From: Culligan, Kevin [mailto:Culligan.Kevin@epa.gov] 

Sent: Saturday, May 24, 2014 11:43 AM Eastern Standard Time 

To: Higgins, Cortney 

Cc: Wayland, Robertj <Wayland.Robertj@epa.gov> 

Subject: FW: 111d RIA Responses to OMB Comments, Revised RIA Executive Summary and 

Revised Chapter 3 

Cortney, 

Per our discussion yesterday, here is a shot at the RIA stuff addressing costs of EE as well as the 

rest of our responses. Per our discussion, we are happy to continue to work with you on the EE 

cost language and I think there is also a potential path on the AQ modeling that I'm happy to 

discuss (either Tuesday, or if you want to talk earlier, I can make time). 

- Kevin 

<< E O 12866 Exec_summary_05_23_14 final.docx >> 

<< E O 12866 Chapter_3_Cost_Economic_Energy_Impacts_5_23_14 final.docx >> 

<< EO 12866 EPA Response to Interagency OMB Comments 052314 final .docx >> 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

Draft Deliberative 

ES-1 

EXECUTIVE SUMMARY 

This Regulatory Impact Analysis (RIA) discusses potential benefits, costs, and economic 

impacts of the proposed Emission Guidelines for Greenhouse Gas Emissions from Existing 

Stationary Sources: Electric Utility Generating Units (herein referred to EGU GHG Existing 

Source Guidelines). This RIA also discusses the potential benefits, costs and economic impacts 

of the proposed Standards of Performance for Greenhouse Gas Emissions from Reconstructed 

and Modified Stationary Sources (EGU GHG Reconstructed and Modified Source Standards). 

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

Greenhouse gas pollution threatens Americans' health and welfare by leading to long 

lasting changes in our climate that can have a range of severely negative effects on human health 

and the environment. Carbon Dioxide (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 

such reduction and any non-air quality health and environmental impact and energy 
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ES-2 

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. 1. ReducingLowering the carbon intensity of generation at individual affected EGUs 

(e.g., through heat-rate improvements). 

2. Reducing emissions fromof the most carbon-intensive affected EGUs in the amount 

that results fromto the extent that this can be accomplished cost-effectively by shifting 

substituting generation at those EGUs with generation fromto less carbon-intensive 

existing fossil fuel-firedaffected EGUs, (including natural gas combined cycle 

([NGCC]) units that are under construction). 

3. Reducing emissions from affectedof carbon-emitting EGUs in the amount that results 

from substituting generation at those EGUs with to the extent that this can be 

accomplished cost-effectively by expandeding the amount of new, low-er or zero- (or 

no) carbon-intensity generation. 

4. Reducing emissions from affected of carbon-emitting EGUs in the amount that results 

from the use ofto the extent that this can be accomplished cost-effectively by 

increasing demand-side energy efficiency that reduces the amount of generation 

required. 

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 policy 

considerations, including varying emission reduction opportunities and existing state programs 

 

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 

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 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 rate-based compliance scenarios for the goals set for Options 1 and 2, as well as a 
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ES-4 

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 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. 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 

 

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 

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

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

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

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

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

Louisiana 948 883 1,052 1,025 

Maine 393 378 418 410 

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

Massachusetts 655 576 715 683 

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

Minnesota 911 873 1,018 999 

Mississippi 732 692 765 743 

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

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

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

Nevada 697 647 734 713 

New Hampshire 546 486 598 557 

New Jersey 647 531 722 676 

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

New York 635 549 736 697 

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

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

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

Oklahoma 931 895 1,019 986 

Oregon 407 372 450 420 

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

Rhode Island 822 782 855 840 

South Carolina 840 772 930 897 

South Dakota 800 741 888 861 

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

Texas 853 791 957 924 

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

Virginia 884 810 1,016 962 

Washington 264 215 312 284 

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

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

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

Table ES-2 shows the emission reductions associated with the compliance scenarios for the 

proposed Options 1 regional and state compliance approaches and Table ES-3 reports emission 
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ES-6 

reductions associated with Option 2. In 2020, the EPA estimates that CO2 emissions 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-2 and ES-3 also show emission reductions for criteria air 

pollutants. 

 

 Table ES-2. Summary of Climate and Air Pollutant Emission Reductions Options 11

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 212N/A

Proposed Guidelines 1,790 1,184 1,213 156

Emissions Change -371 -292 -345 -56

2025 Regional Compliance Approach 

Base Case 2,231 1,515 1,587 209

Proposed Guidelines 1,730 1,120 1,166 N/A150

Emissions Change -501 -395 -421 -59

2030 Regional Compliance Approach 

Base Case 2,256 1,530 1,537 198N/A

Proposed Guidelines 1,711 1,106 1,131 144

Emission Change -545 -424 -407 -54

2020 State Compliance Approach 

Base Case 2,161 1,476 1,559 212

Proposed Guidelines 1,777 1,140 1,191 154N/A

Emissions Change -383 -335 -367 -58

2025 State Compliance Approach 

Base Case 2,231 1,515 1,587 209N/A

Proposed Guidelines 1,724 1,090 1,151 145

Emission Change -506 -425 -436 -63

2030 State Compliance Approach 

Base Case 2,256 1,530 1,537 198N/A

Proposed Guidelines 1,701 1,059 1,109 142

Emissions Change -555 -471 -428 -56

Source: Integrated Planning Model, 2014. N/A – not yet available 1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air pollutantpollution health co-benefits of the proposed 

guidelines. 
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ES-7 

Table ES-3. Summary of Climate and Air Pollutant Emission Reductions Option 21

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 212N/A

Proposed 

GuidelinesOption 2

1,878 1,231 1,290 166

Emissions Change -283 -244 -268 -46

2025 Regional Compliance Approach 

Base Case 2,231 1,515 1,587 209

Option 2Proposed 

Guidelines

1,862 1,218 1,279 165N/A

Emissions Change -368 -297 -309 -44

2030 Regional Compliance Approach 

Base Case 2,256 1,530 1,537 198

Option 2Proposed 

Guidelines

1,903 1,243 1,286 158N/A

Emissions Change -353 -287 -252 -40

2020 State Compliance Approach 

Base Case 2,161 1,476 1,559 212

Option 2Proposed 

Guidelines

1,866 1,208 1,277 163

Emissions Change -295 -267 -281 N/A-49

2025 State Compliance Approach 

Base Case 2,231 1,515 1,587 209

Option 2Proposed 

Guidelines

1,855 1,188 1,271 161

Emissions Change -376 -327 -317 -48N/A

2030 State Compliance Approach 

Base Case 2,256 1,530 1,537 198

Option 2Proposed 

Guidelines

1,882 1,209 1,264 154

Emissions Change -373 -321 -274 -44N/A

Source: Integrated Planning Model, 2014. N/A– not yet available. 

1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air pollutantpollution health co-benefits of the guidelines.

ES.4 Costs of Existing EGU Guidelines 

 The ‘‘compliance costs’’ of this proposed action are represented in this analysis as the 

change in electric power generation costs between the base case and policy case in which states 

pursue a distinct set of strategies not limited to the technologies and measures included in the 

BSER to meet the EGU GHG emission guidelines, and include cost estimates for demand side 

energy efficiency. The compliance assumptions, and therefore the projected “compliance costs” 
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ES-8 

set forth in this analysis, are illustrative in nature and do not represent the full suite of 

compliance flexibilities states may ultimately pursue. 

