## 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### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative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 Re constructed 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 (CO 2 

### ) is the primary greenhouse gas pollutant, accounting for nearly three-quarters of global greenhouse gas emissions and 84 percent of U.S. greenhouse gas emissions. Fossil fuel-fired electric generating units (EGUs) are, by far, the largest emitters of GHGs, primarily in the form of CO 2 

### , among stationary sources in the U.S.

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

ES.2 Summary of Proposed EGU GHG Existing Source Guidelines

Under Clean Air Act (CAA) section 111(d), state plans must establish standards of performance that reflect the degree of emission limitation achievable through the application of the “best system of emission reduction” (BSER) that, taking into account the cost of achieving 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 foll owing 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 expand eding 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 guidel ines 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 reduc tion opportunities and existing state programs

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

emission reduction achievable through applying the BSER; and the state is authorized to identify the emission standard that reflects that amount of emission reduction. In addition, the State is required to include in its state plan the standards of performance and measures to implem ent and enforce those standards. The State submits the plan to EPA, and EPA approves it if the standards of performance and implementing and enforcing measures are satisfactory.

### ***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-3 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-sta te basis, in recognition of the fact that electricity is transmitted across state lines, and measures often impact regional EGU CO 2 

### emissions.

While the EPA must establish BSER and is proposing goals and guidelines that reflect BSER, CAA section 111(d) also provides the EP A with the flexibility to design goals and guidelines that recognize, and are tailored to, the uniqueness and complexity of the power generation sector and CO 2 

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

ES.3 Control Strategies for Existing EGUs

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

Table ES-1 shows the proposed state goal s 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

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

ES-4 regional and state compliance approaches for each option. With the state compliance approach, states are assumed to comply with the guidelin es 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. Th e guidance allows flexibility of compliance, including the possib ility of using a mass-based approach. The analysis in the
illustrative scenarios does not assume that stat es 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 scenario s, there may be less costly a pproaches that the states may adopt to achieve their state goals.

Table ES-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO 2 

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

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

2 The EPA has not developed goals for Vermont and the Dist rict of Columbia because current information indicates those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. territories at this time.

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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 asso ciated with the compliance scenarios for the proposed Options 1 regional and state compliance approaches and Table ES-3 reports emission

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

ES-6 reductions associated with Option 2. In 2020, the EPA estimates that CO 2 

### 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. CO 2 

### 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 1 1
CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (thousands of tons) 2020 Regional Compliance Approach Base Case 2,161 1,476 1,559 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 Complia nce 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 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 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 2 1
CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (thousands of tons) 2020 Regional Compliance Approach Base Case 2,161 1,476 1,559 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 Complia nce 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 Complian ce 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 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 emission reductions are relevant for estimating air pollu tantpollution health co-benefits of the guidelines.


### ES.4 Costs of Existing EGU Guidelines

The ‘‘compliance costs’’ of this proposed acti on are represented in this analysis as the change in electric power generation costs between the base case and policy case in which states pursue a distinct set of strategies not limited to the technologies and measures included in 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 monitori ng, reporting, and recordkeeping. The estimated cost of Option 2 is between $4.2 and $5.4 billion in 2020 and between $1.9 and $3.1 billion in 2030 (2011$). The estimated monitoring, reporting and recordkeeping costs for both options are $ 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 va rious fuels, demand-side energy efficiency measures, and other actions associated with comp liance. 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 efficien cy participant costs (pai d 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 usi ng IPM (v5.13) documentation, available at:

http://www.epa.gov/powersector modeling/BaseCasev513.html Formatted: Font color: Auto

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

ES-9 use energy efficiency program costs are estimat ed 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 efficien cy 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, comp liance 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 He alth Co-benefits for Existing EGUs

Implementing the proposed guidelines is expected to reduce emissions of CO 2 

### and have
human health co-ben efits associated with reducedancillary emission reduction s (i.e., co-benefits)
of SO 2 

### , NO 2 

### , and directly emitted PM 2.5 

### , which would lead to lower ambient concentrations of
PM 2.5 

### and ozone. The se 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 So cial 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 Tran sportation, Environmental Protection Agency, National Comment [A1]: OMB edit: “Implementing the proposed guidelines is expected to reduce emissions of CO 2 and, as a secondary or cobenefit of control technologies introduced to reduce CO2 emissions, there will be reduced emissions of SO 2 , NO 2 , and directly emitted PM 2.5 , which would lead to lower ambient concentrations of PM 2.5 and ozone.”
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 reflect s 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, wW e were unable to quantify or
monetize all of the climate benefits and health a nd environmental co-benefits associated with the proposed emission guidelines, including reducing exposure to SO 2 

### , NO x 

### , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment. These unquantified benefits could be substantial, but it is difficult to approximate the potential magnitude of these unquantified bene fits 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/asse ts/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 a nd Unquantified Benefits

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

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

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

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

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

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

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

Reduced incidence of

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

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

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

Reduced vegetation and ecosystem effects from exposure to

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

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

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

1 We assess these co-benefits qualitatively due to data and resour ce 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 eviden ce is only suggestive of causality or there are other significa nt 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 (CO 2 

### ) 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 CO 2 

### emissions in a given year. It includes a wide range of anticipated climate impacts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is ty pically 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 CO 2 

