indicated that there was evidence that exposure to methylmercury in
humans and animals can have adverse effects on both the developing and
adult cardiovascular system. Infant exposure in the womb to
methylmercury has been associated with altered blood-pressure and
heart-rate variability in children. In adults, dietary exposure to
methylmercury has been linked to a higher risk of acute myocardial
infarction (MI), coronary heart disease, or cardiovascular heart
disease. To date, the EPA has not attempted to utilize a quantitative
dose-response assessment for cardiovascular effects associated with
methylmercury exposures because of a lack of consensus among scientists
on the dose-response functions for these effects and inconsistency
among available studies as to the association between methylmercury
exposure and various cardiovascular system effects.
    However, additional studies have become available that have
increased the EPA's confidence in characterizing the dose-response
relationship between methylmercury and adverse cardiovascular outcomes.
These new studies were leveraged to inform new quantitative screening
analyses (described in section III.A.3, below) to estimate one
cardiovascular endpoint--incidence of MI mortality--that may
potentially be linked to U.S. EGU mercury emissions as well as the
number of U.S. EGU impacted watersheds. In addition to a new meta-
analysis (Hu et al., 2021) \42\ on the association of methylmercury
generally with cardiovascular disease (CVD), stroke, and ischemic heart
disease (IHD), there is a limited body of existing literature that has
examined associations between mercury and various cardiovascular
outcomes. These include acute MI, hypertension, atherosclerosis, and
heart rate variability (Roman et al., 2011).\43\
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    \42\ Hu, X. F., Lowe, M., Chan, H.M., Mercury exposure,
cardiovascular disease, and mortality: A systematic review and dose-
response meta-analysis. Environmental Research 193 (2021),110538.
    \43\ Roman HA, Walsh TL, Coull BA, Dewailly [Eacute], Guallar E,
Hattis D, Mari[euml]n K, Schwartz J, Stern AH, Virtanen JK, Rice G.
Evaluation of the cardiovascular effects of methylmercury exposures:
Current evidence supports development of a dose-response function
for regulatory benefits analysis. Environ Health Perspect. 2011
May;119(5):607-14. doi: 10.1289/ehp.1003012. Epub 2011 Jan 10.

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    Immunotoxic Effects of Exposure to Methylmercury. Although exposure
to some forms of mercury can result in a decrease in immune activity or
an autoimmune response (ATSDR, 1999), evidence for immunotoxic effects
of methylmercury is limited (NAS Study).
    Other Mercury-Related Human Toxicity Data Including Potential
Carcinogenicity. The Mercury Study noted that methylmercury is not a
potent mutagen but is capable of causing chromosomal damage in a number
of experimental systems. The NAS Study indicated that the evidence that
human exposure to methylmercury causes genetic damage is inconclusive;
it noted that some earlier studies showing chromosomal damage in
lymphocytes may not have controlled sufficiently for potential
confounders. One study of adults living in the Tapajos River region in
Brazil (Amorim et al., 2000) \44\ reported a relationship between
methylmercury concentration in hair and DNA damage in lymphocytes, as
well as effects on chromosomes. Long-term methylmercury exposures in
this population were believed to occur through consumption of fish,
suggesting that genotoxic effects (largely chromosomal aberrations) may
result from dietary, chronic methylmercury exposures similar to and
above those seen in the populations studied in the Faroe Islands and
Republic of Seychelles. Since 2000, more recent studies have evaluated
methylmercury genotoxicity in vitro in human and animal cell lines and
in vivo in rats.
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    \44\ Amorim MI, Mergler D, Bahia MO, Dubeau H, Miranda D, Lebel
J, Burbano RR, Lucotte M. Cytogenetic damage related to low levels
of methyl mercury contamination in the Brazilian Amazon. An Acad
Bras Cienc. 2000 Dec;72(4):497-507. doi: 10.1590/s0001-
37652000000400004.
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    Based on limited human and animal data, methylmercury is classified
as a ``possible human carcinogen'' by the International Agency for
Research on Cancer (IARC, 1993) \45\ and in IRIS (USEPA, 2001).
However, a quantitative estimate of the carcinogenic risk of
methylmercury has not been assessed under the IRIS program at this
time. Multiple human epidemiological studies have found no significant
association between methylmercury exposure and overall cancer
incidence, although a few studies have shown an association between
methylmercury exposure and specific types of cancer incidence (e.g.,
acute leukemia and liver cancer) (NAS Study).
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    \45\ International Agency for Research on Cancer (IARC) Working
Group on the Evaluation of Carcinogenic Risks to Humans. Beryllium,
Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry.
Lyon (FR): International Agency for Research on Cancer; 1993. (IARC
Monographs on the Evaluation of Carcinogenic Risks to Humans, No.
58.) Mercury and Mercury Compounds. Available from: https://www.ncbi.nlm.nih.gov/books/NBK499780.
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    Some evidence of reproductive and renal toxicity in humans from
methylmercury exposure exists. However, overall, human data regarding
reproductive, renal, and hematological toxicity from methylmercury are
very limited and are based on studies of the two high-dose poisoning
episodes in Iraq and Japan or animal data, rather than epidemiological
studies of chronic exposures at the levels of interest in this
analysis.
b. Review of Health Effects for Non-Mercury HAP
    As noted earlier, EGUs are the largest source of HCl, HF, and
selenium emissions, and are a major source of metallic HAP emissions
including arsenic, chromium, nickel, cobalt, and others. Exposure to
these HAP, depending on exposure duration and levels of exposures, is
associated with a variety of adverse health effects. These adverse
health effects may include chronic health disorders (e.g., irritation
of the lung, skin, and mucus membranes; decreased pulmonary function,
pneumonia, or lung damage; detrimental effects on the central nervous
system; damage to the kidneys; and alimentary effects such as nausea
and vomiting).
    As of 2021, three of the key metal HAP emitted by EGUs (arsenic,
chromium, and nickel) have been classified as human carcinogens, while
three others (cadmium, selenium, and lead) are classified as probable
human carcinogens. Overall (metal and non-metal), the EPA has
classified four of the HAP emitted by EGUs as human carcinogens and
five as probable human carcinogens. See 76 FR 25003-25005 (May 3, 2011)
for a fuller discussion of the health effects associated with these
pollutants.
    As summarized in the Supplement to the Non-Hg Case Study Chronic
Inhalation Risk Assessment In Support of the Appropriate and Necessary
Finding for Coal- and Oil-Fired Electric Generating Units (2011 Non-Hg
HAP Assessment),\46\ the EPA previously completed a refined chronic
inhalation risk assessment for 16 EGU case studies in order to assess
potential public health risk associated with non-mercury HAP. The 16
case studies included one unit that used oil and 15 that used coal. As
noted in the 2015 Proposal, this set of case studies was designed to
include those facilities with potentially elevated cancer and non-
cancer risk based on an initial risk screening of prospective EGU units
completed utilizing the Human Exposure Model paired with HAP emissions
data obtained from the 2005 National Emissions Inventory. For each of
the 16 case study facilities, we conducted refined dispersion modeling
with the EPA's AERMOD (American Meteorological Society/Environmental
Protection Agency Regulatory Model) system to calculate annual ambient
concentrations (see 2011 Non-Hg HAP Assessment). Average annual
concentrations were calculated at census block centroids. We calculated
the MIR for each facility as the cancer risk associated with a
continuous lifetime (24 hours per day, 7 days per week, and 52 weeks
per year for a 70-year period) exposure to the maximum concentration at
the centroid of an inhabited census block, based on application of the
unit risk estimate from the EPA's IRIS program. Based on estimated
actual emissions, the highest estimated individual lifetime cancer risk
from any of the 16 case study facilities was 20-in-1 million, driven by
nickel emissions from the one case study facility with oil-fired EGUs.
Of the facilities with coal-fired EGUs, five facilities had MIR greater
than 1-in-1 million (the highest was 5-in-1 million), with the risk
from four due to emissions of chromium VI and the risk from one due to
emissions of nickel. There were also two facilities with coal-fired
EGUs that had MIR equal to 1-in-1 million. Based on this analysis, the
EPA concludes that cancer risks associated with these HAP emissions
supports a finding that it is appropriate to regulate HAP emissions
from EGUs.
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    \46\ U.S. EPA. 2011. Supplement to the Non-Hg Case Study Chronic
Inhalation Risk Assessment In Support of the Appropriate and
Necessary Finding for Coal- and Oil-Fired Electric Generating Units.
Office of Air Quality Planning and Standards. November. EPA-452/R-
11-013. Docket ID Item No. EPA-HQ-OAR-2009-0234-19912.
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c. Review of Other Adverse Environmental Effects Associated With EGU
HAP Emissions
    Ecological Effects of Methylmercury. Along with the human health
hazards associated with methylmercury, it is well-established that
birds and mammals are also exposed to methylmercury through fish
consumption (Mercury Study). At higher levels of exposure, the harmful
effects of methylmercury include slower growth and development, reduced
reproduction, and premature mortality. The effects of methylmercury on
wildlife are variable across species but have been observed in the
environment

for numerous avian species and mammals including polar bears, river
otters, and panthers. These adverse effects can propagate into impacts
on human welfare to the extent they influence economies that depend on
robust ecosystems (e.g., tourism).
    Ecological Effects of Acid Gas HAP. Even after the ARP was largely
implemented in 2005, EGU sources comprised 82 percent of all
anthropogenic HCl (a useful surrogate for all acid gas HAP) emissions
in the U.S. When HCl dissolves in water, hydrochloric acid is formed.
When hydrochloric acid is deposited by rainfall into terrestrial and
aquatic ecosystems, it results in acidification of those systems. The
MATS rule was expected to result in an 88 percent reduction in HCl
emissions. As part of a recent Integrated Science Assessment (EPA,
2020),\47\ the EPA concluded that the body of evidence is sufficient to
infer a causal relationship between acidifying deposition and adverse
changes in freshwater biota. Affected biota from acidification of
freshwater include plankton, invertebrates, fish, and other organisms.
Adverse effects can include physiological impairment, as well as
alteration of species richness, community composition, and biodiversity
in freshwater ecosystems. This evidence is consistent and coherent
across multiple species. More species are lost with greater
acidification.
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    \47\ U.S. EPA. Integrated Science Assessment (ISA) for Oxides of
Nitrogen, Oxides of Sulfur and Particulate Matter Ecological
Criteria (Final Report). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R-20/278, 2020.
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3. Post-2016 Screening-Level Risk Assessments of Methylmercury Impacts
    This section of the preamble describes three screening-level risk
assessments completed since the 2016 Supplemental Finding that further
strengthen the conclusion that U.S. EGU-sourced mercury represents a
hazard to public health. These ``screening-level'' assessments are
designed as broad bounding exercises intended to illustrate the
potential scope and public health importance of methylmercury risks
associated with U.S. EGU emissions. In some cases, they incorporate
newer peer-reviewed literature that was not available to the Agency
previously. Remaining uncertainties, however, prohibit the EPA from
generating a more precise estimate at this time. Two of the three risk
assessments focus on the potential for methylmercury exposure to
increase the risk of MI-related mortality in adults and for that
reason, section III.A.3.a begins by describing the methodology used in
the analyses, including discussion of the concentration response (CR)
function \48\ for MI-related mortality and the incorporation of
confidence cutpoints designed to address uncertainty. Then, the EPA
describes an extension of the original watershed-level subsistence
fisher methylmercury risk assessment to evaluate the potential for
elevated MI-mortality risk among subsistence fishers (section
III.A.3.b). In addition, a separate risk assessment is presented for
elevated MI mortality among all adults utilizing a bounding approach
that explores potential risks associated with exposure of the general
U.S. population to methylmercury (sourced from U.S. EGUs) through fish
consumption (section III.A.3.c). Finally, focusing on
neurodevelopmental outcomes, another bounding analysis is presented
that focuses on the risk of IQ points loss in children exposed in utero
through maternal fish consumption by the population of general U.S.
fish consumers (section III.A.3.d). Each of these analyses quantify
potential impacts on incidence of adverse health effects. Section
III.A.4 provides illustrative examples of how these incidence estimates
translate to monetized benefits.
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    \48\ Concentration-response functions relate levels of exposure
for the chemical of interest to the probability or rate of response
for the adverse health outcome in the exposed individual or
population. Typically these mathematical relationships are based on
data obtained either from human epidemiology studies, clinical
studies, or toxicological (animal) studies. In this case, CR
functions for MI-related mortality are based on epidemiology studies
as discussed further below.
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a. Methodology for Estimating MI-Mortality
    This section describes the methodology used in the new screening-
level risk assessments related to mortality, including the EPA's
application of a CR function characterizing the relationship between
increased MI-mortality and methylmercury exposure. As discussed further
in the 2021 Risk TSD,\49\ which is contained in the docket for this
action, the approach draws on recommendations provided by an expert
panel convened by the EPA in 2010 to evaluate the cardiovascular
effects associated with methylmercury exposure (the findings of the
expert panel were summarized as a peer-reviewed paper, Roman et al.,
2011). The panel ``found the body of evidence exploring the link
between [methylmercury] and acute myocardial infarction (MI) to be
sufficiently strong to support its inclusion in future benefits
analyses, based both on direct epidemiological evidence of [a
methylmercury]-MI link and on [methylmercury's] association with
intermediary impacts that contribute to MI risk.'' Given the likely
mechanism of action associated with MI, the panel further recommended
that either hair-mercury or toenail-mercury be used as an exposure
metric because both reflect a longer-term pattern of exposure.
Regarding the shape of the CR function, the panel noted that the
EURAMIC study (Guallar et al., 2002) \50\ had identified a log-linear
model form with log-of exposure providing the best fit using toenail
mercury as the biomarker of exposure. The panel also discussed the
issue of potential effect modification by cardioprotective compounds
including polyunsaturated fatty acids (PUFA).\51\ Kuopio Ischaemic
Heart Disease Risk Factor Study (KIHD) and European Multicenter Case-
Control Study on Antioxidants, Myocardial Infarction, and Cancer of the
Breast Study (EURAMIC) datasets ``provide the strongest and most useful
data sets for quantifying methylmercury-related incidence of MI.''
However, the panel did note the disconnect between typical levels of
exposure to methylmercury in the U.S. population and the relatively
higher levels of exposure reflected in the two recommended epidemiology
studies (KIHD and EURAMIC). Therefore, the panel suggested that
consideration be given to restricting modeling MI mortality to those
with higher concentrations reflecting the levels of exposure found in
the two key epidemiology studies (corresponding to roughly 75th to 95th
percentile hair-mercury levels for U.S. women of child-bearing age, as
characterized in National Health and Nutrition Examination

