ES-1

EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) presents the health and welfare benefits, costs, 

and other impacts of the final Mercury and Air Toxics Standards (MATS) in 2016.

ES.1 Key Findings

This rule will reduce emissions of Hazardous Air Pollutants (HAP) including mercury from 

the electric power industry. As a co-benefit, the emissions of certain PM2.5 precursors such as 

SO2 will also decline. EPA estimates that this final rule will yield annual monetized benefits (in 

2007$) of between $37 to $90 billion using a 3% discount rate and $33 to $81 billion using a 7% 

discount rate. The great majority of the estimates are attributable to co-benefits from 4,200 to 

11,000 fewer PM2.5-related premature mortalities. The monetized benefits from reductions in 

mercury emissions, calculated only for children exposed to recreationally caught freshwater 

fish, are expected to be $0.004 to $0.006 billion in 2016 using a 3% discount rate and $0.0005 

to $0.001 billion using a 7% discount rate. The annual social costs, approximated by the 

compliance costs, are $9.6 billion (2007$) and the annual monetized net benefits are $27 to $80

billion using 3% discount rate or $24 to $71 billion using a 7% discount rate. The benefits 

outweigh costs by between 3 to 1 or 9 to 1 depending on the benefit estimate and discount 

rate used. There are some costs and important benefits that EPA could not monetize, such as 

those for the HAP being reduced by this final rule other than mercury. Upon considering these 

limitations and uncertainties, it remains clear that the benefits of the MATS are substantial and 

far outweigh the costs. Employment impacts associated with the final rule are estimated to be 

small.

The benefits and costs in 2016 of the final rule are in Table ES-1. The emission 

reductions from the electricity sector that are expected to result from the rule are reported in 

Table ES-2. 
ES-2

Table ES-1. Summary of EPA’s Estimates of AnnualizedaBenefits, Costs, and Net Benefits of 

the Final MATS in 2016b(billions of 2007$)

Description

Estimate

(3% Discount Rate)

Estimate

(7% Discount Rate)

Costsc$9.6 $9.6

Benefitsd,e,f $37 to $90 + B $33 to $81 + B

Net benefits (benefits-costs)g$27 to $80 + B $24 to $71 + B

a

All estimates presented in this report represent annualized estimates of the benefits and costs of the final MATS 

in 2016 rather than the net present value of a stream of benefits and costs in these particular years of analysis.

b

Estimates rounded to two significant figures and represent annualized benefits and costs anticipated for the 

year 2016.

c Total social costs are approximated by the compliance costs. Compliance costs consist of IPM projections, 

monitoring/reporting/record-keeping costs, and oil-fired fleet analysis costs. For a complete discussion of these 

costs refer to Chapter 3.

d

Total benefits are composed primarily of monetized PM-related health benefits. The reduction in premature 

fatalities each year accounts for over 90% of total monetized benefits. Benefits in this table are nationwide and 

are associated with directly emitted PM2.5 and SO2 reductions. The estimate of social benefits also includes 

CO2-related benefits calculated using the social cost of carbon, discussed further in chapter 5. 

e

Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Data limitations prevented us from quantifying these endpoints, and as 

such, these benefits are inherently more uncertain than those benefits that we were able to quantify. Estimates 

here are subject to uncertainties discussed further in the body of the document. Potential benefit categories 

that have not been quantified and monetized are listed in Table ES-4.

f Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. 

Results reflect the use of 3% and 7% discount rates consistent with EPA and OMB guidelines for preparing 

economic analyses (EPA, 2000; OMB, 2003).

g

Net benefits are rounded to two significant figures. Columnar totals may not sum due to rounding.

Table ES-2: Projected Electricity Generating Unit (EGU) Emissions of SO2, NOX, Mercury, 

Hydrogen Chloride, PM, and CO2 with the Base Case and with MATS, 2015 a,b

Million Tons

Mercury 

(Tons)

Thousand Tons CO2

(Million Metric 

SO2 NOX HCl PM2.5 Tonnes)

Base All EGUs 3.4 1.9 28.7 48.7 277 2,230

Covered EGUs 3.3 1.7 26.6 45.3 270 1,906

MATS All EGUs 2.1 1.9 8.8 9.0 227 2,215

Covered EGUs 1.9 1.7 6.6 5.5 218 1,883

a

Source: Integrated Planning Model run by EPA, 2011

b

The year 2016 is the compliance year for MATS, though as we explain in later chapters, we use 2015 as a proxy 

for compliance in 2016 for IPM emissions and costs due to availability of modeling impacts in that year. 
ES-3

ES.1.1 Health Co-Benefits

The final MATS Rule is expected to yield significant health co-benefits by reducing 

emissions not only of HAP such as mercury, but also significant co-benefits by reducing directly 

emitted fine particles (PM2.5), and sulfur dioxide emissions, which contribute to the formation 

of PM2.5. Approximately 95% of the monetized co-benefits are due to reduced exposure to 

sulfate particles.