### 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 Info rmation 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 EP A 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 es timates represent global measures because of the distinctive nature of the climate change pr oblem. 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. Th erefore, the SCC estimates incorporate the worldwide damages caused by carbon dioxide emission s 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, a nd 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 CO 2 

### emissions in the year 2020 (2011$), and
each estimate increases over time. These SCC estimat es are associated with different discount rates. The first three estimates are the model average at 5 percent discount rate, 3 percent, and 2.5 percent, respectively, and the fourth estimate is the 95 th 

### percentile at 3 percent.

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

### emission reductions to inform the benefitcost analysis.

5 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for Uncertainty, Negotiation, and Distribution (FUND); and Po licy 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 Po llution Health Co-Benefits

The proposed guidelines would reduce emi ssions of precursor pollutants (e.g., SO 2 

### , NOx, and directly emitted particles), which in turn would lower ambient concentrations of PM 2.5 

### 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 PM 2.5 

### and ozone from emission reductions of precursor pollutants (e.g., SO 2 

### , NOx, and directly emitted particles) anticipated by implementing the proposed guidelines

. 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 PM 2.5 

### cobenefits include avoided premature deaths (derived from effect coefficients in two cohort studies [Krewski et al. 2009 and Lepeule et al. 2012] fo r 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 epidem iology studies [Bell et al. (2004) and Levy et al. (2005)]), as well as avoided morbidity effects fo r 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 es timate the health co-benefits. To create the benefit-per-ton estimates for PM 2.5 

### , this approach uses an air quality model to convert emissions
of PM 2.5 

### precursors (e.g., SO 2 

### , NO x 

### ) and directly emitted particle s into changes in ambient PM 2.5 

### concentrations and BenMAP to estimate the ch anges in human health associated with that change in air quality. We then divide these health impacts by the emissions in specific sectors at the regional level (i.e., East, West, and Californi a). We followed a similar process to estimate benefit-per-ton estimates for the ozone precursor NO x 

### . To calculate the co-benefits for the proposed guidelines, we then multiplied the regional benefit-per-ton estimates for the EGU sector by the corresponding emission reduc tions. All benefit-per-ton estimates reflect the

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

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

health co-benefits in Alaska and Hawaii anticipated from implementation of the proposed guidelines. Comment [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., SO 2 , 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 PM 2.5 

### -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 

### health co-benefits:

1. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differ entiation of effect es timates by particle type. The PM ISA concluded that “many constituents of PM 2.5 

### can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates include health co-benefits from reducing fine particles in areas with varied concentrations of PM 2.5 

### , including both areas that do not meet the fine particle stan dard and those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

### exposures occur in a distributed fashion over the 20 years following exposure based on the advice of theSAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates.
Every benefits analysis examining the potential effects of a change in environmental protection requirements is limited, to some ex tent, by data gaps, model capabilities (such as geographic coverage) and uncertainties in the unde rlying scientific and economic studies used to configure the benefit and cost models. Despite these uncertainties, we believe this analysis

### 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 PM 2.5 

### National Ambient Air Quality Standard (NAAQS) RIA (U.S. EPA, 2012) because we lack the necessary air quality input and monitoring data to conduct a complete benefits assessment. In addition, using a benefit-per-ton approach adds another important source of uncertainty to the benefits estimates.

ES 5.3 Combined Benefits Estimates

The EPA has evaluated the range of potential impacts by combining all four SCC values with health co-benefits values at the 3 percent and 7 percent discount rates. Different discount rates are applied to SCC than to the health co-benefit estimates; because CO 2 

### emissions are longlived and subsequent damages occur over many years. Moreover, several 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 CO 2 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% (95 th percentile) $51 $67$65 to $88$85 $65$64 to $85$81 In 2025 501 million metric tonnes CO 2

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% (95 th percentile) $76 $99$98 to $130$13 0 $97$96 to $120$1 20 In 2030 545 million metric tonnes CO 2 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% (95 th percentile) $92 $120$12 0 to $150$15 0 $120$11 0 to $150$1 40 Option 2 In 2020 283 million metric tonnes CO 2 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% (95 th percentile) $39 $52$51 to $70$67 $51$50 to $67$64 In 2025 368 million metric tonnes CO 2 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% (95 th percentile) $56 $73$72 to $96$93 $72$71 to $93$90 In 2030 353 million metric tonnes CO 2

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% (95 th 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 CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The heal th co-benefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., fr om Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles are not yet av ailable.See chapter 4 for mo re 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 CO 2 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% (95 th percentile) $52 $69$68 to $92$89 $68$66 to $88$85 In 2025 506 million metric tonnes CO 2

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% (95 th percentile) $77 $100$99 to $130$13 0 $98$97 to $130$1 20 In 2030 555 million metric tonnes CO 2 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$9 8 3% (95 th percentile) $94 $120$120 to $160$15 0 $120$12 0 to $150$1 50 Option

2 In 2020 295 million metric tonnes CO 2 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% (95 th percentile) $40 $54$53 to $72$69 $53$51 to $69$66 In 2025 376 million metric tonnes CO 2 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% (95 th percentile) $57 $75$74 to $98$95 $73$72 to $95$92 In 2030 373 million metric tonnes CO 2

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% (95 th percentile) $63 $81$80 to $110$10 0 $80$79 to $100$9 9 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The heal th co-benefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., fr om Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles are not yet av ailable.See chapter 4 for mo re 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 a nd 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 Op tion 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-benefit s in 2020 are estimated to be $15 billion to $34 billion (2011$) for a 3 percent discount rate an d $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 $4 5 billion (2011$) 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 us ing a 3 percent discount rate (model average). The air pollution health co-benefit s in 2020 are estimated to be $1 5 billion to $36 billion for a 3 percent discount rate and $14 billion to $33 bill ion (2011$) for a 7 percent discount rate. The annual compliance costs including monitoring and reporting cost s, 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 (Tab le ES-8 below) and $4 7 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-CO 2 

### greenhouse gases and cobenefits from reducing exposure to SO 2 

### , NO x 

### , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effect s and visibility impairment. Upon considering these limitations and uncertainties, it remains cl ear that the benefits of this proposal are substantial and far outweigh the costs.

