0.003700 0.061000 0.023000Acute 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 0.041000 0.015000Non-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 fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
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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 PM2.5. The key difference is substituting the ozone impacts from EGUs in the 

sector modeling for the PM2.5 impacts. We have not historically generated ozone benefit-per-ton 

estimates due to the complex non-linearity in the atmospheric response to changes in ozone 

precursor pollutants from different sectors in different geographic areas. However, for EGUs, we 

have increased confidence in the magnitude and location of ozone-season NOx emissions and 

ambient ozone concentrations attributable to these emissions. In addition, the sector modeling 

provides information regarding the total EGU contribution to ozone formation, which provides 

greater transferability than an air quality modeling scenario for a regulation that might include 

both increases and decreases in emissions in different locations. Figure 4A-6 provides the 

average ambient ozone concentrations from the EGU sector in the sector modeling for 2016.10

 

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 may 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 (see Chapter 3), there is no double-counting concern with 

the resulting co-benefits estimates.

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

8-hour maximum, May-September, in ppb) 

We assume that all of the ozone impacts from EGUs are attributable to NOx emissions. 

VOC emissions, which are also a precursor to ambient ozone formation, are insignificant from 

the EGU sector relative to NOx emissions from EGUs and the total VOC emissions inventory. 

Therefore, we believe that our assumption that EGU-attributable ozone formation at the regionallevel is due to NOx alone is reasonable. 

Similar to PM2.5, this method provides estimates of the regional average benefit-per-ton. 

Due to the non-linear chemistry between NOx emissions and ambient ozone, using an average 

benefit-per-ton estimate for NOx adds uncertainty to the ozone co-benefits estimated for the 

proposed guidelines. Because most of the estimated co-benefits for the proposed guidelines are 

attributable to changes in ambient PM2.5, the added uncertainty is likely to be small. 

In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates 

calculated using NOX emissions from U.S. EGUs during the ozone-season only (May to 

September). As shown in Table 4A-1, ozone-season NOx emissions from EGUs are 

approximately half of all-year NOX emissions. Because we estimate ozone health impacts from 

May to September only, this approach underestimates ozone co-benefits in areas with a longer 
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4A-18

ozone seasons such as southern California and Texas. When the underestimated benefit-per-ton 

estimate is multiplied by ozone-season only NOx emission reductions, this results in an 

underestimate of the monetized ozone co-benefits. For illustrative purposes, Tables 4A-8 through 

4A-10 provide the ozone benefit-per-ton estimates using both all-year NOx emissions and ozoneseason only NOx for 2020, 2025, and 2030, respectively. Tables 4A-11 through 4A-13 provide 

the ozone season incidence-per-ton estimates for 2020, 2025, and 2030, respectively. 

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

EGUs in 2020 (2011$)* 

Ozone precursor 

Pollutant National Regional 

East West California 

Ozone season NOx $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000 

All-year NOx $1,900 to $8,000 $2,300 to $9,600 $450 to $2,000 $3,800 to $16,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx 

precursor emissions to ambient ozone. 

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

EGUs in 2025 (2011$)* 

Ozone precursor 

Pollutant National Regional 

East West California 

Ozone season NOx $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000 

All-year NOx $2,500 to $11,000 $3,000 to $13,000 $650 to $2,800 $5,200 to $22,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx 

precursor emissions to ambient ozone. 

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

EGUs in 2030 (2011$)* 

Ozone precursor 

Pollutant National Regional 

East West California 

Ozone season NOx $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000 

All-year NOx $2,700 to $12,000 $3,200 to $14,000 $730 to $3,200 $5,700 to $25,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx 

precursor emissions to ambient ozone. 