Survey (NHANES) data and referenced by the panel).
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    \49\ U.S. EPA. 2021. National-Scale Mercury Risk Estimates for
Cardiovascular and Neurodevelopmental Outcomes for the National
Emission Standards for Hazardous Air Pollutants: Coal- and Oil-Fired
Electric Utility Steam Generating Units--Revocation of the 2020
Reconsideration, and Affirmation of the Appropriate and Necessary
Supplemental Finding; Notice of Proposed Rulemaking.
    \50\ Guallar E, Sanz-Gallardo MI, van't Veer P, Bode P, Aro A,
G[oacute]mez-Aracena J, Kark JD, Riemersma RA, Mart[iacute]n-Moreno
JM, Kok FJ; Heavy Metals and Myocardial Infarction Study Group.
Mercury, fish oils, and the risk of myocardial infarction. N Engl J
Med. 2002 Nov 28;347(22):1747-54. doi: 10.1056/NEJMoa020157.
    \51\ Virtanen JK, Voutilainen S, Rissanen TH, Mursu J, Tuomainen
TP, Korhonen MJ, Valkonen VP, Sepp[auml]nen K, Laukkanen JA, Salonen
JT. Mercury, fish oils, and risk of acute coronary events and
cardiovascular disease, coronary heart disease, and all-cause
mortality in men in eastern Finland. Arterioscler Thromb Vasc Biol.
2005 Jan;25(1):228-33. doi: 10.1161/01.ATV.0000150040.20950.61. Epub
2004 Nov 11.
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    In the intervening period since the release of the expert panel's
findings in 2011 (Roman et al., 2011), the EPA has continued to review
literature characterizing the relationship between methylmercury
exposure and cardiovascular effects. While the EPA has not yet
conducted a systematic review, two recent studies are of particular
interest for quantifying the potential relationship between U.S. EGU
mercury emissions and acute MI that informed a modeling approach. Giang
and Selin (2016) \52\ presented an approach for modeling MI mortality
reflecting a number of the recommendations presented in Roman et al.,
2011 including the use of the KIHD and EURAMIC studies as the basis for
a CR function including both the log-linear functional form and the
effect estimate derived from the KIHD study results. A second study, Hu
et al. 2021,\53\ presented a meta-analysis looking at the relationship
between methylmercury exposure and mortality. That paper utilized eight
studies each determined to be of good quality and reflecting at a
minimum, adjustments for age, sex, and n-3 PUFA in specifying dose-
response relationships. Historically, studies which account for n-3
PUFA have assumed a linear relationship between PUFAs and risk of MI
(Roman et al., 2011). However, the association between PUFA intake and
cardiovascular risk may not be linear (Mozaffarian and Rimm, 2006).\54\
The potential for confounding and effect modification by PUFA and
selenium makes it difficult to interpret the relationship between
methylmercury and MI, particularly at lower doses where there is
potential for masking of methylmercury toxicity. The results of the
meta-analysis by Hu et al., 2021 illustrated this phenomenon with their
J-shaped functions for both IHD and CVD, both of which showed an
initial region of negative slope (diminishing net risk with
methylmercury exposure) before reaching an inflection point (between 1
and 2 microgram per gram ([micro]g/g) hair-mercury depending on the
endpoint) where the function turns positive (increasing risk).
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    \52\ Giang A, Selin NE. Benefits of mercury controls for the
United States. Proc Natl Acad Sci U S A. 2016 Jan 12;113(2):286-91.
doi: 10.1073/pnas.1514395113. Epub 2015 Dec 28.
    \53\ Hu XF, Lowe M, Chan HM. Mercury exposure, cardiovascular
disease, and mortality: A systematic review and dose-response meta-
analysis. Environ Res. 2021 Feb;193:110538. doi: 10.1016/
j.envres.2020.110538. Epub 2020 Dec 5.
    \54\ Mozaffarian D, Rimm EB. Fish intake, contaminants, and
human health: Evaluating the risks and the benefits. JAMA. 2006 Oct
18;296(15):1885-99. doi: 10.1001/jama.296.15.1885. Erratum in: JAMA.
2007 Feb 14;297(6):590.
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    For the EPA's new screening-level assessment, we have considered
the recommendations presented in Roman et al., 2011, as well as the J-
shaped functions presented in Hu et al., 2021, and their implications
for considering overall confidence in specifying the relationship
between cardiovascular-related mortality and methylmercury exposure. In
particular, the EPA has higher confidence in the log-linear
relationship at levels of hair-mercury exposure above the selected
confidence cutpoints. In specifying these confidence cutpoints (for
modeling MI mortality) we have looked to recommendations presented in
Roman et al., 2011, specifically that we consider modeling risk for
levels of exposure reflected in the EURAMIC and KIHD studies (with
these equating to roughly 0.66 and 1.9 [micro]g/g hair-mercury,
respectively, or approximately the 75th-95th percentile of hair-mercury
levels seen in women of childbearing age in available 1999-2000 NHANES
survey data \55\). Further, we note that these confidence cutpoints
roughly match the inflection point for IHD and CVD seen in the J-shaped
plot presented in Hu et al., 2021, which further supports their use in
defining regions of methylmercury exposure above which we have
increased confidence in modeling MI mortality. However, as noted
earlier, we are not concluding here that there is an absence of risk
below these cutpoints, as such conclusions would require a weight of
the evidence analysis and subsequent independent peer review. Rather,
we are less confident in our ability to specify the nature of the CR
function in those lower exposure regions due to possible effect
modification and/or confounding by PUFA and/or selenium. Therefore, in
applying the CR function in modeling MI mortality, we included a set of
three functions-two including the cutpoints described above and a third
no-cutpoint version of the function reflecting the assumption that risk
extends across the entire range of methylmercury exposure. In terms of
the other elements of the CR function (shape and effect estimate), we
have also followed the advice presented in Roman et al., 2011, as
further illustrated through the analysis published by Giang and Selin
2016, and utilized a log-linear form and an effect estimate of 0.10 for
MI mortality obtained from the KIHD study (see 2021 Risk TSD). As with
the other risk estimates presented for methylmercury, these estimates
reflect the baseline for U.S. EGUs prior to implementation of MATS
(i.e., 29 tons).
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    \55\ NHANES has not continued to collect hair-mercury data in
subsequent years since the NHANES dataset referenced here. While
NHANES has continued with total blood-mercury monitoring, hair
mercury is a better biomarker for characterizing methylmercury
exposure over time. Given that the CR functions based on the KIHD
study (as well as observations presented in Roman et al. 2011
regarding cardio-modeling) were all based on hair-mercury, this was
chosen as the anchoring analytical biometric. The potential for bias
due to the use of the 1999-2000 NHANES data is further discussed in
the 2021 Risk TSD.
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b. Increased MI-Mortality Risk in Subsistence Fishers Exposed to
Methylmercury
    This screening-level analysis of MI-mortality risk is an extension
of the female subsistence-fisher-based at-risk watershed analysis
originally completed as part of the 2011 risk assessment supporting the
appropriate and necessary determination (USEPA, 2011) and documented in
the 2011 Final Mercury TSD. In that original analysis, a series of
female subsistence fisher risk scenarios was evaluated for a subset of
3,141 watersheds within the continental U.S. for which there were
sampled methylmercury fish tissue data (that fish tissue data allowing
a higher-confidence empirically-based assessment of methylmercury risk
to be generated for those watersheds). For each watershed, we used the
fish tissue methylmercury data to characterize total mercury-related
risk and then we estimated the portion of that total risk attributable
to U.S. EGUs (based on the fraction of total mercury deposition to
those watersheds associated with U.S. EGU emissions as supported by the
Mercury Maps approach, USEPA, 2011).\56\
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    \56\ A detailed discussion of the Mercury Maps approach
(establishing a proportional relationship between mercury deposition
and methylmercury concentrations in fish at the watershed level) is
presented in section <IP_ADDRESS> of the 2011 Final Mercury TSD which in
turn references: Mercury Maps--A Quantitative Spatial Link Between
Air Deposition and Fish Tissue Peer Reviewed Final Report. U.S. EPA,
Office of Water, EPA-823-R-01-009, September, 2001.
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    We have now extended the at-risk watershed analysis completed in
2011 for the subsistence fisher scenarios to include an assessment of
the potential for increased MI mortality risk.\57\ Specifically, we
have utilized the U.S. EGU-attributable methylmercury exposure
estimates ([micro]g/kg-day methylmercury intake) generated for the
subsistence fisher scenario in each

