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EO12866MACT_NSPSUtilitiesMATS2060_AP52RIA20111104Ch3.docx

CHAPTER 3

COST, ECONOMIC, AND ENERGY IMPACTS 

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

performed for the Mercury and Air Toxics Standards (MATS). EPA used the Integrated Planning 

Model (IPM), developed by ICF Consulting, to conduct its analysis. IPM is a dynamic linear 

programming model that can be used to examine air pollution control policies for SO2, NOx, Hg, 

HCl, and other air pollutants throughout the United States for the entire power system.

Documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm, 

and updates specific to the MATS modeling are in the “Documentation Supplement for EPA 

Base Case v.4.10_MATS – Updates for Final Mercury and Air Toxics Standards (MATS) Rule” 

(hereafter IPM 4.10 Supplemental Documentation for MATS).

3.1 Background

Over the last decade, EPA has on several occasions used IPM to consider pollution

control options for reducing power-sector emissions.

1 Most recently EPA used IPM extensively 

in the development and analysis of the impacts of the Cross-State Air Pollution Rule (CSAPR).2

As discussed in Chapter 2, MATS coincides with a period when many new pollution controls are 

being installed. Many are needed for compliance with NSR settlements and state rules, while 

others may have been planned in expectation of CAIR and its replacement, the CSAPR.

The emissions scenarios for the RIA reflects the Cross-State Air Pollution Rule (CSAPR) as 

finalized in July 2011 and the emissions reductions of SOX, NOX, directly emitted PM, and CO2

are consistent with application of federal rules, state rules and statutes, and other binding, 

enforceable commitments in place by December 2010 for the analysis timeframe.

3

 

1Many EPA analyses with IPM have focused on legislative proposals with national scope, such as EPA’s IPM 

analyses of the Clean Air Planning Act (S.843 in 108th Congress), the Clean Power Act (S.150 in 109th Congress), 

the Clear Skies Act of 2005 (S.131 in 109th Congress), the Clear Skies Act of 2003 (S.485 in 108th Congress), and 

the Clear Skies Manager's Mark (of S.131). These analyses are available at EPA’s website: 

(http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). EPA also analyzed several multi-pollutant 

reduction scenarios in July 2009 at the request of Senator Tom Carper to illustrate the costs and benefits of 

multiple levels of SO2 and NOX control in the power sector.

2

Additionally, IPM has been used to develop the NOX Budget Trading Program, the Clean Air Interstate Rule 

programs, the Clean Air Visibility Programs, and other EPA regulatory programs for the last 15 years. 

3

Consistent with the mercury risk deposition modeling for MATS, EPA did not model non-federally enforceable 

mercury-specific emissions reduction rules in the base case or MATS policy case (see preamble section III.A). 
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EPA has made these base case assumptions recognizing that the power sector will install 

a significant amount of pollution controls in response to several requirements. The inclusion of 

CSAPR and other regulatory actions (including federal, state, and local actions) in the base case 

is necessary in order to reflect the level of controls that are likely to be in place in response to 

other requirements apart from MATS. This base case will provide meaningful projections of 

how the power sector will respond to all the regulatory requirements for air emissions in 

totality, while isolating the incremental impacts of MATS relative to a base case with other air 

emission reduction requirements separate from today’s action. 

The model’s base case features an updated Title IV SO2 allowance bank assumption and 

incorporates updates related to the Energy Independence and Security Act of 2007. Some 

modeling assumptions, most notably the projected demand for electricity, are based on the 

2010 Annual Energy Outlook from the Energy Information Administration (EIA). In addition, the 

model includes existing policies affecting emissions from the power sector: the Title IV of the 

Clean Air Act (the Acid Rain Program); the NOx SIP Call; various New Source Review (NSR) 

settlements4; and several state rules5affecting emissions of SO2, NOx, and CO2 that were 

finalized through June of 2011. IPM includes state rules that have been finalized and/or 

approved by a state’s legislature or environmental agency, with the exception of non-federal 

mercury-specific rules. The IPM 4.10 Supplemental Documentation for MATS contains details

on all of these other legally binding and enforceable commitments for installation and 

operation of pollution controls. This chapter focuses on results of EPA’s analysis with IPM for 

the model’s 2015 run-year in connection with the compliance date for MATS.

MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAPS) 

for the “electric utility steam generating unit” source category, which includes those units that 

 

Note that this approach does not significantly affect SO2 and NOX projections underlying the cost and benefit 

results presented in this RIA

4

The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company (Vectren), 

Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia Electric & Power 

Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power (AEP), East Kentucky 

Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio Edison, Kentucky Utilities, 

Hoosier Energy, Salt River Project, Westar, Puerto Rico Power Authority, Duke Energy, American Municipal Power, 

and Dayton Power and Light. These agreements lay out specific NOx, SO2, and other emissions controls for the 

fleets of these major Eastern companies by specified dates. Many of the pollution controls are required between 

2010 and 2015.

5

These include current and future state programs in Alabama, Arizona, California, Colorado, Connecticut, 

Delaware, Georgia, Illinois, Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, 

Montana, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Tennessee, Texas, Utah, 

Washington, West Virginia, and Wisconsin the cover certain emissions from the power sector.
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combust coal or oil for the purpose of generating electricity for sale and distribution through 

the national electric grid to the public.

Coal-fired electric utility steam generating units include electric utility steam generating 

units that burn coal, coal refuse, or a synthetic gas derived from coal either exclusively, in any 

combination together, or in any combination with other supplemental fuels. Examples of 

supplemental fuels include petroleum coke and tire-derived fuels. The NESHAP establishes 

standards for HAP emissions from both coal- and oil-fired EGUs and will apply to any existing, 

new, or reconstructed units located at major or area sources of HAP. Although all HAP are 

pollutants of interest, those of particular concern are hydrogen fluoride (HF), hydrogen chloride 

(HCl), dioxins/furans, and HAP metals, including antimony, arsenic, beryllium, cadmium, 

chromium, cobalt, mercury, manganese, nickel, lead, and selenium.

This rule affects any fossil fuel fired combustion unit of more than 25 megawatts electric 

(MWe) that serves a generator that produces electricity for sale. A unit that cogenerates steam 

and electricity and supplies more than one-third of its potential electric output capacity and 

more than 25 MWe output to any utility power distribution system for sale is also considered 

an electric utility steam generating unit. The rule affects roughly 1,400 EGUs: approximately 

1,100 existing coal-fired generating units and 300 oil-fired steam units, should those units 

combust oil. Of the 600 power plants potentially covered by this rule, about 430 have coal-fired 

units only, 30 have both coal- and oil- or gas-fired steam units, and 130 have oil- or gas-fired 

steam units only. Note that only steam electric units combusting coal or oil are covered by this 

rule.