### ES-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 95 th 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 SO 2 and NO 2 1.5 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.2 tons of Hg Ecosystem Effects Visibility impairment Option 1 - regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $4.7 $17 $25 $51 $3.6 $13 $19 $39 Air pollution health co-benefits c $1 56 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 SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 0.9 tons of Hg Ecosystem Effects Visibility impairment

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

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

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

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

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

### 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 95 th 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 43 to $ 7074 $ 39 41 to $ 6669 $ 30 31 to $ 5255 $ 28 29 to $ 4851

Non-Monetized Benefits Direct exposure to SO 2 and NO 2 2.0 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $7.5 $25 $37 $76 $5.5 $18 $27 $56 Air pollution health co-benefits c $ 21 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 SO 2 and NO 2 1.7 tons of Hg Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.3 tons of Hg Ecosystem Effects Visibility impairment

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

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

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

### 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 ef ficiency program and participant costs.

e The estimates of net benefits in this summary table are cal culated using the global SCC at a 3 percent discount rate (model average). The RIA includes combined climate and heal th estimates based on these additional discount rates. 

### Table ES-10. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guid elines –2030 (billions of 2011$) a Option 1 – state Option 2 – state 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate
Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.5 $31 $44 $94 $6.4 $21 $30 $63
Air pollution health co-benefits c $ 25 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 SO 2 and NO 2 2.1 tons of Hg and 590 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.5 tons of Hg and 130 tons of HCl Ecosystem Effects Visibility impairment Option 1 – regional Option 2 – regional 3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate Climate Benefits b 5% discount rate 3% discount rate 2.5% discount rate 95 th percentile at 3% discount rate $9.3 $30 $44 $92 $6.0 $20 $28 $60 Air pollution health co-benefits c $ 24 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 SO 2 and NO 2 1.7 tons of Hg and 580 tons of HCl Ecosystem Effects Visibility impairment Direct exposure to SO 2 and NO 2 1.1 tons of Hg and 30 tons of HCl Ecosystem Effects Visibility impairment

a All estimates are for 2030, and are rounded to two significant figures, so figures may not sum.
b The climate benefit estimates in this su mmary table reflect global impacts from CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount ra tes are applied to SCC than to the other estimates because CO 2 emissions are long-lived and subsequent damages occur over many years. The SCC estimates are year-specific and increase over time.

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

### 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 ef ficiency program and participant costs.

e The estimates of net benefits in this summary table are cal culated using the global SCC at a 3 percent discount rate (model average). The RIA includes combined climate and heal th estimates based on these additional discount rates.



### ES.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-deliv ered natural gas prices will increase by 9 to 12 percent in 2020, with negligible changes by 20 30 relative to the base cas e. 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 ch anges in production and profitabi lity for firms that supply those goods and services and the guidelines provide flexibility for states that may want to enhance demand for goods and services from those sectors.

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

States have the responsibility and flexibi lity to implement policies and practices for compliance with Proposed Electric Generating Unit Greenhouse Gas Existing Source Guidelines. Given the wide range of approaches that may be used, quantifying the associated employment impacts is difficult. EPA’s illustrative employ ment analysis includes an estimate of projected employment impacts associated with these guide lines 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 in crease 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 CO 2 

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

### 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, Tec hnical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with partic ipation 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/tec hnical-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 Indust rial, Area and Mobile Emission Sectors Across the U.S.” Environment International, 49 41–151.
Krewski D; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes and Y. Shi, et al . 2009. Extended FollowUp and Spatial Analysis of the American 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/pub lications_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 Methylmerc ury. 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 fo r Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. Available at <http://www.epa.gov/ttnecas1/re gdata/RIAs/6-ozoneriachapter6.pdf>. U.S. EPA. 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur: Ecological Criteria National (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. (EPA document number EPA/600/R-08/139, December). Available at <http://cfpub.epa.gov/ncea/ cfm/recordisplay.cfm?deid=201485>. U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report). Research Triangle Park, NC: National Center for Envi ronmental Assessment, RTP Division. (EPA document number EPA-600-R-08-139F, December). Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. EPA. 2010d. Section 3: Re

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

### 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 En vironmental Impacts Division. (EPA document number EPA-452/R-12-003, December). Available at <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Report). Washington, DC: U.S. 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 BB etween Selected Causes of Pp ostneonatal Ii nfant 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 CO 2 

### , SO 2 

### , NO X 

### , Hg, HCl, and other air pollutants throughout the United States for the entire power system. The IPM analysis is complemented by an analysis of the cost and scope of reductions in electricity demand that can be achieved through energy efficiency programs.