watershed to generate equivalent hair-mercury exposure estimates for
that subsistence fisher scenario in each watershed (see 2021 Risk TSD
for additional detail on the conversion of daily methylmercury intake
rates into hair-mercury levels). We then compare those hair-mercury
levels to the confidence cutpoints developed for the MI mortality
screening-level risk assessment described above in section III.A.3.a.
If the hair-mercury level for a particular watershed is above either
the EURAMIC or KIHD confidence cutpoint (i.e., above 0.66 and 1.9
[micro]g/g hair-mercury, respectively), then we consider that watershed
to be at increased risk for MI mortality exclusively due to that U.S.
EGU-attributable methylmercury exposure.\58\ Note, that this is not to
suggest that exposures at watersheds where U.S. EGU-attributable
contributions are below these cutpoints are without risk, but rather
that when exposure levels exceed these cutpoints, we have increased
confidence in concluding there is an increased risk of MI mortality for
subsistence fishers active within that watershed. It is also important
to note that in many cases, total methylmercury exposure (i.e., EGU
contribution plus contributions from other sources) may exceed these
confidence cutpoints such that subsistence fishers active at those
watersheds would be at increased risk of MI mortality at least in part
due to EGU emissions. See White Stallion, 748 F.3d at 1242-43 (finding
reasonable the EPA's decision to consider cumulative impacts of HAP
from EGUs and other sources in determining whether HAP emissions from
EGUs pose a hazard to public health under CAA section 112(n)(1)(A));
see also CAA section 112(n)(1)(B) (directing the EPA to study the
cumulative impacts of mercury emissions from EGUs and other domestic
stationary sources of mercury).
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    \57\ Note that while the 2011 Final Mercury TSD, in utilizing an
RfD-based approach reflecting neurodevelopmental effects, focused on
female subsistence fishers; the analysis focused on MI-mortality
risk covers all adult subsistence fishers, and we use our cutpoint
bounding analysis because there is not an RfD focused specifically
on cardiovascular effects for methylmercury.
    \58\ Although we have used the MI-mortality CR function
described in section III.A.3.a of this preamble to generate
mortality incidence estimates for the general fish consuming
population (see section III.A.3.c), this is not possible for
subsistence fishers since we are not able at this point to enumerate
them. Consequently, we use the confidence cutpoints associated with
that CR function to identify exposures associated with MI mortality
risk as described here.
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    Table 3 of the 2021 Risk TSD presents the results of the analysis
of risk for MI-mortality for the subsistence fisher scenarios. As with
the original RfD-based risk estimates, these results are dimensioned on
two key parameters (self-caught fish consumption rate and the watershed
percentile exposure level--hair-mercury [micro]g/g). Those watershed
percentile hair-mercury values that exceed the EURAMIC-based MI
mortality confidence cutpoints (0.66 [micro]g/g hair-mercury) are
shaded in the table and those cells that also exceed the KIHD-based MI
mortality confidence cutpoint (1.9 [micro]g/g hair-mercury) are bolded.
Once again, these thresholds identify levels of methylmercury exposure
(hair-mercury) associated with a clear association with MI-related
health effects (i.e., increased risk). Unlike the RfD-based risk
estimates, for MI-mortality estimates we only focus on U.S. EGU-
attributable methylmercury (i.e., whether U.S. EGU-attributable hair-
mercury exceeds the cutpoints of interest).
    Results for the typical subsistence fisher, representing high-end
self-caught fish consumption in the U.S. population, suggest that up to
10 percent of the watersheds modeled are associated with hair-mercury
levels (due to U.S. EGU mercury emissions alone) that exceed the lower
EURAMIC cutpoint for MI-mortality risk, with 1 percent of modeled
watersheds also exceeding the KIHD cutpoint (due to U.S. EGU-mercury
emissions alone). For low-income Black subsistence fishers active in
the Southeast, up to 25 percent of the watersheds exceed the lower
EURAMIC confidence threshold (assuming the highest rate of fish
consumption), with only the upper 1 percent of watersheds exceeding the
KIHD threshold (again based only on U.S. EGU-sourced mercury exposure).
c. Characterization of MI-Mortality Risk for the General U.S.
Population Resulting From the Consumption of Commercially-Sourced Fish
    The second of the three new screening-level risk analyses estimates
the incidence of MI mortality in the general U.S. population resulting
from consumption of commercially-sourced fish containing methylmercury
emitted from U.S. EGUs.\59\ This is accomplished by first estimating
the total burden of methylmercury-related MI mortality in the U.S.
population and then estimating the fraction of that total increment
attributable to U.S. EGUs. The task of modeling this health endpoint
can involve complex mechanistic modeling of the multi-step process
leading from U.S. EGU mercury emissions to mercury deposition over
global/regional fisheries to bioaccumulation of methylmercury in
fisheries stocks to exposure of U.S. fish consumers through consumption
of those commercially-sourced fish (e.g., Giang and Selin, 2016).
However, in recognition of the uncertainty associated with attempting
to model this more complex multi-step process, we have instead
developed a simpler screening analysis approach intended to generate a
range of risk estimates that reflects the impact of critical sources of
uncertainty associated with this exposure scenario. Rather than
attempting to generate a single high-confidence estimate of risk, which
in our estimation is challenging given overall uncertainty associated
with this exposure pathway, the goal with the bounding approach is
simply to generate a range of risk estimates for MI mortality that
furthers our understanding of the significant public health burden
associated with EGU HAP emissions.
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    \59\ Although the analysis presented here focuses on
methylmercury exposure associated with fish consumption which, as
noted earlier, is the primary source of methylmercury exposure for
the U.S. population, EGU mercury deposited to land can also impact
other food sources including those associated with agricultural
production (e.g., rice). In the context of fish consumption,
commercially-sourced fish refers to fish consumed in restaurants or
from food stores.
---------------------------------------------------------------------------

    The bounding approach developed for this particular scenario is
based on the assumption that fish sourced from global commercial
fisheries are loaded by mercury deposited to those fisheries and that
the fraction of that deposited mercury originating from U.S. EGUs will
eventually be reflected as a fraction of methylmercury in those fish
and subsequently as a fraction of MI mortality risk associated with
those U.S. EGUs. One of the challenges associated with this screening
analysis is how to attribute domestic EGU contributions to global
fisheries and how that might vary from location to location. For
simplicity, the bounding analysis includes two assumptions: (1) A
potential lower-bound reflecting the assumption that U.S. fish
consumption is largely sourced from global fisheries and consequently
the U.S. EGU contribution to total global mercury emissions
(anthropogenic and natural) can be used to approximate the U.S. EGU
fractional contribution to MI mortality and (2) a potential upper-bound
where we assume that fisheries closer to U.S. EGUs (e.g., within the
continental U.S. or just offshore and/or along the U.S. Atlantic and
Pacific coastlines) supply most of the fish and seafood consumed within
the U.S., and therefore U.S. EGU average deposition over the U.S. (as a
fraction of total mercury deposition) can be used to approximate the
U.S. EGU fractional contribution to MI mortality (see 2021 Risk TSD for
more detail).\60\ The EPA is

continuing to review the literature (including consideration of
research by FDA) to better define the relative contributions for
sources of fish consumed within the U.S. Note that the bounding
analysis also includes consideration for another key source of
uncertainty, namely, the specification of the CR function linking
methylmercury exposure to increased MI mortality and, in particular,
efforts to account for increased confidence in specifying the CR
function for higher levels of methylmercury exposure through the use of
confidence cutpoints (section III.A.3.a). Additional detail on the
stepwise process used to first generate the total U.S. burden of MI-
mortality related to total methylmercury exposure and then apportion
that total risk estimate to the fraction contributed by U.S. EGUs is
presented the 2021 Risk TSD. Based on the 29 tons of mercury emitted by
U.S. EGUs prior to implementation of MATS, the bounding estimates from
the fraction of total mercury deposition attributable to U.S. EGUs at
the global scale is 0.48 percent (lower bound) and 1.8 percent (upper
bound). These estimated bounding percentages are important since they
have a significant impact on the overall incidence of MI mortality
ultimately attributable to U.S. EGU-sourced mercury.
---------------------------------------------------------------------------

    \60\ Another way of stating this is that the lower-bound
estimate reflects an assumption that U.S. EGU mercury is diluted as
part of a global pool and impacts commercial fish sourced from
across the globe (with lower levels of methylmercury contribution)
while the upper-bound estimate reflects a focus on more near-field
regional impacts by U.S. EGU mercury to fish sourced either within
the continental U.S. or along its coastline (with greater relative
contribution to methylmercury levels).
---------------------------------------------------------------------------

    Reflecting both the spread in the apportionment of U.S. EGU-sourced
mercury (as described above) and application of the three possible
applications of the CR function for MI mortality (no confidence-
cutpoint, KIHD cutpoint, EURAMIC cutpoint), the estimated MI-mortality
attributable to U.S. EGU-sourced mercury for the general U.S.
population associated primarily with consumption of commercially-
sourced fish ranges from 5 to 91 excess deaths each year.\61\ For those
Americans with high levels of methylmercury in their body (i.e., above
certain cutpoints), the science suggests that any additional increase
in methylmercury exposure will raise the risk of fatal heart attacks.
Based on this screening analysis, even after imposition of the ARP and
other CAA criteria pollutant requirements that also reduce HAP
emissions from domestic EGU sources, we find that mercury emissions
from EGUs pose a risk of premature mortality due to MI.
---------------------------------------------------------------------------

    \61\ Inclusion of 95th percentile confidence intervals for the
effect estimate used in modeling MI mortality extends this range to
from 3 to 143 deaths (reflecting the 5th percentile associated with
the 5 lower bound estimate to the 95th percentile for the upper
bound estimate of 91).
---------------------------------------------------------------------------

d. Characterization of IQ Loss for Children Born to Mothers in the
General U.S. Population Resulting From the Consumption of Commercially
Sourced Fish (and Other Food Items Containing Methylmercury)
    The third new screening-level risk analysis estimates the incidence
of IQ loss in children in the general U.S. population resulting from
maternal consumption of commercially sourced fish containing
methylmercury attributable to U.S. EGUs (resulting in subsequent
prenatal exposure to methylmercury). The approach used in estimating
incidence of this adverse health effect shares several elements with
the approach described above for modeling MI mortality in the general
U.S. population, including in particular, the method used to apportion
the total methylmercury-related health burden to the fraction
associated with U.S. EGU mercury emissions (e.g., use of lower and
upper bound estimates of the fractional contribution of domestic EGU
sources). Other elements of the modeling approach, including the
specification of the number of children born annually in the U.S., the
specification of maternal baseline hair-mercury levels (utilizing
NHANES data) and the characterization of the linkage between
methylmercury exposure (in utero) and IQ loss, are based on methods
used in the original 2011 benefits analysis completed for MATS (USEPA,
2011) and are documented in the 2021 Risk TSD.
    As with the MI-mortality estimates described earlier, the two
bounding estimates for the fraction of total mercury deposition
attributable to U.S. EGUs at the global and regional scales (0.48
percent and 1.8 percent, respectively) have a significant impact on the
overall magnitude of IQ points lost (for children born to the general
U.S. population) which are ultimately attributable to U.S. EGUs.
However, the EPA has relatively high confidence in modeling this
endpoint due to greater confidence in the IQ loss CR function. The
range in IQ points lost annually due to U.S. EGU-sourced mercury is
estimated at 1,600 to 6,000 points, which is distributed across the
population of U.S. children covered by this analysis.\62\ Given
variation in key factors related to maternal methylmercury exposure, it
is likely that modeled IQ loss will not be uniformly distributed across
the population of exposed children and may instead, display
considerable heterogeneity.\63\ The bounding analysis described here
was not designed to characterize these complex patterns of
heterogeneity in IQ loss across the population of children simulated
and we note that such efforts would be subject to considerable
uncertainty. However, it does provide evidence of specific adverse
outcomes with real implications to those affected. Even small
degradations in IQ in the early stages of life are associated with
diminished future outcomes in education and earnings potential.
---------------------------------------------------------------------------

    \62\ Inclusion of 95th percentile confidence intervals for the
effect estimate used in modeling this endpoint extends this range to
from 80 to 12,600 IQ points lost (reflecting the 5th and 95th
percentiles).
    \63\ Maternal exposure (and hence IQ impacts to children) from
U.S. EGU-sourced mercury can display considerable variation due to
(a) spatial patterns of U.S. EGU mercury fate and transport
(including deposition and methylation) which affects impacts on fish
methylmercury and (b) variations in fish consumption by mothers
(including differences in daily intake, types of fish consumed and
geographical origins of that fish).
---------------------------------------------------------------------------