 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 $64268.3

million in 2020, $8.92 million in 2025, and $8.29 million in 2030 (2011$). The annual 

incremental cost is the projected additional cost of complying with the proposed rule in the year 

analyzed and includes the 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 in 

Table ES-4 below and discussed more extensively in Chapter 3 of this RIA. 

Table ES-4. Summary of Illustrative Compliance Costs 

 Incremental Cost from Base Case (billions of 2011$) 

 2020 2025 2030 

Option 1 

State Compliance $7.4 $5.5 $8.8 

Regional Compliance $5.4 $4.6 $7.3 

Option 2 

State Compliance $5.4 $5.5 $3.1 

Regional Compliance $4.2 $4.5 $1.9 

Source: Integrated Planning Model, 2014. Compliance costs shown here do not include monitoring, reporting, and 

recordkeeping costs.

The costs reported in Table ES-4 represent the estimated incremental electric utility 

generating costs changes from the base case, plus end-use energy efficiency program costs (paid 

by electric utilities) and end-use energy efficiency participant costs (paid by electric utility 

consumers). For example in 2020 for the proposed Option 1 regional compliance approach, end-

 

3

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

http://www.epa.gov/powersectormodeling/BaseCasev513.html 

Formatted: Font color: Auto
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ES-9 

use energy efficiency program costs are estimated to be $5.1 billion and end-use efficiency 

participant costs are $5.1 billion using a 3% discount rate. This estimate for end-use energy 

efficiency costs of $10.2 billion is combined with the costs generated by the Integrated Planning 

Model (IPM) that include the costs of states’ compliance with state goals associated with 

changes to reduce the carbon-intensity of electricity production and the energy demand 

decreases expected from end-use energy efficiency assumed in the illustrative scenarios. Thus 

the end-use energy efficiency costs are combined with the costs of reducing carbon-intensity of 

production less the energy demand reductions due to end-use energy efficiency or by a net value 

derived by IPM of -$4.8 billion. The resulting compliance cost estimate of the rule considering 

all components is $5.4 billion ($10.2 billion - $4.8 billion) in 2020 for the Option 1 regional 

compliance approach. Note that when monitoring, reporting and recordkeeping costs of $68.3 

million are added to this estimate, compliance costs become $5.5 billion in 2020. The 

compliance costs reported in Table ES-4 are not social costs. These costs represent the 

illustrative real resources costs for states to comply with the BSER goals for Options 1 and 2. 

Electric sector compliance costs and monitoring, recordkeeping and reporting costs are compared 

to social benefits in Tables ES-8, ES-9 and ES-10 to derive illustrative net benefits of the 

guidelines. For a more extensive discussion of social costs, see Chapter 3 of this RIA. 

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

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

human health co-benefits associated with reducedancillary emission reductions (i.e., co-benefits) 

of SO2, NO2, and directly emitted PM2.5, which would lead to lower ambient concentrations of 

PM2.5 and ozone. These climate benefits estimates have been calculated using the estimated 

values of marginal climate impacts presented in the Technical Support Document: Technical 

Update of the Social Cost of Carbon for Regulatory Impact Analysis Uunder Executive Order 

12866, henceforth denoted as the 2013 SCC TSD.

4

 benefits value deemed central by the federal 

 

4

 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by 

Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of 

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

Comment [A1]: OMB edit: “Implementing 

the proposed guidelines is expected to reduce 

emissions of CO2 and, as a secondary or cobenefit of control technologies introduced to 

reduce CO2 emissions, there will be reduced 

emissions of SO2, NO2, and directly emitted 

PM2.5, which would lead to lower ambient 

concentrations of PM2.5 and ozone.” 

EPA response: We believe that the edited 

sentence more accurately reflects the impacts 

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

Draft Deliberative 

ES-10 

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-6 and 

ES-7. Also, the range of combined benefits reflects different concentration-response functions 

for the air pollution health co-benefits, but it does not capture the full range of uncertainty 

inherent in the health co-benefits estimates. Furthermore, wWe 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-5 provides the list of the quantified 

and unquantified environmental and health benefits in this analysis. 

 

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

Comment [A2]: OMB edit: “but it does not 

capture the full range of uncertainty inherent in 

the health co-benefits estimates that are 

typically reported in EPA’s regulatory impact 

analyses. 

EPA response: We have not incorporated this 

text edit because we do not believe that the 

additional language adds clarity. 
ES-11 

Table ES-5. Quantified and Unquantified Benefits 

Benefits Category Specific Effect 

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information 

Improved Environment 

Reduced climate 

effects 

Global climate impacts from 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

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

Comment [A3]: OMB edit: “Potential 

Quantified and Unquantified Benefits” 

EPA response: We did not incorporate this edit 

because it may cause confusion. The purpose 

of this table is to identify which benefit 

categories are included and excluded in the 

quantified estimates. Adding the word 

“potential” indicates uncertainty as to whether 

specific effects were quantified in this analysis.
ES-12 

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. 

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-13 

ES.5.1 Estimating Global Climate Benefits 

We estimate the global social benefits of carbon dioxide (CO2) emission reductions 

expected from this rulemaking using the SCC estimates presented in the 2013 SCC TSD. We 

refer to these estimates, which were developed by the U.S. government, as “SCC estimates” for 

the remainder of this document. 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 impact on 

cumulative global CO2 emissions). 

The federal SCC estimates of SCC used in this analysis have been developed over many 

years, using the best science available, and with input from the public. The EPA and other 

federal agencies have considered the extensive public comments on ways to improve SCC 

estimation received via the notice and comment period that was part of numerous rulemakings 

since 2006. In addition, OMB’s Office of Information and Regulatory Affairs recently sought 

public comment on the approach used to develop the SCC estimates. The comment period ended 

on February 26, 2014, and OMB is reviewing the comments received. 

An interagency process that included the EPA and other executive branch entities used 

three integrated assessment models (IAMs) to develop SCC estimates and selected four global 

values for use in regulatory analyses. The SCC estimates represent global measures because of 

the distinctive nature of the climate change problem. Emissions of greenhouse gases contribute 

to damages around the world, even when they are released in the United States, and the world’s 

economies are now highly interconnected. Therefore, the SCC estimates incorporate the 

worldwide damages caused by carbon dioxide emissions in order to reflect the global nature of 

the problem, and we expect other governments to consider the global consequences of their 

greenhouse gas emissions when setting their own domestic policies. See RIA Chapter 4 for more 

discussion. 

Comment [A4]: OMB Comment: Replace 

with document citation and insert 

“...consistent with OMB requirements for 

quantifying and monetizing the SCC.” 

EPA response: We presented full citation in 

ES.5 (preceding section) and defined it as 

“2013 SCC TSD,” so unnecessary to repeat in 

ES 5.1. Also, the suggested text edit states that 

EPA has developed SCC estimates consistent 

with OMB requirements. We modified the text 

to clarify that EPA applied the SCC estimates 

developed by the US government, which have 

been cited with the 2013 TSD. 

Comment [A5]: OMB comment: Add 

sentence and citation of how public input was 

obtained. 

EPA response: We described how public input 

was obtained, but we do not understand what 

you mean by citation for this description 

because there is no one citation. EPA and 

other agencies have sought public comment 

through numerous rulemakings. 
ES-14 

The federal government first released the estimates in February 2010 and updated them in 

2013 using new versions of each IAM. The general approach to estimating the SCC values in 

2010 and 2013 was to run the three integrated assessment models (DICE, FUND, and PAGE)5

using the following three inputs in each model: a probabilistic distribution for climate sensitivity; 

five scenarios capturing economic, population, and emission trajectories; and constant annual 

discount rates. The 2010 SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methods used to develop the estimatesmethodology 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 

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. 