EPA analyzed for the RIA the input-based (lbs/MMBtu) MATS control requirements 

shown in Table 3-1. In this analysis, EPA does not model an alternative SO2 standard. For further 

discussion about the scope and requirements of MATS, see the preamble or Chapter 1 of this 

RIA.
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Table 3-1. Emissions Limitations for Coal-Fired and Solid Oil-Derived Fuel-Fired Electric 

Utility Steam Generating Units

Subcategory

Total Particulate 

Matter

Hydrogen 

Chloride Mercury

Existing coal-fired unit designed for coal > 8,300 Btu/lb 

Existing coal-fired unit designed for coal < 8,300 Btu/lb

Existing - IGCC 

Existing – Solid oil-derived 

New coal-fired unit designed for coal > 8,300 Btu/lb

New coal-fired unit designed for coal < 8,300 Btu/lb

New – IGCC 

New – Solid oil-derived 

Note: lb/MMBtu = pounds pollutant per million British thermal units fuel input

lb/TBtu = pounds pollutant per trillion British thermal units fuel input

lb/MWh = pounds pollutant per megawatt-electric output

lb/GWh = pounds pollutant per gigawatt-electric output

* Beyond-the-floor limit as discussed elsewhere.

Table 3-2. Emissions Limitations for Liquid Oil-Fired Electric Utility Steam Generating Units

Subcategory

Total HAP 

Metals

Hydrogen 

Chloride

Hydrogen 

Fluoride Mercury

Existing – Liquid oil 

New – Liquid oil

EPA used the Integrated Planning Model (IPM) v.4.10 to assess the impacts of the MATS 

emission limitations for coal-fired electricity generating units (EGU) in the contiguous United 

States. IPM modeling did not subject oil-fired units to policy criteria.6Furthermore, IPM 

modeling did not include generation outside the contiguous U.S., where EPA is aware of only 2 

facilities that would be subject to the coal-fired requirements of the final rule. Given the limited 

number of potentially impacted facilities, limited availability of input data to inform the 

modeling, and limited connection to the continental grid, EPA did not model the impacts of the 

rule beyond the contiguous U.S.

 

6

EPA did not model the impacts of MATS on oil-fired units using IPM. Rather, EPA performed an analysis of 

impacts on oil-fired units for the final rule. The results are provided in a TSD titled: “Assessment of MATS 

Compliance Costs for Oil-Fired Units in the Continental U.S.”
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Mercury emissions are modeled as a function of mercury content of the fuel type(s) 

consumed at each plant in concert with that plant’s pollutant control configuration. HCl 

emissions are projected in a similar fashion using the chlorine content of the fuel(s). For both 

mercury and HCl, EGUs in the model must emit at or below the final mercury and HCl emission 

rate standards in order to operate from 2015 onwards. EGUs may change fuels and/or install 

additional control technology to meet the standard, or they may choose to retire if it is more 

economic for the power sector to meet electricity demand with other sources of generation. 

See IPM 4.10 documentation and IPM 4.10 Supplemental Documentation for MATS for more 

details.

Total PM emissions are calculated exogenously to IPM, using EPA’s Source Classification 

Code (SCC) and control-based emissions factors. SCC is a classification system that describes a 

generating unit’s characteristics.

Instead of emission limitations for the organic HAP, EPA is proposing that if requested, 

owners or operators of EGUs submit to the delegated authority or EPA, as appropriate, 

documentation showing that an annual performance test meeting the requirements of the rule 

was conducted. IPM modeling of the MATS policy assumes compliance with these work practice 

standards.

Electricity demand is anticipated to grow by roughly 1 percent per year, and total 

electricity demand is projected to be 4,103 billion kWh by 2015. Table 3-3 shows current 

electricity generation alongside EPA’s base case projection for 2015 generation using IPM. EPA’s 

IPM modeling for this rule relies on EIA’s Annual Energy Outlook for 2010’s electric demand 

forecast for the US and employs a set of EPA assumptions regarding fuel supplies and the 

performance and cost of electric generation technologies as well as pollution controls. The base 

case includes CSAPR as well as other existing state and federal programs for air emissions 

control from electric generating units, with the exception of state mercury rules. 
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Table 3-3. 2009 U.S. Electricity Net Generation and EPA Base Case Projections for 

2030 (Billion kWh)

Coal

Oil

Natural Gas

Nuclear

Hydroelectric

Non-hydro Renewables

Other

Total

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run 

by EPA, 2011.

Figure 3-1. Geographic Distribution of 

2012

Source/Notes: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010).

fossil facilities in the NEEDS 4.10 IPM frame

this includes committed new builds and committed retirements.

some facilities may be obscured.

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Electricity Net Generation and EPA Base Case Projections for 

Historical Base Case

2009 2015 2020

1,756 1,982 2,002

39 0.11 0.13

921 710 847

799 828 837

273 286 286

144 252 289

18 45 45

3,950 4,103 4,307

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run 

Geographic Distribution of Affected Units, by Facility, Size and Fuel Source

Source/Notes: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010). This map displays all 

fossil facilities in the NEEDS 4.10 IPM framework. NEEDS reflects available capacity on-line by the end of 2011; 

this includes committed new builds and committed retirements. In areas with a dense concentration of facilities, 

Release During Review ***

Electricity Net Generation and EPA Base Case Projections for 2015-

2030

2,027

0.21

1,185

817

286

333

55

4,702

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run 

Affected Units, by Facility, Size and Fuel Source in 

This map displays all 

line by the end of 2011; 

In areas with a dense concentration of facilities, 
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As noted above, IPM has been used for evaluating the economic and emission impacts

of environmental policies for over two decades. The economic modeling presented in this 

chapter has been developed for specific analyses of the power sector. Thus, the model has 

been designed to reflect the industry as accurately as possible. To that end, EPA uses a series of 

capital charge factors in IPM that embody financial terms for the various types of investments 

that the power sector considers for meeting future generation and environmental constraints.

The model applies a discount rate of 6.15% for optimizing the sector’s decision-making over 

time. IPM’s discount rate, designed to represent a broad range of private-sector decisions for 

power generation, rates differs from discount rates used in other analyses in this RIA, such as

the benefits analysis which each assume alternative social discount rates of 3% and 7%. These 

discount rates represent social rates of time preference, whereas the discount rate in IPM 

represents an empirically-informed price of raising capital for the power sector. Like all other 

assumed price inputs in IPM, EPA uses the best available information from utilities, financial 

institutions, debt rating agencies, and government statistics as the basis for the capital charge 

rates and the discount rate used for power sector modeling in IPM.

More detail on IPM can be found in the model documentation, which provides 

additional information on the assumptions discussed here as well as all other assumptions and 

inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa-ipm). Updates specific to 

MATS modeling are also in the IPM 4.10 Supplemental Documentation for MATS.

3.2 Projected Emissions

MATS is anticipated to achieve substantial emissions reductions from the power sector.