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

1 All costs represent real dollars ($2011).

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3-2 3.3 Power Sector Modelling Framework IPM, developed by ICF Consulting, is a stat e-of-the-art, peer-rev iewed, dynamic linearprogramming model that can be used to project power sector behavior under future business-asusual conditions and examine prospective air pollution control policies throughout the United States for the entire electric power system. EPA us ed IPM, inclusive of the electricity demand reductions achieved through the energy efficiency scenario included in the proposed rule, to project likely future electricity market conditions with and without the proposed rule. The level of energy efficiency-driven reductions in electrici ty demand and the associated costs are reported
in Section 3.6.
The Integrated Planning Model (IPM) is a mu lti-regional, dynamic, deterministic linear programming model of the U.S. electric power sect or. It provides forecasts of least cost capacity expansion, electricity dispatch , and emission 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 emis sion impacts of prospective environmental policies. The model is designed to reflect electrici ty markets as accurately as possible. EPA uses the best available information from utilities, indu stry experts, gas and coal market experts, financial institutions, and government statistic s as the basis for the detailed power sector modeling in IPM. The model documentation provid es additional information on the assumptions
discussed here as well as all ot her model assumptions and inputs. 23

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

3 At the time the documentation was published, CO2 emi ssions from biomass were not included in EPA Base Case v.5.13. EPA is currently developing methods to estima te the amount of CO2 emitted on-site during biomass cofiring at coal fired power plants. For this analysis, the base case assumes an emissions factor of 195 lbs/MMBtu for all biomass.

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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 mo del includes an endogenous representation of the North American natural gas supply system thro ugh a natural gas module that reflects a partial supply/demand equilibrium of the North American gas market accounting for varying levels of potential power sector gas demand and corresponding gas production and price levels. 4 

### Thismodule consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas regasification facility locations that are tied togeth er by a series of linkages (i.e., pipelines) that represent the North American natural gas transmission and distribution network.
IPM also endogenously models the partial equilibrium of coal supply and EGU coal demand levels throughout the continental U.S., taking into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM, which are publicly available, were developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that power plants wo uld face if selecting that coal over the modeling time horizon. The IPM documentation outlines the methods and data used to quantify the economically recoverable coal reserves, character ize their cost, and build the 36 coal regions curves. The coal supply curves were developed in consultation with Wood Mackenzie, one of the leading energy consulting firms and specialists in coal supply. These curves have been independently reviewed by industry experts and have been made available for public review prior to this rulemaking process. 5

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

5 See Chapter 9 of EPA’s Base Case usi ng IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

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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 ha s been used to forecast the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule, Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon
Pollution Standards for New Power Plants.
The model and EPA's assumptions input into the model undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variety of stakeholders, including owners and operato rs of capacity in the electricity sector that is represented by the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highly detailed review of key input assumptions, model representation, and modeling results. IPM has received extensive review by energy and environmental modeling experts in a variety of contexts. For example, in the late 1990’s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 prospective studies that are periodically condu cted. The model has also undergone considerable interagency scrutiny when it has been used to conduct over a dozen legislative analyses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of comparative modeling exercises sponsored by Stanford University’s Energy Modeling Forum over the past 15 years.
IPM has also been employed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, environmental groups, and industry, all of w hom 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 architectur e. This is the fifth major iteration of EPA’s base case using IPM, and calibrates certain information and data from the Energy Information Agency's (EIA) Annual Energy Outlook (AEO), in this case AEO 2013 (hence the platform name of v5.13).

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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 regul ations whose provisions were either in effect or enacted and clearly delineated at the time the base case was finalized in August 2013. The
EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), 6 

### the Mercury and Air Toxics Rule (MATS), and other state and Federal regulations to the extent that they contain measures, permits, or other air-related limitations or requirements.
EPA has also updated the National Electric Energy Data System (NEEDS). This database
contains the unit-level data that is used to constr uct the "model" plants that represent existing and
committed 7 

### units in EPA modeling applications of IPM. NEEDS includes basic geographic, operating, air emissions, and other data on these generating units. 8


### Other routine updates were also adopted in v5.13. These include changes based on public comments that have been received over the last few years, updates reflecting planned new power plant construction, retirements, new power plan t cost and performance, pollution control costs and performance, emission rate assignments, and state rules and enforcement actions. The update also included further refinement to the modeled regions to reflect more recent power market structure based on NERC, FERC, EIA, and other data and planning sources, and now includes more regions that better reflect limitations on current power system dispatch and transmission
behavior.

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

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

8 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.13), <http://www.epa.gov/powersector modeling/BaseCasev513.html>.

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3-6 3.4 State Goals in this Proposal In this action, the EPA is proposing state-speci fic rate-based goals to guide states in the development of their plans. The agency is proposing one option (Option 1) for state-specific goals and requesting comment on a second set of state-specific goals and compliance period (Option 2).
Table 3-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO 2 

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

State 9 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 Dist rict of Columbia because current information indicates those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. territories at this time.

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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 capacit y, fossil units included in Indian Country, the treatment of cogeneration in historical data, and the assumed applicability for some units. The resulting changes to the state goals are minor, and any differences in results obtained by analyzing these new goals would be negligible. Furthermore, these small changes in the goals would be offset by comparable changes in the illustrative compliance scenarios (in which compliance is consistent with the assumptions used in setting state goals).