 

5

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

Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE).

Comment [A6]: EPA response: redline 

responds to OMB comment in next paragraph 

Comment [A7]: OMB Comment: Refer to 

the key assumptions – including climate 

sensitivity – see response to request for 

correction 

EPA response: Did you have a particular 

sentence in mind? The redline addition in the 

paragraph above describes the 3 input 

assumptions. There is a phrase about the 3 

input assumptions in OMB’s 1/24/14 response 

to API et al, but it needed to be modified (e.g., 

I would not describe all 3 discount rates as 

intergenerational.) Also, we do not view the 3 

input assumptions as limitations of the analysis 

and therefore described them in the preceding 

methodology paragraph. We have provided 

more discussion in RIA Chapter 4 about how 

the three input parameters allowed us to 

explore key uncertainties. 

Comment [A8]: OMB Edit: “current??” 

EPA Response: Cannot insert current because 

AR4 was based on estimates published prior to 

2007. Also, AR5 does not update this specific 

sentence on directionality. 
ES-15 

ES 5.2 Estimating Air Pollution Health Co-Benefits 

The proposed guidelines would reduce emissions of precursor pollutants (e.g., SO2, NOx, 

and directly emitted particles), which in turn would lower ambient concentrations of PM2.5 and 

ozone. This co-benefits analysis quantifies the monetized benefits associated with the reduced 

exposure to these two pollutantsThe 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.

6

 Unlike the global SCC estimates, the air pollution health 

co-benefits are only estimated for the contiguous U.S.7 The estimates of monetized PM2.5 cobenefits include avoided premature deaths (derived from effect coefficients in two cohort studies 

[Krewski et al. 2009 and Lepeule et al. 2012] for adults and one for infants [Woodruff et al. 

1997]), as well as avoided morbidity effects for ten non-fatal endpoints ranging in severity from 

lower respiratory symptoms to heart attacks (U.S. EPA, 2012). The estimates of monetized 

ozone co-benefits include avoided premature deaths (derived from the range of effect 

coefficients represented by two short-term 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). 

We 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 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 

 

6

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

7

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

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

Comment [A9]: OMB edit: The health cobenefits estimates represent the total monetized 

human health benefits for populations 

associated with the reduced exposure to PM2.5 

and ozone associated with emission reductions 

of precursor pollutants (e.g., SO2, NOx, and 

directly emitted particles) anticipated by 

implementing the proposed guidelines 

EPA response: We modified this sentence to 

be more clear. 

Comment [A10]: OMB edit: “include 

modeled avoided premature deaths” 

EPA response: We did not incorporate this text 

edit because we believe that the current text is 

more clear than the suggested revision. 

Comment [A11]: OMB edit: “modeled 

avoided morbidity” 

EPA response: See above. 

Comment [A12]: OMB edit: “modeled 

avoided premature deaths” 

EPA response: See above. 

Comment [A13]: OMB edit: “modeled 

avoided morbidity” 

EPA response: See above. 
ES-16 

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 

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 
ES-17 

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 discount rates are 

applied to SCC because the literature shows that it the estimate of SCC is sensitive to 

assumptions about discount rate and because no consensus exists on the appropriate rate to use in 

an intergenerational context. The U.S. government 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-6 and ES-7 provide the combined climate benefits and health co-benefits for each 

option evaluated for 2020, 2025 and 2030 for Options 1 and 2, respectively for each discount rate 

combination. 
ES-18 

Table ES-6. Combined Estimates of 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 plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

Option 1 In 2020 371 million metric tonnes CO2

5% $4.7 $21$19 to $42$39 $19$18 to $39$36

3% $17 $33$32 to $54$51 $32$30 to $51$48

2.5% $25 $41$40 to $63$59 $40$38 to $59$56

3% (95th percentile) $51 $67$65 to $88$85 $65$64 to $85$81

In 2025 501 million metric tonnes CO2

5% $7.5 $30$29 to $61$57 $28$27 to $56$53

3% $25 $48$46 to $78$75 $46$44 to $74$70

2.5% $37 $60$58 to $90$86 $57$56 to $85$82

3% (95th percentile) $76 $99$98 to $130$13

0 $97$96 to $120$120

In 2030 545 million metric tonnes CO2

5% $9.3 $35$33 to $68$65 $32$31 to $63$60

3% $30 $55$54 to $89$86 $53$52 to $84$81

2.5% $44 $69$67 to $100$99 $66$65 to $97$94

3% (95th percentile) $92 $120$12

0 to $150$150

$120$11

0 to $150$140

Option 2 In 2020 283 million metric tonnes CO2

5% $3.6 $17$16 to $34$32 $16$15 to $32$29

3% $13 $26$25 to $44$41 $25$24 to $41$38

2.5% $19 $33$31 to $50$47 $31$30 to $47$45

3% (95th percentile) $39 $52$51 to $70$67 $51$50 to $67$64

In 2025 368 million metric tonnes CO2

5% $5.5 $23$21 to $46$43 $21$20 to $42$39

3% $18 $36$34 to $59$56 $34$33 to $55$52

2.5% $27 $44$43 to $67$64 $43$42 to $64$61

3% (95th percentile) $56 $73$72 to $96$93 $72$71 to $93$90

In 2030 353 million metric tonnes CO2

5% $6.0 $23$22 to $46$43 $21$20 to $42$40

3% $20 $37$35 to $59$56 $35$34 to $56$53

2.5% $28 $45$44 to $68$65 $44$43 to $64$62

3% (95th percentile) $60 $77$76 to $100$97 $75$74 to $96$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.See chapter 4 for more information about these 

estimates and regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average. 

Formatted Table

Comment [A14]: Note: EPA updated the 

health co-benefits to include the benefits 

associated with reducing emissions of directly 

emitted particles. 

Comment [A15]: OMB edit: As noted 

above, the numbers in this table do not 

quantify the uncertainty inherent in these 

estimates. 

EPA response: We believe that the added text 

addresses this comment. 
ES-19 

Table ES-7. Combined Estimates of 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 plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

Option 

1 

In 2020 383 million metric tonnes CO2

5% $4.9 $22$20 to $45$41 $20$19 to $41$38

3% $18 $35$33 to $57$54 $33$32 to $54$50

2.5% $26 $43$42 to $66$62 $42$40 to $62$59

3% (95th percentile) $52 $69$68 to $92$89 $68$66 to $88$85

In 2025 506 million metric tonnes CO2

5% $7.6 $31$29 to $62$58 $29$27 to $57$54

3% $25 $49$47 to $80$76 $46$45 to $75$71

2.5% $37 $61$59 to $92$88 $58$57 to $87$83

3% (95th percentile) $77 $100$99 to $130$13

0 $98$97 to $130$120

In 2030 555 million metric tonnes CO2

5% $9.5 $36$34 to $72$68 $34$32 to $66$63

3% $31 $57$56 to $93$89 $55$53 to $87$84

2.5% $44 $71$69 to $110$10

0 $69$67 to $100$98

3% (95th percentile) $94 $120$120 to $160$15

0

$120$12

0 to $150$150

Option 

2 

In 2020 295 million metric tonnes CO2

5% $3.8 $17$16 to $35$32 $16$15 to $32$30

3% $14 $27$26 to $45$42 $26$25 to $42$40

2.5% $20 $34$32 to $52$49 $32$31 to $49$46

3% (95th percentile) $40 $54$53 to $72$69 $53$51 to $69$66

In 2025 376 million metric tonnes CO2

5% $5.6 $23$22 to $47$44 $22$20 to $43$40

3% $19 $36$35 to $60$57 $35$34 to $56$54

2.5% $28 $45$44 to $69$66 $44$42 to $65$62

3% (95th percentile) $57 $75$74 to $98$95 $73$72 to $95$92

In 2030 373 million metric tonnes CO2

5% $6.4 $24$23 to $48$45 $23$22 to $44$42

3% $21 $39$37 to $63$60 $37$36 to $59$56

2.5% $30 $48$47 to $72$69 $46$45 to $68$65

3% (95th percentile) $63 $81$80 to $110$10

0 $80$79 to $100$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.See chapter 4 for more information about these 

estimates and regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average. 