4. Most HAP Benefits Cannot Be Quantified or Monetized
    Despite the array of adverse health and environmental risks
associated with HAP emissions from U.S. coal- and oil-fired EGUs
documented above, as the above discussion demonstrates, it can be
technically challenging to estimate the extent to which EGU HAP
emissions will result in adverse effects quantitively across the U.S.
population absent regulation. In fact, the vast majority of the post-
control benefits of reducing HAP cannot be quantified or monetized with
sufficient quality to inform regulatory decisions due to data gaps,
particularly with respect to sensitive populations. But that does not
mean that these benefits are small, insignificant, or nonexistent.
There are numerous unmonetized effects that contribute to additional
benefits realized from emissions reductions. These include additional
reductions in neurodevelopmental and cardiovascular effects from
exposure to methylmercury, adverse ecosystem effects including mercury-
related impacts on recreational and commercial fishing, health risks
from exposure to non-mercury HAP, and health risks in EJ subpopulations
that face disproportionally high exposure to EGU HAP.
    Congress well understood the challenges in monetizing risks. As
discussed in section II.B above, the statutory language in CAA section
112 clearly supports a conclusion that the intended benefit of HAP
regulation is a reduction in the volume of HAP emissions to reduce
assumed and

identified risks from HAP with the goal of protecting even the most
exposed and most sensitive members of the population. The statute
requires the EPA to move aggressively to quickly reduce and eliminate
HAP, placing high value on doing so in the face of uncertainty
regarding the full extent of harm posed by hazardous pollutants on
human health and welfare. The statute also clearly places great value
on protecting even the most vulnerable members of the population, by
instructing the EPA, when evaluating risk in the context of a
determination of whether regulation is warranted, to focus on risk to
the most exposed and most sensitive members of the population. See,
e.g., CAA sections 112(c)(9)(B), 112(f)(2)(B), and 112(n)(1)(C). For
example, in evaluating the potential for cancer effects associated with
emissions from a particular source category under CAA section
112(f)(2), the EPA is directed by Congress to base its determinations
on the maximum individual risk (MIR) to the most highly exposed
individual living near a source. Similarly, in calculating the
potential for non-cancer effects to occur, the EPA evaluates the impact
of HAP to the most exposed individual and accounts for sensitive
subpopulations.
    Notably, Congress in CAA section 112 did not require the EPA to
quantify risk across the entire population, or to calculate average or
``typical'' risks. The statutory design focusing on maximum risk to
individuals living near sources acknowledges the inherent difficulty in
enumerating HAP effects, given the large number of pollutants and the
uncertainties associated with those pollutants, as well as the large
number of sources emitting HAP. However, this does not mean that these
effects do not exist or that society would not highly value these
reductions, despite the fact that the post-control effects of the
reductions generally cannot be quantified. The EPA has long
acknowledged the difficulty of quantifying and monetizing HAP benefits.
In March 2011, the EPA issued a report on the post-control benefits and
costs of the CAA. This Second Prospective Report \64\ is the latest in
a series of EPA studies that estimate and compare the post-control
benefits and costs of the CAA and related programs over time. Notably,
it was the first of these reports to include any attempt to quantify
and monetize the impacts of reductions in HAP, and it concentrated on a
small case study for a single pollutant, entitled ``Air Toxics Case
Study--Health Benefits of Benzene Reductions in Houston, 1990-2020.''
As the EPA summarized in the Second Prospective Report, ``[t]he purpose
of the case study was to demonstrate a methodology that could be used
to generate human health benefits from CAAA controls on a single HAP in
an urban setting, while highlighting key limitations and uncertainties
in the process. . . . Benzene was selected for the case study due to
the availability of human epidemiological studies linking its exposure
with adverse health effects.'' (pg. 5-29). In describing the approach,
the EPA noted: ``[b]oth the Retrospective analysis and the First
Prospective analysis omitted a quantitative estimation of the benefits
of reduced concentrations of air toxics, citing gaps in the
toxicological database, difficulty in designing population-based
epidemiological studies with sufficient power to detect health effects,
limited ambient and personal exposure monitoring data, limited data to
estimate exposures in some critical microenvironments, and insufficient
economic research to support valuation of the types of health impacts
often associated with exposure to individual air toxics.'' (pg. 5-29).
These difficulties have long hindered the Agency's ability to quantify
post-control HAP impacts and estimate the monetary benefits of HAP
reductions.
---------------------------------------------------------------------------

    \64\ U.S. EPA Office of Air and Radiation, April 2011. The
Benefits and Costs of the Clean Air Act from 1990 to 2020, Final
Report--Rev. A. Available at https://www.epa.gov/sites/production/files/2015-07/documents/fullreport_rev_a.pdf.
---------------------------------------------------------------------------

    In preparing the benzene case study for inclusion in the Second
Prospective Report, the Agency asked the Advisory Council on Clean Air
Compliance Analysis (the Council) to review the approach. In its 2008
consensus advice to the EPA after reviewing the benzene case study,\65\
the Council noted that ``Benzene . . . has a large epidemiological
database which OAR used to estimate the health benefits of benzene
reductions due to CAAA controls. The Council was asked to consider
whether this case study provides a basis for determining the value of
such an exercise for HAP benefits characterization nationwide.'' They
concluded:
---------------------------------------------------------------------------

    \65\ U.S. EPA Advisory Council on Clean Air Act Compliance
Analysis, Review of the Benzene Air Toxics Health Benefits Case
Study. July 11, 2008. Available at https://nepis.epa.gov/Exe/ZyPDF.cgi/P1000ZYP.PDF?Dockey=P1000ZYP.PDF.

    As recognized by OAR, the challenges for assessing progress in
health improvement as a result of reductions in emissions of
hazardous air pollutants (HAPs) are daunting. Accordingly, EPA has
been unable to adequately assess the economic benefits associated
with health improvements from HAP reductions due to a lack of
exposure-response functions, uncertainties in emissions inventories
and background levels, the difficulty of extrapolating risk
estimates to low doses and the challenges of tracking health
progress for diseases, such as cancer, that have long latency
periods. . . .
    The benzene case study successfully synthesized best practices
and implemented the standard damage function approach to estimating
the benefits of reduced benzene, however the Council is not
optimistic that the approach can be repeated on a national scale or
extended to many of the other 187 air toxics due to insufficient
epidemiological data. With some exceptions, it is not likely that
the other 187 HAPs will have the quantitative exposure-response data
needed for such analysis. Given EPA's limited resources to evaluate
a large number of HAPs individually, the Council urges EPA to
consider alternative approaches to estimate the benefits of air
toxics regulations.

    In addition to the difficulties noted by the Council, there are
other challenges that affect the EPA's ability to fully characterize
post-control impacts of HAP on populations of concern, including
sensitive groups such as children or those who may have underlying
conditions that increase their risk of adverse effects following
exposure to HAP. Unlike for criteria pollutants such as ozone and PM,
the EPA lacks information from controlled human exposure studies
conducted in clinical settings which enable us to better characterize
dose-response relationships and identify subclinical outcomes. Also, as
noted by the Council and by the EPA itself in preparing the benzene
case study, the almost universal lack of HAP-focused epidemiological
studies is a significant limitation. Estimated risks reported in
epidemiologic studies of fine PM (PM2.5) and ozone enable
the EPA to estimate health impacts across large segments of the U.S.
population and quantify the economic value of these impacts.
Epidemiologic studies are particularly well suited to supporting air
pollution health impact assessments because they report measures of
population-level risk that can be readily used in a risk assessment.
    However, such studies are infrequently performed for HAP. Exposure
to HAP is typically more uneven and more highly concentrated among a
smaller number of individuals than exposure to criteria pollutants.
Hence, conducting an epidemiologic study for HAP is inherently more
challenging; for starters, the small population size means such studies
often lack sufficient statistical power to detect effects. For example,
in the case of mercury, the most exposed and most sensitive members of
the population

may be both small and highly concentrated, such as the subsistence
fishers that the EPA has identified as likely to suffer deleterious
effects from U.S. EGU HAP emissions. While it is possible to estimate
the potential risks confronting this population in a case-study
approach (an analysis that plays an important role in supporting the
public health hazard determination for mercury as discussed above in
sections III.A.2 and III.A.3), it is not possible to translate these
risk estimates into post-control quantitative population-level impact
estimates for the reasons described above.
    Further, for many HAP-related health endpoints, the Agency lacks
economic data that would support monetizing HAP impacts, such as
willingness to pay studies that can be used to estimate the social
value of avoided outcomes like heart attacks, IQ loss, and renal or
reproductive failure. In addition, the absence of socio-demographic
data such as the number of affected individuals comprising sensitive
subgroups further limits the ability to monetize HAP-impacted effects.
All of these deficiencies impede the EPA's ability to quantify and
monetize post-control HAP-related impacts even though those impacts may
be severe and/or impact significant numbers of people.
    Though it may be difficult to quantify and monetize most post-
control HAP-related health and environmental benefits, this does not
mean such benefits are small. The nature and severity of effects
associated with HAP exposure, ranging from lifelong cognitive
impairment to cancer to adverse reproductive effects, implies that the
economic value of reducing these impacts would be substantial if they
were to be quantified completely. By extension, it is reasonable to
expect both that reducing HAP-related incidence affecting individual
endpoints would yield substantial benefits if fully quantified, and
moreover that the total societal impact of reducing HAP would be quite
large when evaluated across the full range of endpoints. In judging it
appropriate to regulate based on the risks associated with HAP
emissions from U.S. EGUs, the EPA is placing weight on the likelihood
that these effects are significant and substantial, as supported by the
health evidence. The EPA's new screening-level analyses laid out in the
Risk TSD for this proposal illustrate this point. Specifically, in
exploring the potential for MI-related mortality risk attributable to
mercury emissions from U.S. EGUs, the EPA's upper bound estimate is
that these emissions may contribute to as many as 91 additional
premature deaths each year. The value society places on avoiding such
severe effects is very high; as the EPA illustrates in the valuation
discussion in the 2021 Risk TSD, the benefit of avoiding such effects
could approach $720 million per year. Similarly, for IQ loss in
children exposed in utero to U.S. EGU-sourced mercury, our upper bound
estimate approaches 6,000 IQ points lost which could translate into a
benefit approaching $50 million per year.
    These estimates are intended to illustrate the point that the HAP
impacts are large and societally meaningful, but not to suggest that
they are even close to the full benefits of reducing HAP. There are
many other unquantified effects of reducing EGU HAP that would also
have substantial value to society. As described above, mercury alone is
associated with a host of adverse health and environmental effects. The
statute clearly identifies this basket of effects as a significant
concern in directing the EPA to study them specifically. If the EPA
were able to account for all of these post-control effects in our
quantitative estimates, the true benefits of MATS would be far clearer.
However, available data and methods currently preclude a full
quantitative accounting of the post-control impacts of reducing HAP
emissions from U.S. EGUs and a monetization of these impacts.
    There are other aspects of social willingness to pay that are not
accounted for in the EPA's quantitative estimate of benefits either.
For example, in previous MATS-related rulemakings and analysis, the EPA
has not estimated what individuals would be willing to pay in order to
reduce the exposure of others who are exposed (even if they are not
experiencing high levels of HAP exposure themselves). These may be
considered and quantified as benefits depending on whether it is the
health risks to others in particular that is motivating them.\66\ For
example, Cropper et al. (2016) found that focus group participants
indicated a preference for more equitable distribution of health risks
than for income, which indicates that it is specifically the risks
others face that was important to the participants.\67\ This result is
particularly important as exposure to HAP is often disproportionately
borne by underserved and underrepresented communities (Bell and Ebisu,
2012).\68\ Unfortunately, studies to quantify the willingness to pay
for a more equitable distribution of HAP exposures are limited, so
quantification of this benefit likely cannot be performed until new
research is conducted.
---------------------------------------------------------------------------

    \66\ Jones-Lee, M.W. Paternalistic Altruism and the Value of
Statistical Life. The Economic Journal, vol. 102, no. 410, 1992, pp.
80-90.
    \67\ Cropper M., Krupnick A., and W. Raich, Preferences for
Equality in Environmental Outcomes, Working Paper 22644 http://www.nber.org/papers/w22644 National Bureau of Economic Research,
September 2016.
    \68\ Bell, Michelle L., and Keita Ebisu. Environmental
inequality in exposures to airborne particulate matter components in
the United States. Environmental Health Perspectives 120.12 (2012):
1699-1704.
---------------------------------------------------------------------------

    The HAP-related legislative history for the 1990 Amendments
includes little discussion of the monetized benefits of HAP, perhaps
due to these attendant difficulties. When such monetized benefits were
estimated in several outside reports submitted to Congress before
passage of the 1990 Amendments, the estimates were based on reduced
cancer deaths and the value of the benefits that are quantified were
estimated to be small as compared to the estimated costs of regulating
HAP emissions under CAA section 112. See, e.g., A Legislative History
of the Clean Air Act Amendments of 1990, Vol. I at 1366-67 (November
1993) (estimating the total annual cost of CAA section 112 to be
between $6 billion and $10 billion per year and the estimated annual
benefits to be between $0 and $4 billion per year); id. at 1372-73
(estimating the total annual cost of CAA section 112 to be between $14
billion and $62 billion per year and the estimated annual benefits to
be between $0 and $4 billion per year). Despite the apparent disparity
of estimated costs and monetized benefits, Congress still enacted the
revisions to CAA section 112. Thus, it is reasonable to conclude that
Congress found HAP emissions to be worth regulating even without
evidence that the monetized benefits of doing so were greater than the
costs. The EPA believes this stems from the value that the statute
places on reducing HAP regardless of whether the post-control benefits
of doing so can be quantified or monetized, and the statute's purpose
of protecting even the most exposed and most sensitive members of the
population.
5. Characterization of HAP Risk Relevant to Consideration of
Environmental Justice
    In assessing the adverse human health effects of HAP pollution from
EGUs, we note that these effects are not borne equally across the
population, and that some of the most exposed individuals and
subpopulations--protection of whom is, as noted, of particular concern
under CAA section 112--are minority and/or low-income populations.
Executive Order 12898 (59 FR 7629;