Since the technologies available to meet the emission reduction requirements of the rule 

reduce multiple air pollutants, EPA expects the rule to yield a broad array of pollutant 

reductions from the power sector. The primary pollutants of concern under MATS from the 

power sector are mercury, acid gases such as hydrogen chloride (HCl), and HAP metals, 

including antimony, arsenic, beryllium, cadmium, chromium, cobalt, mercury, manganese, 

nickel, lead, and selenium. EPA has extensively analyzed mercury emissions from the power 

sector, and IPM modeling assesses the mercury contents in all coals and the removal 

efficiencies of relevant emission control technologies (e.g., ACI). EPA also models emissions and 

the pollution control technologies associated with HCl (as a surrogate for acid gas emissions). 

Like SO2, HCl is removed by both scrubbers and DSI (dry sorbent injection). Projected emissions 

are based on both control technology and detailed coal supply curves used in the model that 

reflect the chlorine content of coals, which corresponds with the supply region, coal grade, and 

sulfur, mercury, and ash content of each coal type. This information is critical for accurately 
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projecting future HCl emissions, and for understanding how the power sector will respond to a 

policy requiring reductions of multiple HAPs.

Generally, existing pollution control technologies reduce emissions across a range of 

pollutants. For example, both FGD and SCR can achieve notable reductions in mercury in 

addition to their primary targets of SO2 and NOX reductions. DSI will reduce HCl emissions while 

also yielding substantial SO2 emission reductions, but is not assumed in EPA modeling to result 

in mercury reductions. Since there are many avenues to reduce emissions, and because the 

power sector is a highly complex and dynamic industry, EPA employs IPM in order to reflect the 

relevant components of the power sector accurately, while also providing a sophisticated view 

of how the industry could respond to particular policies to reduce emissions. For more detail on 

how EPA models emissions from the power sector, including recent updates to include acid 

gases, see IPM 4.10 Supplemental Documentation for MATS.

Under MATS, EPA projects annual HCl emissions reductions of 88 percent in 2015, Hg 

emissions reductions of 68 percent in 2015, and PM2.5 emissions reductions of 20 percent in 

2015 from coal-fired EGUs greater than 25 MW. In addition, EPA projects SO2 emission 

reductions of 41 percent, and annual CO2 reductions of 1 percent from coal-fired EGUs greater 

than 25 MW by 2015, relative to the base case (see Table 3-4).7 Mercury emission projections in 

EPA’s base case are affected by the incidental capture in other pollution control technologies 

(such as FGD and SCR) as described above. 

Table 3-4. Projected Emissions of SO2, NOX, Mercury, Hydrogen Chloride, PM, and CO2 with 

the Base Case and with MATS, 2015

Million Tons

Mercury 

(Tons)

Thousand Tons CO2

(Million Metric 

SO Tonnes) 2 NOX HCl PM2.5

Base All EGUs 3.4 1.9 28.7 48.7 277 2,230

Coal > 25 MW 3.3 1.7 26.6 45.3 270 1,906

MATS All EGUs 2.1 1.9 10.6 9.0 224 2,215

Coal > 25 MW 1.9 1.7 8.4 5.5 215 1,881

Source: Integrated Planning Model run by EPA, 2011

 

7

The CO2 emissions reported from IPM account for the direct CO2 emissions from fuel combustion and CO2 created 

from chemical reactions in pollution controls to reduced sulfur
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Figure 3-2. SO2 Emissions from the Power Sector in 2015 with and without MATS

Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM v4.10 

base case and control case projections (EPA, February 2011)

Figure 3-3. NOX Emissions from the Power Sector in 2015 with and without MATS

Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM 

v4.10_MATS base case and control case projections (EPA, 2011)
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Figure 3-4. Mercury Emissions from the Power Sector in 2015 with and without MATS

Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM 

v4.10_MATS base case and control case projections (EPA, 2011)

Figure 3-5. Hydrogen Chloride Emissions from the Power Sector in 2015 with and without 

MATS

Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM 

v4.10_MATS base case and control case projections (EPA, 2011)
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3.3 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 pollution control approaches to meet the final HAP emission standards. In simple 

terms, these costs are the resource costs of what the power industry will directly expend to 

comply with EPA’s requirements.

EPA projects that the annual incremental compliance cost of MATS is $9.2 billion in 2015 

($2007). The annual incremental cost is the projected additional cost of complying with the 

final rule in the year analyzed, and includes the amortized cost of capital investment and the 

ongoing costs of operating additional pollution controls, needed new capacity, shifts between 

or amongst various fuels, and other actions associated with compliance.

Table 3-5. Annualized Compliance Cost for MATS Requirements on Coal-fired Generation

2015 2020 2030

Annualized Compliance Cost (billions of 2007$) $9.2 $8.5 $7.5

Source: Integrated Planning Model run by EPA, 2011.

EPA’s projection of $9.2 billion in additional costs in 2015 should be put into context for 

power sector operations. As shown in section 2.7, the power sector is expected in the base case

to expend over $320 billion in 2015 to generate, transmit, and distribute electricity to end-use 

consumers. Therefore, the projected costs of compliance with MATS amount to less than a 3% 

increase in the cost to meet electricity demand, while securing public health benefits that are 

several times more valuable (as described in Chapters 4 and 5).

3.4 Projected Compliance Actions for Emissions Reductions 

Fossil fuel-fired electric generating units are projected to achieve HAP emission 

reductions through a combination of compliance options. These actions include improved 

operation of existing controls, additional pollution control installations, coal switching 

(including blending of coals), and generation shifts towards more efficient units and loweremitting generation technologies (e.g., some reduction of coal-fired generation with an 

increase of generation from natural gas). In addition, there will be some affected sources that 

find it uneconomic to invest in new pollution control equipment and will be removed from 

service. These facilities are generally amongst the oldest and least efficient power plants, and 

typically run infrequently. In order to ensure that any retirements resulting from MATS do not 

adversely impact the ability of affected sources and electric utilities from meeting the demand 
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for electricity, EPA has conducted an analysis of the impacts of projected retirements on 

electric reliability. This analysis is discussed in TSD titled: “Resource Adequacy and Reliability in 

the IPM Projections for the Toxics Rule” which is available in the docket.

The requirements under MATS are largely met through the installation of pollution 

controls (see Figure 3-6). To a lesser extent, there is a small degree of shifting within and across 

various ranks and types of coals, and a relatively small shift from coal-fired generation to 

greater use of natural gas and non-emitting sources of electricity (e.g., hydro and nuclear)(see 

Table 3-6). The largest share of emissions reductions occur from coal-fired units installing new 

pollution control devices, such as FGD, ACI, and fabric filters; a smaller share of emission 

reductions come from fuel shifts and unit retirements. Mercury emission reductions are largely 

driven by SCR/FGD combinations and ACI installations. HCl emission reductions are largely 

driven by FGD and DSI installations, which also incidentally provide substantial SO2 reductions 

in the policy case. Mercury, PM2.5, and HCl emission reductions are also facilitated by the 

installation of fabric filters, which boost mercury and HCl removal efficiencies of ACI and DSI, 

respectively.