Formatted Table

Comment [A16]: OMB edit: As noted 

above, the numbers in this table do not 

quantify the uncertainty inherent in these 

estimates. 

EPA response: We believe that the added text 

addresses this comment. 
ES-20 

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

for Existing Sources 

In this summary, the EPA provides the 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-8, the 

EPA estimates that in 2020 the proposed Option 1 regional compliance approach will yield 

monetized climate benefits of $17 billion using a 3 percent discount rate (model average, 2011$). 

The air pollution health co-benefits in 2020 are estimated to be $15 billion to $34 billion (2011$) 

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

The annual compliance costs, including monitoring and reporting costs, are approximately $5.5 

billion (2011$) in 2020. The quantified net benefits (the difference between monetized benefits 

and costs) are $26 billion to $45 billion (2011$) for 2020 (Table ES-8 below) and $47 billion to 

$78 billion (2011$) for 2030 (Table ES-10 below), using a 3 percent discount rate (model 

average). For the Option 1 state compliance approach in 2020, the EPA estimates monetized 

climate benefits of approximately $18 billion using a 3 percent discount rate (model average). 

The air pollution health co-benefits in 2020 are estimated to be $15 billion to $36 billion for a 3 

percent discount rate and $14 billion to $33 billion (2011$) for a 7 percent discount rate. The 

annual compliance costs including monitoring and reporting costs, are approximately $7.5 billion 

(2011$) in 2020. The quantified net benefits (the difference between monetized benefits and 

costs) are $26 billion to $46 billion for 2020 (Table ES-8 below) and $47 billion to $80 billion 

(2011$) for 2030 (Table ES-10 below), Benefit and cost estimates for Options 1 and 2 regional 

and state compliance approaches for 2020, 2025, and 2030 are presented in Tables ES-8, ES-9, 

and ES-10. 

The EPA could not monetize some important benefits of the guidelines. Unquantified 

benefits include climate benefits from reducing emissions of non-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-21 

Table ES-8. Summary of Estimated Monetized Benefits, Compliance Costs, and Net 

Benefits for the Proposed Guidelines – 2020 (billions of 2011$) a

Option 1 - state Option 2 – state 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b 

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$4.9 

$18 

$26 

$52 

$3.8 

$14 

$20 

$40 

Air pollution health co-benefits c $15 17 to $3640 $14 15 to 

$3336 $12 14 to $2932 $11 12 to $2629

Total Compliance Costs d $7.5 $5.5 

Net Benefits e $26 27 to $4650 $24 26 to 

$4346 $20 22 to $3740 $19 20 to $3437

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 $156 to $3437 $13 15 to 

$3134 $12 13 to $2831 $11 12 to $2528

Total Compliance Costs d $5.5 $4.3 

Net Benefits e $26 28 to $4549 $25 26 to 

$4245 $21 22 to $3740 $20 21 to $3437

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. 
ES-22 

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. 

Table ES-9. Summary of Estimated Monetized Benefits, Compliance Costs, and Net 

Benefits for the Proposed Guidelines – 2025 (billions of 2011$) a

Option 1 – state Option 2 – state 

3% Discount 

Rate 

7% Discount 

Rate 

3% Discount 

Rate 

7% Discount 

Rate 

Climate Benefits b 

5% discount rate 

3% discount rate 

2.5% discount rate 

95th percentile at 3% discount rate 

$7.6 

$25 

$37 

$77 

$5.6 

$19 

$28 

$57 

Air pollution health co-benefits c $22 23 to $5154 $20 21 to 

$4649 $16 18 to $3841 $15 16 to $3537

Total Compliance Costs d $5.5 $5.5 

Net Benefits e $41 43to $7074 $39 41 to 

$6669 $30 31 to $5255 $28 29 to $4851

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 23 to $5053 $19 21 to 

$4548 $16 17 to $3740 $14 16 to $3436

Total Compliance Costs d $4.6 $4.5 

Net Benefits e $42 43 to $7074 $40 41 to 

$6669 $30 31 to $51554 $28 29 to $48510

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.
ES-23 

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. 

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. 

Table ES-10. Summary of Estimated 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 27 to $5862 $23 24 to 

$5356 $17 18 to $3942 $15 16 to $3638

Total Compliance Costs d $8.8 $3.1 

Net Benefits e $47 49 to $8084 $45 46 to 

$7579 $34 36 to $575960 $33 34 to $5356

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 25 to $5559 $21 23 to 

$5054 $16 17 to $3740 $14 15 to $3436

Total Compliance Costs d $7.3 $1.9 

Net Benefits e $47 48 to $7882 $44 46 to 

$7377 $33 35 to $5557 $32 33 to $5154

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 
ES-24 

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. 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.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. 

Formatted: Not Highlight
ES-25 

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 

energy efficiency can result in changes 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. 

Given 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 28,000 to 25,900 job-years in 2020 for Option 1, state and regional compliance 
ES-26 

approaches, respectively. For Option 2, the state and regional compliance approach estimates 

both an increase of approximately 29,800 to 26,700 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.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. 

 
ES-27 

ES.10 References 

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): pp. 2372-8. 

Docket ID EPA-HQ-OAR-2009-0472-114577, 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>. 

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>. 
ES-28 

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. 

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 >. 
ES-29 

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>. 

Woodruff, T.J.,; J. Grillo, and K.C. Schoendorf. 1997. “The Relationship BBetween Selected

Causes of Ppostneonatal Iinfant mortality Mortality and particulate Particulate air Air 

pollution Pollution in the United States,.” Environmental Health Perspectives, 105 (6): 608-

612. 
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3-1 

CHAPTER 3: COST, ECONOMIC, AND ENERGY IMPACTS 

3.1 Introduction 

This chapter reports the compliance cost, economic, and energy impact analysis 

performed for the proposed rule. EPA used the Integrated Planning Model (IPM), developed by 

ICF International, to conduct its analysis. IPM is a dynamic linear programming model that can 

be used to examine air pollution control policies for 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. 

3.2 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). 
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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. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3-3 

Although the Agency typically focuses on broad system effects when assessing the 

economic impacts of a particular policy, EPA’s application of IPM includes a detailed and 

sophisticated regional representation of key variables affecting power sector behavior. 

The model incorporates a detailed representation of the fossil-fuel supply system that is 

used to forecast equilibrium fuel prices. The 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 
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3-4 

EPA has used IPM extensively over the past two decades to analyze options for reducing 

power sector emissions. Previously, the model 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. 