February 16, 1994) establishes Federal executive policy on EJ issues.
That Executive Order's main provision directs Federal agencies, to the
greatest extent practicable and permitted by law, to make EJ part of
their mission by identifying and addressing, as appropriate,
disproportionately high and adverse human health or environmental
effects of their programs, policies, and activities on minority
populations and low-income populations. Executive Order 14008 (86 FR
7619; February 1, 2021) also calls on Federal agencies to make
achieving EJ part of their missions ``by developing programs, policies,
and activities to address the disproportionately high and adverse human
health, environmental, climate-related and other cumulative impacts on
disadvantaged communities, as well as the accompanying economic
challenges of such impacts.'' That Executive Order also declares a
policy ``to secure environmental justice and spur economic opportunity
for disadvantaged communities that have been historically marginalized
and overburdened by pollution and under-investment in housing,
transportation, water and wastewater infrastructure, and health care.''
Under Executive Order 13563, Federal agencies may consider equity,
human dignity, fairness, and distributional considerations, where
appropriate and permitted by law.
    In the context of MATS, exposure scenarios of clear relevance from
an EJ perspective include the full set of subsistence fisher scenarios
included in the watershed-level risk assessments completed for the
rule. Subsistence fisher populations are potentially exposed to
elevated levels of methylmercury due to their elevated levels of self-
caught fish consumption which, in turn, are often driven either by
economic need (i.e., poverty) and/or cultural practices. In the context
of MATS, we completed watershed-level assessments of risks for a broad
set of subsistence fisher populations covering two health endpoints of
clear public health significance including: (a) Neurodevelopmental
effects in children exposed prenatally to methylmercury (the
methylmercury-based RfD analysis described in the 2011 Final Mercury
TSD) and (b) potential for increased MI-mortality risk in adults due to
methylmercury exposure (section III.A.3.b above).
    The general subsistence fisher population that was evaluated
nationally for both analyses was not subdivided by socioeconomic
status, race, or cultural practices.\69\ Therefore, the risk estimates
derived do not fully inform our consideration of EJ impacts, although
the significantly elevated risks generated for this general population
are clearly relevant from a public health standpoint. However, the
other, more differentiated subsistence fisher populations, which are
subdivided into smaller targeted communities, are relevant in the EJ
context and in some instances were shown to have experienced levels of
risk significantly exceeding those of the general subsistence fisher
population, as noted earlier in section III.A.3.b.
---------------------------------------------------------------------------

    \69\ Note that the RfD-based analysis described in the 2011
Final Mercury TSD and referenced here addressed the potential for
neurodevelopmental effects in children and therefore focused on the
ingestion of methylmercury by female subsistence fishers. By
contrast, the analysis focusing on increased MI-mortality risk for
subsistence fishers described in the 2021 Risk TSD and referenced
here was broader in scope and encompassed all adult subsistence
fishers.
---------------------------------------------------------------------------

    In particular, for the watershed analysis focusing on the
methylmercury RfD-based analysis (i.e., neurodevelopmental risk for
children exposed prenatally), while the general female fisher scenario
suggested that modeled exposures (from U.S. EGU-sourced mercury alone)
exceeded the methylmercury RfD in approximately 10 percent of the
watersheds modeled (2011 Final Mercury TSD, Table 2-6), for low-income
Black subsistence fisher females in the Southeast, modeled exposures
exceeded the RfD in approximately 25 percent of the watersheds. These
results suggest a greater potential for adverse effects in low-income
Black populations in the Southeast. Similarly, while the general
subsistence fisher had exposure levels suggesting an increased risk for
MI-mortality risk in 10 percent of the watersheds modeled, two sub-
populations were shown to be even further disadvantaged. Low-income
Black and white populations in the Southeast and tribal fishers active
near the Great Lakes had the potential for increased risk in 25 percent
of the watersheds modeled.\70\ Both of these results (the
neurodevelopmental RfD-based analysis and the analysis of increased MI-
mortality risk) suggest that subsistence fisher populations that are
racially or culturally, geographically, and income-differentiated could
experience elevated risks relative to not only the general population
but also the population of subsistence fishers generally. We think
these results are relevant in considering the benefits of regulating
EGU HAP.
---------------------------------------------------------------------------

    \70\ Recognizing challenges in obtaining high-end consumption
rates for tribal populations active in areas of high U.S. EGU impact
(e.g., Ohio River valley, areas of the central Southeast such as
northern Georgia, northern South Carolina, North Carolina and
Tennessee) there is the potential for our analysis of tribal-
associated risk to have missed areas of elevated U.S. EGU-sourced
mercury exposure and risk. In that case, estimates simulated for
other subsistence populations active in those areas (e.g., low-
income whites and Blacks in the Southeast as reported here and in
Table 3 of the 2021 Risk TSD) could be representative of the ranges
of risk experienced by tribal populations to the extent that
cultural practices result in similar levels of increased fish
consumption.
---------------------------------------------------------------------------

6. Overview of Health and Environmental Effects Associated With Non-HAP
Emissions From EGUs
    Alongside the HAP emissions enumerated above, U.S. EGUs also emit a
substantial quantity of criteria pollutants, including direct
PM2.5, nitrogen oxides (NOX) (including
NO2), and SO2, even after implementation of the
ARP and numerous other CAA requirements designed to control criteria
pollutants. In the 2011 RIA, for example, the EPA estimated that U.S.
EGUs would emit 3.4 million tons of SO2 and 1.9 million tons
of NOX in 2015 prior to implementation of any controls under
MATS (see Table ES-2). These EGU SO2 emissions were
approximately twice as much as all other sectors combined (EPA
SO2 Integrated Science Assessment, 2017).\71\ These
pollutants contribute to the formation of PM2.5 and ozone
criteria pollutants in the atmosphere, the exposure to which is
causally linked with a range of adverse public health effects.
SO2 both directly affects human health and is a precursor to
PM2.5. Short-term exposure to SO2 causes
respiratory effects, particularly among adults with asthma.
SO2 serves as a precursor to PM2.5, the exposure
to which increases the risk of premature mortality among adults, lung
cancer, new onset asthma, exacerbated asthma, and other respiratory and
cardiovascular diseases. Likewise, EGU-related emissions of
NOX will adversely affect human health in the form of
respiratory effects including exacerbated asthma. NOX is a
precursor pollutant to both PM2.5 and ground-level ozone.
Exposure to ozone increases the risk of respiratory-related premature
death, new onset asthma, exacerbated asthma, and other outcomes. Fully
accounting for the human health impacts of reduced EGU emissions under
MATS entails quantifying both the direct impacts of HAP as well as the
avoided premature deaths and illnesses associated with reducing these
co-emitted criteria pollutants. Similarly,

U.S. EGUs emit substantial quantities of CO2, a powerful
greenhouse gas (GHG): The EPA estimated these emissions at 2.23 million
metric tpy in 2015 (2011 RIA, Table ES-2). The environmental impacts of
GHG emissions are accounted for through the social cost of carbon,\72\
which can be used to estimate the benefits of emissions reductions due
to regulation.
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    \71\ U.S. EPA. Integrated Science Assessment for Sulfur Oxides--
Health Criteria (Final Report). U.S. Environmental Protection
Agency, Washington, DC, EPA/600/R-17-451, December 2017.
    \72\ See https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html: ``EPA and other federal agencies use
estimates of the social cost of carbon (SC-CO2) to value
the climate impacts of rulemakings. The SC-CO2 is a
measure, in dollars, of the long-term damage done by a ton of carbon
dioxide (CO2) emissions in a given year. This dollar
figure also represents the value of damages avoided for a small
emission reduction (i.e., the benefit of a CO2
reduction). The SC-CO2 is meant to be a comprehensive
estimate of climate change damages and includes changes in net
agricultural productivity, human health, property damages from
increased flood risk, and changes in energy system costs, such as
reduced costs for heating and increased costs for air conditioning.
However, given current modeling and data limitations, it does not
include all important damages.''
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    Not all of the non-HAP benefits of MATS were quantified or
monetized in the 2011 RIA. However, the EPA thoroughly documented these
potential effects and identified those for which quantification and/or
monetization was possible. Specifically, the EPA calculated the number
and value of 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 (2011 RIA, p. ES-3). We also estimated substantial
additional health improvements for children from reductions in upper
and lower respiratory illnesses, acute bronchitis, and asthma attacks.
In addition, we included in our monetized co-benefits estimates the
effect from the reduction in CO2 emissions resulting from
this rule, based on the interagency SC-CO2 estimates. These
benefits stemmed from imposition of MATS and would be coincidentally
realized alongside the HAP benefits.
7. Summary of Public Health Hazards Associated With Emissions From EGUs
    The EPA is proposing to find that the evidence provided in this
section of the preamble, informed where possible with new scientific
evidence available since the publication of the 2016 Supplemental
Finding, once again demonstrates that HAP released from U.S. EGUs
represent a significant public health hazard absent regulation under
CAA section 112. As noted earlier, the EPA found that even after
imposition of the other requirements of the CAA, EGUs were the largest
domestic source of mercury, HF, HCl, and selenium and among the largest
domestic contributors of arsenic, chromium, cobalt, nickel, hydrogen
cyanide, beryllium, and cadmium. The EPA has documented a wide range of
adverse health effects in children and adults associated with mercury
including, in particular, neurodevelopmental effects in children
exposed prenatally (e.g., IQ, attention, fine motor-function, language,
and visual spatial ability) and a range of cardiovascular effects in
adults including fatal MI and non-fatal IHD. Non-mercury HAP have also
been associated with a wide range of chronic health disorders (e.g.,
irritation of the lung; decreased pulmonary function, pneumonia, or
lung damage; detrimental effects on the central nervous system; and
damage to the kidneys). Furthermore, three of the key metal HAP emitted
by EGUs (arsenic, chromium, and nickel) have been classified as human
carcinogens and there is evidence to suggest that, prior to MATS,
emissions from these sources had the potential to result in cancer
risks greater than 1-in-1 million.
    Further, this section describes the results from several new
screening-level risk assessments considering mercury from domestic EGU
sources. These risk assessments focused on two broad populations of
exposure: (a) Subsistence fishers exposed to mercury through self-
caught fish consumption within the continental U.S. and (b) the general
U.S. population exposed to mercury through the consumption of
commercially-sourced fish (i.e., purchased from restaurants and food
stores). The results of these screening-level risk assessments are
useful for informing our understanding about the potential scope and
public health importance of these impacts, but remaining uncertainties
prohibit precise estimates of the size of these impacts currently. For
example, numerous studies considering multiple, large cohorts have
shown that people exposed to high amounts of mercury are at higher risk
of fatal and non-fatal CVD. While U.S. EGUs are only one of multiple
global sources that contribute to this mercury exposure, the EPA's
screening analysis suggests the potential for U.S. EGU emissions of
mercury to contribute to premature mortality in the general U.S.
population.
    Furthermore, as part of the subsistence fisher analyses, we
included scenario modeling for a number of EJ-relevant populations
showing that several populations (including low-income Blacks and
whites in the Southeast and tribal populations near the Great Lakes)
had risk levels that were significantly above the general subsistence
fisher population modeled for the entire U.S. As noted earlier, the EPA
believes that Congress intended in CAA section 112 to address risks to
the most exposed and most sensitive members of the public. These
additional risk assessments suggest that there are populations that are
particularly vulnerable to EGU HAP emissions, including populations of
concern from an EJ standpoint.
    MATS plays a critical role in reducing the significant volume and
risks associated with EGU HAP emissions discussed above. Mercury
emissions have declined by 86 percent, acid gas HAP by 96 percent, and
non-mercury metal HAP by 81 percent since 2010 (pre-MATS). See Table 4
at 84 FR 2689 (February 7, 2019). MATS is the only Federal requirement
that guarantees this level of HAP control from EGUs. At the same time,
the concomitant reductions in CO2, NOX, and
SO2, also provide substantial public health and
environmental benefits. Given the numerous and important public health
and environmental risks associated with EGU emissions, the EPA again
concludes that the advantages of regulating HAP emissions from this
sector are significant. Acknowledging the difficulties associated with
characterizing risks from HAP emissions discussed earlier in this
section, we solicit comments about the health and environmental hazards
of EGU HAP emissions discussed in this section and the appropriate
approaches for quantifying such risks, as well any information about
additional risks and hazards not discussed in this proposal.