Our analyses suggest this rule would yield co-benefits in 2016 of $37 to $90 billion 

(based on a 3% discount rate) and $33 to $81 billion (based on a 7% discount rate). This 

estimate reflects the economic value of a range of avoided health outcomes including 510 

fewer mercury-related IQ points lost as well as avoided PM2.5-related impacts, including 4,200 

to 11,000 premature deaths, 4,700 nonfatal heart attacks, 2,600 hospitalizations for respiratory

and cardiovascular diseases, 540,000 lost work days and 3.2 million days when adults restrict 

normal activities because of respiratory symptoms exacerbated by PM2.5. We also estimate 

substantial additional health improvements for children from reductions in upper and lower 

respiratory illnesses, acute bronchitis, and asthma attacks. See Table ES-2 for a list of the annual 

reduction in health effects expected in 2016 and Table ES -3 for the estimated value of those 

reductions. In addition, we include in our monetized co-benefits estimates the effect from the 

reduction in CO2 emissions resulting from this rule. We calculate the co-benefits associated 

with these emission reductions using the interagency estimates of the social cost of carbon 

(SCC)1.

It is important to note that the health co-benefits from reduced PM2.5 exposure

reported here contain uncertainty, including from the following key assumptions: 

1. The PM2.5-related co-benefits of the regulatory alternatives were derived through a

benefit per-ton approach, which does not fully reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local 

factors that might lead to an over-estimate or under-estimate of the actual cobenefits of controlling PM precursors. In addition, differences in the distribution of 

 

1

Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with 

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

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

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

Office of Science and Technology Policy, and Department of Treasury (February 2010). Also available at 

http://www.epa.gov/otaq/climate/regulations.htm
ES-4

emissions reductions across states between the modeled scenario and the final rule 

scenario add uncertainty to the final benefits estimates.

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

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

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but the scientific evidence is not yet sufficient to allow differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates include 

health co-benefits from reducing fine particles in areas with varied concentrations of 

PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard down to the lowest modeled concentrations. It 

is important to emphasize that National Ambient Air Quality Standards (NAAQS) are 

not set at a level of zero risk. Instead, the NAAQS reflect the level determined by the 

Administrator to be protective of public health within an adequate margin of safety, 

taking into consideration effects on susceptible populations based on the scientific 

literature available at the time the standard is set. While benefits occurring below 

the standard may be less certain than those occurring above the standard, EPA 

considers them to be legitimate components of the total benefits estimate.

A large fraction of the PM2.5-related benefits occur below the level of the National 

Ambient Air Quality Standard (NAAQS) for annual PM2.5 at 15 μg/m3, which wasset in 2006. It is 

important to emphasize that NAAQS are not set at a level of zero risk. Instead, the NAAQS 

reflect the level determined by the Administrator to be protective of public health within an 

adequate margin of safety, taking into consideration effects on susceptible populations. EPA set 

the 2006 standard taking into consideration the best scientific literature available at the time 

EPA conducted the scientific review for the standard. 

Based on the modeled interim baseline which is approximately equivalent to the final 

baseline (see Appendix 5A), 11% and 73% of the estimated avoided premature deaths occur at 

or above an annual mean PM2.5 level of 10 μg/m3(the LML of the Laden et al. 2006 study) and 

7.5 μg/m3 (the LML of the Pope et al. 2002 study), respectively. These are the source studies for 

the concentration-response functions used to estimate mortality benefits. As we model 

avoided premature deaths among populations exposed to levels of PM2.5 that are successively 

lower than the LML of each study our confidence in the results diminishes. However, studies 
ES-5

using data from more recent years, during which time PM concentrations have fallen, continue 

to report strong associations with mortality. EPA briefly describes these uncertainties below 

and in more detail in the benefits chapter of this RIA.

ES.1.2 Welfare Co-Benefits

The term welfare co-benefits covers both environmental and societal benefits of 

reducing pollution, such as reductions in damage to ecosystems, improved visibility and 

improvements in recreational and commercial fishing, agricultural yields, and forest 

productivity. EPA did not quantify any of the important welfare co-benefits expected from the 

final MATS, but these are discussed in detail in Chapter 5.
ES-6

Table ES-3. Estimated Reduction in Incidence of Adverse Health Effects of the Mercury and 

Air Toxics Standards (95% confidence intervals)a,b

Impact Eastern U.S.c Western U.S. Total

Mercury-Related Endpoints

IQ Points Lost 510.8

PM-Related Endpoints

Premature death

Pope et al. (2002) (age 

>30)