Figure 3-6. Operating Pollution Control Capacity on Coal-fired Capacity (by Technology) with 

the Base Case and with MATS, 2015 (GW)

Note: The difference between controlled capacity in the base case and under the MATS may not necessarily equal 

new retrofit construction, since controlled capacity above reflects incremental operation of dispatchable 

controls in 2015. Additionally, existing ACI installed on those units online before 2008 are not included in the 

base case to reflect removal of state mercury rules from IPM modeling. For these reasons, and due to rounding, 

numbers in the text above may not reflect the increments displayed in this figure. See IPM Documentation for 

more information on dispatchable controls.

Source: Integrated Planning Model run by EPA, 2011.

As shown in Figure 3-6, this analysis projects that by 2015, the final rule will drive the 

installation of an additional 17 GW of FGD (scrubbers), 45 GW of DSI, 92 GW of additional ACI, 
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106 GW of additional fabric filters, and 52 GW of ESP upgrades. For more information, see 

section 8.14. 

The difference between operating controlled capacity in the base case and under MATS 

in Figure 3-6 may not necessarily equal new retrofit construction, since total controlled capacity 

in the figure reflects incremental operation of existing controls that are projected to operate 

under MATS but not under the base case. With respect to the increase in operating ACI, some 

of this increase represents existing ACI capacity on units built before 2008. EPA’s modeling does 

not reflect the presence of state mercury rules, and EPA assumes that ACI controls on units 

built before 2008 do not operate in the absence of these rules. In the policy case, these controls 

are projected to operate and the projected compliance cost thus reflects the operating cost of 

these controls. Since these controls are in existence, EPA does not count their capacity toward 

new retrofit construction, nor does EPA’s compliance costs projection reflect the capital cost of 

these controls (new retrofit capacity is reported in the previous paragraph). 

3.5 Projected Generation Mix

Table 3-6 and Figure 3-7 show the generation mix in the base case and in MATS. In 2015, 

coal-fired generation is projected to decline slightly and natural-gas-fired generation is 

projected to increase slightly relative to the base case. Coal-fired generation is projected to 

increase above 2009 actual levels. The vast majority (over 98%) of base case coal capacity is 

projected to remain in service under MATS. In addition, the operating costs of complying coalfired units are not so affected as to result in major changes in the electricity generation mix.

Table 3-6. Generation Mix with the Base Case and the MATS, 2015 (Thousand GWh)

2009 2015

Historical Base Case Policy Case

Change from 

Base Percent Change

Coal 1,756 1,982 1,957 −25 −1.3%

Oil 39 0.11 0.11 0.00 2.7%

Natural Gas 921 710 732 22 3.1%

Nuclear 799 828 831 3 0.4%

Hydroelectric 273 286 288 2 0.7%

Non-hydro Renewables 144 252 250 −2 −0.7%

Other 18 45 45 0.0 0.0%

Total 3,950 4,103 4,104 1 0.0%

Note: Numbers may not add due to rounding.

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; 2015 projections are from the Integrated 

Planning Model run by EPA, 2011.
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Figure 3-7. Generation Mix with the Base Case and with MATS, 2015-2030

Source: Integrated Planning Model run by EPA, 2011.

3.6 Projected Withdrawals from Service

Relative to the base case, about 5.5 GW of coal-fired capacity is projected to be 

uneconomic to maintain (less than 2 percent of all coal-fired capacity) by 2015 after 

consideration of various regional factors (e.g., other available capacity and fuel prices) and unit 

attributes (e.g., efficiency and age). Uneconomic units, for the most part, are older, smaller, and 

less frequently used generating units that are dispersed throughout the country (see Table 3-7).

Table 3-7. Characteristics of Covered Operational Coal Units and Additional Coal Units 

Projected to Withdraw as Uneconomic under MATS, 2015

Average Age 

(Years)

Average Capacity

MW Factor in Base

Withdrawn as Uneconomic 52 117 53%

Operational 43 327 71%

Source: Integrated Planning Model run by EPA, 2011.

There could be a variety of local factors that would make plant owners decide to keep 

various units in service to meet local reliability and other local requirements that are beyond 

EPA’s ability to assess in modeling. This is an important limitation to recognize in these results, 

which should be used to consider “potential” closures using these numbers. To the extent that 
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plants operate in response to local factors unaccounted for in EPA’s modeling, these results 

could be overstating the capacity removed from service as a result of this rule.

For the final rule, EPA has examined whether the IPM-projected closures may adversely 

impact reserve margins and reliability planning. The IPM model is specifically designed to 

ensure that generation resource availability is maintained in the projected results subject to 

reserve margins in 32 modeling regions for the contiguous US, which must be preserved either 

by using existing resources or through the construction of new resources. IPM also addresses 

reliable delivery of generation resources by limiting the ability to transfer power between 

regions using the bulk power transmission system. Within each model region, IPM assumes that 

adequate transmission capacity is available to deliver any resources located in, or transferred 

to, the region. The IPM model projects available capacity given certain constraints such as 

reserve margins and transmission capability but does not constitute a detailed reliability 

analysis. For example, the IPM model does not examine frequency response. For more detail on 

IPM’s electric load modeling and power system operation, please see IPM documentation 

(http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html) and the TSD on Resource 

Adequacy and Reliability in the IPM Projections for MATS.

Total operational capacity is lower in the policy scenario, primarily as a result of 

additional coal projected to be uneconomic to maintain. Since most regions are projected to 

have excess capacity above their target reserve margins, most of these withdrawals from 

service are absorbed by a reduction in excess reserves. Operational capacity changes from the 

base case in 2015 are shown in Table 3-8.

Table 3-8. Total Generation Capacity by 2015 (GW)

2010 Base Case MATS

Pulverized Coal 317 310 304

Natural Gas Combined Cycle 201 206 206

Other Oil/Gas 253 233 233

Non-Hydro Renewables 31 70 70

Hydro 99 99 99

Nuclear 102 104 105

Other 5 4 4

Total 1,009 1,026 1,020

Source: 2010 data from EPA’s NEEDS v.4.10_PTox. Projections from Integrated Planning Model run by EPA.

Note: “Non-Hydro Renewables” include biomass, geothermal, solar, and wind electric generation capacity. 2015 

capacity reflects plant closures planned to occur prior to 2015.
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The policy case analyzed maintains resource adequacy in each region projected to 

decrease in coal capacity by using excess reserve capacity within the region, reversing base case

withdrawals of non-coal capacity, building new capacity, or by importing excess reserve 

capacity from other regions. Although any closure of a large generation facility will need to be 

studied to determine potential local reliability concerns, EPA analysis suggests that projected 

economic withdrawals from service under the final rule could have little to no overall impact on 

electric reliability. Not only are projected withdrawals under MATS limited in scope, but the 

existing state of the power sector is also characterized by substantial excess capacity. The 

weighted average reserve margin at the national level is projected to be approximately 25% in

the base case, while the North American Electric Reliability Corporation (NERC) recommends a 

margin of 15%. EPA projects that MATS would only reduce total operational capacity by less 

than one percent in 2015.