B. Consideration of Cost of Regulating EGUs for HAP

1. Introduction
    In evaluating the costs and disadvantages of MATS, we begin with
the costs to the power industry of complying with MATS. This assessment
uses a sector-level (or system-level) accounting perspective to
estimate the cost of MATS, looking beyond just pollution control costs
for directly affected EGUs to include incremental costs associated with
changes in fuel supply, construction of new capacity, and costs to non-
MATS units that were also projected to adjust operating decisions as
the power system adjusted to meet MATS requirements. Such an approach
is warranted due to the nature of the power sector, which is a large,
complex, and interconnected industry.

This means that while the MATS requirements are directed at a subset of
EGUs in the power sector, the compliance actions of the MATS-regulated
EGUs can affect production costs and revenues of other units due to
generation shifting and fuel and electricity price changes. Thus, the
EPA's projected compliance cost estimate represents the incremental
costs to the entire power sector to generate electricity, not just the
compliance costs projected to be incurred by the coal- and oil-fired
EGUs that are regulated under MATS. Limiting the cost estimate to only
those expenditures incurred by EGUs directly regulated by MATS would
provide an incomplete estimate of the costs of the rule.
    Using this broad view, in the 2011 RIA we projected that the
compliance cost of MATS would be $9.6 billion per year in 2015.\73\
This estimate of compliance cost was based on the change in electric
power generation costs between a base case without MATS and a policy
case where the sector complies with the HAP emissions limits in the
final MATS. The EPA generated this cost estimate using the Integrated
Planning Model (IPM).\74\ This model is designed to reflect electricity
markets as accurately as possible using the best available information
from utilities, industry experts, natural gas and coal market experts,
financial institutions, and government statistics. Notably, the model
includes cost and performance estimates for state-of-the-art air
pollution control technologies with respect to mercury and other HAP
controls. But there are inherent limits to what can be predicted ex
ante. And because the estimate was made 5 years prior to full
compliance with MATS, stakeholders, including a leading power sector
trade association, have indicated that our initial cost projection
significantly overestimated actual costs expended by industry. There
are significant challenges to producing an ex post cost estimate that
provides an apples-to-apples comparison to our initial cost
projections, due to the complex and interconnected nature of the
industry. However, independent analyses provided to the EPA indicate
that we may have overestimated the cost of MATS by billions of dollars
per year. Moreover, there have been significant changes in the power
sector in the time since MATS was promulgated that were not anticipated
in either EPA or U.S. Energy Information Administration (EIA)
projections at the time.\75\ Entirely outside of the realm of EPA
regulation, there were dramatic shifts in the cost of natural gas and
renewables, state policies, and Federal tax incentives, which have also
further encouraged construction of new renewables. These have led to
significantly faster and greater than anticipated retirement of coal
capacity and coal-fired generation.
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    \73\ All costs were reported in 2007 dollars.
    \74\ IPM, developed by ICF International, is a state-of-the-art,
peer-reviewed, dynamic, deterministic linear programming model of
the contiguous U.S. electric power sector. IPM provides forecasts of
least-cost capacity expansion, electricity dispatch, and emission
control strategies while meeting electricity demand and various
environmental, transmission, dispatch, and reliability constraints.
The EPA has used IPM for over 2 decades to understand power sector
behavior under future business-as-usual conditions and to evaluate
the economic and emission impacts of prospective environmental
policies.
    \75\ In 2009, coal-fired generation was by far the most
important source of utility scale generation, providing more power
than the next two sources (natural gas and nuclear) combined. By
2016, natural gas had passed coal-fired generation as the leading
source of generation in the U.S. While natural gas-fired generation,
nuclear generation and renewable generation have all increased since
2009, coal-fired generation has significantly declined.
---------------------------------------------------------------------------

    While there are significant limitations to producing an ex post
cost estimate, we have endeavored, where possible, to approximate the
extent of our overestimate. The unexpected shifts in the power sector,
including the rapid increase in natural gas supplies that occurred
after promulgation of MATS, resulted in our projected estimates of
natural gas prices to be approximately double what they were in
actuality. Incremental natural gas expenditures accounted for
approximately 25 percent of the $9.6 billion compliance cost estimate
for 2015 in the 2011 RIA. The market trends of the power sector also
had major impacts on the number of controls installed and operated on
coal-fired EGUs in the years following promulgation of MATS. With
respect to just pollution control installation and operation, we
project that we overestimated annual compliance costs by at least $2.2
to 4.4 billion per year, simply as a result of fewer pollution controls
being installed than were estimated in the 2011 RIA. Though this range
of an overestimate is limited to costs associated with pollution
controls and operation, those costs made up 70 percent of the projected
$9.6 billion figure.
    We additionally find that the controls that were installed at MATS-
regulated EGUs were likely both less expensive and more effective in
reducing pollution than originally projected, resulting in our estimate
likely being too high for these reasons as well. Lastly, since
completing the 2011 RIA, we have updated several assumptions in our
modeling that would also have resulted in a lower cost estimate had
they been incorporated into our modeling at the time of the rule.
Taking into account the above considerations, we believe we
overestimated the cost of MATS by billions of dollars.
    We next examine the projected cost of MATS--both total cost and
specific types of costs--using sector-level metrics that put those cost
estimates in context with the economics of the power sector. The reason
we examine these metrics is to better understand the disadvantages that
expending these costs had on the EGU industry and the public more
broadly, just as on the benefits side we look beyond the volume of
pollution reductions to the health and environmental advantages
conferred by the reductions.
    For purposes of these analyses, we use the 2011 RIA projections,
keeping in mind our newer analyses, which indicated that those
projections were almost certainly overestimated. Specific to the power
sector, we evaluate the projected costs of the rule to revenues from
electricity sales across nearly 20 years, and we compare the projected
expenditures required under the rule with historic expenditures by the
industry over the same time period. We additionally evaluate broader
impacts on the American public by looking at projected effects of MATS
on retail electricity prices and our analyses of whether the power
sector could continue to provide adequate and reliable electricity
after imposition of the rule. We find that, when viewed in context, the
projected costs of MATS to both the power sector and the public were
small relative to these metrics and well within the range of historical
variability. Moreover, experience has borne out our projection that the
EGU sector could continue to provide adequate, reliable, and affordable
electricity to the American public after the imposition of the rule.
    Section III.B.2 contains our discussion of the ways in which the
compliance costs for MATS were likely overestimated. Section III.B.3
expands upon and re-evaluates the cost metrics used in the 2016
Supplemental Finding by adding post-promulgation information to our
analysis, and we discuss impacts on power sector generating capacity.
In section III.B.4, we propose to reaffirm additional cost
considerations regarding the availability and cost of control
technologies discussed in earlier rulemakings, and in section III.B.5,
we provide our proposed conclusions regarding the costs, or
disadvantages, of regulating HAP from EGUs.

2. Compliance Cost Projections in the 2011 RIA Were Likely
Significantly Overestimated
    In issuing this proposal, the EPA finds itself in a position
Congress was not likely to have contemplated when it promulgated the
1990 Amendments. The statute contemplated that the EPA would have
completed the required studies and presumably made its determination
more than 20 years ago. Due to litigation and multiple changes of
administration following Michigan, we are, at this point, nearly 10
years after promulgation of the regulation about which we are making a
threshold determination, and 5 years after full implementation of that
regulation. The vast majority of MATS-affected sources were required to
be in compliance with the rule's requirements by April 2016, and
installation of new controls-or upgrades to existing controls-were in
place by 2017.\76\ This means we now have on hand unit-level data
regarding installations, a clearer picture about market trends, and
updated, more accurate assumptions that, taken together, produce a very
different picture of the actual costs of MATS than what we projected
when we reaffirmed the appropriate and necessary determination and
promulgated the rule in 2012. Therefore, while the Agency considers
that the information that was available at the time of MATS
promulgation provided a valid analytical basis for the threshold
appropriate and necessary determination, because many years have
elapsed since then, the EPA believes it is reasonable to examine how
the power sector has evolved since MATS was finalized and, with the
benefit of hindsight, compare important aspects of the 2011 RIA
projections with what actually happened since MATS was promulgated.
Because our obligation under CAA section 112(n)(1)(A) is to fully
consider the advantages and disadvantages of regulating a large,
critically important industry, whose role impacts the lives of every
American, we think it is important to evaluate and consider the best,
currently available information, even if, as discussed in sections
III.B.3 and 4, the pre-existing record supports the same conclusion.
This ex post examination demonstrates that the EPA almost certainly
significantly overestimated compliance costs in the 2011 RIA, which
further supports the determination that regulation is appropriate and
necessary after considering cost. We also do not view this updated,
post-hoc evaluation of what happened post-promulgation as undermining
the record we established in 2012. Models are not invalidated ``solely
because there might be discrepancies between those predictions and the
real world. That possibility is inherent in the enterprise of
prediction.'' EME Homer City Generation, L.P. v. EPA, 795 F.3d 118,
135-36 (D.C. Cir. 2015).
---------------------------------------------------------------------------

    \76\ Affected sources were required to be in compliance with the
requirements in MATS within 3 years after the effective date of the
rule (i.e., by April 2015). However, sources were allowed to request
an additional year to comply with the rule and the vast majority of
sources were required to be in compliance with the rule's
requirements by April 2016. We therefore think 2017 is a reasonable
year in which to analyze installed controls on the EGU fleet.
---------------------------------------------------------------------------

    In an ideal world, with perfect information, we would be able to
generate an ex post analysis of regulatory costs that could be compared
to our ex ante cost estimate prepared at the time MATS was issued.
However, it is extremely challenging to produce rigorous retrospective
estimates of regulatory costs. A literature review and series of case
studies performed by EPA staff provides insights on how analysts can
perform retrospective cost analysis.\77\ Kopits et al. (2015)
identifies several challenges associated with ex post cost assessments,
including data limitations with respect to how facilities chose to
comply with regulations and comprehensive facility-level pollution
abatement costs. A key component to a rigorous retrospective analysis
noted by the authors that can be particularly difficult to achieve is
an accurate definition of the counterfactual, that is, what would have
occurred absent the rule. It is this counterfactual that provides the
baseline against which the incremental costs of regulation are
estimated.
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    \77\ Kopits, E., A. McGartland, C. Morgan, C. Pasurka, R.
Shadbegian, N. B. Simon, D. Simpson and A. Wolverton (2015).
Retrospective cost analyses of EPA regulations: a case study
approach. Journal of Benefit-Cost Analysis 5(2): 173-193.
---------------------------------------------------------------------------

    In the case of MATS, to construct an estimate of ex post
implementation costs that is directly comparable to the ex ante 2011
RIA cost estimate, we would first need to accurately attribute changes
in the power sector that were due to MATS requirements rather than to
market and technological changes, other regulations, or, importantly,
combinations of these factors (i.e., properly specify the
counterfactual). Second, we would need actual information of the
incremental costs that had been associated with facility-level
operational changes due to MATS, such as observed changes in dispatch,
actual fuel consumption, and how controls in MATS-affected units were
actually operated. Even the operation of non-MATS affected units would
be relevant to such an analysis, because operational decisions are
interconnected on the grid via dispatch decisions as well as through
fuel markets. While there may be approaches such as econometric
analysis, simulation modeling, and event study analysis that could
capture and estimate components of the problem identified above and
derive an estimate of ex post MATS costs, the approach would very
likely require different methods and assumptions than the 2011 RIA
estimates which were based on the comparison of two forward-looking
sets of projections. Even if we undertook such additional analysis or
modeling, ultimately we would still only be able to provide a new
estimate of regulatory costs, not an actual cost. Given how challenging
it is to produce rigorous retrospective estimates of regulatory costs,
particularly at a system-level, an ex post analysis is better suited to
comparing particular aspects of the analysis, which can help us
understand whether costs in the 2011 RIA were over- or under-estimated
and can yield a general sense of how much reality diverged from the
projection, than to attempting to generate a new and precise ``actual''
total compliance cost estimate for MATS.
    Estimating retrospective costs for a rule of the magnitude of MATS
is an especially significant challenge because the rule regulates
hundreds of units within a complex, interdependent, and dynamic
economic sector. Units within the power sector are also subject to many
regulatory requirements and other economic drivers. While we can
observe the decisions of the sector and individual units in terms of
decisions on controls, fuels, and retirement, we cannot pinpoint the
reason(s) behind each unit-level decision. With respect to identifying
the counterfactual against which to evaluate retrospective compliance
costs, several unforeseen factors since MATS promulgation have driven
changes in the power sector that have led to the composition of the
current fleet being different than the fleet projected in the 2011 RIA.
For example, dramatic increases in the supply of natural gas, along
with advances in cost and performance of renewable generation
technologies and low electricity demand growth, none of which were
fully anticipated in the 2011 RIA, have made strong contributions to
shifts away from coal-fired generation.78 79 Additionally,
other