4,100

(1,100 – 7,000)

130

(30 – 220)

4,200

(1,200 – 7,200)

Laden et al. (2006) (age 

>25)

10,000

(4,800 – 16,000)

320

(140 – 510)

11,000

(5,000 – 17,000)

Infant (< 1 year) 19

(-21 – 59)

1

(-1 – 2)

20

(-22 – 61)

Chronic bronchitis 2,700

(89 – 5,400)

100

(-1 – 210)

2,800

(88 – 5,600)

Non-fatal heart attacks (age > 

18)

4,600

(1,200 – 8,100)

120

(25 – 210)

4,700

(1,200 – 8,300)

Hospital admissions—

respiratory (all ages)

820

(320 – 1,300)

17

(6 – 27)

830

(330 – 1,300)

Hospital admissions—

cardiovascular (age > 18)

1,800

(1,200 – 2,100)

42

(27 – 50)

1,800

(1,200 – 2,200)

Emergency room visits for 

asthma (age < 18)

3,000

(1,500 – 4,500)

110

(52 – 160)

3,100

(1,600 – 4,700)

Acute bronchitis (age 8-12) 6,000

(-1,400 – 13,000)

250

(-69 – 560)

6,300

(-1,400 – 14,000)

Lower respiratory symptoms 

(age 7-14)

77,000

(30,000 – 120,000)

3,100

(1,100 – 5,200)

80,000

(31,000 – 130,000)

Upper respiratory symptoms 

(asthmatics age 9-18)

58,000

(11,000 – 110,000)

2,400

(360 – 4,400)

60,000

(11,000 – 110,000)

Asthma exacerbation 

(asthmatics 6-18)

130,000

(4,500 – 430,000)

5,200

(-6 – 18,000)

130,000

(4,500 – 450,000)

Lost work days (ages 18-65) 520,000

(440,000 – 600,000)

21,000

(18,000 – 24,000)

540,000

(460,000 – 620,000)

Minor restricted-activity days 

(ages 18-65)

3,100,000

(2,500,000 – 3,700,000)

120,000

(99,000 – 150,000)

3,200,000

(2,600,000 – 3,800,000)

a

Estimates rounded to two significant figures; column values will not sum to total value.

b

The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.

c

Includes Texas and those states to the north and east.
ES-7

Table ES-4. Estimated Economic Value of Health and Welfare Co-Benefits of the Mercury and 

Air Toxics Standards (95% confidence intervals, billions of 2007$)a

Impact Pollutant Eastern U.S.b Western U.S. Total

Avoided IQ loss associated with methylmercury exposure from self-caught fish consumption among recreational 

anglers

3% discount rate Hg $0.004 – $0.006

7% discount rate Hg $0.0005 – $0.001

Adult premature death (Pope et al., 2002 PM mortality estimate)

3% discount rate PM2.5 $33

($2.6 - $99)

$1.0

(<$0.01 - $3.1)

$34

($2.6 - $100)

7% discount rate PM2.5 $30

($2.3 - $90)

$0.9

(<$0.01 - $2.8)

$30

($2.4 - $92)

Adult premature death (Laden et al., 2006 PM mortality estimate)

3% discount rate PM2.5 $84

($7.4 - $240)

$2.6

($0.1 - $7.6)

$87

($7.5 - $250)

7% discount rate PM2.5 $76

($6.7 - $220)

$2.3

($0.1 - $6.9)

$78

($6.8 - $230)

Infant premature death PM2.5 $0.2

($-0.2 – $0.8)

<$0.01 $0.2

($-0.2 - $0.8)

Chronic Bronchitis PM2.5 $1.3

($0.1 - $6.1)

$0.1

(<$0.01 - $0.2)

$1.4

($0.1 - $6.4)

Non-fatal heart attacks 

3% discount rate PM2.5 $0.5

($0.1 - $1.3)

<$0.01 $0.5

($0.1 - $1.3)

7% discount rate PM2.5 $0.4

($0.1 - $1.0)

<$0.01 $0.4

($0.1 - $1.0)

Hospital admissions—respiratory PM2.5 $0.01

(<$0.01 - $0.02)

<$0.01 $0.01

($0.01 - $0.02)

Hospital admissions—

cardiovascular

PM2.5 $0.03

(<$0.01 - $0.05)

<$0.01 $0.03

(<$0.01 - $0.05)

Emergency room visits for asthma PM2.5 <$0.01 <$0.01 <$0.01

Acute bronchitis PM2.5 <$0.01 <$0.01 <$0.01

Lower respiratory symptoms PM2.5 <$0.01 <$0.01 <$0.01

Upper respiratory symptoms PM2.5 <$0.01 <$0.01 <$0.01

Asthma exacerbation PM2.5 <$0.01 <$0.01 <$0.01

Lost work days PM2.5 $0.1

($0.1 - $0.1)

