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Subject: MATS RIA OMB Submission

Date: December 9, 2011

This version of the Mercury and Air Toxics Standards (MATS) Regulatory Impact Analysis 

(RIA) incorporates edits made since the since the version delivered on 11/23. These include 

small changes based on post-processed PM emission changes from the final policy case 

modeled in IPM that were not available in time for the 11/23 version and revisions based on 

comments received during the interagency review process. Below is a summary of EPA’s 

responses to the RIA-related comments received during the interagency process.

Preamble Comments Pertaining to the RIA

Impacts of the Rule on Ratepayers

 It does not appear as though EPA took into adequate consideration the real potential 

utilities will close and the surrounding communities experience economic hardship due 

to these regulations. 

EPA analyzes the impact of the rule and assesses potential closures, includes any 

electricity rate impact or other power sector changes resulting from those closures in 

its analysis, estimates the potential employment changes from those closures (and 

changes in fuel use as well), and conducted an analysis of the impacts on small 

businesses as required by SBREFA. Fully detailed information on the economic costs 

of the rule, as well as its benefits, are presented in the RIA. 

 The coal units affected by this regulation would be faced with a large investment of 

funds that will result in no added production from the plants. On the contrary, the 

parasitic load (electricity necessary to run the equipment in the plant itself) that each 

plant normally requires would increase substantially due to the installation of emission 

controls devices that require power to operate. So, in essence, the power plants will 

ultimately produce fewer megawatts due to the increased parasitic load and associated 

revenues would be decreased. Has this effect been accounted for in the costs of the 

rule and effect on ratepayers?

Parasitic load is fully accounted for in EPA modeling.

 Page 555: We refer to a statement on this page: ““The EPA’s estimates of increase in 

the retail electricity price range from 1.3 percent to 5.9 percent regionally in 2015...Lowincome households will thus see some increase in electricity price, but this increase 

should be modest.” This percent price increase does not take into account the 

cumulative effect of numerous EPA rules that could place upward pressure on electricity

prices. Additionally, it is unclear why a price increase as high as 5.9 percent would not 

be considered a major increase to a low-income consumer.
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Electricity prices are not anticipated to be much different from historical levels, even 

after the rule. Some regions will experience greater rate impacts than others, but EPA 

estimates that the average household impact will be less than $3 per month.

 We strongly recommend that EPA consider providing context for the projected change 

in electricity prices by comparing either the projected prices or the magnitude of the 

projected increase to recent historical data. For example, based on a quick look at EIA 

data(http://38.96.246.204/electricity/monthly/excel/epmxlfile5_3.xls), it appears that 

EPA could explain its projected average price of electricity with the EGU MACT rule 

would still be less than average electricity prices for 2008-2010 (EPA should confirm that

those numbers are comparable). Alternatively, EPA could say that the 3% increase is on 

par (or even less) than typical year-to-year price variability (again, after verifying that is 

in fact the case).

EPA plans to include this information in communications materials, and presents a 

chart illustrating historical electricity prices Chapter 2 of the RIA.

 In response to retirement projections and reliability concerns cited by a commenter, 

EPA responds that most studies projecting over 10 GW of retirements do not hold up to 

scrutiny and that many tools exist to maintain reliability (including new generation, 

demand response, energy efficiency, transmission upgrades, and energy storage). First, 

EPA’s retirement analysis relied heavily on the installation of DSI for compliance, which 

few of the many studies disparaged by the EPA did. More detail on EPA’s sensitivity case 

where “less DSI systems and more scrubber systems were installed” is required. In this 

case, DSI was limited to 35GW on installation and only one more GW is retired as 

compared to the base analysis. However, in modeling frameworks, it is likely that the 

most optimal units for DSI will get the technology, then other units will install FGD. 

Where the real sensitivity would become apparent is limiting DSI by size and coal type, 

which would show the limitations of the technology. In addition, the retirement 

projections rely heavily on low natural gas prices – a higher gas price sensitivity analysis 

is required. In addition, many of the reliability measures cited by the EPA are still in the 

developmental stages or potentially very costly – EPA should have also factored the 

costs of maintaining reliability into their analysis.

Using higher natural gas prices would almost certainly result in lower levels of coal 

retirements and is unlikely to result in major differences with the primary policy 

scenario. With regards to DSI, EPA believes that is has appropriately modeled DSI and 

has provided a sensitivity that is also informative. In EPA’s analysis, certain limitations 

are included when modeling DSI. For example, it can only be used in conjunction with 

lower sulfur fuels, it is not allowed at units with fluidized bed or an existing scrubber, 

units must have a fabric filter or ESP (or retrofit with FF or ESP). In addition, the costs 

of DSI vary by unit size and rank of coal that is burned. In the 35 GW sensitivity, we 

allow the model to most cost-effectively determine which units will install the 

technology subject to the aforementioned constraints. There is no evidence that 
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larger units are unable to install the technology, and several large units over 500 MW 

have tested and are currently installing DSI. In addition, arguments have consistently 

been made that the technologies to reduce emissions are not ready for wider 

adoption when new requirements are set forth (Acid Rain Program and scrubbers, 

NOx SIP Call and SCRs, mercury control, etc). History has shown in every instance that 

technology in fact, ends up performing much better in practice than originally thought 

and that industry is adept at handling particular challenges to widespread technology 

adoption. 

Employment Effects

 Page 549: We refer to a statement on this page: “The Agency found that the impact on 

employment nationally should be small on net...” EPA should also note that there would 

be a decrease in labor productivity (i.e., more labor required to produce the same 

amount of electricity), which would cause the short-run aggregate supply curve to shift 

to the left, resulting in higher prices and businesses producing less (i.e., lower GDP).

Note added.

 Pages 569 and 550, Last Paragraph: Please confirm if any of the commenters has 

referenced a study or provided any data to back up their claims on the adequacy of 

labor availability. We would also appreciate receiving a copy of the TSD referenced in 

EPA’s response.

Some commenters have said labor availability will be an issue (primarily utilities) and 

other commenters (including boilermakers and the pollution control industry) argued 

that there will be more than adequate labor availability to support MATS compliance 

activities. Historically, the pollution control sector has effectively responded to the 

increased demand for pollution controls/labor, and there is substantial evidence that 

labor availability will not be an issue.

 Page 603: We refer to the statement on this page: ““...each additional $1 million 

spending on pollution abatement results in an small net increase of 1.6 jobs; the 

estimated effect is not statistically significant different from zero.” It seems better to 

simply state the study finds spending on pollution abatement does not have a 

statistically significant effect on jobs. EPA’s analysis should also point out that there 

would be a decrease in labor productivity (i.e., more labor required to produce the same 

amount of electricity), which would cause the short-run aggregate supply curve to shift 

to the left, resulting in higher prices and businesses producing less (i.e., lower GDP).

Because of the interest in employment EPA has used this study for a number of 

regulations to estimate the mean net employment and provided the 95% confidence 

interval results to reflect the high degree of uncertainty (the interval includes zero so 

we are showing we don’t know the sign of the effect but the interval itself is helpful 

when others give exaggerated claims of huge job changes well outside our 95% 

confidence interval).
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The impact of a decrease in productivity has been noted in this section.

Cumulative Effects of EPA Rules

 Page 557: We refer to a statement on this page: “...the direct benefits from the 1990 

Clean Air Act Amendments reach almost $2 trillion for the year 2020...” What was the 

effect on real GDP? This is a more relevant measure for decision makers than social 

costs, as changes to GDP growth are what determine whether the economy is 

experiencing a recession. We suggest EPA delete the discussion of this report, since it is 

not relevant to the rulemaking at hand.

The Agency disagrees with the assertion that the effects of a program on GDP would 

be “more relevant” information for decision makers than an understanding of the net 

benefits of a program (or collection of related programs) obtained through rigorous 

benefit-cost analysis based on standard principles and practices of welfare economics. 