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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3-5 

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

3-6 

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 3-1. 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 

State9

Option 1 Option 2 

Interim Goal 

(2020-2029) 

Final Goal 

(2030 Forward) 

Interim Goal 

(2020-2024) 

Final Goal 

(2025 Forward) 

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

Alaska 1,097 1,003 1,170 1,131 

Arizona * 735 702 779 763 

Arkansas 968 910 1,083 1,058 

California 556 537 582 571 

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

Connecticut 597 540 651 627 

Delaware 913 841 1,007 983 

Florida 794 740 907 884 

Georgia 891 834 997 964 

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

Idaho 244 228 261 254 

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

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

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

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

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

Louisiana 948 883 1,052 1,025 

Maine 393 378 418 410 

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

Massachusetts 655 576 715 683 

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

Minnesota 911 873 1,018 999 

 

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

3-7 

Mississippi 732 692 765 743 

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

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

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

Nevada 697 647 734 713 

New Hampshire 546 486 598 557 

New Jersey 647 531 722 676 

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

New York 635 549 736 697 

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

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

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

Oklahoma 931 895 1,019 986 

Oregon 407 372 450 420 

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

Rhode Island 822 782 855 840 

South Carolina 840 772 930 897 

South Dakota 800 741 888 861 

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

Texas 853 791 957 924 

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

Virginia 884 810 1,016 962 

Washington 264 215 312 284 

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

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

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

* Excludes EGUs located in Indian country. 

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 

would be offset by comparable changes in the illustrative compliance scenarios (in which 

compliance is consistent with the assumptions used in setting state goals). 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3-8 

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 regard 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 

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 2024. 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 Total 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. . 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.

EPA also analyzed a set of compliance scenarios that assume greater geographic 

flexibility for compliance where states may choose to cooperate in order to achieve more costeffective outcomes, since some states can reduce their emissions more easily relative to others. 

This scenario is modeled for both Option 1 and Option 2 and is referred to as the “Regional” 

scenario. The regional scenarios allow emission 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. 

 West (WECC) (CA, WA, OR, ID, MT, UT, NV, CO, WY, NM, AZ) 

 North Central (MISO)15 – 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 

 

15 Note that the MISO region expanded to integrate Entergy territory at the end of 2013. 
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Figure 3-11. NERC Regions 

Figure 3-2. NERC Assessment Areas 

Formatted: Font: 10 pt, Complex Script

Font: 10 pt, Do not check spelling or

grammar
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 Figure 3-3. Regional Compliance Zones Used in this Analysis 

These regional groupings of states allow the state goals in each option analyzed to be met 

collectively by averaging emission rate performance across all affected units located in that 

region. 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 region16 must be less than or equal to the weighted average17 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. 

 

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

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

respective state 
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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. 

3.6 Demand Side Energy Efficiency 

3.6.1 Projected Demand-Side Energy Savings18

To estimate the potential electricity demand reduction that could be achieved, and 

associated costs incurred, through implementation of demand-side energy efficiency policies, 

EPA developed scenarios that reflect increased levels of demand-side energy efficiency, rooted 

in what leading states have already accomplished or have requirements in place to accomplish.19

For Option 1, adjustments were made to each state’s annual incremental reduction in electricity 

consumption by ramping up from an historical basis20 to a target rate of 1.5% of electricity 

demand annually over a period of years starting in 2017, and maintain that rate throughout the 

modeling horizon. Twelve leading states have 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 

 

18 For a more detailed discussion of the demand-side energy efficiency savings projections, refer to the Greenhouse 

Gas Abatement Measures TSD.

19 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. 

20 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/. 
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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.21

To reflect the implementation of demand-side energy efficiency in modeling, the fixed 

total electricity demand in IPM was adjusted exogenously to reflect the estimated future-year 

energy savings calculated from the approach described above. State energy savings in sales were 

scaled up to account for transmission losses and applied to base case generation demand in each 

model year to derive adjusted demand for each state, reflecting the energy efficiency scenario 

energy savings.22 The demand adjustments were applied proportionally across all segments (peak 

and non-peak) of the load duration curve.23 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. 

 

21 See Greenhouse Gas Abatement Measures TSD and the State Goal Setting TSD 

22 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. 

23 For more information on load duration curves, see Chapter 2 of EPA’s Base Case using IPM (v5.13) 

documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html 
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 Table 3-2. 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% 

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% 

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% 
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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% 

Source: See Greenhouse Gas Abatement Measures TSD 

3.6.2 Demand-Side Energy Efficiency Total Costs24

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.25 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 

 

24 For a more detailed discussion of the demand-side energy efficiency savings cost estimates, refer to the 

Greenhouse Gas Abatement Measures TSD. 

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

Abatement Measures TSD. 
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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 8.5 cents 

per kWh in 2020, 8.9 cents per kWh in 2025, and 9.0 cents per kWh in 2030.26

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 the recovery of these 

program costs through the following procedure.27 For each state, the first-year EE program costs 

are calculated for each year, equivalent to 50% of the total first-year EE 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. 28 

 

26 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. 

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

28 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. 
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3.7 Projected Power Sector Impacts 

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 

emissions29), 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 3-3. Projected CO2 Emission Impacts, Relative to Base Case

 CO2 Emissions 

(MM Tonnes) 

CO2 Emissions 

Reductions from Base 

Case 

(MM Tonnes) 

CO2 Emissions 

Reductions: Percent 

Change from Base 

Case 

 2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 2,161 2,231 2,256 

Option 1 Regional 1,790 1,730 1,711 371 501 545 17% 22% 24% 

Option 1 State 1,777 1,724 1,701 383 506 555 18% 23% 25% 

Option 2 Regional 1,878 1,862 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

 Table 3-4. Projected CO2 Emission Impacts, Relative to 2005 

 CO2 Emissions 

(MM Tonnes) 

CO2 Emissions 

Reductions from Base 

Case 

(MM Tonnes) 

CO2 Emissions 

Reductions: Percent 

Change from Base 

Case 

 2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 2,161 2,231 2,256 273 203 178 11% 8% 7% 

Option 1 Regional 1,790 1,730 1,711 644 704 723 26% 29% 30% 

 

29 For purposes of these calculations, EPA has used historical CO2 emissions from eGRIDrid for 2005, which reports 

EGU emissions as 2,434 million metric tonnes in the contiguous US. 
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Option 1 State 1,777 1,724 1,701 657 710 733 27% 29% 30% 

Option 2 Regional 1,878 1,862 1,903 556 572 531 23% 23% 22% 

Option 2 State 1,866 1,855 1,882 568 579 552 23% 24% 23% 

 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 3-5Table 3-8 below demonstrate similar trends 

in 2025 and 2030. 

 Table 3-54. 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 

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) 212 156 154 166 163 -26.4% -27.2% -21.5% -22.9%

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) 209 150 146 165 161 -28.1% -30.1% -21.0% -23.2%

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% 
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PM2.5

(thousand tons) 198 144 142 158 154 -27.2% -28.5% -20.3% -22.1%

 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 

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. 

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. 30 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 

 

30 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the 

electricity sector to serve demand. 
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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,31 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 3-65. 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 

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 3-65Table 3-6 include the exogenously 

determined annualized total cost of demand-side energy efficiency measures, presented here in

Table 3-7 6Table 3-10. Table 3-7Table 3-8 provides the total first-year or “up front” cost of 

demand-side energy efficiency measures.