EPA regulations such as the Disposal of Coal Combustion Residuals from
Electric Utilities final rule, the Steam Electric Power Generating
Effluent Guidelines--2015 Final Rule, and the 2020 Steam Electric
Reconsideration Rule, were promulgated after MATS.\80\ While the
compliance periods of these rules all postdate the MATS compliance
date, utilities are likely to consider multiple regulations
simultaneously when making planning decisions, a likelihood that also
complicates the identification of the counterfactual scenario of a
world without MATS that is needed to generate an ex post incremental
cost estimate of MATS that would be directly comparable to the ex ante
2011 RIA cost estimate.
---------------------------------------------------------------------------

    \78\ Linn, J. and K. McCormack (2019). The Roles of Energy
Markets and Environmental Regulation in Reducing Coal-Fired Plant
Profits and Electricity Sector Emissions. RAND Journal of Economics
50: 733-767.
    \79\ Coglianese, J., et al. (2020). The Effects of Fuel Prices,
Environmental Regulations, and Other Factors on U.S. Coal
Production, 2008-2016. The Energy Journal 41(1): 55-82.
    \80\ 85 FR 53516 (August 28, 2020), 80 FR 67838 (November 3,
2015), and 85 FR 64650 (October 13, 2020), respectively.
---------------------------------------------------------------------------

    Even though it is extremely challenging to produce the type of ex
post incremental cost estimate discussed above, several stakeholders
have conducted analyses, focusing on different components of the
regulation's cost, to assess actual costs of compliance. While none of
these estimates can be precisely compared against the EPA ex ante
estimates because they use different methods than the power sector
modeling the EPA used in the 2011 RIA, all of the independent analyses
suggested that the actual compliance costs expenditures were
significantly lower--by billions of dollars--than the EPA estimated in
the 2011 RIA.
    First, a 2015 analysis by Andover Technology Partners focused on
the capital and operating costs associated with the actual installation
and operation of pollution control equipment at MATS-regulated units
and made two key findings: the number of installed controls was
significantly lower than the number of controls that was projected in
the 2011 RIA and the cost of the installed controls was generally lower
than the control costs that the EPA assumed in the 2011 RIA modeling.
Based on these findings, the study estimated that the EPA's projected
cost of compliance was over-estimated by approximately $7
billion.81 82 In other words, the Andover Technology
Partners estimated that the EPA's projected cost was approximately four
times higher than their retrospective estimate of cost, which they
estimated to be approximately $2 billion per year.
---------------------------------------------------------------------------

    \81\ Declaration of James E. Staudt, Ph.D., CFA, at 3, White
Stallion Energy Center v. EPA, No. 12-1100 (DC Cir., December 24,
2015). Also available at Docket ID Item No. EPA-HQ-OAR-2009-0234-
20549.
    \82\ In addition to the 2015 study, Andover Technology Partners
produced two other analyses in 2017 and 2019, respectively, that
estimated the ongoing costs of MATS. The 2017 report estimated that
the total annual operating cost for MATS-related environmental
controls was about $620 million, an estimate that does not include
ongoing payments for installed environmental capital. The 2019
report estimates the total annual ongoing incremental costs of MATS
to be about $200 million; again, this estimate does not include
ongoing MATS-related capital payment. The 2017 report is available
in Docket ID Item No. EPA-HQ-OAR-2018-0794-0794. The 2019 report is
available in Docket ID Item No. EPA-HQ-OAR-2018-0794-1175.
---------------------------------------------------------------------------

    Second, a 2017 study performed by M.J. Bradley & Associates (MJB&A)
used information from the EIA and estimated that owners and operators
of coal-fired EGUs incurred total capital expenditures on environmental
retrofits of $4.45 billion from December 2014 to April 2016.\83\ To the
EPA's understanding, the MJB&A cost estimate represents total upfront
capital costs (not ongoing operating and maintenance expenditures), and
is not annualized as was the capital expenditure in the 2011 RIA-based
projected cost estimate. For comparison, the estimated total upfront
(not annualized) capital expenditures underpinning the 2011 RIA annual
compliance cost estimate is about $36.5 billion, which is more than
eight times higher than the MJB&A estimates. This result suggests that
the capital cost component of the 2011 RIA cost projections was
significantly overestimated, potentially by a factor of more than
eight.
---------------------------------------------------------------------------

    \83\ Available in Docket ID Item No. EPA-HQ-OAR-2018-0794-1145.
---------------------------------------------------------------------------

    Third, the Edison Electric Institute (EEI), the association that
represents all U.S. investor-owned electric companies, estimated that
by April 2019, owners and operators of coal- and oil-based EGUs
incurred cumulative (not annual) compliance costs of more than $18
billion to comply with MATS, including both capital and operations and
maintenance costs since MATS became effective in April 2012.\84\ In
order to provide a simple comparison between the EEI figure, which was
incurred over 7 years, and the annualized amount presented in the 2011
RIA ($9.6 billion), we can divide the EEI figure by 7 to estimate an
average annual amount of approximately $2.6 billion, which is similar
to the Andover Technology Partners estimate of approximately $2
billion. Also in line with the Andover Technology Partners estimate,
EEI's estimate suggests that the annual costs related to MATS
compliance were overestimated in the 2011 RIA by approximately $7
billion. While there is some uncertainty in the amount of time over
which those costs were incurred, as well as the exact nature of those
expenditures, it is clear that the information provided by EEI supports
a conclusion that the costs of compliance with MATS were significantly
lower than the Agency's projections.
---------------------------------------------------------------------------

    \84\ Available in Docket ID Item No. EPA-HQ-OAR-2018-0794-2267.
---------------------------------------------------------------------------

    In summary, it is the EPA's understanding that two of these studies
indicate that the 2011 RIA may have overestimated annual compliance
costs by approximately $7 billion, and the third study finds that the
projected total upfront capital costs may have been overestimated by a
factor of more than eight. While each of these retrospective cost
estimates is developed from bases that are dissimilar from one another
and, in particular, from how the EPA developed the prospective cost
estimates in the 2011 RIA, each of the independent analyses indicate
that the costs of MATS are likely significantly less than the EPA
estimated in the 2011 RIA.
    For this proposal, the EPA has evaluated whether the ex ante
estimates in the 2011 RIA were likely accurate, overestimated, or
underestimated, and the details of the EPA's new analysis are contained
in the docketed TSD (referred to herein as the ``Cost TSD'').\85\
Consistent with our systems-level approach, we begin our analysis with
an evaluation of natural gas expenditures during the relevant time
period. The rapid decrease in the price of natural gas during this time
period affected U.S. power generation profoundly, including U.S. EGU
fuel expenditures; this has significant implications for our ex post
analysis because natural gas expenditures constituted approximately 25
percent of the projected 2015 compliance costs in the 2011 RIA.\86\
These market shifts in the industry also impacted expenditures
associated with the installation and operation of pollution control
equipment at MATS-affected facilities. Those costs constituted a
majority--about 70 percent--of the projected annual compliance costs in
2015. The following

sections closely examine these two components of the compliance cost
and use available information to evaluate whether the projected
compliance costs reported in the 2011 RIA were likely higher or lower
than actual costs. We also review important cost assumptions used in
the 2011 RIA. Taken together, this suite of quantitative and
qualitative evaluations indicates that the projected costs in the 2011
RIA were almost certainly significantly overestimated. We find that the
2011 RIA's estimate of the number of installations alone led to an
overestimate of about $2.2 to $4.4 billion, and that if recent updates
to the cost and performance assumption for pollution controls had been
reflected in the 2011 RIA modeling, the projected compliance costs
would likely have been even lower (suggesting the overestimate could be
greater than $4.4 billion).
---------------------------------------------------------------------------

    \85\ U.S. EPA. 2021. Supplemental Data and Analysis for the
National Emission Standards for Hazardous Air Pollutants: Coal- and
Oil-Fired Electric Utility Steam Generating Units--Revocation of the
2020 Reconsideration, and Affirmation of the Appropriate and
Necessary Supplemental Finding; Notice of Proposed Rulemaking
(``Cost TSD'').
    \86\ We projected that regulation of coal- and oil-fired EGUs
under MATS would induce units to switch to natural gas, which in
turn would increase the price of natural gas and the cost of those
expenditures.
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a. Natural Gas Supply
    The natural gas industry has undergone significant change in recent
years. Starting in the mid-2000s, technological changes in natural gas
drilling and extraction initiated major market changes that resulted in
significant increases to domestic supplies of natural gas. As these
technologies have continued to advance, they have had a lasting impact
on natural gas markets, resulting in major shifts in the economics of
electric sector operations given the abundant supply of natural gas at
relatively low costs. This section summarizes these changes and the
implications for the cost projection presented in the 2011 RIA.
    In 2005, the EIA estimated that proved reserves of natural gas were
213 trillion cubic feet (tcf).\87\ In 2019, the estimate of proved
reserves was 495 tcf, an increase of 132 percent. The market effects of
this major supply shift were profound across the economy, but
especially for the power sector. By the end of 2019, aided by advances
in drilling and hydraulic fracturing techniques, natural gas production
from tight and shale gas formations was the major source of domestic
production (see Table 1 below) and had increased three-fold from 2005
production levels.
---------------------------------------------------------------------------

    \87\ U.S. Crude Oil and Natural Gas Proved Reserves, Year-end
2019 (Table 9: U.S. proved reserves of natural gas). EIA, January
11, 2021 release available at https://www.eia.gov/naturalgas/crudeoilreserves. Accessed July 23, 2021.

                                 Table 1--U.S. Natural Gas Production, by Source
                                              [Trillion cubic feet]
----------------------------------------------------------------------------------------------------------------
                                                    Tight/shale     Other lower      Lower 48
                      Year                              gas         48 onshore       offshore          Other
----------------------------------------------------------------------------------------------------------------
2005............................................             7.2             5.1             3.4             2.3
2006............................................             8.0             5.1             3.2             2.3
2007............................................             9.0             4.9             3.1             2.3
2008............................................            10.3             4.9             2.6             2.4
2009............................................            11.1             4.5             2.7             2.4
2010............................................            12.4             4.2             2.5             2.2
2011............................................            14.8             4.0             2.0             2.1
2012............................................            16.7             3.7             1.6             2.0
2013............................................            17.6             3.5             1.4             1.7
2014............................................            19.5             3.4             1.3             1.6
2015............................................            21.0             3.2             1.4             1.5
2016............................................            21.1             2.8             1.3             1.4
2017............................................            22.2             2.7             1.1             1.3
2018............................................            25.7             2.7             1.0             1.3
2019............................................            29.3             2.4             1.0             1.2
2020............................................            29.2             2.3             1.2             1.2
----------------------------------------------------------------------------------------------------------------
Source: U.S. EIA, https://www.eia.gov/energyexplained/natural-gas/where-our-natural-gas-comes-from.php, accessed
  July 25, 2021.

    Note: ``Other'' includes production from Alaska and Coalbed
Methane sources.

    As a result, the natural gas market underwent a long period of
sustained low prices (see Table 2 below). These market shifts were not
fully anticipated or predicted by observers, as indicated by natural
gas futures prices at the time of MATS promulgation. Although these
changes took root in the mid-2000s, the lasting market disruption would
take more time to cement itself. From 2010 through 2019, the U.S became
one of the world's leading producers of natural gas, breaking domestic
production records year-on-year through the decade, while maintaining
record-low prices. During this timeframe, the U.S. shifted from a total
net energy importer to an exporter,\88\ while maintaining some of the
lowest relative natural gas prices globally.\89\
---------------------------------------------------------------------------

    \88\ Monthly Energy Review, EIA (June 24, 2021) and Today in
Energy (``U.S. total energy exports exceed imports in 2019 for the
first time in 67 years''), EIA (April 20, 2020) available at https://www.eia.gov/todayinenergy/detail.php?id=43395. Accessed July 23,
2021.
    \89\ BP, Statistical Review of World Energy 2021 available at
https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed July 23, 2021.