In fact, prevailing OMB guidance issued pursuant to relevant Executive Orders 

(especially Circular A-4 issued in response to EO 12866) explicitly provides for 

assessing a program’s net benefits through benefit-cost analysis but does not explicitly 

call for an assessment of a program’s effect on GDP growth to be a routine part of 

major rule Regulatory Impact Analyses. This is because most economists recognize 

that the projected net benefits of program is more salient information for 

policymaking than the effects of that program on GDP, which is not a reliable measure 

of utility, or economic welfare. Furthermore, the public comments to which the draft 

preamble text responds pertain to expressed concern about cumulative effects of 

multiple regulations. The most comprehensive assessment available of the overall 

effect of air pollution regulatory programs implemented pursuant to the Clean Air Act 

as a whole is provided by the March 2011 Second Prospective Study of the benefits 

and costs of the 1990 Clean Air Act Amendments. This study clearly demonstrates the 

substantial positive net benefits of cumulative clean air programs from 1990 to 2020. 

These analytical results are both relevant and useful for responding to the comments 

regarding cumulative effects of air pollution rules. Therefore the Agency disagrees 

with the suggestion to delete references to a study which provides useful perspective 

in response to the submitted public comments.

Benefits 

 For the final rule, the only mercury benefit that can be quantified either in economic 

terms or by a widely understood measure – the total nationwide increase in IQ which 

results from a 90% reduction in utility mercury emissions – are removed from the main 

body and are put in an appendix. These findings are replaced by a “hazard quotient.”

People understand what IQ points mean: if the MATS rule were to have caused children 

born of mothers who ate fresh caught fish (whose mercury levels were influenced by 

power plant emissions) to gain an average IQ of 5 points when power plant mercury 
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emissions were sharply reduced, we would all have understood what an important 

public health gain had been achieved. No explanation would have been needed.

However, EPA has now found that of the 240,000 children born of mothers who eat fish 

influenced by power plant emissions, the total IQ gain nationwide from the 90% 

reduction in power plant emissions is 510 IQ points, also expressed in the documents as 

2/1,000 of an IQ point per child. EPA calculates the monetized benefit of this increase of 

510 IQ points nationwide to be between $500,000 and $6 million, depending on 

assumptions such as discount rates. The benefit is based upon total lower lifetime 

wages due to the reduction of 510 IQ points, and contrasts with costs of the regulation 

of about $11 billion.

This finding is good news. It means that pregnant women can now be told that US 

mercury emissions will not harm the intelligence of their unborn child. Wouldn’t it be a 

good idea to make sure that readers of this document now know that they are safer 

than they might have previously thought? Public health findings aren’t always about 

harm we have found, but also about harm we suspected but later found not to be of 

concern.

In contrast, the use of a “hazard quotient” has no intuitive meaning to people. Further, 

it isn’t clear what actual harm exists if the hazard quotient is above 1.5 (the level at 

which a hazard is said to exist). There is suggestion of risk, but is the risk of a similar 

magnitude of the IQ risks, now quantified? Of less risk of harm to health than the IQ 

risks? 

Therefore the suggestion is to also include, in the body of the Final Rule (instead of in an 

Appendix), the quantifiable (and positive) findings that virtually no adverse effect on IQs 

have occurred from US mercury emissions (because virtually no benefit occurs from a 

90% reduction in such emissions). This appears to be news which anyone interested in 

health effects of mercury would want to know. 

The commenter conflates the National-Scale Mercury Risk Assessment (Hg Risk TSD) 

supporting the Appropriate and Necessary finding with the mercury benefits analysis 

in the RIA. Each analysis uses somewhat different methods because each document 

serves a different purpose. For the Hg Risk TSD, we moved the IQ analysis to an 

appendix in response to a clear recommendation from the SAB peer review, but we 

retained the IQ analysis for the Hg benefits analysis in the main body of the RIA. 

The decision to focus on the (reference dose) RfD-based hazard quotient (HQ) 

estimates, rather than IQ loss, in the Hg Risk TSD reflects concerns that the IQ loss 

metric may not capture the full range of neurodevelopmental effects associated with 

mercury exposure. Therefore, while we acknowledge that IQ loss provides a readily 

interpretable health effect endpoint, the HQ metric captures other neurologicalrelated health effects that could occur at lower levels of exposure.

The decision to focus on IQ decrements for the benefits assessment in the RIA was 

driven by data and methodology availability for IQ analysis. Data and methodology of 

sufficient quality and quantity were not available for any of the other neurological 
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endpoints considered.

In addition, we disagree with the commenter regarding the use of the centraltendency measure (e.g., 2/1000 of an IQ point) for the recreational angler because it 

does not capture the skewed distribution of mercury-related impacts. Relative to the 

freshwater recreational angler cited by the commenter, freshwater subsistence fisher 

populations are likely to have substantially elevated mercury risk due to their higher 

self-caught fish consumption rates. The Hg Risk TSD shows that IQ loss estimates for 

freshwater subsistence fishers can be orders of magnitude higher than the central 

tendency numbers cited by the commenter for recreational anglers.

 Page 619: Table 9: Since some studies have found no significant effect of PM2.5 on

mortality, perhaps zero should be included in the range of PM2.5 benefits, as Electric 

Power Research Institute has suggested. EPRI indicated: “Since some models in some 

studies do not find a statistically significant effect of PM2.5 on mortality, the effects 

range could even include zero...” (Source: EPRI PM2.5 Reductions and Impact on 

Premature Death: An EPRI perspective; Issue Brief 1018632.) Also, the same EPRI briefing 

states the EPRI-Washington University Veterans Study did not find any statistically

significant mortality associations with PM2.5, but found some PM components that 

were significantly associated with mortality. The EPRI briefing goes on to say: “Of great 

significance for CAIR, this study did not find a significant association between the sulfate 

component of PM and mortality...if this study were used to derive estimates of health 

benefits attributable to reductions in SO2 emissions, the numbers would differ from 

those derived from the Pope et al...some studies have shown that estimated impacts of 

PM diminish when other co-pollutants are considered in the analyses. If the copollutants are indeed associated with increased mortality and are correlated with PM 

levels, then the PM effects estimated from this single pollutant model approach can be 

biased upwards.”

EPA disagrees with this recommendation. The Integrated Science Assessment (ISA) for 

Particulate Matter (U.S. EPA, 2009), which was twice reviewed by the Science Advisory 

Board, concluded that there is a causal relationship between exposure to PM2.5 and 

premature mortality after reviewing the entire body of scientific evidence. In 

addition, the ISA also concluded that the scientific evidence is not yet sufficient to 

allow differentiation of effect estimates by particle type. This means that while some 

PM mixtures may be more toxic than other mixtures, the scientific literature does not 

support the commenter’s assertion that some particles are not toxic. Although we 

note this uncertainty in the RIA, we assume that all particles are equally toxic for the 

purpose of calculating benefits. This assumption was supported by the National 

Academies of Science and the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis.

 Page 628: We refer to the statement on this page: “The fourth value is the 95th

percentile of the SCC from all three models at a 3 percent discount rate.” Why not 
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include a fifth value at the 5th percentile in order to represent lower-than-expected 

impacts from temperature change

The Social Cost of Carbon (SCC) values used to estimate climate benefits for the MATS 

final rule are consistent with the recommendations of the Interagency Working Group 

on the SCC discussed extensively in the Technical Support Document available on 

OMB’s website at: http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf. The main purpose of this exercise was to 

develop a schedule of SCC values that can be applied consistently across regulatory 

analyses and Agencies. The TSD clearly states that:

The interagency group selected four SCC values for use in regulatory 

analyses. Three values are based on the average SCC across models and 

socio-economic and emissions scenarios at the 2.5, 3, and 5 percent discount 

rates. The fourth value is included to represent the higher-than-expected 

economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95

th 

percentile at a 

3 percent discount rate. (The full set of distributions by model and scenario 

combination is included in the Appendix.) As noted above, the 3 percent 

discount rate is the central value, and so the central value that emerges is the 

average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we 

emphasize the importance and value of considering the full range.