Moreover, coal units projected to withdraw as uneconomic are distributed throughout 

the power grid with limited effect at the regional level, such that any potential impacts should 

not adversely affect reserve margins and should be manageable through the normal industry 

processes. For example, in the RFC NERC reliability Region, containing coal-fired generating 

area in Pennsylvania, West Virginia and the Midwest, there is a decrease of less than 2% in the 

reserve margin in the policy case and a remaining overall reserve margin of over 20%.

Furthermore, subregions may share each other’s excess reserves to ensure adequate reserve 

margins within a larger reliability region. EPA’s IPM modeling accommodates such transfers of 

reserves within the assumed limits of reliability of the inter-regional bulk power system. For 

these reasons, the projected closures of coal plants are not expected to raise broad reliability 

concerns.

3.7 Projected Capacity Additions

Due in part to a low growth rate anticipated for future electricity demand levels in the 

latest EIA forecast, EPA analysis indicates that there is sufficient excess capacity through 2015 

to compensate for capacity that is retired from service under MATS. In the short-term, most 

new capacity is projected as a mix of wind and natural gas in response to low fuel prices and 

other energy policies (such as tax credits and state renewable portfolio standards). In addition, 

future electricity demand expectations have trended downwards in recent forecasts, reducing 

the need for new capacity in the 2015 timeframe (see Chapter 2 for more discussion on future 

electricity demand). 
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Table 3-9. Total Generation Capacity by 2030 (GW)

2010 Base Case MATS Change

Pulverized Coal 317 308 303 −4.7

Natural Gas Combined Cycle 201 275 279 3.6

Other Oil/Gas 253 235 235 0.4

Non-Hydro Renewables 31 79 80 0.2

Hydro 99 99 99 0.0

Nuclear 102 103 103 0.3

Other 5 4 4 0.0

Total 1,009 1,103 1,102 −0.2

Note: “Non-Hydro Renewables” include biomass, geothermal, solar, and wind electric generation capacity.

Source: 2010 data from EPA’s NEEDS v.4.10_PTox. Projections from Integrated Planning Model run by EPA.

3.8 Projected Coal Production for the Electric Power Sector

Coal production for electricity generation under MATS is expected to decline modestly 

relative to the base case without the rule. The reductions in emissions from the power sector 

will be met through the installation and operation of pollution controls for HAP removal. Many 

available pollution controls achieve emissions removal rates of up to 99 percent (e.g., HCl 

removal by new scrubbers), which allows industry to rely more heavily on local bituminous coal 

in the eastern and central parts of the country that has higher contents of HCl and sulfur, and it 

is less expensive to transport than western subbituminous coal. Overall demand for coal is 

reduced as a result of MATS, with a slight reduction in bituminous coal, and more of a reduction 

in subbituminous coal (see Tables 3-10 and 3-11). The trend reflects the reduced demand for 

lower-sulfur coal under MATS, where nearly all units are operating with a post-combustion 

emissions control. In this case, because of the additional pollution controls, many of these units 

no longer find it economic to pay a transportation premium to purchase lower-sulfur 

subbituminous coals. Instead, EGUs are generally shifting consumption towards nearby 

bituminous coal, which can achieve low emissions when combined with post-combustion 

emissions controls. This explains the increase in coal supplied from the Interior region, which is 

located in relatively close proximity to many coal-fired generators subject to MATS. The 

increase in lignite use occurs at units blending subbituminous and lignite coals, and reflects a

small shift in blended balance towards a greater use of lignite.
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Table 3-10. 2015 Coal Production for the Electric Power Sector with the Base Case and MATS

(Million Tons)

Supply Area 2009 2015 Base 2015 MATS Change in 2015

Appalachia 246 184 173 −6%

Interior 129 216 236 9%

West 553 554 536 −3%

Waste Coal 14 14 13 −5%

Imports 30 30 0%

Total 942 998 988 −1%

Source: Production: U.S. Energy Information Administration (EIA), Coal Distribution — Annual (Final), web site 

http://www.eia.doe.gov/cneaf/coal/page/coaldistrib/a_distributions.html (posted February 18, 2011); Waste 

Coal: U.S. EIA, Monthly Energy Review, January 2011 Edition, Table 6.1 Coal Overview, web site 

http://www.eia.doe.gov/emeu/mer/coal.html (posted January 31, 2011). All projections from Integrated 

Planning Model run by EPA, 2011.

Table 3-11. 2015 Power Sector Coal Use with the Base Case and the MATS, by Coal Rank 

(TBtu)

Coal Rank Base MATS Change

Bituminous 11,314 11,268 −0.4%

Subbituminous 7,736 7,532 −3%

Lignite 849 895 5%

Total 19,900 19,695 −1%

Source: Integrated Planning Model run by EPA, 2011.
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Figure 3-8. Total Coal Production by Coal-Producing Region, 2007 (Million Short Tons)

Note: Regional totals do not include refuse recovery

Source: EIA Annual Coal Report, 2007

3.9 Projected Retail Electricity Prices

EPA’s analysis projects a near-term increase in the average retail electricity price of 3%

in 2015 falling to 2% by 2020 under the final rule in the contiguous U.S. The projected price 

impacts vary by region and are provided in Table 3-12 (see Figure 3-9 for regional 

classifications).

Regional retail electricity prices are projected to range from 1 to 6 percent higher with MATS in 

2015. The extent of regional retail electricity increases correlates with states that have 

considerable coal-fired generation in total generation capacity and that coal-fired generation is 

less well-controlled (such as in the ECAR and SPP regions).
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Table 3-12. Projected Contiguous U.S. and Regional Retail Electricity Prices with the Base 

Case and with the MATS (2007 cents/kWh)

Base Case MATS Percent Change

2015 2020 2030 2015 2020 2030 2015 2020 2030

ECAR 8.2 8.2 9.8 8.5 8.5 9.9 4.3% 2.9% 1.3%

ERCOT 8.9 8.8 11.3 9.2 8.9 11.3 3.1% 0.9% −0.1%

MAAC 9.5 10.4 12.7 9.8 10.4 12.7 2.7% 0.4% −0.1%

MAIN 8.1 8.4 9.7 8.3 8.6 9.7 2.7% 2.2% 0.1%

MAPP 8.0 7.9 8.5 8.4 8.3 8.8 4.4% 4.7% 2.7%

NY 13.8 13.4 16.6 14.0 13.5 16.6 2.0% 0.7% 0.1%

NE 12.3 11.8 13.8 12.6 11.9 13.8 1.9% 0.8% 0.1%

FRCC 10.2 9.7 11.0 10.4 9.8 11.0 2.1% 1.0% 0.7%

STV 7.9 7.8 8.4 8.2 8.0 8.6 3.2% 2.4% 1.8%

SPP 7.7 7.4 8.1 8.1 7.8 8.4 5.9% 5.8% 4.4%

PNW 7.1 6.8 7.6 7.3 7.0 7.6 2.7% 2.6% 1.1%

RM 9.2 9.5 11.0 9.4 9.6 11.1 2.1% 1.8% 1.1%

CALI 13.0 12.5 12.7 13.2 12.6 12.7 1.3% 0.8% 0.0%

Contiguous U.S. 