<$0.01 $0.1

($0.1 - $0.1)

(continued)
ES-8

Table ES-4. Estimated Economic Value of Health and Welfare Co-Benefits of the Mercury and 

Air Toxics Standards (95% confidence intervals, billions of 2007$)a(continued)

Impact Pollutant Eastern U.S.b Western U.S. Total

Minor restricted-activity days PM2.5 $0.2

($0.1 - $0.3)

<$0.01 $0.2

($0.1 - $0.3)

CO2-related benefits 

(3% discount rate) CO2 $0.36

Monetized total Benefits (Pope et al., 2002 PM2.5 mortality estimate)

3% discount rate $35+B

($2.8 - $110)

$1.1+B

($0.03 - $3.4)

$37+B

($3.2 - $110)

7% discount rate $32+B

($2.5 - $98)

$1.0+B

($0.03 - $3.1)

$33+B

($2.9 - $100)

Monetized total Benefits (Laden et al., 2006 PM2.5 mortality estimate)

3% discount rate $87+B

($7.5 - $250)

$2.7+B

($0.1 - $7.9)

$90+B

($8.0 - $260)

7% discount rate $78+B

($6.8 - $230)

$2.4+B

($0.1 - $7.2)

$81+B

($7.3 - $240)

a

Economic value adjusted to 2007$ using GDP deflator. Estimates rounded to two significant figures. The negative 

estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these 

health impacts and do not suggest that increases in air pollution exposure result in decreased health impacts. 

Confidence intervals reflect random sampling error and not the additional uncertainty associated with 

accounting for differences in air quality baseline forecasts described in chapter 5. The net present value of 

reduced CO2 emissions are calculated differently than other benefits. The same discount rate used to discount 

the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of 

SCC for internal consistency. This table shows monetized CO2 co-benefits at discount rates at 3 and 7 percent 

that were calculated using the global average SCC estimate at a 3% discount rate because the interagency 

workgroup on this topic deemed this marginal value to be the central value. In section 5.6 we also report CO2 

co-benefits using discount rates of 5 percent (average), 2.5 percent (average), and 3 percent (95th percentile).

b

Includes Texas and those states to the north and east.

Figure ES-1 summarizes an array of PM2.5-related monetized benefits estimates based 

on alternative epidemiology and expert-derived PM-mortality estimate.

Figure ES-2 summarizes the estimated net benefits for the final rule by displaying all 

possible combinations of health and climate co-benefits and costs. Each of the 14 bars in each 

graph represents a separate point estimate of net benefits under a certain combination of cost 

and benefit estimation methods. Because it is not a distribution, it is not possible to infer the 

likelihood of any single net benefit estimate.
Figure ES-1. Economic Value of Estimated PM

Epidemiology or Expert-Derived PM Mortality Risk Estimate

a

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 

5A)

b

Column total equals sum of PM2.5-related mortality 

ES-9

. Economic Value of Estimated PM2.5-Related Health Co-Benefits According to 

Derived PM Mortality Risk Estimatea,b

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 

related mortality and morbidity benefits.

According to 
Figure ES-2. Net Benefits of the 

Derived Mortality Risk Estimate

a

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Append

5A)

b

Column total equals sum of PM2.5-related mortality and morbidity benefits.

ES.2 Not All Benefits Quantified

EPA was unable to quantify or monetize all of the health and environmental benefits 

associated with the final MATS R

substantial, including the overall value associated with HAP reductions, value of increased 

agricultural crop and commercial forest yields, visibility improvements, and reductions in 

nitrogen and acid deposition and the resu

ES-5 provide a list of these benefits.

ES-10

2. Net Benefits of the MATS Rule According to PM2.5 Epidemiology or Expert

erived Mortality Risk Estimatea,b

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Append

related mortality and morbidity benefits.

Not All Benefits Quantified

EPA was unable to quantify or monetize all of the health and environmental benefits 

final MATS Rule. EPA believes these unquantified benefits could be

substantial, including the overall value associated with HAP reductions, value of increased 

agricultural crop and commercial forest yields, visibility improvements, and reductions in 

nitrogen and acid deposition and the resulting changes in ecosystem functions.

a list of these benefits.