In keeping with the interagency directive, EPA used this approach for the Light Duty 

Vehicle Rule to estimate benefits from CO2 emission reductions and in all subsequent 

EPA regulatory actions to date for this purpose. OMB led and several other 

interagency reviewers for the MATS final rule actively participated in the SCC 

Interagency Working Group to develop this approach. It is our understanding that any 

deviation from the approach outlined in the TSD would have to be reviewed and 

considered by the interagency group before it could be implemented by the EPA or 

other agencies.

On page 30 of the SCC TSD, limitations of the current SCC estimates are discussed. A 

key question remaining from the interagency process is the relevant assumption to be 

made about relative risk aversion with regard to high-impact climate outcomes. The 

current SCC values do not consider the possibility that individuals may have a higher 

willingness to pay to reduce the likelihood of low-probability, high impact outcomes 

than they do to reduce the likelihood of higher-probability but lower impact damages 

with the same expected costs. “The inclusion of the 95th percentile estimate in the 

final set of SCC values was largely motivated by this concern.” The interagency group 

also noted that estimates of climate damages are far more uncertain under more 

extreme climate change scenarios.
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 We strongly recommend that EPA provide a much clearer explanation regarding the 

mercury benefits of this rule. The mercury benefits quantified in the cost/benefit 

sections include only a small portion of the total benefits of the mercury reductions 

projected from the rule – only the IQ loss from children exposed to recreationallycaught freshwater fish. It is entirely unclear from the preamble to the rule, the 

executive summary in the RIA and other summaries of the costs/benefits of the rule that 

the “Hg-related benefits” represent such a small subset of the Hg benefits. We strongly 

suggest clarification of these benefits by, for example, changing tables to read “Partial 

Hg-related benefits (recreational freshwater fish IQ effects only)” or some other 

similarly explicit language.

Text has been added to the Executive Summary, the Cost/Benefits Comparison 

Chapter and in appropriate areas of the Preamble to address this recommendation.

RIA-Specific Comments

 Industry does not make retirement decisions based upon one regulation, but on the 

entire group of regulations. Therefore, a more accurate and comprehensive RIA would 

look at the aggregate effect of proposed and recently promulgated EPA rules. EPA 

should include these aggregate costs for all proposed and recently promulgated rules. 

Executive Order 13563 directs each agency to tailor its regulations to take into account 

the costs of cumulative regulations.

The Agency did not prepare a cumulative impact analysis to accompany the rule for 

the following reasons: 1) the various EO requirements that the Agency must comply 

with require us to estimate impacts specific to this rule; 2) decision makers and the 

public need to know the impacts specific to a particular rule in order to judge the 

merits of the regulation; 3) estimates specific to a particular rule are more transparent 

than those from a cumulative impact analysis. A cumulative impact analysis lumps 

several regulations together and can potentially mask a high-cost/low benefit 

regulation among other rules that may have large net benefits. By analyzing each 

regulation separately, EPA makes clear statements about what impacts, costs, and 

benefits will be generated as a result of this particular regulation. 

This does not, however, mean EPA has failed to consider cumulative impacts of the 

regulation. The inclusion of CSAPR and other regulatory actions (including federal, 

state, and local actions) in the IPM 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 provides meaningful projections of how the power sector will 

respond to the cumulative regulatory requirements for air emissions, while isolating 

the incremental impacts of MATS. These results are presented in Chapter 3 of the RIA.

Additionally, the Agency does reflect on the broader cumulative impacts of our 

regulations. In March 2011, EPA issued the Second Clean Air Act Prospective Report 
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which assessed at the benefits and costs of regulations pursuant to the 1990 Clean Air 

Act Amendments. The study examines the cumulative impact of these regulations 

(found at http://www.epa.gov/air/sect812/feb11/summaryreport.pdf). As shown in 

the report, the direct benefits from the 1990 Clean Air Act Amendments reach almost 

$2 trillion for the year 2020, a figure that dwarfs the direct costs of implementation 

($65 billion). The full report is at http://www.epa.gov/air/sect812/prospective2.html. 

The direct benefits of the 1990 Clean Air Act Amendments and associated programs 

significantly exceed their direct costs, which means economic welfare and quality of 

life for Americans were improved by passage of the 1990 Amendments. The wide 

margin by which benefits exceed costs combined with extensive uncertainty analysis 

suggest it is very unlikely this result would be reversed using any reasonable 

alternative assumptions or methods. This conclusion is likely to also be attributable to 

this rule as shown in the RIA.

 EPA may want to include in the RIA an analysis of the effect of the regulation on U.S. 

Gross Domestic Product (GDP). Such an analysis would be a useful measure of whether 

the policy is cost-effective. Similarly, it would also be useful to estimate the economic 

effect of the rule at the state level (e.g., state GDP). This would serve to satisfy 

Executive Order 13563, which states that the regulatory system should promote 

economic growth.

Prevailing OMB guidance issued pursuant to relevant Executive Orders (especially 

Circular A-4 issued in response to EO 12866) explicitly provides for assessing a 

program’s net benefits through benefit-cost analysis but does not explicitly call for an 

assessment of a program’s effect on GDP growth to be a routine part of major rule 

Regulatory Impact Analyses. This is because most economists recognize that the 

projected net benefits of program is more salient information for policymaking than 

the effects of that program on GDP, which is not a reliable measure of utility, or 

economic welfare. 

 Employment Impacts Due to Increases in Electricity Prices Are Not Accounted For

The Executive Summary (ES) of the RIA (and ensuing portions of subsequent chapters of 

the RIA) discuss two types of employment impacts stemming from these regulations –

see pg. 14 of the ES. The first type is changes in employment over time in the effected 

industry, in this case the electric utility industry. The second type is short term increases 

in employment for construction of pollution control equipment prior to compliance 

dates. 

These impacts are summarized in Table ES-6. Long term employment changes in the 

electric utility industry are estimated to be about 8,000 jobs, but the estimate is said to 

be not statistically different than zero. Short term employment gains are estimated to 

be about 47,000 jobs. Both these estimates are for increased employment.

The comment is that by far the largest employment effect due to the MATS regulation is 

likely to be a substantial increase in unemployment because of increased electricity 
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prices. Regulations which increase electricity prices result in lost jobs because of 

reduced purchasing power across the economy because of these higher electricity 

prices. Many firms across the US will do marginally less business because households 

and businesses will have less disposable income, as a result of paying more for 

electricity. And when these firms do less business, they need fewer workers. Thus 

fewer workers will be hired in expanding businesses and more workers will be let go in 

contracting ones; some such firms will close down. 

Deschenes (2010) finds that every 1% increase in electricity prices causes a nationwide 

reduction in employment of between 0.1% and 0.16%. The total US civilian labor force 

as of Oct. 2011 is 154.2 million. In his preface, Deschenes thanks Michael Greenstone, 

the Obama Administration’s first Research Director of the Council of Economic Advisors 

for helpful advice.

The RIA ES states (pg. 14): “Retail electricity prices are projected to increase in the 

contiguous US by an average of 3.0% in 2015 with the final MATS Rule.” It also states 

that natural gas prices will be higher than they would otherwise be, but we do not try to 

estimate unemployment impacts of higher natural gas prices. 

Multiplying 154 million US workers by 0.3% and by 0.48% (three times 0.1% to 0.16% to 

reflect the estimated 3% increase in electricity prices) gives a total unemployment 

increase of from 462,000 (low end) to 739,000 workers (high end). These would be the 

dominant estimated unemployment effects caused by the MATS regulations. If instead 

of total number of employed people, the 154 million person civilian work force, we used 

Full Time Equivalent (FTE), as in Deschenes (2010), the unemployment estimates would 

be lower by about 43%; however, FTE would not include self employed workers 

(Deschenes, 2010), so FTE estimates of unemployment would be biased low. Thus the 

estimate of the number of unemployed workers as a result of the higher electricity 

prices stemming from MATS would be between the FTE and Civilian Work Force 

estimates.