Average

9.0 9.0 10.2 9.3 9.2 10.3 3.0% 2.0% 0.9%

Source: EPA’s Retail Electricity Price Model, 2011.
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Figure 3-9. Retail Price Model Regions

3.10 Projected Fuel Price Impacts

The impacts of the final Rule on coal and natural gas prices before shipment are shown 

below in Tables 3-13 and 3-14. Overall, average coal price changes are related to changes in 

demand for a wide variety of coals based upon a number of parameters (e.g., chlorine or 

mercury content, heat content, proximity to the power plant, etc.). Generally, total demand for 

coal decreases slightly under MATS, most notably subbituminous coal. This is reflected in the 

projected average minemouth price of coal, which goes up by about 3 percent even though 

total demand for coal is reduced slightly (1 percent reduction). Notwithstanding the projected 

“mine-mouth” coal price changes, many units may in fact be realizing overall fuel cost savings 

by switching to more local coal supplies (which reduces transportation costs) after installing 

additional pollution control equipment. Gas price changes are directly related the projected 

increase in natural gas consumption under MATS. This increase in demand is met by producing 

additional natural gas at some increase in regional costs, resulting over time in a small price 

increase.
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Table 3-13. Average Minemouth and Delivered Coal Prices with the Base Case and with 

MATS (2007$/MMBtu)

2007

2015 2030

Base Case MATS

Percent 

Change 

from Base Base Case MATS

Percent 

Change 

from Base

Minemouth 1.27 1.35 1.39 2.8 1.51 1.56 3.2

Delivered 1.76 2.11 2.15 1.9 2.29 2.33 1.7

Source: Historical data from EIA AEO 2010 Reference Case Table 15 (Coal Supply, Distribution, and Prices); 

projections from the Integrated Planning Model run by EPA, 2011.

Table 3-14. 2015-2030 Weighted Average Henry Hub (spot) and Delivered Natural Gas Prices 

with the Base Case and with MATS (2007$/MMBtu)

Base Case MATS Percent Change from Base

Henry Hub 5.29 5.33 0.8

Delivered - Electric Power 5.56 5.60 0.7

Delivered - Residential 10.94 10.98 0.4

Source: Projections from the Integrated Planning Model run by EPA (2011) adjusted to Henry Hub prices using 

historical data from EIA AEO 2011 reference case to derive residential prices.

IPM modeling of natural gas prices uses both short- and long-term price signals to 

balance supply of and demand in competitive markets for the fuel across the modeled time 

horizon. As such, it should be understood that the pattern of IPM natural gas price projections 

over time is not a forecast of natural gas prices incurred by end-use consumers at any particular 

point in time. The natural gas market in the United States has historically experienced 

significant price volatility from year to year, between seasons within a year, and even sees 

major price swings during short-lived weather events (such as cold snaps leading to short-run 

spikes in heating demand). These short-term price signals are fundamental for allowing the 

market to successfully align immediate supply and demand needs; however, end-use 

consumers are typically shielded from experiencing these rapid fluctuations in natural gas 

prices by retail rate regulation and by hedging through longer-term fuel supply contracts. IPM 

assumes these longer-term price arrangements take place “outside of the model” and on top of 

the “real-time” shorter-term price variation necessary to align supply and demand. Therefore, 

the model’s natural gas price projections should not be mistaken for traditionally experienced 

consumer price impacts related to natural gas, but a reflection of expected average price 

changes over the time period 2015 to 2030.
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For this analysis, in order to represent a natural gas price evolution that end-use 

consumers can anticipate under retail rate regulation and/or typical hedging behavior, EPA is 

displaying the weighted average of IPM’s natural gas price projections for the 2015-2030 time 

horizon (see Table 3-14). In that framework, consumer natural gas price impacts are anticipated 

to range from 0.4% to 0.7% based on consumer class in response to MATS.

3.11 Key Differences in EPA Model Runs for MATS Modeling

In this analysis, we use the Integrated Planning Model (IPM), which is a multiregional, 

dynamic, deterministic linear programming model of the U.S. electric power sector. 8The length 

of time required to conduct emissions and photochemical modeling precluded the use of IPM 

version 4.10_MATS. Thus the air quality modeling for MATS relied on EGU emission projections 

from an interim IPM platform that was subsequently updated during the rulemaking process for 

the base case and policy scenario summarized in this chapter. The 2015 base case EGU 

emissions projections of mercury, hydrogen chloride, SO2, and PM used in air quality modeling 

were obtained from an earlier version of IPM, 4.10_FTransport. IPM version 4.10_FTransport 

reflects all state rules and consent decrees adopted through December 2010. Units with SO2 or 

NOX advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with 

Title IV, New Source Review (NSR), state settlements, or state-specific rules were allowed in 

IPM to decide on the basis of economic efficiency whether to operate those controls. Note that 

this base case includes CSAPR, which was finalized in July 2011. Further details on the EGU 

emissions inventory used for this proposal can be found in the IPM Documentation.

The results presented in this chapter, from IPM version 4.10_MATS, reflect updates 

made to the 4.10_FTransport base case. These revisions are fully documented in the IPM 4.10 

Supplemental Documentation for MATS and include: updated assumptions regarding the 

removal of HCl by alkaline fly ash in subbituminous and lignite coals; an update to the fuelbased mercury emission factor for petroleum coke, which was corrected based on reexamination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life 

extension costs; corrected variable operating and maintenance cost (VOM) for ACI retrofits; 

adjusted coal rank availability for some units, consistent with EIA From 923 (2008); updated 

state rules in Washington and Colorado; and numerous unit-level revisions based on comments 

received through the notice and comment process. Additionally, IPM v.4.10_MATS does not 

reflect mercury-specific state regulations (see section 1 above).

 

8

http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html
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3.12 Projected Primary PM Emissions from Power Plants

IPM does not endogenously model primary PM emissions from power plants. These 

emissions are calculated as a function of IPM outputs, emission factors and control 

configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based

in part on the presence of PM-relevant pollution control devices) to determine PM emissions.