Epidemiology or ExpertBased on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 

EPA was unable to quantify or monetize all of the health and environmental benefits 

could be

substantial, including the overall value associated with HAP reductions, value of increased 

agricultural crop and commercial forest yields, visibility improvements, and reductions in 

Tables ES-4 and 
ES-11

Table ES-5. Human Health Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Informationa

Improved Human Health

Reduced incidence of 

premature mortality 

from exposure to PM2.5

Adult premature mortality based on cohort 

study estimates and expert elicitation 

estimates (age >25 or age >30)

  Section 5.4

Infant mortality (age <1)   Section 5.4

Reduced incidence of 

morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   Section 5.4

Hospital admissions—respiratory (all ages)   Section 5.4

Hospital admissions—cardiovascular (age

>18)

  Section 5.4

Emergency room visits for asthma (<18)   Section 5.4

Acute bronchitis (age 8–12)   Section 5.4

Lower respiratory symptoms (age 7–14)   Section 5.4

Upper respiratory symptoms (asthmatics 

age 9-11)

  Section 5.4

Asthma exacerbation (asthmatics age 6–18)   Section 5.4

Lost work days (age 18-65)   Section 5.4

Minor restricted-activity days (age 18–65)   Section 5.4

Chronic Bronchitis (age >26)   Section 5.4

Other cardiovascular effects (e.g., other 

ages)

— — PM ISAc

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, nonbronchitis chronic diseases, other ages and 

populations)

— — PM ISAc

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, etc)

— — PM ISAc, d

Cancer, mutagenicity, and genotoxicity 

effects

— — PM ISAc, d

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term 

study estimates (all ages)

— — Ozone CD, Draft 

Ozone ISAb

Premature mortality based on long-term 

study estimates (age 30–99)

— — Ozone CD, Draft 

Ozone ISAb

Reduced incidence of 

morbidity from 

exposure to ozone

Hospital admissions—respiratory causes 

(age > 65)

— — Ozone CD, Draft 

Ozone ISAb

Hospital admissions—respiratory causes 

(age <2)

— — Ozone CD, Draft 

Ozone ISAb

Emergency room visits for asthma (all ages) — — Ozone CD, Draft 

Ozone ISAb

Minor restricted-activity days (age 18–65) — — Ozone CD, Draft 

Ozone ISAb

(continued)
ES-12

Table ES-5. Human Health Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

School absence days (age 5–17) — — Ozone CD, Draft 

Ozone ISAb

Decreased outdoor worker productivity (age 

18-65)

— — Ozone CD, Draft 

Ozone ISAb

Other respiratory effects (e.g., premature 

aging of lungs)

— — Ozone CD, Draft 

Ozone ISAc

Cardiovascular and nervous system effects — — Ozone CD, Draft 

Ozone ISAd

Reproductive and developmental effects — — Ozone CD, Draft 

Ozone ISAd

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISAb

Chronic lung disease hospital admissions (age 

> 65)

— — NO2 ISAb

Respiratory emergency department visits (all 

ages)

— — NO2 ISAb

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

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

Premature mortality — — NO2 ISAc,d

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISAc,d

Reduced incidence of 

morbidity from 

exposure to SO2

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

Asthma emergency room visits (all ages) — — SO2 ISAb

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

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

Premature mortality — — SO2 ISAc,d

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISAc,d

Reduced incidence of 

morbidity from 

exposure to methyl

mercury (through 

reduced mercury 

deposition as well as 

the role of sulfate in 

methylation )

Neurologic effects—IQ loss   IRIS; NRC, 2000b

Other neurologic effects (e.g., developmental 

delays, memory, behavior)

— — IRIS; NRC, 2000c

Cardiovascular effects — — IRIS; NRC, 2000c,d

Genotoxic, immunologic, and other toxic 

effects

— — IRIS; NRC, 2000c,d

a

For a complete list of references see Chapter 5.

b We assess these benefits qualitatively due to time and resource limitations for this analysis.

c We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

d We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over 

the strength of the association.
ES-13

Table ES-6. Environmental Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Informationa

Improved Environment

Reduced visibility 

impairment

Visibility in Class I areas in SE, SW, and 

CA regions

— — PM ISAb

Visibility in Class I areas in other regions — — PM ISAb

Visibility in residential areas — — PM ISAb

Reduced climate 

effects

Global climate impacts from CO2 —  Section 5.6

Climate impacts from ozone and PM — — Section 5.6

Other climate impacts (e.g., other GHGs, 

other impacts) 

— — IPCCc

Reduced effects on 

materials

Household soiling — — PM ISAc

Materials damage (e.g., corrosion, 

increased wear)

— — PM ISAc

Reduced effects from 

PM deposition (metals 

and organics)