Presumably there are other estimates of the cross elasticity of unemployment with 

respect to energy prices, so these estimates using cross elasticities from Deschenes 

(2010) may be low or high. The important point is that the unemployment created by 

higher electricity prices should be included in the RIA. 

Reference: Deshenes, O. 2010. Climate Policy and Labor Markets. Working Paper 

16111. National Bureau of Economic Research.

The recommended study is too preliminary for regulatory use. The NBER website 

where the study can be found (http://www.nber.org/books/full10-1) notes that "The 

following chapters are preliminary drafts unless otherwise noted. They have not yet 

been subjected to the formal review process of the NBER."

Additionally, EPA has identified a number of issues with the application of this paper 

for the purpose of estimating employment impacts in the RIA. There is not enough 

discussion of control variables for the reader to evaluate how well the model is 

controlling for other influences on employment. As mentioned in section C of the 

paper, omitted factors might bias the estimates. 
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The author's choice of methods ignores longer term economic adjustments that are 

made in response to a shock. EPA believes that an employment impact analysis needs 

consider short term impacts as well as longer term adjustment paths.

Deschenes himself, as well as his conference discussant Matt Kahn, both emphasize 

quite clearly in the text of the paper (and Kahn’s comments) that the methodology 

used in this study does not identify a causal relationship between electricity prices and 

employment. That is, the econometric approach that Deschenes uses (OLS with fixed 

effects) cannot isolate whether either increases in electricity prices lead to declines in 

employment or if changes in employment lead to changes in electricity prices. At best 

the Deschenes approach can estimate the correlation between the two; not causation.

 Electricity Price Increases (and thus Increased Unemployment) from MATS May Be 

Understated When Interaction with Other Electricity Rate Impacts from Other 

Regulations are Taken Into Account

According to the ES, only 5.5 MW of coal units are expected to retire as a result of the 

MATS rule (pg. 14). Does this estimate include retirements which might occur when a 

unit which must comply with the new MATS rule must also comply with other new air 

quality and water quality rules, including (for air quality) rules regarding (1 through 3) 

NO2, SO2, and PM National Ambient Air Quality Standards, (4) Visibility Rule, and (5) 

Cross State Air Pollution Rule?

Some EGUs which might be able to continue to operate economically while installing 

technology to meet MATS may no longer be able to operate economically if they also 

have to add additional technologies to meet other rules noted above. If each rule 

analyzes coal unit retirements based upon the impact of each rule only by itself, e.g., if 

the MATS rule does not take into account costs that will occur due to other regulations, 

then plant retirements due to the combination of new regulations, and thus electricity 

price increases, may both be greater than impacts analyzed for MATS alone, or on a rule 

by rule basis. 

It would therefore be crucial for EPA to estimate the number of coal units retired based 

upon the combined rules, and the resulting electricity price increases. Further, the 

increase in unemployment as a result of the combined rules should also be calculated. 

Pg. 14 (not a typo) of the RIA for the Cross State Air Pollution Rule states: “Retail 

electricity prices are projected to increase nationally by an average of 2.5 % in 2012 and 

1.5 % in 2014 with the proposed Transport Rule.”

This appears to be an additional 4% increase in electricity costs, relative to the MATS 

rule. Would these costs increase, and added coal plants (other than those assumed to 

be retired in the Cross State [Transport] rule) retire, when MATS requirements are 

added to those of the Cross State rule on a plant by plant basis? The same argument 

pertains when other air quality rules (such as the upcoming PM rule) are promulgated. 

Thus the actual electricity price increases, and the actual unemployment estimates due 

to higher electricity prices, may be understated when interactive effects are considered. 
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We would like to ask EPA to clarify whether costs impacts on individual EGUs from rules 

other than MATS are considered when determinations are made as to whether MATS 

will cause such units to retire. If the answer is affirmative, which of the new rules are 

considered in this context? If the answer is negative, would EPA be willing to consider 

joint effects, in order to re-estimate the number of coal units retired, and the resulting 

additional electricity price increases?

The MATS analysis includes existing and enforceable environmental requirements for 

the power sector (both state and Federal), including the finalized Cross-State Air 

Pollution Rule. The IPM documentation has an extensive list of all the power sector 

rules and regulations that are included in the modeling. EPA has not included in this 

analysis any speculative constraints regarding rules which have not yet been finalized. 

The MATS regulatory impact analysis looks at the impact of the MATS regulation as it 

is implemented on top of a base case which accounts for all other existing and 

enforceable air pollution control requirements, including the final Cross-State Air 

Pollution Rule. As a result, the projections of the MATS policy case are a valid 

reflection of the outcome in the power sector of complying with MATS, CSAPR, and 

other existing regulations. As noted above, the projected electricity prices under the 

MATS policy scenario, which also incorporates CSAPR, are well within historic norms. 

The MATS impact analysis is explicitly designed to determine “whether MATS will 

cause such units to retire” and its results are a valid reflection of the economic 

impacts of this final rule. Finally, it appears as though the above interagency 

comment may have derived the estimate of a 4% increase in retail electricity price by 

adding the CSAPR RIA’s 2012 estimate of 2.5% and 2014 estimate of 1.5% - however, it 

is improper to add those impacts together, as they are each a proportional measure of 

conditions with and without CSAPR in the year analyzed, and rather than showing a 

4% increase, they show an initial 2.5% increase in 2012 that declined to only 1.5% by 

2014. Also, there are different years of analysis for the two rules.

 Estimated Benefits in MATS Include Reduction in Mortality from PM2.5 at Levels Below 

the National Ambient Air Quality Standard (NAAQS) for PM2.5

The current NAAQS as well as the new NAAQS that is under consideration both set levels 

for daily and annual PM2.5 concentrations which are said to be protective of public 

health. If PM2.5 concentrations are above either the daily or annual level in a given 

locality, states must create a plan to reduce concentrations down to that regulatory 

permissible level. If PM2.5 concentrations are below these legal concentrations, then 

nothing more need be done to protect the public health. NAAQS standards are subject 

to public review.

In estimating benefits from MATS, EPA assumes that PM2.5 concentrations at levels well 

below the NAAQS standards, down to nearly background levels, cause premature 

mortality. In making this assumption, EPA essentially changes the level of harm in the 

PM2.5 NAAQS to levels far below those in the NAAQS. Two questions thus arise:

 Should the assumption of a linear dose-response function down to roughly 

background levels be subject to public review before being used in a RIA?
13

 What would be the benefits (and cost to benefit ratios) in the RIA if only 

mortality above, but not below, the NAAQS were included?

In implementing these rules, emission controls may lead to reductions in ambient 

PM2.5 below the PM NAAQS in some areas. 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 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.

Additionally, in this RIA we binned the estimated population exposed to projected 

future baseline PM2.5 air quality levels for comparison against the “Lowest Measured 

Level” (LML) of PM2.5 air quality in the mortality studies. The purpose of this analysis is 

to show whether the estimated premature deaths associated with reduced PM2.5 

exposure occur at or above the range of ambient PM2.5 observations studied in Pope et 

al. (2002) and Laden et al. (2006), which are the two epidemiological studies that EPA 

uses to estimate PM2.5-related premature mortality co-benefits. We found that a 

significant proportion of the avoided PM-related premature deaths we estimated in 

this analysis occurred among populations exposed at or above the LML of each study in 

the baseline, increasing our confidence in our estimate of the magnitude of the PMrelated premature deaths avoided. Approximately 11% of the 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 about 73% occur at or above an annual mean PM2.5 level of 7.5 

μg/m3 (the LML of the Pope et al. 2002 study). 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. 