Primary PM emissions are calculated by adding the filterable PM and condensable PM 

emissions. 

Filterable PM emissions for each unit are based on historical information regarding 

existing emissions controls and types of fuel burned and ash content of the fuel burned, as well 

as the projected emission controls (e.g., scrubbers and fabric filters).

Condensable PM emissions are based on plant type, sulfur content of the fuel, and 

SO2/HCl and PM control configurations. Although EPA’s analysis is based on the best available 

emission factors, these emission factors do not account for the potential changes in 

condensable PM emissions due to the installation and operation of SCRs. The formation of 

additional condensable PM (in the form of SO3 and H2SO4) in units with SCRs depends on a 

number of factors, including coal sulfur content, combustion conditions and characteristics of 

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

designed and operated to minimize increases in condensable PM. This limitation means that 

IPM post-processing is potentially underestimating condensable PM emissions for units with 

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

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

EPA plans to continue improving and updating the PM emission factors and calculation 

methodologies. For a more complete description of the methodologies used to post-process 

PM emissions from IPM, see “IPM ORL File Generation Methodology” (March, 2011).

3.13 Illustrative Dry Sorbent Injection Sensitivity

Several commenters believe that EPA’s IPM modeling assumptions regarding the 

efficacy and cost of DSI are based on too little data and are too optimistic. Some commenters 

believe that in practice there will be a need for many more FGD scrubbers for MATS compliance 

than projected by EPA for effective acid gas control, and at a corresponding higher cost. EPA 

disagrees with these opinions for several reasons (see Response to Comments) and believes 

that EPA’s modeling assumptions regarding DSI cost and performance are reasonable.
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However, to examine the potential impacts of limited DSI availability, EPA analyzed a 

scenario that limited total DSI capacity to 35 GW in 2015. In this scenario, which reduces the 

capacity of DSI by 19 GW compared to the primary MATS scenario, an additional 15 GW of coal 

capacity chooses to install scrubbers, and an additional 1 GW of capacity is projected to 

withdraw from service. Additionally, about 3 GW of capacity that installed DSI in the primary 

policy scenario switches to a very low chlorine subbituminous coal, which allows for compliance 

with the HCl standard absent additional acid gas control technology.

Limiting total DSI capacity to 35 GW results in a $1.2 billion (2007$) increase in 

annualized compliance costs in 2015. Additionally, SO2 is further reduced in 2015 by an 

additional 55,000 tons (a 4% increase in SO2 reductions).

3.14 Limitations of Analysis

EPA’s modeling is based on expert judgment of various input assumptions for variables 

whose outcomes are in fact uncertain. Assumptions for future fuel supplies and electricity 

demand growth deserve particular attention because of the importance of these two key model 

inputs to the power sector. As a general matter, the Agency reviews the best available 

information from engineering studies of air pollution controls to support a reasonable modeling 

framework for analyzing the cost, emission changes, and other impacts of regulatory actions.

The annualized cost estimates of private compliance costs provided in this analysis are 

meant to show the increase in production (generating) costs to the power sector in response to 

the final rule. To estimate these annualized costs, EPA uses a conventional and widely-accepted 

approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds 

that to the annual incremental operating expenses. The CRF is derived from estimates of the 

cost of capital (private discount rate), the amount of insurance coverage required, local 

property taxes, and the life of capital. The private compliance costs presented earlier are EPA’s 

best estimate of the direct private compliance costs of MATS.

The annualized cost of the final rule, as quantified here, is EPA’s best assessment of the 

cost of implementing the rule. These costs are generated from rigorous economic modeling of 

changes in the power sector due to implementation of MATS. This type of analysis using IPM 

has undergone peer review, and federal courts have upheld regulations covering the power 

sector that have relied on IPM’s cost analysis.

The direct private compliance cost includes, but is not limited to, capital investments in 

pollution controls, operating expenses of the pollution controls, investments in new generating 
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sources, and additional fuel expenditures. EPA believes that the cost assumptions used for the 

final rule reflect, as closely as possible, the best information available to the Agency today. The 

cost associated with monitoring emissions, reporting, and record keeping for affected sources is 

not included in these annualized cost estimates, but EPA has done a separate analysis and 

estimated the cost to be approximately $128.6 million annually (see Section 7.3, Paperwork 

Reduction Act).

Cost estimates for MATS are based on results from ICF’s Integrated Planning Model. The 

model minimizes the costs of producing electricity (including abatement costs) while meeting 

load demand and other constraints (full documentation for IPM can be found at 

http://www.epa.gov/airmarkets/progsregs/epa-ipm and in the IPM 4.10 Supplemental 

Documentation for MATS. IPM assumes “perfect foresight” of market conditions over the time 

horizon modeled; to the extent that utilities and/or energy regulators misjudge future 

conditions affecting the economics of pollution control, costs may be understated as well.

In the policy case modeling, EPA exogenously determines that a subset of covered units 

might require a retrofit fabric filter (also known as a baghouse) retrofit, or might need to 

upgrade existing ESP control in order to meet the PM standard. EPA’s methodology for 

assigning these controls to EGUs in policy case modeling is based on historic PM emission rates 

and reported control efficiencies, and is explained in the TSD titled: “XXX,” which is available in 

the docket.

Additionally, this modeling analysis does not take into account the potential for 

advancements in the capabilities of pollution control technologies as well as reductions in their 

costs over time. In addition, EPA modeling cannot anticipate in advance the full spectrum of 

compliance strategies that the power sector may innovate to achieve the required emission 

reductions under MATS, which would potentially reduce overall compliance costs. Where 

possible, EPA designs regulations to assure environmental performance while preserving 

flexibility for affected sources to design their own solutions for compliance. Industry will 

employ an array of responses, some of which regulators may not fully anticipate and will 

generally lead to lower costs associated with the rule than modeled in this analysis. For 

example, unit operators may find opportunities to improve or upgrade existing pollution 

control equipment without requiring as many new retrofit devices (i.e., meeting the PM 

standard with an existing ESP without requiring installation of a new fabric filter).

With that in mind, MATS establishes emission rates on key HAPs, and although this 

analysis projects a specific set of technologies and behaviors as EPA’s judgment of least-cost 
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compliance, the power sector is free to adopt alternative technologies and behaviors to achieve 

the same environmental outcome EPA has deemed in the public interest as laid out in the Clean 

Air Act. Such regulation serves to promote innovation and the development of new and 

cheaper technologies. As an example, cost estimates of the Acid Rain SO2 trading program by 

Resources for the Future (RFF) and MIT’s Center for Energy and Environmental Policy Research 

(CEEPR) have been as much as 83 percent lower than originally projected by the EPA (see 

Carlson et al., 2000; Ellerman, 2003). It is important to note that the original analysis for the 

Acid Rain Program done by EPA also relied on an optimization model like IPM. Ex ante, EPA cost 

estimates of roughly $2.7 to $6.2 billion9in 1989 were an overestimate of the costs of the 

program in part because of the limitation of economic modeling to perfectly anticipate

technological improvement of pollution controls and economic improvement of other 

compliance options such as fuel switching. Ex post estimates of the annual cost of the Acid Rain 

SO2 trading program range from $1.0 to $1.4 billion.