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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 indivi dual state (these scenarios are referred to as “state”), and another set of scenarios where gr oups of states are assumed to collaborate and achieve compliance across larger regions (referred to as “regional”). Estimates of the benefits, costs, and economic impacts of this proposed action are presented for both state and regional compliance. 10 

### These illustrative compliance scenarios ar e designed to reflect, to the extent possible, the scope and nature of the proposed guidelines. However, there is considerable uncertainty with regard to the precise measures that states will adopt to meet the proposed requirements, since there are consid erable flexibilities afforded to the states in developing their state plans. Nonetheless, the analysis of the bene fits, costs, and relevant impacts of the proposed rule attempts to encapsulate some of those flexib ilities in order to inform states and stakeholders of the potential overall impacts of the proposal. The relevant impacts, costs, and benefits are provided for 2020, 2025, and 2030 for Options 1 and 2 (with both state and regional compliance).
It is also important to note that the an alysis does not specify any particular CO 2 

### reduction 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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3-9 affected by the illustrative state compliance scen arios are only those included in the calculation of the state goals. 11 

### These affected sources are: 12



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

 Existing NGCC units with nameplate capacity greater than 25 MW

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

 New and existing non-hydro renewable capacity

 At risk and under construction nuclear. 13



### Furthermore, all compliance scenarios modeled include an assumption that affected sources within states are able to meet state goals collectively, by averaging all of their emissions relative to all of their generation. This approach enables some sources to emit at rates higher than the relevant goal, as long as there is corresponding generation coming from sources that emit at a lower rate such that the goal (in lbs/MWh) is met across all affected sources collectively. The
emissions rate at covered sources 14 

### must be less than or equal to the applicable state goal, on average, over the entire compliance period, but not in any particular year.
The illustrative compliance scenarios further as sume that the states adopt intertemporal averaging in the initial compliance period for both Option 1 and Option 2. That is, the average
emissions rate at covered sources in each state must be less than or equal to the applicable state goal over the compliance period. The initial compliance period for Option 1 is 2020 to 2029 and for Option 2 it is 2020 to 2024. After the initial compliance period the average emission rate of

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

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

13 See Technical Support Document – GHG Abatement Measures

14 Total emissions from covered sources divided by the su m of total generation from covered sources plus demandside energy savings

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3-10 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 th e 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 choos e 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 ra te averaging across affected sources within six multi-state regions, informed by North Amer ican 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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3-11 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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3-12 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 st ate scenarios, affected sources in each state must respond to their respective state goal. However, the ability to average is extended to all affected sources in each compliance region. In these scenarios, the emissions rate from affected sources in each region 16 

### must be less than or equal to the weighted average 17 

### 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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3-13 The analysis in the illustrative scenarios does not assume that states use any specific policy mechanism to achieve the state goals. Wh ile IPM finds a least cost way to achieve the state goals to achieve the rate-based constraints imposed in the illustrative scenarios, there may be less costly approaches that the states may adopt to achieve their state goals.

3.6 Demand Side Energy Efficiency 3.6.1 Projected Demand-Side Energy Savings 18


### 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 stat e’s annual incremental reduction in electricity consumption by ramping up from an historical basis 20 

### 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 e ither 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 ener gy efficiency savings projections, refer to the Greenhouse Gas Abatement Measures TSD

.

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

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

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3-14 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 in cremental savings rate are assumed to sustain a 1.0% rate beginning in 2017. Twenty leading stat es 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 border s, energy savings from a bisected state were distributed between the applicable IPM model regions using a distribution approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities.

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 co mpliance modeling for each option, the cumulative savings rate is applied to 2012 electricity consumption in the calculation of st ate goals whereas compliance modeling applies that rate to forecasted base case demand. Thus, for a given rate of demand reduction, the qua ntity of electricity demand reduced is greater in the compliance modeling than in se tting the state goals, consistent with forecasted base case demand growth from 2012 to 2020 and beyond.

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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3-15 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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3-16 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 Costs 24


### 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 reach ed 0.5% (a 20% additional cost escalation is

24 For a more detailed discussion of the demand-side en ergy 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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3-17 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-s ide energy efficiency progra ms (to cover program costs) is typically collected through a standard per kWh surcharge to the rate-payer; the regional retail price impacts analyzed from this RIA’s compliance scenarios assumes 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 assu mption of fixed demand. However, the modeling also does not capture the effect of higher costs of producing el ectricity, which is attributable to other methods of complying with the state goals, on electricity demand for the same reason (i.e., fixed demand in IPM). For further discussion of these issues see the Limi tations section at the end of this chapter.

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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3-18 3.7 Projected Power Sector Impacts 3.7.1 Projected Emissions Under the proposed rule, EPA projects annual CO 2 

### reductions between 17% and 18% below base case projections for Option 1 in 2020 (reaching 26% to 27% below 2005
emissions 29 

### ), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005 emissions). For Option 2, EPA projects annual CO 2 

### reductions between 13% and 14% in 2020 (reaching 23% below 2005 emissions) and between 16% and 17% in 2030 (reaching 22% to 23% below 2005 emissions). For each Option, the regional scenario achieves fewer emissions reductions largely because the ability to average emissions regionally allows those states that were projected to emit below their compliance targ et in the base case to offset reductions that
other states would otherwise have made.