 Determining Toxicity of Individual PM2.5 Species, Relevance to MATS RIA

 Since 1996, scientific advisory bodies to EPA (such as the National Research Council of 

the National Academy of Sciences) have suggested that different PM2.5 species are likely 

to have different toxicity, and that EPA should use toxicology and other means to 

determine which PM2.5 species are most, or least harmful.

In March, 2009, on-the-record public comments were submitted to EPA by DOE/FE staff 

suggesting a means by which toxicity of specific PM2.5 species could be determined. In 

the 2009 Integrated Science Assessment (ISA) for PM, EPA has established several 

excellent criteria for judging the degree to which PM2.5 may be causally related to 

various health endpoints. The comments suggested that EPA could take advantage of 

the large number of new studies available, then and especially now, and use the criteria 

in the ISA to estimate the degree to which individual PM2.5 species may be causally 

related to the health endpoints considered (such as cardiovascular mortality). To be 

clear, the suggestion was not that EPA create “speciated standards” for particularly toxic

PM2.5 species (e.g., black carbon). To do so might take several years. Rather, the 
14

suggestion was that EPA use its excellent criteria to determine the strength of evidence 

for the toxicity of important PM2.5 species. To do so would enable states, in developing 

plans to reduce PM2.5 concentrations, to preferentially reduce the most toxic species.

The suggestions made in March 2009 are relevant to the MATS RIA, because only 

particular PM2.5 species are reduced using the technologies needed to reduce mercury 

emissions by 90%. These species are largely inorganic salts. Should an EPA analysis of 

the degree to which individual PM2.5 species may be related to mortality be undertaken, 

the results could have relevance to the benefits estimates in the MATS RIA.

The Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009), which 

underwent two rounds of peer review by EPA’s independent Science Advisory Board 

and public comment, already evaluated the body of scientific evidence regarding the 

strength of the evidence on particle component toxicity, as recommended by the 

commenter. This assessment concluded that “many constituents of PM2.5 can be 

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

differentiation of those constituents or sources that are more closely related to 

specific health outcomes”. 

The assumption that all particles are equally toxic for the purpose of calculating 

benefits was supported by the National Academies of Science (NRC, 2002) and the 

Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis 

(U.S. EPA-SAB, 2010). We specifically note this uncertainty in the RIA: “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 effect estimates by particle type.” 

 We will not repeat in detail here why it is crucial to utilize studies which include many 

PM2.5 species (if an important PM2.5 species is not included in a study, different PM2.5

species might rise to the level of a statistically significant risk only in the absence of such 

species). Nor will we repeat in detail why it is important to use pollution data that 

reflects subject exposure reasonably accurately (without good subject exposure, the 

significance and levels of health risks for pollutants whose concentrations are variable 

across a city will likely be understated, and risks might be “transferred” to pollutants 

with less spatial variability). We merely note the importance of these issues for 

reference.

No response needed.

 Section ES.2, Page ES-8: “EPA believes these unquantified benefits are substantial...” It 

is not clear what value signifies a cutoff between what is substantial and what is 

insubstantial.
15

Revised to indicate the unquantified benefits “could be substantial.” Since the 

monetized benefits of this rule are so large, if the unquantified benefits were only 1% 

of the total, they would be worth hundreds of millions of dollars.

 Page ES-3: “We also include in our monetized benefits estimates the effect from the 

reduction in CO2 emissions resulting from this rule.”

EPA repeatedly makes a point of not analyzing the impacts of other emission rules on 

coal retirements, but does not seem to have a problem assigning the benefits to other 

emissions from this rule.

This is both inaccurate and a non sequitur. The inclusion of CSAPR and other 

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

reflects the industry behavior that will likely occur in response to other requirements 

apart from MATS. This base case provides meaningful projections of how the power 

sector will respond to the cumulative regulatory requirements for air emissions, while 

isolating the incremental impacts of MATS. Additionally, while the rule targets 

mercury and other HAP emissions, the technology used to control these pollutants 

also results in reductions of PM and SO2. These reductions are expected to result in 

significant health co-benefits and are an important category to quantify. Analysis of 

only one pollutant misrepresents the total impacts of rule.

 Table ES-3: there appears to be discrepancies in the calculated monetary value of 

avoided IQ loss that should be reconciled. Table ES-3 states that, at the 7% discount 

rate, the estimated monetary value of avoided IQ loss associated with methylmercury 

exposure from self-caught fish consumption among recreational anglers is reported 

between $0.000005–$0.000009 (in billions of 2007$). The Table also states that, at the 

3% discount rate, the value is reported between $0.004–$0.006. The values at 3% and 

7% appear to be inconsistent with each other.

Revised in 11/23 version.

 Section ES.4, Page ES-15: “Of the 83 small entities affected, 49 are projected to have 

costs greater than 1 percent of their revenues.” Perhaps EPA should implement a policy 

to help protect these 49 small entities from proportionally large cost increases.

As described in the FRFA in the preamble and Chapter 7 of the RIA, EPA has taken 

steps to minimize the significant economic impact on small entities. Because this rule 

does not affect units with a generating capacity of less than 25 MW, small entities that 

do not own at least one generating unit with a capacity greater than 25 MW are not 

subject to the rule. According to the EPA's analysis, among the coal- and oil-fired EGUs 

(i.e., excluding combined cycle gas turbines and gas combustion turbines) about 26 

potentially small entities only own EGUs with a capacity less than or equal to 25 MW, 

and none of those entities are subject to the final rule based on the statutory 

definition of potentially regulated units. For those units the commenter references 
16

that are affected by the proposed rule, the EPA considered a number of comments 

received, both during the Small Business Advocacy Review (SBAR) Panel and the public 

comment period. While none of the alternatives adopted in the final rule are 

specifically applied to small entities, the EPA believes these modifications will make 

compliance less onerous for all regulated units, including those owned by small 

entities.

Chapter 3: 

 Page 3-5: “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.”

Assuming that EPA uses the USGS’ COALQUAL database, there a some issues that could 

arise from the use of this data. First, COALQUAL is over 10 years old (1998) and second, 

it does not account for Hg reductions that could occur from coal washing, and only has 

the Hg content of coal as it is in the ground.

EPA does not use the USGS COALQUAL database for its IPM modeling assumptions. 

Instead, EPA has developed its own assumptions for mercury content in coals through 

ICRs, independent studies, and industry experts. See IPM documentation for more 

detail.

 Page 3-5: “Total PM emissions are calculated exogenously to IPM, using EPA’s Source 

Classification Code (SCC) and control-based emissions factors.” Combined with ES-1 

“The great majority of the estimates are attributable to co-benefits from 4,300 to 

11,000 fewer PM2.5-related premature mortalities.”

There seems to be a large disconnect between the fact that PM emissions are 

exogenously calculated and the fact that they show the largest portion of the benefits.

While PM is exogenously calculated, it is based upon the types of fuel used, emissions 

control types and costs, and emission modification factors in EPA modeling (all 

endogenous to the model). In effect, emission estimates are all determined in a 

similar fashion (reflecting modeled fuel choice and pollution control retrofitting) with 

certain emissions reported as a direct model output and other emissions (including 

PM) generated through post-processing calculations. The process EPA uses to project 

EGU PM emissions is fully described in the IPM documentation.

 Page 3-7: “As noted above, IPM has been used for evaluating the economic and 

emission impacts of environmental policies for over two decades.”

Has any retrospective analysis been done comparing IPM projections and actual reality? 

There is no indication that even though the model has been used for decades that is 

does a good job predicting what will really happen.