 

31 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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Table 3-Table 76. Annualized Total Gross Cost of Demand-Side Energy Efficiency (at 

discount rates of 3% and 7%, billions 2011$) 

 2018 2020 2025 2030 

Option 1 at 3% 4.1 10.2 28.9 42.7 

Option 1 at 7% 4.9 12.3 35.0 51.8 

Option 2 at 3% 3.6 8.0 20.6 29.8 

Option 2 at 7% 4.3 9.7 24.9 36.1 

Source: Greenhouse Gas Abatement Measures TSD 

Table 3- 78. First-Year Total Costs of Demand-Side Energy Efficiency (billions 2011$) 

 2018 2020 2025 2030 

Option 1 20.5 30.7 43.6 43.5 

Option 2 17.0 22.2 29.6 30.0 

Source: Greenhouse Gas Abatement Measures TSD 

The following example uses results from Option 1 Regional in the year 2020 to illustrate 

how different components of estimated expenditures are combined to form the full compliance 

costs presented in Table 3-6. Comparing the IPM modeling results in 2020 for this scenario to 

results from the base case, one can see an incremental decline of $4.8 billion in the annualized 

expenditures required to supply enough electricity to meet demand. This incremental decline is a 

net outcome of two simultaneous forces having opposite effects. Imposing the CO2 constraints 

represented in the Option 1 Regional scenario on electric generators would, ceteris paribus, yield 

an incremental increase in expenditures to supply a given level of electricity. However, the IPM 

modeling also reflects a substantial reduction in electricity demand as a result of end-use energy 

efficiency (EE) improvements, which necessarily results in substantial reductions in the 

expenditures needed to supply a correspondingly lower amount of electricity. The incremental 

decline in IPM-modeled expenditures shows that before the expenditures to reduce electricity 

demand through end-use energy efficiency are taken into account, the reduced expenditures of 

electricity supply (due to EE) outweigh the increased expenditures to meet the policy’s CO2

constraints, such that the net outcome is a decline of $4.8 billion in annualized expenditures to 

supply electricity in 2020.

In order to reflect the full cost attributable to the policy, it is necessary to pair this 

incremental -$4.8 billion in electricity supply expenditures with the annualized expenditures 

needed to secure the end-use energy efficiency improvements. EPA has estimated these EE-
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3-23 

related expenditures to be $10.2 billion in 2020 (using a 3% discount rate). As a result, this 

analysis finds the cost of the Option 1 Regional scenario in 2020 to be $5.4 billion (the sum of 

incremental supply-related and demand-related expenditures). 

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

 2020 2025 2030 

Base Case $177.8 $202.9 $224.7 

Option 1 Regional $173.0 $178.6 $189.2 

Option 1 State $175.0 $179.5 $190.7 

Option 2 Regional $174.0 $186.9 $196.8 

Option 2 State $175.2 $187.8 $198.0 

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 

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.32

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. 

 

32 See Technical Support Document – GHG Abatement Measures. 

Formatted: Indent: Before: 0"
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3-24 

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-

10Table 3-11. 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 3-107. Projected Capacity Factor of Existing Coal Steam and Natural Gas 

Combined Cycle Capacity 

 Existing Coal Steam Existing Natural Gas 

Combined Cycle 

 2020 2025 2030 2020 2025 2030 

Base Case 78% 80% 79% 52% 48% 42% 

Option 1 Regional 77% 74% 73% 55% 54% 50% 

Option 1 State 76% 74% 73% 56% 55% 51% 

Option 2 Regional 75% 74% 76% 57% 55% 49% 

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.33 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 

 

33 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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3-25 

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. 

3.7.4 Projected Generation Mix 

Table 3-11Table 3-12 and Figure 3-4Figure 3-7 show the generation mix in the base case 

and under the proposed rule. The ability to average the emissions rate at covered sources (which 

exclude new NGCC) over the 2020-2030 compliance period provides an incentive to shift 

generation within the covered sources to less carbon-intensive generation. In 2020, for Option 1, 

total generation 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. Coalfired 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. Gasfired generation from new combined cycle capacity increases in 2020, as projected new natural 

gas combined cycle capacity replaces retired coal capacity. By 2030, generation from newly 

built natural gas combined cycle decreases between 36% and 40% relative to the base case, 

consistent with the decrease in new capacity (see section 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, 
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3-26 

consistent with the projected decrease in total natural gas use. The projected increase in pipeline 

capacity in the near term is largely the result of building pipeline capacity a few years earlier 

than in is projected in the base case. Given the relatively small amount of additional 

infrastructure development that is projected to shift to earlier years, construction of this 

additional capacity should be easily manageable and not raise any reliability or cost concerns. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

3-27 

Table 3-118. 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
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Figure 3-4. 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
9-0 

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.34

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 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 3-12Table 3-13. 

 

34 For more detail on IPM’s electric load modeling and power system operation, please see IPM documentation (http://www.epa.gov/powersector modeling). 
9-1 

 Table 3-1210. 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 

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 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 nonhydro 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 nonhydro renewable capacity is projected to increase by about 12 GW (67% increase) above the base case in 2020, and 8 to 9 GW (24%

to 28% increase) by 2030. 

Under 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. 
9-2 

 Table 3-1311. Projected Capacity Additions, Gas (GW) 

 Cumulative Capacity Additions: 

Gas Combined Cycle 

Incremental Cumulative 

Capacity Additions: Gas 

Combined Cycle

 2020 2025 2030 2020 2025 2030 

Base Case 11.9 38.9 83.8 

Option 1 Regional 32.7 34.4 49.0 20.8 -4.4 -34.8 

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 

 

 Table 3-1412. Projected Capacity Additions, Non-hydro Renewable (GW) 

 Cumulative Capacity Additions: 

Renewables 

Incremental Cumulative 

Capacity Additions: Renewables 

 2020 2025 2030 2020 2025 2030 

Base Case 17.8 28.4 32.7 

Option 1 Regional 29.9 38.4 41.9 12.1 9.9 9.2 

Option 1 State 29.6 37.0 40.6 11.8 8.5 7.9 

Option 2 Regional 26.4 35.5 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 

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 3-15Table 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 3-1513. Coal Production for the Electric Power Sector, 2020 

 Coal Production (MM Tons) Percent Change from Base Case 

 Base 

Case 

Option 1 Option 2 Option 1 Option 2 

 Reg. State Reg. State Reg. State Reg. State 

Appalachia 140 87 91 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 3-1614. Power Sector Gas Use 

 Power Sector Gas Use 

(TCF) 

Percent Change in Power 

Sector Gas Use 

 2020 2025 2030 2020 2025 2030 

Base Case 8.35 8.88 9.89 

Option 1 Regional 9.32 9.31 9.37 11.7% 4.8% -5.3% 

Option 1 State 9.54 9.52 9.61 14.3% 7.2% -2.9% 
9-3 

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 

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 3-17Table 3-18, 

Table 3-18Table 3-19, Table 3-19Table 3-20, and 

Table 3-20Table 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. 