Table 3-3. Projected CO 2 

### Emission Impacts , Relative to Base Case

CO 2 Emissions (MM Tonnes) CO 2 Emissions Reductions from Base Case (MM Tonnes) CO 2 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 CO 2 

### Emission Impacts, Relative to 2005

CO 2 Emissions (MM Tonnes) CO 2 Emissions Reductions from Base Case (MM Tonnes) CO 2 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 CO 2 emissions from eG RIDrid for 2005, which reports EGU emissions as 2,434 million metric tonnes in the contiguous US.

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3-19 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 SO 2 

### , 22% to 24% reduction of NO X 

### , and a 15% to 18% reduction of mercury, as a result of the proposed Option 1. EPA projects fewer emission reductions overall as a result of the proposed Option 2 in 2020: a 17% to 18%
reduction in SO 2 

### , 17% to 18% reduction in NO X 

### , and an 11% to 14% reduction in mercury. The projected non-CO 2 

### reductions presented in Table 3-5Table 3-8 below demonstrate similar trends
in 2025 and 2030.

Table 3- 54 . Projected Non-CO 2 

### Emissions Reductions, 2020-2030

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

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

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

PM 2.5 (thousand tons) 212 156 154 166 163 -26.4% -27.2% -21.5% -22.9%

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

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

PM 2.5 (thousand tons) 209 150 146 165 161 -28.1% -30.1% -21.0% -23.2%

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

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

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3-20 PM 2.5 (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 produc tion is likely to be positive or negative and also assessed the potential magni tude of changes relative to CO 2 

### emissions reductions anticipated at power plants. This assessment included CO 2 

### emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 

### emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH 4 

### emissions from natural gas systems and coal mines and CO 2 

### emissions from flaring of methane will likely decrease under the proposed guidelines. Furthermore, the changes in upstream methane emissions are small relative to the changes in direct emissions fro m power plants. The technical details supporting this analysis can be found in the Appendix to this chapter.
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 change s in equilibrium fuel prices on the expenditures of the electricity sector to serve demand.

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3-21 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 fi nal 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 expect ed in the base case to expend over $359 billion in 2020 to generate, transmit, and distribute electricity to end-use consumers. Therefore, the projected costs of compliance with the proposed rule amount to a one to two percent increase in the cost to meet electricity demand, while securi ng 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 usi ng IPM (v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

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3-22 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 CO 2 

### 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 incr eased expenditures to meet the policy’s CO 2 

### constraints, such that the net outcome is a dec line 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 (u sing 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 EG U to generate the same amount of electricity using less fuel. The decrease in required foss il fuel results in a lower output-based CO 2 

### emissions rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling 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 CO 2 

### -intensive generation to less CO 2 

### -intensive generation. Compared to the Base Case, existing coal steam capacity is projected to operate at a lower capacity factor for both Option 1 and Option 2, on average, although projected average capacity factors for existing coal steam boilers remains around 75% across scenarios. Existing natural gas combined cycle units, which are less carbon-intensive than coal steam capacity on an output basis, operate at noticeably higher capacity factor in Options 1 and 2, on average. See Table 3-
10Table 3-11 . The utilization of existing natural gas co mbined 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 CO 2 

### reduction strategies that were part of BSER, instead of employing re-dispatch to the maximum extent. In addition, future electric demand is considerably lower than the Base Case.

Table 3- 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 appr oach for reducing the average output-based
emission rate is to consider reductions in load attributable to demand-side energy efficiency savings, which will reduce the need for higher emitting generation. 33 

### In the compliance scenario analyses presented in this RIA, each state is cred ited for the total demand-side energy efficiency savings equal to the levels that are assumed in the state goals. See section 13.6.1 for a description

33 Because fossil fuel-fired EGUs typically have higher va riable costs than other EGUs (such as nuclear and renewable EGUs), they are typically the firs t to be displaced when demand is reduced.

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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 averag e 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 carbo n-intensive generation. In 2020, for Option 1, total generation declines approximately three pe rcent 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 to tal 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-firedgeneration 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 tota l demand attributable to the demand-side energy savings under the proposed rule. Coal generation is projected to decrease between 16% and 17% in 2020, and natural gas-fired generation from existing combined cycle units is projected to increase nine percent, relative to the base case.
Under both options, additional pipeline capacity is projected to be built through 2020
relative to the base case. In all of the illu strative 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.