The Agency’s use of IPM is intended to provide accurate estimates of the incremental 

impact of regulations or policy (e.g., X% increase in electricity prices, X GW of 
17

additional retirements, $X/ton allowance prices), and is not used as an absolute 

forecast for the power sector. IPM results are generally consistent with other 

Agencies’ power sector forecasts (e.g., EIA). Analyses from other researchers 

(Resources for the Future, MIT) have generally concluded that EPA tends to overstate 

the costs of regulations.

 Page 3-12, Figure 3-6: Based on information presented during the discussions on the 

proposed rule, EPA was showing a substantial number of existing FGD upgrades 

resulting from this rule. These upgrades have not been mentioned in the preamble or in 

the RIA. If the number of these upgrades is substantial, they should be shown in Figure 

3-6 and their impact should be discussed in detail. In the IPM manual documents, a cost 

of a scrubber upgrade is shown as an allowance of $100/kW. However, this cost is 

shown only as a fixed O&M cost. We do not agree with this, since a scrubber upgrade 

would involve expenditure of capital. Such an upgrade would normally require 

modifications to the scrubber vessel, replacement of auxiliary equipment, etc. If a 

substantial number of such upgrades are required under this rule, these capital costs 

cannot be ignored. In addition, the fixed O&M costs will go up as well as there would be 

additional variable O&M costs, since sorbent injection rates would generally have to be 

increased and/or injection of additives may be required.

EPA agrees and also assumes that there is a capital cost associated with the scrubber 

upgrade. However, EPA implements this cost assumption in the current IPM 

environment through the use of an annualized cost adder equivalent to capital 

expense amortization but is included in the model’s “accounting category” of FOM. 

The appearance of this cost as FOM is an artifact of current IPM design, even though 

the scrubber upgrade capital expenditure is accurately accounted for in the modeling 

results. In addition, EPA acknowledges your observation about displaying the 

magnitude of scrubber upgrading and has added that information to the updated RIA.

 Page 3-12, Figure 3-6: Since ESP upgrade was not an option used in the analyses for the 

proposed rule, we recommend that additional information be provided on this option, 

either here or in the IPM manual document. We would like a confirmation that only 

those ESP upgrade techniques have been used that do not require extended plant 

outages to install. For example, a project involving extension of ESP surface area may 

require an outage lasting more than two years. Such long outages may become another 

factor contributing to the grid reliability issue.

The approach for ESP upgrades will be fully described in the IPM documentation. The 

supplemental documentation for final MATS explains the procedure used to 

determine the type and extent of ESP upgrades used. Included is a table that defines 

each of the three levels of ESP upgrade that were considered (including 

characteristics, trigger points, associated costs, and performance improvements). A 

detailed spreadsheet is included indicating the type of upgrade selected for each 

evaluated coal unit. About 34 GW of capacity is projected to receive ESP upgrades, of 
18

which 87% is projected receive upgrades that involve no internal flow or area 

adjustments. EPA believes that these upgrades will require only very short outages 

that will have no impact on grid reliability. The remaining 13% is projected to incur 

ESP upgrade costs that could possibly involve internal adjustments. However, these 

adjustments (assumed to occur at only about 4 GW of capacity total nationwide, 

about 1% of total existing coal capacity) are unlikely to necessitate an outage of a 

length that it cannot be scheduled appropriately such that the grid operator can 

accommodate the absence of a particular unit by drawing on other sources of 

generation to meet load during the outage period. Furthermore, EPA believes even 

this projection is conservative because as explained in the IPM documentation, EPA 

assumed a filterable PM limit for this ESP upgrade analysis that is roughly ten percent 

below (i.e., more stringent than) the filterable PM limit in the final MATS rule.

 Page 3-12, Figure 3-6: Please provide the updated IPM manual document that shows 

how the costs of ESP upgrade were estimated.

IPM documentation will be transmitted.

 

 Page 3-25: “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 sources, and additional fuel expenditures.”

No accounting for reliability measures in the compliance costs.

IPM represents the cost of reliability measures through the requirement to maintain 

adequate reserves in each region. A higher level of retirements may cause capacity 

costs to rise as a result of increased costs of building new capacity needed to satisfy 

the reserve requirement, or from additional costs for transferring reserves from 

neighboring regions. IPM modeling captures these costs as increases in capacity prices 

when the reserve margin constraint is binding, and these costs are reflected in all EPA 

modeling outputs (e.g., electricity prices or annualized costs).

 Section 4.1, Page 4-1, Paragraph 2: “It is not expected that mercury benefits would be 

substantially changed, and given the small magnitude of the benefits...” It seems odd 

that the majority of the benefits of the rule do not come from Mercury, since a major 

goal of the rule is to reduce mercury. Since the vast majority of benefits come from 

reducing PM 2.5, is there a more cost-effective approach to reducing PM 2.5 than the 

Mercury and Air Toxics Standard? This would serve to satisfy Executive Order 13563, 

which indicates regulations should be chosen that maximize net benefits.

The MACT standard is a technology-based standard that, under the Clean Air Act

Amendments of 1990, must not be less than the average emission level achieved by 

controls on the best performing 12 percent of existing sources by category. While EPA 

cannot consider cost when setting the MACT floor, EPA has appropriately 

incorporated provisions in the proposed rule to provide regulated units with flexibility 
19

to reduce emissions in the most cost-effective and efficient manner for that particular 

unit.

 Section 5.2.2, Page 5-11: “The appropriate economic measure is therefore ex ante 

Willingness to Pay (WTP) for changes in a risk.” In general, willingness to pay (WTP) is 

problematic because there are an infinite number of areas individuals could be asked to 

place a value on, yet individuals have a finite budget; thus, it seems likely that WTP 

would likely overstate what an individual would actually be willing and able to pay. 

Therefore, WTP should be constrained to not exceed a typical individual's budget.

While we agree that budget constraints are important considerations for welldesigned WTP studies, we disagree that budget constraints disqualify the use of all 

WTP studies. . EPA’s use of WTP estimates to quantify the economic value of avoided 

deaths and certain illnesses is consistent with advice received from the EPA Science 

Advisory Board (SAB). In particular, the SAB noted that “conventional benefit-cost 

analysis is based on the identification of potential Pareto improvements (i.e., the 

"Kaldor-Hicks criterion" or "compensation test"). Straightforward application of the 

Kaldor-Hicks criterion implies EPA should account for any and all individual 

characteristics that influence an individual's (or household's) willingness to pay for 

reduced health risk.” The SAB also noted that, “[c]oncerning morbidity, the Agency 

should continue to use Willingness-to-Pay (WTP) estimates for morbidity values, 

rather than cost-of-illness (COI) estimates, should these be available. Where WTP is 

unavailable, COI estimates can be used as placeholders, awaiting further research, 

provided suitable caveats are included in the analysis.” Finally, the SAB recommended 

that the Agency should “continue to use WTP estimates when these are available, 

rather than COI estimates... [and] it is important to recognize that the COI estimates 

are not appropriate alternative estimates to be substituted for WTP estimates 

because they do not reflect the preferred concept of valuation” (U.S. EPA-SAB 2004a 

and U.S. EPA-SAB 2004b). In addition, each of the WTP estimates that EPA uses have 

been peer reviewed and supported by the SAB.

 Section 5.3, Pages 5-19 and 5-20: The assumption that all fine particles are equally 

potent in causing premature mortality is important, as changes to premature mortalities 

make up the vast majority of total benefits. Since some studies have found no 

significant effect of PM2.5 on mortality, perhaps zero should be included in the range of 

PM2.5 benefits, as Electric Power Research Institute has suggested. EPRI indicated: 

“Since some models in some studies do not find a statistically significant effect of PM2.5 

on mortality, the effects range could even include zero...” (Source: EPRI PM2.5 

Reductions and Impact on Premature Death: An EPRI perspective; Issue Brief 1018632.)