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 shortlived 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 3-1715. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) 

 Minemouth Delivered - Electric Power Sector 

 2020 2025 2030 2020 2025 2030 

Base Case 1.73 1.88 2.06 2.62 2.80 2.98 

Option 1 Regional 1.46 1.57 1.72 2.19 2.29 2.44 

Option 1 State 1.45 1.56 1.70 2.18 2.30 2.44 

Option 2 Regional 1.49 1.62 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 3-1816. 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 

Table 3-1917. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu) 

 Henry Hub Delivered - Electric 

Power Sector 

 2020 2025 2030 2020 2025 2030 

Base Case 4.98 5.68 6.00 5.36 6.11 6.39 

Option 1 Regional 5.50 5.60 6.02 5.86 5.91 6.33 

Option 1 State 5.61 5.57 6.07 5.98 5.90 6.39 

Option 2 Regional 5.40 5.79 5.85 5.76 6.13 6.18 

Option 2 State 5.43 5.80 5.86 5.80 6.16 6.22 

Formatted: 05 Body Text
9-4 

 Source: Integrated Planning Model run by EPA, 2014 

Table 3-2018. 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 

3.7.9 Projected Retail Electricity Prices 

EPA’s analysis projects an increase in the national average (contiguous U.S.) retail electricity price between 5.9% and 6.5% in 

2020 and between 2.7% and 3.1% by 2030 under the proposed Option 1, compared to the modeled base case price estimate in those 

years. Under Option 2, on average, EPA projects an average retail price increase ranging from 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. 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 3-2119. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 

 2020 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 

Base 

Case 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

ERCT 9.9 10.6 10.8 10.4 10.5 7.4% 9.9% 5.3% 6.0% 

FRCC 10.6 11.3 11.6 11.2 11.4 6.5% 8.7% 5.2% 7.1% 

MROE 10.4 10.9 11.0 10.8 10.9 4.7% 5.8% 3.4% 4.2% 

MROW 9.2 9.8 9.8 9.6 9.6 6.4% 7.0% 4.6% 4.9% 

NEWE 13.8 15.2 15.4 14.5 14.8 10.1% 11.4% 5.1% 6.9% 

NYCW 18.0 19.7 19.8 18.8 18.9 9.5% 10.0% 4.6% 5.0% 

NYLI 14.7 16.1 16.1 15.3 15.3 9.3% 9.5% 3.7% 4.2% 

NYUP 12.7 14.0 14.0 13.4 13.3 9.9% 9.8% 4.8% 4.5% 

RFCE 12.2 13.3 13.2 12.6 12.8 8.6% 7.7% 3.2% 4.6% 

RFCM 10.7 11.2 11.3 11.1 11.1 5.1% 6.0% 3.6% 3.9% 

RFCW 10.1 10.7 10.8 10.4 10.3 6.1% 6.3% 2.5% 2.2% 

SRDA 9.0 9.4 9.4 9.3 9.2 4.1% 4.5% 3.0% 2.8% 

SRGW 9.3 9.7 9.7 9.5 9.5 4.9% 4.7% 2.1% 1.7% 

SRSE 10.4 10.7 10.7 10.6 10.6 2.8% 3.4% 2.2% 2.2% 

SRCE 8.2 8.4 8.4 8.3 8.4 1.5% 1.7% 1.3% 1.6% 

SRVC 10.7 10.9 10.9 10.8 10.8 2.4% 2.2% 1.6% 1.5% 

SPNO 10.6 11.7 10.7 11.4 10.7 9.9% 0.6% 7.5% 1.1% 

SPSO 8.3 9.0 9.3 8.8 9.0 8.3% 11.7% 5.3% 7.7% 

AZNM 10.5 11.1 11.3 10.9 11.1 5.3% 7.4% 3.9% 5.6% 

CAMX 14.3 15.2 15.2 14.9 14.9 6.3% 6.4% 4.7% 4.5% 

NWPP 7.3 7.7 7.8 7.6 7.6 6.1% 6.7% 4.5% 4.5% 

RMPA 8.9 9.5 9.9 9.4 9.6 6.3% 10.4% 5.0% 6.9% 

Contiguous 

U.S. 10.4 11.1 11.1 10.8 10.9 5.9% 6.5% 3.6% 4.0% 
9-5 

Table 3-2220. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 

 2025 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 

Base 

Case 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

ERCT 11.2 11.5 11.4 11.6 11.6 2.9% 1.8% 3.3% 3.7% 

FRCC 10.9 11.4 11.5 11.3 11.5 4.5% 5.4% 4.1% 5.4% 

MROE 10.5 10.9 11.0 10.8 10.9 4.0% 4.2% 2.5% 4.0% 

MROW 9.2 9.8 9.8 9.6 9.6 5.8% 5.7% 4.3% 4.4% 

NEWE 14.2 14.4 14.5 14.3 14.5 1.4% 1.9% 0.2% 1.6% 

NYCW 18.8 19.1 19.1 18.9 18.9 1.5% 1.3% 0.5% 0.4% 

NYLI 15.6 15.5 15.4 15.4 15.3 -0.4% -1.4% -1.4% -1.5% 

NYUP 13.2 13.3 13.2 13.2 13.1 0.2% -0.4% -0.3% -0.8% 

RFCE 12.6 12.7 12.9 13.0 13.0 0.4% 2.2% 3.0% 3.1% 

RFCM 10.7 11.2 11.2 11.1 11.1 4.0% 4.5% 3.1% 3.6% 

RFCW 10.9 10.9 11.0 10.9 11.0 -0.4% 0.0% -0.1% 0.3% 

SRDA 9.3 9.7 9.7 9.7 9.7 4.1% 3.7% 3.9% 3.6% 

SRGW 10.1 10.1 10.1 10.1 10.1 -0.1% -0.1% -0.1% -0.1% 

SRSE 10.3 10.7 10.7 10.6 10.7 3.5% 3.8% 2.8% 2.9% 

SRCE 8.2 8.5 8.5 8.4 8.4 3.1% 3.2% 2.2% 2.2% 

SRVC 10.6 10.9 10.8 10.8 10.8 3.0% 2.5% 2.4% 2.0% 

SPNO 10.3 11.8 10.8 11.3 10.7 14.1% 4.5% 8.7% 3.0% 

SPSO 8.8 9.5 9.7 9.4 9.5 7.4% 9.4% 6.0% 7.7% 

AZNM 10.8 11.2 11.3 11.0 11.2 3.9% 5.0% 2.4% 4.0% 

CAMX 13.9 14.4 14.3 14.3 14.3 3.2% 2.9% 2.9% 2.9% 

NWPP 7.4 7.8 7.7 7.6 7.6 5.0% 4.6% 3.4% 3.3% 

RMPA 9.4 9.6 10.0 9.6 9.8 2.3% 6.4% 2.7% 4.8% 

Contiguous 

U.S. 10.8 11.1 11.1 11.0 11.1 2.7% 2.9% 2.4% 2.7% 

Table 3-2321. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) 

 2030 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 

Base 

Case 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

Option 1 

Regional 

Option 1 

State 

Option 2 

Regional 

Option 2 

State 

ERCT 11.6 12.0 12.0 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% 

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% 
9-6 

Figure 3-58. Electricity Market Module Regions 

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

3.7.10 Projected Electricity Bill Impacts 

The electricity price changes addressed in section 3.7.9 combine with the significant policy-induced reductions in electricity 

demand to affect average electricity bills. The estimated changes to average bills are summarized in Table 3-24Table 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 3-2422. 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% 

3.8 Projected Primary PM Emissions from Power Plants 

IPM is not configured to endogenously model primary PM emissions from power plants. These emissions are calculated as a 

function of IPM outputs, emission factors and control configuration. IPM-projected fuel use (heat input) is multiplied by PM emission 

factors (based in part on the presence of PM-relevant pollution control devices) to determine PM emissions. Primary PM emissions are 

calculated by adding the filterable PM and condensable PM emissions. 

Filterable PM emissions for each unit are based on historical information regarding existing emissions controls and types of 

fuel burned and ash content of the fuel burned, as well as the projected emission controls (e.g., scrubbers and fabric filters). 

Condensable PM emissions are based on plant type, sulfur content of the fuel, and SO2/HCl and PM control configurations. 