Effects on Individual organisms and 

ecosystems

— — PM ISAc

Reduced vegetation 

and ecosystem effects 

from exposure to 

ozone

Visible foliar injury on vegetation — — Ozone CD, Draft 

Ozone ISAc

Reduced vegetation growth and 

reproduction

— — Ozone CD, Draft 

Ozone ISAb

Yield and quality of commercial forest 

products and crops

— — Ozone CD, Draft 

Ozone ISAb,d

Damage to urban ornamental plants — — Ozone CD, Draft 

Ozone ISAc

Carbon sequestration in terrestrial 

ecosystems

— — Ozone CD, Draft 

Ozone ISAc

Recreational demand associated with 

forest aesthetics

— — Ozone CD, Draft 

Ozone ISAc

Other non-use effects Ozone CD, Draft 

Ozone ISAc

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary 

productivity, leaf-gas exchange, 

community composition)

— — Ozone CD, Draft 

Ozone ISAc

(continued)
ES-14

Table ES-6. Environmental Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOxISAb

Tree mortality and decline — — NOx SOxISAc

Commercial fishing and forestry effects — — NOx SOxISAc

Recreational demand in terrestrial and 

aquatic ecosystems

— — NOx SOxISAc

Other nonuse effects NOx SOxISAc

Ecosystem functions (e.g., 

biogeochemical cycles)

— — NOx SOxISAc

Reduced effects from 

nutrient enrichment

Species composition and biodiversity in 

terrestrial and estuarine ecosystems

— — NOx SOxISAc

Coastal eutrophication — — NOx SOxISAc

Recreational demand in terrestrial and 

estuarine ecosystems

— — NOx SOxISAc

Other non-use effects NOx SOxISAc

Ecosystem functions (e.g., 

biogeochemical cycles, fire regulation)

— — NOx SOxISAc

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from SO2 exposure — — NOx SOxISAc

Injury to vegetation from NOx exposure — — NOx SOxISAc

Reduced incidence of 

morbidity from 

exposure to methyl

mercury (through 

reduced mercury 

deposition as well as 

the role of sulfate in 

methylation )

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

reproductive effects)

— — Mercury Study 

RTCc,d

Commercial, subsistence and 

recreational fishing

— — Mercury Study 

RTCc

a

For a complete list of references see Chapter 5.

b We assess these benefits qualitatively due to time and resource limitations for this analysis.

c We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

d We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over 

the strength of the association.
ES-15

ES.3 Costs and Employment Impacts

The projected annual incremental private costs of the final MATS Rule to the electric 

power industry are $9.6 billion in 20151. These costs represent the total cost to the electricitygenerating industry of reducing HAP emissions to meet the emissions limits set out in the rule.

Estimates are in 2007 dollars. These total costs of the rule are estimated using the Integrated 

Planning Model (IPM), as well as additional analyses for oil-fired units and monitoring/recordkeeping costs.

There are several national changes in energy prices that result from the final MATS Rule. 

Retail electricity prices are projected to increase in the contiguous US by an average of 3.1% in 

2015 with the final MATS Rule. On a weighted average basis between 2015 and 2030, 

consumer natural gas price anticipated to increase from 0.3% to 0.6% depending on consumer 

class in response to the final MATS Rule.

There are several other types of energy impacts associated with the final MATS Rule. A 

small amount of coal-fired capacity, about 4.7 GW (less than 2 percent of all coal-fired capacity 

in 2015), is projected to become uneconomic to maintain by 2015. These units are 

predominantly smaller and less frequently-used generating units dispersed throughout the 

contiguous US. 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. Coal production for use in the power sector is projected to decrease by 1 percent 

by 2015, and we expect slightly reduced coal demand in Appalachia and the West with the final 

MATS Rule.

In addition to addressing the costs and benefits of the final MATS Rule, EPA has 

estimated a portion of the employment impacts of this rulemaking. We have estimated two 

types of impacts. One provides an estimate of the employment impacts on the regulated 

industry over time. The second covers the short-term employment impacts associated with the 

construction of needed pollution control equipment until the compliance date of the 

regulation. We expect that the rule’s impact on employment will be small, but will (on net) 

result in an expected increase in employment.

 

1

The year 2016 is the compliance year for MATS, though as we explain in later chapters, we use 2015 as a proxy 

for compliance in 2016 for IPM emissions, costs and economic impact analysis due to availability of modeling 

impacts in that year.
ES-16

The approaches to estimate employment impacts use different analytical techniques 

and are applied to different industries during different time periods, and they use different 

units of analysis. No overlapping estimates are summed. Estimates of employment changes 

per dollar of expenditure on pollution control from Morgenstern et al. (2002) are used to 

estimate the ongoing annual employment impacts for the regulated entities (the electric power 

sector) as a result of this rule. The short term estimates for employment needed to design, 

construct, and install the control equipment in the three year period before the compliance 

date are also provided using an approach that estimates employment impacts for the 

environmental protection sector based on forecast changes from IPM on the number and scale 

of pollution controls and labor intensities in relevant sectors. Finally some of the other types of 

employment impacts that will be ongoing are estimated using IPM outputs and labor 

intensities, as reported in chapter 6, but not included in this table because they omit some 

potentially important categories.