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

Health Endpoint East West California 

Premature Mortality – adult 

Bell et al. (2004) 0.000260 0.000053 0.000420 

Levy et al. (2005) 0.001200 0.000250 0.001900 

Morbidity 

Hospital Admissions, Respiratory (ages > 65) 0.001600 0.000250 0.002200 

Hospital Admissions, Respiratory (ages < 2) 0.000710 0.000220 0.001200 

Emergency Room Visits, Respiratory 0.000840 0.000150 0.001300 

Acute Respiratory Symptoms 1.500000 0.380000 3.000000 

School Loss Days 0.510000 0.140000 1.000000 

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

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

Health Endpoint East West California 

Premature Mortality – adult 

Bell et al. (2004) 0.000530 0.000110 0.000890 

Levy et al. (2005) 0.002400 0.000520 0.004100 

Morbidity 

Hospital Admissions, Respiratory (ages > 65) 0.003500 0.000590 0.004900 

Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000440 0.002300 

Emergency Room Visits, Respiratory 0.001700 0.000300 0.002700 

Acute Respiratory Symptoms 3.000000 0.770000 5.900000 

School Loss Days 1.000000 0.290000 2.100000 

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

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

Health Endpoint East West California 

Premature Mortality – adult 

Bell et al. (2004) 0.000570 0.000130 0.001000 

Levy et al. (2005) 0.002600 0.000580 0.004500 

Morbidity 

Hospital Admissions, Respiratory (ages > 65) 0.004000 0.000690 0.005900 

Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000460 0.002400 

Emergency Room Visits, Respiratory 0.001800 0.000320 0.002800 

Acute Respiratory Symptoms 3.000000 0.810000 6.000000 

School Loss Days 1.100000 0.310000 2.200000 

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

4A.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 

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relative to benefits estimated using scenario-specific air quality modeling. First, we evaluated 

how well the spatial distribution of emissions underlying the regional benefit-per-ton estimates 

match the spatial distribution of the emissions for the illustrative compliance scenarios for the 

proposed guidelines. Second, we evaluated how well the national and regional benefit-per-ton 

estimates predicted modeled benefits estimated for MATS (U.S. EPA, 2011b) and CSAPR (U.S. 

EPA, 2011c). 

For the first evaluation, we provide graphs of the EGU emissions in each state in the 

sector modeling base case used to generate the regional benefit-per-ton estimates and the 

emissions in the IPM base case for this RIA, as shown in Figures 4A-7 through 4A-9. It is 

important to note that these graphs do not show the emission reductions anticipated from 

implementation of the proposed guidelines. Rather, these graphs illustrate the similarities and 

differences in the spatial distribution of the emissions from the EGU sector from the different 

base cases in order to evaluate the potential uncertainty associated with using the benefit-per-ton 

estimates to estimate the health co-benefits of the proposed guidelines. As noted in section 4A-2 

above, the sector modeling base case does not reflect emission reductions anticipated from the 

EGU sector as a result of the recently promulgated MATS rule,11 but the IPM base case does 

reflect those emission reductions. Even though the sector modeling has substantially more SO2

emissions from EGUs (3.8 million tons in 2016 vs. 1.5 million tons in 2020), the spatial 

distribution is relatively similar across states and thus the resulting benefit-per-ton would likely 

be similar. Therefore, we would not expect this difference to have a substantial effect on the 

resulting co-benefits estimated for the proposed guidelines. 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 SO2

emission reductions anticipated from MATS. 

Formatted: Space After: 3 pt
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4A-21

 ‐

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Alabama

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

District of Columbia

Florida

Georgia

Idaho

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Oregon

Pennsylvania

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Wyoming

Tons

EGU sector modeling

IPM Base

Formatted: Complex Script Font: 12 pt
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4A-22

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

 ‐

50,000

100,000150,000200,000250,000300,000350,000400,000450,000

Alabama

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

District of Columbia

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Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

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Montana

Nebraska

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

Tons

EGU sector air quality modeling base caseIPM base case for this RIA
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4A-23