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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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-28Figure 3-4. Generation Mix with the Base Caseand 111(d) Options, 2020-2030 (million GWh)Source: Integrated Planning Model run by EPA, 20140.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. State Reg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclearCoal2020 2025 2030Option1Option2Option1 Option2Option1Option29-0 3.7.5 Projected Incremental Retirements Relative to the base case, about 30 to 49 GW of coal-fired cap acity is projected to be uneconomic to maintain (about 12% to 19% of all coal-fired capacity proj ected 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 enou gh existing capacity and/or building enough new capacity. IPM also addresses reliable delivery of generation resources at a region al level by limiting the ability to transfer power between regions using the bulk power transmission system. Within each model region, I PM 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 ex amined key parameters from the IPM projec tions to assess whether concerns over reg ional resource adequacy would be likely to arise or whether changes in generation and flow pattern im pacts would raise issues for rel iability 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. Capac ity that is otherwise uneconomic to operate will be retired onl y 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 fo r 2020 through 2030 to determine what types of changes in the gene ration fleet were projected to oc cur through retirements, addit ional generation and energy efficiency. Although there were changes fr om the base case to the policy cases, none of the policy cases were found to raise concerns over regional resource adequacy. More over, 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 o f 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 sugg ested 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 ru le 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 reduc es the overall load on the grid and thus generally reduces the burden on the transmission infrastructure needed to maintain reliab ility. 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 proce sses. 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/p owersector 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 cy cle 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 affe cted 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 20 30.9-2 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 gen eration 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 t o the operation under both options of less than 1 GW of coal stea m 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% an d 14% in 2020 under Option 1 and between 10% and 12% under Option 2. In later years, gas use declines under both options. These tr ends 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 se ctor. In 2030, gas demand and price decrease below the base case projectio ns, due to the cumulative impact of demand-side energy efficie ncy savings and reduced overall demand.
IPM modeling of natural gas prices uses both short- and long-t erm price signals to balance supply and demand for the fuel across the modeled time horizon. As such, it should be understoo d that the pattern of IPM natural gas price projections over ti me 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 U nited States has historically experienced some degree of price volatility from year to year , between seasons with in a year, and durin g 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 th rough longer-term fuel supply contracts by the power sector. IPM assume s 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 shou ld not be mistaken for traditio nally experienced consumer pri ce impacts related to natural ga s, but a reflection of expected average price changes over th e 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 Text9-4 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 genera tion, transmission, distribution, and ut ility demand-side EE program costs. IPM modeling projects changes in regional wholesale power prices and capacity payments related to imposition of the represented pol icy that are combined with EIA regional transmis sion 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, an d development of new generating capacity. These projected change s vary regionally under each compliance option in response to th e 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 Regi onal 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 Regi onal 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 Regi onal 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-indu ced 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 emissi ons from power plants. These emissions are calculated as a function of IPM outputs, emission factors an d control configuration. IPM-projected fuel use (heat input) is multiplied by PM em ission factors (based in part on the presence of PM-relevant pollution control devices) to determine PM emissions. Primary PM emission s 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 SO 2 /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 potentia l changes in condensable PM emissions due to the installation and operation of SCRs. The formation of additional condensable PM ( in
the form of SO 3 and H 2 SO 4 ) 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 designe d and operated to minimize increases in condensable PM. This limitatio n 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 pr ojections 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 pol icy scenarios over the 2020-2030 time horizon using simplified emissi ons factors. These factors were developed for the eastern and9-7 western regions (excluding California) using EPA’s emissions and fuel use projections for fossil plus biomass. Separate factor s were developed for base case and policy case scenarios. These factor s were applied to the projected fuel use from fossil and biomas s 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 suffic ient for the purpose of estimat ing 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 a ssumptions for variables whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available info rmation 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 an nualized 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 (genera ting) costs to the power sector in response to the final rule. To es timate these annualized costs, EPA uses a conventional and widely

accepted approach that applies a capital r ecovery 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 reflec ting data and methodol ogical limitations. Th e method used for estimating the demand-side energy efficiency cost s 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 savi ngs potential studies, and discusses the associated uncertainties. Th e evaluation, measurement and verification (EM&V) of demand-si de 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 po licies 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 repr esentation of state policies in the EPA’s base case alters the impacts asse ssment, 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 economi cs 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: Acti ons that Significantly Affect Energy Supply, Distribution, or Use. Under th e provisions of this rule, EPA projects that approximately 46 to 49 GW of addition al

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 increas e 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.

3.1 01 Monitoring, Reporting, and Recordkeeping Costs EPA projected monitoring, reporting and recordkeeping costs fo r both state entities and affected EGUs for the compliance years 2020, 2025 and 2030. In calculating the costs for state en tities, EPA estimated personnel costs to oversee compliance, an d 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 d o 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 describe d in the emission guidelines. The EPA estimates that it would take 3 wo rking 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 co mply with the rule. Also, after all modi fications are made at a facility to measu re net energy output, each EGUs Data Acquisition System (DAS) would ne ed to be upgraded to supply the rate-based emissions value to either the state or EPA’s Emissions Collection and Monitoring Pl an 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 co sts 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 ne eded, download data from the ECMPS for their annual reporting and develop their annua l 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 altern ative Option 2 including monitoring reporting and recordkeeping costs are shown in Table 3-2 53 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 3.19-9 Source: Integrated Planning Model run by EPA, 2014. Monitoring, reporting and recordkeeping costs calculated outside IPM.