In Table ES-6, we show the employment impacts of the MATS Rule as estimated by the 

environmental protection sector approach and by the Morgenstern approach.

Table ES-7. Estimated Employment Impact Table

Annual (Reoccurring)

One Time (Construction During 

Compliance Period)

Environmental protection sector 

approacha

Not applicable 46,000

Net effect on electric utility sector 

employment from Morgenstern 

et al., approachc

8,000b

−15,000 to 30,000d

Not Applicable

a

These one-time impacts on employment are estimated in terms of job-years.

b

This estimate is not statistically different from zero.

c

These annual or reoccurring employment impacts are estimated in terms of production workers as defined by 

the US Census Bureau’s Annual Survey of Manufacturers (ASM).

d

95% confidence interval

ES.4 Small Entity and Unfunded Mandates Impacts

After preparing an analysis of small entity impacts, EPA cannot certify that this proposal 

will not have a no SISNOSE (significant economic impacts on a substantial number of small 

entities). Of the 82 small entities affected, 40 are projected to have costs greater than 1 

percent of their revenues. EPA’s decision to exclude units smaller than 25 Megawatt capacity 

(MW) as per the requirements of the Clean Air Act has already significantly reduced the burden 
ES-17

on small entities, and EPA participated in a Small Business Regulatory Enforcement Fairness Act 

(SBREFA) to examine ways to mitigate the impact of the proposed Toxics Rule on affected small 

entities

EPA examined the potential economic impacts on state and municipality-owned entities 

associated with this rulemaking based on assumptions of how the affected states will 

implement control measures to meet their emissions. These impacts have been calculated to

provide additional understanding of the nature of potential impacts and additional information.

According to EPA’s analysis, of the 97 government entities considered in this, EPA 

projects that 42 government entities will have compliance costs greater than 1 percent of base 

generation revenue in 2015, based on our assumptions of how the affected states implement 

control measures to meet their emissions budgets as set forth in this rulemaking.

Government entities projected to experience compliance costs in excess of 1 percent of 

revenues may have some potential for significant impact resulting from implementation of the 

Toxics Rule.

ES.5 Limitations and Uncertainties

Every analysis examining the potential benefits and costs of a change in environmental 

protection requirements is limited to some extent by data gaps, limitations in model 

capabilities (such as geographic coverage), and variability or uncertainties in the underlying 

scientific and economic studies used to configure the benefit and cost models. Despite the 

uncertainties, we believe this benefit-cost analysis provides a reasonable indication of the 

expected economic benefits and costs of the final MATS Rule.

For this analysis, such uncertainties include possible errors in measurement and 

projection for variables such as population growth and baseline incidence rates; uncertainties 

associated with estimates of future-year emissions inventories and air quality; variability in the 

estimated relationships between changes in pollutant concentrations and the resulting changes 

in health and welfare effects; and uncertainties in exposure estimation.

Below is a summary of the key uncertainties of the analysis:

Costs

 Compliance costs are used to approximate the social costs of this rule. Social costs 

may be higher or lower than compliance costs and differ because of preexisting 
ES-18

distortions in the economy, and because certain compliance costs may represent 

shifts in rents.

 Analysis does not capture employment shifts as workers are retrained at the same 

company or re-employed elsewhere in the economy.

 We do not include the costs of certain relatively small permitting costs associated 

with updating Title V permits or increases in monitoring costs for affected units.

 Technological innovation is not incorporated into these cost estimates. Thus, these 

cost estimates may be potentially higher than what may occur in the future, all other 

things being the same.

Benefits

 The mercury concentration estimates for the analysis come from several different 

sources

 The mercury concentration estimates used in the model were based on simple 

temporal and spatial averages of reported fish tissue samples. This approach 

assumes that the mercury samples are representative of “local” conditions (i.e., 

within the same HUC 12) in similar waterbodies (i.e., rivers or lakes).

 State-level averages for fishing behavior of recreational anglers are applied to each 

modeled census tract in the state; which does not reflect within-state variation in 

these factors.

 Application of state-level fertility rates to specific census tracts (and specifically to 

women in angler households.

 Applying the state-level individual level fishing participation rates to approximate 

the household fishing rates conditions at a block level.

 Populations are only included in the model if they are within a reasonable distance 

of a waterbody with fish tissue MeHg samples. This approach undercounts the 

exposed population (by roughly 40 to 45%) and leads to underestimates of national 

aggregate baseline exposures and risks and underestimates of the risk reductions 

and benefits resulting from mercury emission reductions.

 Assumption of 8 g/day fish consumption rate for the general population in 

freshwater angler households.