Also, the same EPRI briefing states the EPRI-Washington University Veterans Study did 

not find any statistically significant mortality associations with PM2.5, but found some 

PM components that were significantly associated with mortality. The EPRI briefing 

goes on to say: “Of great significance for CAIR, this study did not find a significant 

association between the sulfate component of PM and mortality...if this study were 
20

used to derive estimates of health benefits attributable to reductions in SO2 emissions, 

the numbers would differ from those derived from the Pope et al...some studies have 

shown that estimated impacts of PM diminish when other co-pollutants are considered 

in the analyses. If the co-pollutants are indeed associated with increased mortality and 

are correlated with PM levels, then the PM effects estimated from this single pollutant 

model approach can be biased upwards.”

EPA disagrees with this recommendation. As described in the RIA, EPA estimates 

avoided PM-related premature mortality based on risk estimates from the American 

Cancer Society cohort (Pope et al., 2002) and the Harvard Six Cities cohort (Laden et 

al., 2006) as well as providing mortalities estimates based on the results of an expert 

elicitation (Roman et al,2008) as a characterization of uncertainty. The choice of these 

studies was supported by the Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance Analysis. In addition, when EPA conducted an expert elicitation 

on the relationship between PM2.5 exposure and premature mortality in 2006, the 

experts placed most weight on the results of these two studies.

The Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2009), which 

was twice reviewed by the Science Advisory Board, concluded that there is a causal 

relationship between exposure to PM2.5 and premature mortality after reviewing the 

entire body of scientific evidence. In addition, the ISA also concluded that the 

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

particle type. This means that while some PM mixtures may be more toxic than other 

mixtures, the scientific literature does not support the commenter’s assertion that 

some particles are not toxic. Although we note this uncertainty in the RIA, we assume 

that all particles are equally toxic for the purpose of calculating benefits. This 

assumption was supported by the National Academies of Science and the Health 

Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. 

 Section 5.4.2.5, Page 5-35: “Although the association was not statistically significant for 

cough, the results were still positive and close to significance; consequently, we decided 

to include this endpoint...” It does not make sense to use a statistically insignificant 

result in deriving estimates. If the sign had been negative, would it have been included?

The health endpoints included in this RIA are consistent with previous RIAs. The 

Science Advisory Board Health Effects Subcommittee recommends the addition of 

asthma-related symptoms (i.e., asthma exacerbations) to the primary analysis, 

provided that the studies use the panel study approach and that they have 

comparable design and baseline frequencies in both asthma prevalence and 

exacerbation rates (SAB-HES, 2004). Note that the SAB-HES, while supporting the 

incorporation of asthma exacerbation estimates, does not believe that the association 

between ambient air pollution, including ozone and PM, and the new onset of asthma 

is sufficiently strong to support inclusion of this asthma-related endpoint in the 

primary estimate. The Ostro et al. (2001) study was included since it was close to 
21

statistical significance, not because it showed a positive result. Note that a negative 

relationship between PM and cough would not be biologically plausible.

 Section 5.4.4.1, Page 5-42: “Given the mixed theoretical finding and empirical evidence 

regarding adjustments to VSL for risk and population characteristics, we use a single VSL 

for all reductions in mortality risk.” It seems more logical to use a range of value of 

statistical life (VSL) estimates, rather than a single value, in order to account for the 

uncertainty in the measure.

As described in the RIA, EPA determined that a single, peer-reviewed estimate applied 

consistently best reflects the advice it has received from the EPA’s independent 

Science Advisory Board until updated guidance on mortality valuation is available. The 

Agency continues its efforts to update its guidance on this issue.

 Section 5.4.4.1, Page 5-43: “An ideal benefits estimate of mortality risk reduction would

reflect these human characteristics, in addition to an individual’s WTP to improve one’s 

own chances of survival plus WTP to improve other individuals’ survival rates.” 

Capturing the WTP to improve other individuals’ survival rates would be an example of a 

non-use value, thus creating a bias. Individuals likely also place a value on non-use 

things such as less government regulation; for instance, Executive Order 13563 directs 

Agencies to “identify and consider regulatory approaches that reduce burdens and

maintain flexibility and freedom of choice for the public.” 

This issue does not impact the benefit estimates presented in the RIA because the 

specific VSL value applied by EPA is not based on studies that include WTP for the 

reduction in mortality risk of other individuals.

 Section 5.4.4.2, Page: 5-46: WTP for avoiding chronic bronchitis ($340,000) likely 

exceeds an average individual’s budget, especially after considering essentials such as 

food, clothing, housing, etc. $340,000 is greater than six years of income for an 

individual earning $50,000 per year.

We believe this comment reflects a misunderstanding of how the WTP value for 

avoided cases of chronic bronchitis was calculated in the economics literature that 

provides the basis of this estimate. In each of the two studies used to quantify the 

WTP (Krupnick and Cropper 1992; Viscusi et al. 1991), the researchers elicit the value 

that survey participants are willing to pay to reduce their risk of contracting chronic 

bronchitis by a small amount. The value of this small change in risk may then be 

aggregated to produce an implied value of a single case of chronic bronchitis. This is 

similar to EPA’s approach to mortality valuation.

 Section 5.4.4.1, Pages 5-40, 5-42: “The VSL approach is a summary measure for the 

value of small changes in mortality risk experienced by a large number of people.” On 
22

page 5-42, EPA indicates it uses a VSL of $6.3 million. This seems to be an unreasonably 

large value. A more practical measure of the value attributed to an increase in expected 

life for the affected individuals would be the amount of income these individuals could 

expect from living longer due to the rule. This would have the effect of preventing the 

number from exceeding an individual’s remaining lifetime income, since one cannot pay 

more than that amount. So, if the average affected individual could be expected to earn 

$250,000 more during his or lifetime, multiplying $250,000 by 11,000 (number of 

premature deaths), would result in an upward bound of total benefits of the rule due to 

premature deaths of $2.75 billion.

We disagree with this comment. EPA’s use of WTP estimates to quantify the economic 

value of avoided deaths is consistent with advice received from the EPA Science 

Advisory Board. In particular, the SAB noted that “VSL is the conceptually appropriate 

method for assessing the benefits of avoided premature mortality.” (U.S. EPA-SAB 

2004a and U.S. EPA-SAB 2004b). Similar to our response on the comment regarding 

chronic bronchitis valuation, the valuation estimate is simply an aggregation of WTP 

for small changes in risk. As described in the RIA, EPA determined that a single, peerreviewed estimate applied consistently best reflects the advice it has received from 

the SAB until updated guidance on mortality valuation is available. The Agency 

continues its efforts to update its guidance on this issue.

 Section 5.4.5.2, Page 5-50, Table 5-12: Willingness to pay (WTP) is problematic 

because there are an infinite number of areas individuals could be asked to place a 

value on, yet individuals have a finite budget; thus, it seems likely that WTP would likely 

overstate what an individual would actually be willing and able to pay. Therefore, WTP 

should be constrained to not exceed a typical individual's budget.

While we agree that budget constraints are important considerations for welldesigned WTP studies, we disagree that budget constraints disqualify the use of all 

WTP studies. We disagree with this comment. EPA’s use of WTP estimates to quantify 

the economic value of avoided deaths and certain illnesses is consistent with advice 

received from the EPA Science Advisory Board. In particular, the SAB noted that 

“conventional benefit-cost analysis is based on the identification of potential Pareto 

improvements (i.e., the "Kaldor-Hicks criterion" or "compensation test"). 

Straightforward application of the Kaldor-Hicks criterion implies EPA should account 

for any and all individual characteristics that influence an individual's (or household's) 

willingness to pay for reduced health risk.” The SAB also noted that, “[c]oncerning 

morbidity, the Agency should continue to use Willingness-to-Pay (WTP) estimates for 

morbidity values, rather than cost-of-illness (COI) estimates, should these be 

available. Where WTP is unavailable, COI estimates can be used as placeholders, 

awaiting further research, provided suitable caveats are included in the analysis.” 