3.1 21 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 co sts incurred due to the regulatory action, where an opportunity cost is the value lost to society of any goods and services that will no t be produced and consumed as a result of reallocating some res ources towards pollution mitigation. Estimates of social costs may be comp ared to the social benefits expected as a result of a regula tion to assess its net impact on society. The social costs of a regulato ry action will not necessarily be equivalent to the expenditure s associated with compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section provides a qualitative dis cussion 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 requir ed by the electricity sector to comply with the proposed guidel ines.These two components are estimated separately. The expenditures required to achieve the assumed demand reductions through enduse energy efficiency programs are estimated using historical da ta 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 electricit y production costs due to the reduction in demand caused by the end- use energy efficiency programs and the increase in electricit y production costs required to achieve the additional emission re ductions 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 equi valent to those applie d in the calculation of their respective go als. The estimated expenditures required to achieve those net demand redu ctions through end-use energy efficiency programs are presented in this chapter and detailed in the GHG Abatement Measures TSD chap ter 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 ma rgin will represent society’s opportunity cost of the resource s required to produce the energy savings.
The social cost of achieving these net electricity demand reduct ions 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 s cenarios includes this limited share of the program costs that is expended on end-users who would have reduced thei r demand in the absence of the program. Thus, with respect to th is particular issue, the social cost of the illustrative scenarios wo uld be less than the estimated compliance cost reported in Ta ble 3-9 to a limited degree. If the program expenditures that do not lead to additional demand reductions are paid for by electricity rate o r tax payer funded programs then that portion of program expenditure s represents a transfer among electricity consumers and has no ne t 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 req uired 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 p urchased 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 motivat ed 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 e fficiency.9-10 Due to the flexibility held by states in implementing their co mpliance with the proposed stan dards these energy efficiency expenditures may be borne by end-users thro ugh direct participant expenditures or elec tricity rate increases or by producers th rough reductions in their profits. While the allocation of these expenditures between consumers and producers is important for unders tanding the distributional impact of pote ntial compliance stra tegies, it does not necessarily affect the opportunity cost required for the production of the energy savings from a social perspective. Howe ver, specific design elements of demand-side or end-use energy efficiency programs included to address distributional outcomes ma y have an effect on the economic efficiency of the programs a nd
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 te chnologies and services adopted under the program relative to t he 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 i n some cases the quality of services, outside of the energy saving s, 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 betwee n more and less energy efficient products is one potential hypoth esis for the energy paradox. The energy paradox is the observati on 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 pr ice. In addition to heterogeneity in product services and cons umer preferences, there are other explanations for the energy parado x, falling both within and outside the neoclassical rational exp ectations paradigm that is used in benefit/cost analysis. The end-use ener gy efficiency chapter of the G HG Abatement Measures TSD discuss es the energy paradox in detail and provides additional hypothesis for why consumers may not make energy efficiency investments th at 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 desi gned 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 throug h 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 remain ing demand while making additional CO2 emissions intensity reductions necessary to comply with the state goals. Included in the estimate of these complianc e costs, developed using IPM, are the cost savings associated with the reduction in required electricity generation due to the demand re ductions from end-use energy efficiency programs and improveme nts 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 increa sed 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 ma rkets providing inputs to th e electricity sector do not meaningfully affect the prices in those markets, and the assump tion of fixed electricity demand, then the social costs associat ed with the regulatory action would be equal to the resource costs measured by IPM (net of tax and subsidy payments and recovery of sun k

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 comp liance cost will equal th e reduction in producer 41 and consumer surplus 42 in the electricity sector from the pre-regulatory action market equilibrium. However, IPM forecasts production and price changes in fu el markets, which implies that there are changes in producer and consumer surplus in those markets, and therefore the resource cos t estimate by IPM may differ from the social cost for this reas on. For a theoretically consistentmethod for estimating partialequilibrium changes in welfare where pric es and outputs change in multiple markets see Chapte r 9 of Just et al. (2004).

3.1 32 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 pu rchased goods or services

beyond their opportunity cost as defined by the market price.

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# 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 th e guidelines in the RIA by individual building blocks and to show energy savings from end-use en ergy 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 disentangli ng the various component costs of the guidelines including cost savings (reduced fuel use, reduced capital expe nditure, etc.) due to re duced electricity demand from end-use energy efficiency. EPA believes that it has appropria tely accounted for the effect of reduced electricity demand in the calculation of total costs. EPA believes that the more appropriate way to address the conc erns that have been raised is to make it clearer how the total
costs were calculated and the role that end-us e 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 guide lines in a more transparent wa y. A revised version of the RIA Executive Summary and Chapter 3 with relevant ed its 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 appropr iately included analys is quantifying end-use energy efficiency jobs in the RIA. Since end-us e energy efficiency expenditures are such a large component of the rulemaking, the EPA feels it woul d be remiss not to attempt to quantify their employment impact. EPA feels it has made the be st projection possible given the official data
and methods available. The EPA also feels it ha s provided the reader with extensive caveats about the approach, and solic ited comment regarding other methods or data sources.
3. OMB requested that the langua ge regarding the Graff-Zivin and Neidell paper in the RIA Chapter 6 Employment Impacts Analysis be revised.
EPA Response: OMB seemed to be concerned th at the reader may infer that the EPA is implicitly recommending the use of Graff-Zivin a nd Neidell (2012) to quantify labor impacts.

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# Furthermore, OMB suggested that EPA clarify it s position on potential uses of the study. It would be helpful if OMB could concretely spec ify (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 ge neral literature survey to provide the reader with a qualitative unders tanding of how environm ental regulation may affect labor demand and supply. Th is 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 aw are of the best available science on this topic.
Furthermore, it is important to note that the EP A is not using this study to quantify impacts of this rule making, nor has it adequa tely considered the advantages and disadvantages of using this or other studies to quantify im pacts for other rulemakings. The ag ency feels it lacks a scientific basis to include any language at this point supporting or rejecti ng its general usefulness for the purpose of regulatory analysis.
To clarify this issue, we propose adding the follo wing sentences as a foot note 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 benefi ts be easily distinguished from air pollution health co-benefits
in the RIA.

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# 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 r eader that air pollutant health benefits are cobenefits for this rulemaking. EPA did add foot notes to the Executive Summary Tables 3.2 and
3.3. A revised version of the Executive Su mmary with these edits is provided.