Although EPA’s analysis is based on the best available emission factors, these emission factors do not account for the potential 

changes in condensable PM emissions due to the installation and operation of SCRs. The formation of additional condensable PM (in 

the form of SO3 and H2SO4) in units with SCRs depends on a number of factors, including coal sulfur content, combustion conditions 

and characteristics of the catalyst used in the SCR, and is likely to vary widely from unit to unit. SCRs are generally designed and 

operated to minimize increases in condensable PM. This limitation means that IPM post-processing is potentially underestimating

condensable PM emissions for units with SCRs. In contrast, it is possible that IPM post-processing overestimates condensable PM

emissions in a case where the unit is combusting a low-sulfur coal in the presence of a scrubber. 

EPA applied this methodology to develop primary PM emission projections for 2025 in the base case, and for Option 1 State 

and Option 2 Regional. Using these results, EPA then estimated primary PM emissions for the remainder of the base case and policy 

scenarios over the 2020-2030 time horizon using simplified emissions factors. These factors were developed for the eastern and
9-7 

western regions (excluding California) using EPA’s emissions and fuel use projections for fossil plus biomass. Separate factors were 

developed for base case and policy case scenarios. These factors were applied to the projected fuel use from fossil and biomass plant 

types. While this methodology provides a reasonable estimate, EPA notes that applying the methodology discussed above would 

likely yield different results. Nevertheless, EPA has determined that this estimation is sufficient for the purpose of estimating benefits 

on a large regional level. 

For a more complete description of the methodologies used to post-process PM emissions from IPM, see “IPM ORL File 

Generation Methodology” (March, 2011). 

3.89 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 widelyaccepted 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.35 

The demand-side energy efficiency costs are developed based on a review of energy efficiency data and studies, and expert 

judgment. EPA recognizes that significant variation exists in these analyses reflecting data and methodological limitations. The 

method used for estimating the demand-side energy efficiency costs is discussed in more detail in the Greenhouse Gas Abatement 

Measures TSD. The TSD also discusses the economic literature of energy efficiency costs and related considerations, energy savings 

potential studies, and discusses the associated 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.36

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. 

3.910 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 

 

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

36 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>. 
9-8 

coal-fired generation (about 19% of all coal-fired capacity and 4.6% of total generation capacity in 2020) may be removed from 

operation by 2020. 

EPA also projects the average delivered coal price decreases by 16.3% to 16.5% with decreased production of 208 to 228 

million tons (24.6% to 27.7% of US production) in 2020 and that electric power sector delivered natural gas prices will increase by 

about 9.3% to 11.5% with 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. 

3.101 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 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 3-24 shows the annual respondent burden costs and costs of reporting and recordkeeping for 2020, . 2025 and 2030.

Table 3-2523. 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 3-253 below. 

Table 3-2624. 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 
9-9 

Source: Integrated Planning Model run by EPA, 2014. Monitoring, reporting and recordkeeping costs calculated outside IPM. 

 3.121 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 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 enduse 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.37 

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. 

The social cost of achieving these net electricity demand reductions may differ from the expenditures associated with the enduse 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.38 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.39 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. 

 

37 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. 

38 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. 

39 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. 
9-10 

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 

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.40 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 perspective even if that is not the case for the 

end-user. In such cases there is the potential for properly designed energy efficiency programs to address the source of underinvestment, such as principal-agent problems where 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 

 

40 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. 
9-11 

costs). Under these assumptions the change in compliance cost will equal the reduction in producer41 and consumer surplus42 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 theoretically consistent method for estimating partialequilibrium changes in welfare where prices and outputs change in multiple markets see Chapter 9 of Just et al. (2004). 

3.132 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

 

41 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. 

42 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. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

1

EPA Response to Interagency OMB Comments from 5/21/14 Discussion 

on the EGU GHG Existing Source RIA 

1. OMB requested EPA to represent costs of the guidelines in the RIA by individual building 

blocks and to show energy savings from end-use energy efficiency as a separate cost line item. 

EPA Responses: Per our discussions with OMB on this issue, EPA continues to assert the 

difficulty involved in disentangling the various component costs of the guidelines including cost 

savings (reduced fuel use, reduced capital expenditure, etc.) due to reduced electricity demand 

from end-use energy efficiency.  EPA believes that it has appropriately accounted for the effect 

of reduced electricity demand in the calculation of total costs. EPA believes that the more 

appropriate way to address the concerns that have been raised is to make it clearer how the total 

costs were calculated and the role that end-use energy efficiency costs and energy savings have 

in estimating those total costs. EPA has made edits to the Executive Summary Section ES.4 

Costs of Existing EGU Guidelines and Chapter 3 Section 3.7.2 Projected Compliance Costs to 

represent the costs of the guidelines in a more transparent way. A revised version of the RIA 

Executive Summary and Chapter 3 with relevant edits shown in redline/strikeout are sent with 

this written response.

2.   OMB requested that the section of Chapter 6 Employment Impact Analysis quantifying enduse energy efficiency jobs be eliminated from the RIA. 

EPA Response: EPA believes that it has appropriately included analysis quantifying end-use 

energy efficiency jobs in the RIA. Since end-use energy efficiency expenditures are such a large 

component of the rulemaking, the EPA feels it would be remiss not to attempt to quantify their 

employment impact. EPA feels it has made the best projection possible given the official data 

and methods available. The EPA also feels it has provided the reader with extensive caveats 

about the approach, and solicited comment regarding other methods or data sources. 

3. OMB requested that the language regarding the Graff-Zivin and Neidell paper in the RIA 

Chapter 6 Employment Impacts Analysis be revised. 

EPA Response: OMB seemed to be concerned that the reader may infer that the EPA is 

implicitly recommending the use of Graff-Zivin and Neidell (2012) to quantify labor impacts. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

2

Furthermore, OMB suggested that EPA clarify its position on potential uses of the study. It 

would be helpful if OMB could concretely specify (preferably in written form) its concern 

regarding how the EPA is describing this study in this RIA, and what specific concern(s) OMB 

has about the study’s potential use and in what context (e.g., this RIA? Other RIAs?). 

That said, EPA would like to stress that this article is cited as part of a general literature survey 

to provide the reader with a qualitative understanding of how environmental regulation may 

affect labor demand and supply. This article is highly relevant to the topic and was recently 

published in a top economics journal. Therefore, the agency feels it would be remiss not to 

include its findings in the literature review to show that it is aware of the best available science 

on this topic. 

Furthermore, it is important to note that the EPA is not using this study to quantify impacts of 

this rule making, nor has it adequately considered the advantages and disadvantages of using this 

or other studies to quantify impacts for other rulemakings. The agency feels it lacks a scientific 

basis to include any language at this point supporting or rejecting its general usefulness for the 

purpose of regulatory analysis. 

To clarify this issue, we propose adding the following sentences as a footnote to the end of the 

paragraph citing the work (Section 6.4.2). 

“The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using 

this study. In light of this recent research, however, the EPA is considering how best to 

incorporate possible productivity effects in the future.” 

4. OMB asked that the technical memos relevant to the RIA be attached to the RIA for the final 

rule. 

EPA Response: EPA will consider attaching the relevant TSDs to the RIA for the final rule. For 

proposal, EPA will add references to relevant TSDs throughout the RIA. 

5. OMB asked that climate benefits be easily distinguished from air pollution health co-benefits 

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

3

EPA Response: EPA has made a concerted effort to label in all parts of the RIA climate benefits 

and health co-benefits to make it clear to the reader that air pollutant health benefits are cobenefits for this rulemaking. EPA did add footnotes to the Executive Summary Tables 3.2 and 

3.3. A revised version of the Executive Summary with these edits is provided.