Finally, the SAB recommended that the Agency should “continue to use WTP 

estimates when these are available, rather than COI estimates... [and] it is important 

to recognize that the COI estimates are not appropriate alternative estimates to be 
23

substituted for WTP estimates because they do not reflect the preferred concept of 

valuation” (U.S. EPA-SAB 2004a and U.S. EPA-SAB 2004b). In addition, each of the WTP 

estimates that EPA uses have been peer reviewed and supported by the SAB.

Section 5.6, Page 5-88, middle paragraph: “The fourth value is the 95th percentile of the 

SCC from all three models at a 3% discount rate. It is included to represent higher-thanexpected impacts from temperature change further out in the tails of the SCC 

distribution.” Why not include a fifth value at the 5th percentile in order to represent 

lower-than-expected impacts from temperature change?

The Social Cost of Carbon (SCC) values used to estimate climate benefits for the MATS 

final rule are consistent with the recommendations of the Interagency Working Group 

on the SCC discussed extensively in the Technical Support Document (SCC TSD) 

available on OMB’s website at: 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-of-Carbon-for-RIA.pdf. The main purpose of this exercise was to develop a 

schedule of SCC values that can be applied consistently across regulatory analyses and 

Agencies. The TSD clearly states that:

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socio-economic and emissions 

scenarios at the 2.5, 3, and 5 percent discount rates. The fourth value is included to 

represent the higher-than-expected economic impacts from climate change further out 

in the tails of the SCC distribution. For this purpose, we use the SCC value for the 95th 

percentile at a 3 percent discount rate. (The full set of distributions by model and 

scenario combination is included in the Appendix.) As noted above, the 3 percent 

discount rate is the central value, and so the central value that emerges is the average 

SCC across models at the 3 percent discount rate. For purposes of capturing the 

uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range.

The SCC modeling exercise, which included 3 models, 3 discount rates, and 5 

socioeconomic scenarios, produced 45 separate distributions of SCC for a given year. 

Prior to seeing the results of the exercise, the interagency group agreed to combine 

the distributions for the models and scenarios at each discount rate and selected the

statistics to present for each distribution in RIAs. In particular, the interagency group 

found it necessary to limit the number of estimates presented to four to maintain 

coherence while also reflecting the uncertainty of the estimation exercise. In short, 

the group concluded that presenting more than four estimates in the summary tables, 

which typically provide estimates for non-GHG benefits and for multiple years, would 

overwhelm the reader. It is also worth noting that the shape of the distributions—

skewed to the right with a long tail—supports the decision to present the 95th

percentile estimate.
24

In keeping with the interagency directive, EPA used this approach for the Light Duty 

Vehicle Rule to estimate benefits from CO2 emission reductions and this approach has 

been used in all subsequent EPA regulatory actions to date. OMB led the SCC 

Interagency Working Group to develop this approach and several other interagency 

reviewers for the MATS final rule actively participated. It is our understanding that 

any deviation from the approach outlined in the SCC TSD would have to be reviewed 

and considered by the interagency group before it could be implemented by the EPA 

or other agencies.

Moreover, key questions about the relevant assumptions to use regarding risk 

aversion motivated the interagency group to present the 95th percentile estimate in 

addition to the average estimates. The SCC TSD elaborates on limitations of the 

analysis and remaining questions about risk aversion (page 30). Specifically, a key 

question is the relevant assumption to be made about relative risk aversion with 

regard to high-impact climate outcomes. The current SCC values do not consider the 

possibility that individuals may have a higher willingness to pay to reduce the 

likelihood of low-probability, high impact outcomes than they do to reduce the 

likelihood of higher-probability but lower impact damages with the same expected 

costs. “The inclusion of the 95th percentile estimate in the final set of SCC values was 

largely motivated by this concern.” The interagency group also noted that estimates of 

climate damages are far more uncertain under more extreme climate change 

scenarios. Based upon the foregoing, EPA recommends that the SCC estimates remain 

as presented in the MATS final RIA submitted to OMB and the interagency review 

group.

 Section 5.8, Page 5-99, first paragraph: We estimate that by 2016 the rule will have 

reduced the number of PM2.5-related premature deaths by between 4,300 and 

11,000...” It would be useful for EPA to provide an estimate of the average amount of 

time these 4,300 to 11,000 individuals would live beyond what would otherwise be a 

premature death without implementation of the rule. 

EPA does not have the necessary data to estimate avoided life years lost for this rule. 

However, in the 2006 PM NAAQS RIA, EPA estimated that the discounted average gain 

in life years is 10 years. In Hubbell (2006), the estimate of average undiscounted life 

years gained was 15.26 years.

 Section 6.1, Page 6-1: “From an economic perspective, labor is an input into producing 

goods and services. If regulation requires that more labor be used to produce a given 

amount of output, the additional labor is reflected by an increase in the cost of 

production.” It should be noted that the decrease in labor productivity would cause the 

short-run aggregate supply curve to shift to the left, with the result being that 

businesses produce less (i.e., lower GDP). 
25

Noted in RIA in a footnote.

 Section 6.1, Page 6-2: “With significant numbers of workers unemployed, the 

opportunity costs associated with displacing jobs in other sectors are likely to be much 

smaller.” It seems that even at unemployment levels of 9 percent, 91 percent of the 

labor force is still working and thus the opportunity costs associated with displacing jobs 

in other sectors is not likely to be much smaller.

Revised to say “smaller.”

 Section 6.3, Page 6-7, first paragraph: “When a regulation is promulgated, the first 

response of industry is to order pollution control equipment and services in order to 

comply with the regulation when it becomes effective.” It should be noted that an 

alternative first response is for individual plants to shut down in order to avoid the 

higher costs associated with the pollution control equipment and services.

EPA acknowledges that there will be some retirements and has included estimates in 

Chapter 3, along with potential employment losses associated with those retirements 

(in additional to estimates for the pollution control sector). In addition, EPA has 

revised the language in this paragraph to indicate that this type of industry response is 

one possible, but not the only, response to the promulgation of regulation that will 

affect them.

 Section 6.3, Page 6-7, first paragraph: “Revenue and employment in the 

environmental technology industry...” A clear definition of what industries (e.g., NAICS 

codes) are included in the environmental technology industry is needed.

The following is now noted in the RIA: The definition of the environmental technology 

industry from the Department of Commerce’s ITA FY 2010 environmental technologies 

industries assessment is not according to NAICS code but instead is “all goods and 

services that generate revenue associated with environmental protection, 

assessment, compliance with environmental regulations, pollution control and 

prevention, waste management, renewable energy, remediation of contaminated 

property, design and operation of environmental infrastructure, and the provision and 

delivery of environmental resources.”

 Section 6.2.1, Page 6-6, Table 6-2: Since the overall results of the Morgenstern et al. 

study are statistically insignificant, it seems more practical to not provide corresponding 

job estimates, but to simply indicate the overall employment impacts were found to be 

statistically insignificant. Additionally, a summary of other studies that have estimated 

the effects of regulation on the economy would be useful.

Because of the interest in employment EPA has used this study for a number of 

regulations to estimate the mean net employment and provided the 95% confidence 
26

interval results to reflect the high degree of uncertainty (the interval includes zero so 

we are showing we don’t know the sign of the effect but the interval itself is helpful 

when others give exaggerated claims of huge job changes well outside our 95% 

confidence interval). There is now a footnote in the RIA that expresses this point. 

Additional studies are being explored at this time. If needed, the 2011 OMB Report to 

Congress on the Costs and Benefits of the Federal Regulations has a short summary of 

the literature which we could cite.

 Section 8.1, Page 2, Table 8-1: It may be more clear to the reader to change “+B” to 

“+NB,” (Net Benefits) because the reader has to search through the various footnotes in 

order to find that “B is the sum of all unquantified benefits and disbenefits.”

EPA disagrees with this suggestion. Readers would still have to reference the 

footnotes to find the meaning of “NB”.