 The dose-response model used to estimate neurological effects on children because 

of maternal mercury body burden has several important uncertainties, including 
ES-19

selection of IQ as a primary endpoint when there may be other more sensitive 

endpoints, selection of the blood-to-hair ratio for mercury, and the dose-response 

estimates from the epidemiological literature. Control for confounding from the 

potentially positive cognitive effects of fish consumption and, more specifically, 

omega-3 fatty acids.

 Valuation of IQ losses using a lost earning approach has several uncertainties, 

including (1) there is a linear relationship between IQ changes and net earnings 

losses, (2) the unit value applies to even very small changes in IQ, and (3) the unit 

value will remain constant (in real present value terms) for several years into the 

future. Each unit value for IQ losses has two main sources of uncertainty (1). The 

statistical error in the average percentage change in earnings as a result of IQ 

changes and (2) estimates of average lifetime earnings and costs of schooling. 

Based on the modeled interim baseline which is approximately equivalent to the final 

baseline (see Appendix 5A), 11% and 73% of the estimated avoided premature 

deaths occur at or above an annual mean PM2.5 level of 10 μg/m3(the LML of the 

Laden et al. 2006 study) and 7.5 μg/m3 (the LML of the Pope et al. 2002 study), 

respectively. These are the source studies for the concentration-response functions 

used to estimate mortality benefits. As we model avoided premature deaths among 

populations exposed to levels of PM2.5 that are successively lower than the LML of 

each study our confidence in the results diminishes. However, studies using data 

from more recent years, during which time PM concentrations have fallen, continue 

to report strong associations with mortality.

 There are uncertainties related to the health impact functions used in the analysis.

These include: within study variability; across study variation; the application of 

concentration-response (C-R) functions nationwide; extrapolation of impact 

functions across population; and various uncertainties in the C-R function, including 

causality and thresholds. Therefore, benefits may be under- or over-estimates.

 Analysis is for 2016, and projecting key variables introduces uncertainty. Inherent in 

any analysis of future regulatory programs are uncertainties in projecting 

atmospheric conditions and source level emissions, as well as population, health 

baselines, incomes, technology, and other factors.

 This analysis omits certain unquantified effects due to lack of data, time and 

resources. These unquantified endpoints include other health and ecosystem 

effects. EPA will continue to evaluate new methods and models and select those 

most appropriate for estimating the benefits of reductions in air pollution. Enhanced 

collaboration between air quality modelers, epidemiologists, toxicologists, 

ecologists, and economists should result in a more tightly integrated analytical 

framework for measuring benefits of air pollution policies.
ES-20

 PM2.5 mortality co-benefits represent a substantial proportion of total monetized 

benefits (over 90%), and these estimates have following key assumptions and 

uncertainties.

o The PM2.5 -related co-benefits of the alternative scenarios were derived 

through a benefit per-ton approach, which does not fully reflect local 

variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate 

or under-estimate of the actual benefits of this rule.

o We assume that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality. This is an 

important assumption, because PM2.5 produced via transported 

precursors emitted from EGUs may differ significantly from direct PM2.5 

released from diesel engines and other industrial sources, but no clear 

scientific grounds exist for supporting differential effects estimates by 

particle type.

o We assume that the health impact function for fine particles is linear 

within the range of ambient concentrations under consideration. Thus, 

the estimates include health benefits from reducing fine particles in areas 

with varied concentrations of PM2.5, including both regions that are in 

attainment with fine particle standard and those that do not meet the 

standard down to the lowest modeled concentrations.

o To characterize the uncertainty in the relationship between PM2.5 and 

premature mortality, we include a set of twelve estimates based on 

results of the expert elicitation study in addition to our core estimates.

Even these multiple characterizations omit the uncertainty in air quality 

estimates, baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the 

reported confidence intervals and range of estimates give an incomplete 

picture about the overall uncertainty in the PM2.5 estimates. This 

information should be interpreted within the context of the larger 

uncertainty surrounding the entire analysis.
ES-21

ES.6 References

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air 

Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 

173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution

Regulations. Washington, DC: The National Academies Press.

Levy JI, Baxter LK, Schwartz J. 2009. Uncertainty and variability in health-related damages from 

coal-fired power plants in the United States. Risk Anal. doi: 10.1111/j.1539-

6924.2009.01227.x [Online 9 Apr 2009]

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002.

“Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution.” Journal of the American Medical Association 287:1132-1141.

U.S. Environmental Protection Agency (EPA). December 2010. Guidelines for Preparing 

Economic Analyses. EPA 240-R-10-001.

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal Agencies 

on Preparation of Regulatory Analysis.

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

of Postneonatal Infant Mortality and Particulate Infant Mortality and Particulate Air 

Pollution in the United States.” Environmental Health Perspectives 105(6):608-612.