 ‐

20,00040,00060,00080,000

100,000120,000140,000160,000

Alabama

Arizona

Arkansas

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Colorado

Connecticut

Delaware

District of Columbia

Florida

Georgia

Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

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Montana

Nebraska

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

Tons

EGU sector air quality modeling base caseIPM base case for this RIA
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4A-24

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

 ‐

25,00050,00075,000

100,000125,000

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

Tons

EGU sector modelingIPM Base
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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

Tons

EGU sector modelingIPM Base
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4A-26

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

 ‐

10,00020,00030,00040,00050,00060,00070,00080,000

Alabama

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

District of Columbia

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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

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Vermont

Virginia

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Tons

EGU sector air quality modeling base caseIPM base case for this RIA
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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 PM2.5 benefits for both rules were from reductions in ambient 

sulfate. We found that the national benefit-per-ton estimates overestimated the modeled CSAPR 

benefits by 7 percent and the MATS benefits by 33 percent. The regional benefit-per-ton 

estimates overestimated the CSAPR benefits by 14 percent and MATS benefits by 29 percent. 

This evaluation shows that the national and regional benefit-per-ton estimates perform 

reasonably well, with a slight overestimate, but this does not mean that the co-benefits estimated 

for the proposed guidelines would necessarily be within a similar range. 

We also evaluated whether available modeling information could be used to generate 

reliable state-level benefit-per-ton estimates for PM2.5.

13 In general, we would expect state-level 

estimates to better reflect the spatial differences in emission reductions and associated health 

impacts across scenarios than regional estimates, which implicitly assume a constant percentage 

of emission reductions across the region. Varying percentage reductions across states are further 

magnified by spatial differences in population density, base line case health incidence rates, air 

quality response, and interstate pollution transport. 

We tested several methods for generating state-level benefit-per-ton estimates. The 

method that performed best used state-by-state contribution modeling in the Eastern U.S. 

conducted for CSAPR (U.S. EPA, 2011a) to adjust the regional benefit-per-ton estimates for the 

EGU sector. Although the CSAPR modeling is the best available for estimating state-level 

benefit-per-ton estimates, it zeroed-out all anthropogenic emission sources within a state rather 

than just zeroing-out the emissions from the EGU sector alone. Currently, we do not have air 

quality modeling that provides information regarding the PM2.5 response to EGU only emissions 

for each state separately. We evaluated the performance of the state-level estimates by 

comparing the modeled benefits for two previous rulemakings (i.e., CSAPR and MATS) to the 

benefits estimated using state-level benefit-per-ton. When we evaluated the state-level estimates 

in the same manner as the national and regional estimates, we found that the state-level estimates 

 

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 specifically for those rulemakings. For this evaluation, we 

compared the benefits estimated from the air quality modeling, not the benefits estimated for the final rules in the 

RIA generated using the benefit-per-ton estimates for those rulemakings. 

13 We did not attempt to generate state-level ozone benefit-per-ton estimates due to inter-state transport, complex 

atmospheric chemistry, and localized impacts from VOC emissions. 
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4A-28

performed similarly, in general, to the regional estimates for estimating total national benefits but 

were unreliable in estimating the benefits that would accrue to each state. In addition, because 

the spatial differences in emission reductions between the options evaluated in this RIA are 

small, using the state-level estimates did not make a difference in the relative co-benefits across 

options. 

 
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4A-29

 
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4A-30

4A.6 References 

Abt Associates, Inc. 2010. “User’s Guide: Modeled Attainment Test Software.” Available at: 

<http://www.epa.gov/scram001/modelingapps_mats.htm>. 

Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research 

Triangle Park, NC: U. S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards. Available at: 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. 

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): pp. 2372-8. 

Bell, M.L.; F. Dominici and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of 

Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution 

Study.” Epidemiology. 16(4): pp. 436-45. 

Fann, N.; C.M. Fulcher and B.J. Hubbell. 2009. “The Influence of Location, Source, and 

Emission Type in Estimates of the Human Health Benefits of Reducing a Ton of Air 

Pollution.” Air Quality and Atmospheric Health, 2:169–176. 

Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International, 49 41–151. 

Fann, N.; K.R. Baker and C.M. Fulcher. 2013. “The Recent and Future Health Burden of Air 

Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technology, 47 (8) 

(2013), pp. 3580–3589. 

Huang Y.; F. Dominici and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for 

Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics, 16, 547-562. 

Ito, K.; S.F. De Leon and M. Lippmann. 2005. “Associations Between Ozone and Daily 

Mortality: Analysis and Meta-Analysis.” Epidemiology, 16(4): p. 446-57. 

Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow-Up 

and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution 

and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspective, Jul; 120(7):965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Metaregression Analysis.” Epidemiology, 16(4): pp. 458-68. 

Roman, H.A.; K. D. Walker; T. L. Walsh; L. Conner; H. M. Richmond; B. J. Hubbell and P. L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient 

Fine Particulate Matter in the U.S.” Environmental Scientific Technology, 42(7):2268-2274. 

Schwartz, J. 2005. “How Sensitive is the Association Between Ozone and Daily Deaths to 

Control for Temperature?” American Journal of Respiratory and Critical Care Medicine, Vol. 

171 (6): 627-31. 
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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/crossstaterule/pdfs/AQModeling.pdf >. 

U.S. EPA. 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. 

Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division. (EPA document number EPA-452/R-11-011, December). 

Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. 

U.S. EPA. 2011c. “Regulatory Impact Analysis (RIA) for the Final Transport Rule.” Research 

Triangle Park: Office of Air and Radiation, July. Available at: 

<http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>. 

U.S. EPA. 2011d. “Air Quality Modeling Technical Support Document: Source Sector 

Assessments.” Research Triangle Park, NC. Available at: 

<http://www.epa.gov/scram001/reports/EPA454_R11_006.pdf>. 

U.S. EPA. 2012a. “Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter.” Research Triangle Park, NC: Office of Air 

Quality Planning and Standards, Health and Environmental Impacts Division. (EPA document 

number EPA-452/R-12-003, December). Available at: 

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. EPA. 2013a. “Technical Support Document: Estimating the Benefit per Ton of Reducing 

PM2.5 Precursors from 17 Sectors.” Research Triangle Park, NC: Office of Air Quality 

Planning and Standards, February. Available at: 

<http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13

.pdf>. 

U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

(Final Report). Washington, DC: U.S. Environmental Protection Agency. (EPA document 

number EPA/600/R-10/076F). Available at: 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 
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Preamble		 These are additional comments received on the preamble since the 1st 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/studies/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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 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/changes/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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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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 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 CO2 emissions from

biomass‐fired generation.  However, it appears that in the draft RIA analysis for the 111(d) rule, EPA assumed a

CO2 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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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 CO2

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. CO2 emissions from flared methane are

included because regulatory and voluntary programs influence the rate of methane flaring over time, but the

CO2 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/Research/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 CH4 emissions

from the distribution segment of the natural gas system. The current projection methodology uses total natural

gas consumption as an activity driver to project emissions from non‐pipeline leak sources in the distribution

segment. EPA examined how the results would differ if the distribution segment were excluded. Depending on

the scenario and year, the net emissions change between the base case and policy cases was reduced by up to

0.9 TgCO2e or increased by up to 0.4 TgCO2e, assuming the change in power plant natural gas consumption had

no effect on distribution‐segment methane emissions.

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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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 case1 that

lead to re‐dispatch in response to CO2 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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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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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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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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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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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 CASE ELECTRICITY SALES 

GROWTH, 2017 to 2030 % GROWTH 

Texas Regional Entity 12.3% 

Florida Reliability Coordinating 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 Corporation / East 7.8% 

Reliability First Corporation / Michigan 4.8% 

Reliability First Corporation / 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 Coordinating Council / Southwest 19.7% 

Western Electricity Coordinating 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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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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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 CO2 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.