In recognition of this historic pattern of overestimated regulatory cost, EPA’s mobile 

source program uses adjusted engineering cost estimates of pollution control equipment and 

installation costs.10 To date, and including this analysis, EPA has not incorporated a similar 

approach into IPM modeling of EGU compliance with environmental constraints. As a result, 

this analysis may overstate costs where such cost savings from as-yet untapped improvements 

to pollution control technologies may occur in the future. Considering the broad and complex 

suite of generating technologies, fuels, and pollution control strategies available to the power 

sector, as well as the fundamental role of operating cost in electricity dispatch, it is not possible 

to apply a single technology-improving “discount” transformation to the cost projections in this 

analysis. The Agency will consider additional methodologies in the future which may inform the

amount by which projected compliance costs could be overstated regarding further 

technological development in analyses of power sector regulations.

As configured in this application, IPM does not take into account demand response (i.e., 

consumer reaction to electricity prices). The increased retail electricity prices shown in 

Table 3-13 would prompt end users to increase investment in energy efficiency and/or curtail 

(to some extent) their use of electricity and encourage them to use substitutes.11 Those

responses would lessen the demand for electricity, resulting in electricity price increases slightly 

 

9

 2010 Phase II cost estimate in $1995.

10

 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy-Duty Diesel 

Vehicle Rules (2000).

11 The degree of substitution/curtailment depends on the costs and performance of the goods that 

substitute for more energy consuming goods, which is reflected in the demand elasticity.
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lower than IPM predicts, which would also reduce generation and emissions. Demand response

would yield certain unquantified cost savings from requiring less electricity to meet the

quantity demanded. To some degree, these saved resource costs will offset the additional costs 

of pollution controls and fuel switching that EPA anticipates from the final rule. Although the 

reduction in electricity use is likely to be small, the cost savings from such a large industry12 are

not insignificant. EIA analysis examining multi-pollutant legislation in 2003 indicated that the 

annualized costs of MATS may be overstated substantially by not considering demand 

response, depending on the magnitude and coverage of the price increases.13

EPA’s IPM modeling of MATS reflects the Agency’s authority to allow facility-level 

compliance with the HAP emission standards rather than require each affected unit at a given 

facility to meet the standards separately. This flexibility would offer important cost savings to 

facility owners in situations where a subset of affected units at a given facility could be 

controlled more cost-effectively such that their “overperformance” would compensate for any 

“underperformance” of the rest of the affected units. EPA’s modeling in this analysis required 

the average emission rate across all affected units at a given facility to meet the standard. This 

averaging flexibility has the potential to offer further cost savings beyond this analysis if 

particular units find ways to achieve superior pollution control beyond EPA’s assumptions of 

retrofit technology performance at the modeled costs (which could then reduce the need to 

control other units at the same facility).

Additionally, EPA has chosen to express most of the control requirements here as 

engineering performance standards (e.g., lbs/MMBtu of heat input), which provide power plant 

operators goals to meet as they see fit in choosing coals with various pollutant concentrations 

and pollutant control technologies that they adopt to meet the requirements. Historically, such 

an approach encourages industry to engineer cheaper solutions over time to achieve the 

pollution controls requirements.

EPA’s IPM modeling is based on retrofit technology cost assumptions which reflect the 

best available information on current and foreseeable market conditions for pollution control 

deployment. In the current economic environment, EPA does not anticipate (and thus this 

analysis does not reflect) significant near-term price increases in retrofit pollution control 

 

12 Investor-owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA).

13 See “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean Air Planning Act of 2003.” 

Energy Information Administration. September, 2003. EIA modeling indicated that the Clear Skies Act of 2003 (a 

nationwide cap and trade program for SO2, NOX, and mercury), demand response could lower present value costs 

by as much as 47% below what it would have been without an emission constraint similar to the Transport Rule.
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supply chains in response to MATS. To the extent that such conditions may develop during the 

sector’s installation of pollution control technologies under the final rule, this analysis may 

understate the cost of compliance.

3.15 Significant Energy Impact

MATS would have a significant impact according to E.O. 13211: Actions that Significantly 

Affect Energy Supply, Distribution, or Use. Under the provisions of this rule, EPA projects that 

approximately 5.5 GW of coal-fired generation (less than 2 percent of all coal-fired capacity and 

0.5% of total generation capacity in 2015) may be removed from operation by 2015. These 

units are predominantly smaller and less frequently-used generating units dispersed 

throughout the area affected by the rule. If current forecasts of either natural gas prices or 

electricity demand were revised in the future to be higher, that would create a greater 

incentive to keep these units operational.

EPA also projects fuel price increases resulting from MATS. Average retail electricity 

price are shown to increase in the contiguous U.S. by 3 percent in 2015. This is generally less of 

an increase than often occurs with fluctuating fuel prices and other market factors. Related to 

this, the average delivered coal price increases by less than 2 percent in 2015 as a result of 

shifts within and across coal types. As discussed above in section 8.10, EPA also projects that 

electric power sector-delivered natural gas prices will increase by about 0.7% percent over the 

2015-2030 timeframe and that natural gas use for electricity generation will increase by less 

than 200 billion cubic feet (BCF) in 2015. These impacts are well within the range of price 

variability that is regularly experienced in natural gas markets. Finally, the EPA projects coal 

production for use by the power sector, a large component of total coal production, will 

decrease by 11 million tons in 2015 from base case levels, which is about 1 percent of total coal 

produced for the electric power sector in that year. The EPA does not believe that this rule will 

have any other impacts (e.g., on oil markets) that exceed the significance criteria.

3.16 References

EIA Annual Coal Report 2008. DOE/EIA-0584 (2008). Available at: 

http://www.eia.doe.gov/cneaf/coal/page/acr/acr_sum.html

EIA Annual Energy Outlook 2003. DOE/EIA-0383 (2003). Available at: 

http://www.eia.doe.gov/oiaf/archive/aeo03/index.html

EIA Electric Power Annual 2008. DOE/EIA-0348 (2008). Available at: 

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm
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EIA Electric Power Monthly March 2010 with Data for December 2009. DOE/EIA-0226 

(2010/03). Available at: http://www.eia.doe.gov/cneaf/electricity/epm/epm_sum.html

Freme, Fred. 2009. U.S. Coal Supply and Demand: 2008 Review. EIA. Available at: 

http://www.eia.doe.gov/cneaf/coal/page/special/tbl1.html

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory Cost 

Estimates.” Journal of Policy Analysis and Management 19(2):297-322.

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