                                           Table 2--Natural Gas Prices
----------------------------------------------------------------------------------------------------------------
                                                         NYMEX natural gas  NYMEX natural gas
                                                         Henry Hub natural  Henry Hub natural    Henry Hub spot
                                                          gas futures ($/    gas futures ($/   natural gas index
                          Year                             MMBtu), annual     MMBtu), annual     annual average
                                                          average, as of:    average, as of:    price ($/MMBtu)
                                                             2011-03-16         2011-12-21
----------------------------------------------------------------------------------------------------------------
2005...................................................  .................  .................               8.63
2006...................................................  .................  .................               6.74
2007...................................................  .................  .................               6.96

2008...................................................  .................  .................               8.90
2009...................................................  .................  .................               3.94
2010...................................................  .................  .................               4.37
2011...................................................               4.24  .................               4.00
2012...................................................               4.91               3.43               2.75
2013...................................................               5.31               4.07               3.73
2014...................................................               5.67               4.43               4.37
2015...................................................               6.04               4.66               2.63
2016...................................................               6.36               4.90               2.51
2017...................................................               6.67               5.16               2.98
2018...................................................               6.97               5.43               3.16
2019...................................................               7.25               5.70               2.56
2020...................................................               7.50               5.96               2.03
2021...................................................               7.76               6.23  .................
2022...................................................               8.02               6.50  .................
2023...................................................               8.28               6.78  .................
2024...................................................  .................               7.06  .................
----------------------------------------------------------------------------------------------------------------
Source: Annual Average Henry Hub Price, EIA. NYMEX price, from S&P Global data. 2015 data from 2011 RIA, Chapter
  3.

    The EPA projected a 2015 natural gas price of roughly $5/MMBtu when
MATS was finalized in December 2011, which was a reasonable expectation
based on prevailing market conditions at that time. However, natural
gas prices post-MATS promulgation ended up being considerably lower
than anticipated, which resulted in major shifts in the economics of
fossil fuel-fired electric generating technologies (see Table 2 above
and Chart A-1 in the Cost TSD). From 2005 through 2010, annual average
natural gas prices (at Henry Hub) averaged about $6.60/MMBtu. Several
years later, as MATS compliance began, prices averaged roughly $2.75/
MMBtu for the years 2015 through 2019. This market shift greatly
changed the economics of power plant operation for fossil fuel-fired
facilities, with the electric sector surpassing the industrial sector
to become the largest consumer of natural gas (38 percent of the total
in 2020),\90\ and gas-fired generators becoming the leading source of
electric generation in the electric sector, representing 40 percent of
total generation in 2020.\91\
---------------------------------------------------------------------------

    \90\ Table 4.3, Monthly Energy Review, EIA, April 2021,
available at https://www.eia.gov/totalenergy/data/monthly/archive/00352104.pdf.
    \91\ EIA, Electricity Data Browser, Net generation, United
States, all sectors, annual, available at https://www.eia.gov/electricity/data/browser/.
---------------------------------------------------------------------------

    The modeling supporting the 2011 RIA did not anticipate this major
change in natural gas supply, which has clearly had a significant
impact on the electric power sector and those sources covered by MATS.
While we do not quantify the impact this change would have on the
projected compliance costs associated with incremental changes in
natural gas use and price (about 25 percent of the total projected
compliance cost in the 2011 RIA), we note that any closures of covered
units that occurred as a result of the changed relative economics of
fuel prices would decrease the MATS-related compliance costs for the
sector. These closures reduced the amount of control capacity necessary
for compliance with MATS, and we estimate below a range of costs
associated with the overestimation of control installations in the 2011
RIA.
    Several researchers have investigated the role of relative fuel
prices as a factor in decisions that were made regarding closures of
coal-fired units around 2015. Generally, these studies attribute
closures primarily to the decrease in natural gas prices, and they also
note smaller factors such as advances in the cost and performance of
renewable generating sources, lower-than-anticipated growth in
electricity demand, and environmental regulations.
    For example, Linn and McCormack (2019) developed a simulation model
of the U.S. Eastern Interconnection that reproduced unit operation,
emissions, and retirements over the 2005-2015 period. The authors use
this model to explain the relative contributions of demand, natural gas
prices, wind generation, and environmental regulations, including MATS,
to the changes in the share of coal in electricity generation. The
results showed that lower electricity consumption and natural gas
prices account for a large majority of the declines in coal plant
profitability and resulting retirements. The authors found that the
environmental regulations they modeled, NOX emissions caps
and MATS, played a relatively minor role in declines of coal plant
profitability and retirements.
    Additionally, Coglianese et al. (2020) developed a statistical
modeling approach to enable the decomposition of changes in U.S. coal
production from 2008-2016 into changes due to a variety of factors,
including changes in electricity demand, natural gas prices relative to
coal, renewable portfolio standards, and environmental regulations that
affect coal-fired plants. The results indicated that declines in
natural gas prices explained about 92 percent of the decrease in coal
production between 2008 and 2016. Air regulations, including MATS,
explained about 6 percent of the drop in coal production. The study
attributed about 5.2 GW of coal-fired EGU retirements to MATS.
    These studies both demonstrate that the decrease in natural gas
prices played a significant role in closures of coal-fired EGUs. While
we do not quantify the impact this change had on the projected costs
included in the 2011 RIA, we note that any closures of covered units
that occurred as a result of the dramatically changed relative
economics of fuel prices would decrease the MATS-related compliance
costs for the sector.

b. Projected Versus Observed Pollution Control Installations
    The 2011 RIA reported a sector-level compliance cost of $9.6
billion annually in 2015. The majority of those costs--about 70
percent--represented the incremental annualized capital and annual
operation and maintenance (O&M) costs associated with installation and
operation of pollution controls for compliance with MATS at coal steam
units. Given the time that has passed, we can now compare the
incremental projected pollution control capacity reported in the 2011
RIA with available information regarding actual (observed) control
installations. For this proposal, therefore, the EPA has compared
observed installations and costs over 2013-2016 to unit-level estimates
of the control installation capacity and associated costs presented in
the 2011 RIA. This analysis demonstrates, subject to the caveats and
uncertainty discussed below, that the 2011 RIA likely overestimated
total pollution control retrofit capacity that would occur in response
to MATS and, thus, likely overestimated MATS compliance costs. For
example, the analysis that follows demonstrates that fabric filter (FF)
systems--which are an expensive and capital-intensive control
technology--were only installed on less than one-third of the capacity
anticipated in the 2011 RIA analysis.
    This comparison of projected to observed control capacity
installations relies on the simplifying assumption that all dry
scrubbers (e.g., dry FGD systems), dry sorbent injection (DSI) systems,
activated carbon injection (ACI) systems, and FF systems installed
during the 2013-2016 period were installed for compliance with the MATS
emissions limits. This assumption is necessitated by the absence of
comprehensive data on the specific reasons EGUs installed pollution
control equipment. While assuming pollution controls of these types
that were installed in this period are singularly attributable to MATS
requirements is a reasonable assumption for this analysis, it is a
highly conservative assumption given that some of the observed
installations likely occurred in response to other regulations to
control criteria air pollutants (e.g., Cross-State Air Pollution Rule,
Regional Haze, Federal implementation plans, or state implementation
plans) or enforcement actions (e.g., consent decrees). Because some of
the observed installations in this analysis likely resulted from non-
MATS requirements, the approach potentially over-attributes the amount
of pollution controls built specifically for MATS compliance, thereby
leading to an overestimate of the control costs associated with MATS.
    Table 3 presents the findings of this analysis in capacity terms.
The total capacity projected to retrofit with each control in the 2011
RIA is reported for the base case (i.e., projected future conditions
absent MATS) and under MATS. The difference is presented in the
`Projected Incremental Controls' column. So, for example, in the 2011
RIA the EPA projected that there would be an incremental 20.3 GW of
capacity retrofitting with dry FGD that is attributable to MATS. We
compare the projected incremental controls capacity value to the
observed installations capacity value. Note that we are unable to
estimate the total capacity of observed upgrades to electrostatic
precipitators (ESP) and scrubbers due to a lack of available data
regarding such upgrades. For additional information, see the docketed
Cost TSD.

                                                        Table 3--Projected vs. Observed Capacity
                                                                    [Gigawatts (GW)]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Percent
                                                                                Projected       Observed       Difference:     difference:
               Pollution control retrofit                    Base case         MATS         incremental    installations  Observed minus  Observed minus
                                                                                controls       (2013-2016)      projected       projected
                                                                                (2013-2016)     (2013-2016)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Dry FGD.................................................             4.6            24.8            20.3            16.0            -4.3             -21
DSI.....................................................             8.6            52.5            43.9            15.8           -28.1             -64
ACI.....................................................               0            99.3            99.3            96.1            -3.2              -3
FF......................................................            12.7           114.7             102            31.4           -70.6             -69
ESP Upgrade.............................................               0            33.9            33.9             N/A             N/A             N/A
Scrubber Upgrade........................................               0            63.1            63.1             N/A             N/A             N/A
--------------------------------------------------------------------------------------------------------------------------------------------------------
Source: Projected Controls: 2011 RIA; Observed Installations: NEEDS v.5.16.
Note: FF installations include installations specifically related to PM control, as well as installations included with dry scrubber, DSI, and some ACI
  retrofits in the modeling. Totals may not sum due to rounding.

    This analysis demonstrates that projected incremental capacity of
dry FGD, DSI, ACI, and FF was likely significantly overestimated in the
2011 RIA. The capacities of actual installed control technologies are
lower, often significantly lower, than projected (and again, this
analysis attributes all control installations of certain types during
this time period to MATS, even though some portion of those
installations were likely made in whole or in part due to other
regulations). For example, the installed DSI capacity is about two-
thirds lower than was projected. The difference between observed
installed control capacities and what we projected those incremental
control capacities would be translates directly into significantly
lower costs than estimated. Because the vast majority of compliance
costs in the 2011 RIA were related to the installation and operation of
pollution controls, and because significant deployment of any higher-
cost compliance strategies did not occur, the large differences
observed in Table 3 suggest that the projected compliance costs were
likely significantly overestimated as well. For example, approximately
$2 billion was estimated to be attributable to the installation and
operation of DSI controls (21 percent of the total annual projected
costs of MATS), when in actuality, only one-third of those
installations occurred (and some were likely attributable to
regulations other than MATS).
    We also conduct an analysis of the approximate costs related to the
overestimate of projected incremental pollution controls. This analysis
is discussed in detail in the Cost TSD. Specifically, we compared
observed installations over 2013-2016 to unit-level estimates of the
control installation capacity and associated costs presented in the
2011 RIA to develop a range of the potential overestimate of compliance
costs related

to projected control installations that did not occur.
    As result of this analysis, we find that based on this one
variable--the number of control technology installations--the 2011 RIA
overestimated control costs by about $2.2 to $4.4 billion (or 2.7
times). If recent updates to the cost and performance assumptions for
pollution controls had been reflected in the 2011 RIA modeling, the
projected compliance costs would likely have been even lower
(suggesting the overestimate could be greater than $4.4 billion). The
EPA did not quantify advances in cost and performance of control
technology between the time of the EPA's modeling and implementation of
the rule due to uncertainty. We note that this may be one reason that
the Andover Technology Partners' overestimate for control costs of $7
billion exceeds the EPA's range of overestimates ($2.2-4.4 billion) for
the same control and operation costs. The next section helps explain
some of the difference quantified above, and provides further
qualitative evidence supporting the EPA's conclusion that the 2011 RIA
likely significantly overestimated the compliance costs associated with
meeting MATS requirements.
c. 2011 RIA Modeling Assumptions
    Since promulgation of MATS, the EPA has found it necessary to
update some of the modeling assumptions used in the IPM modeling that
informed the RIA cost estimate, in order to capture the most recently
available information and best reflect the current state of the power
sector. Several of these recent updates are directly related to
pollution control retrofits that were projected to be installed for
MATS in the 2011 RIA. Had these updates been reflected in our modeling,
it likely would have projected fewer controls needing to be installed
and therefore a lower cost estimate overall.
    The full suite of assumptions utilized in the IPM modeling are
reported in the model documentation, which provides additional
information on the assumptions discussed here as well as all other
assumptions and inputs to the model.\92\ Updates specific to MATS
modeling are also in the IPM 4.10 Supplemental Documentation for
MATS.\93\ As was included in the 2011 RIA discussion regarding
uncertainty and limitations of the power sector modeling analysis
(Section 3.15), the cost and emissions impact projections did not take
into account the potential for advances in the capabilities of
pollution control technologies or reductions in their costs over time.
EPA modeling cannot anticipate in advance the full spectrum of
compliance strategies that the power sector may innovate to achieve
required emission reductions, and experience has shown that regulated
industry often is able to comply at lower costs through innovation or
efficiencies. Where possible, the EPA designs regulations to assure
environmental performance while preserving flexibility for affected
sources to design their own solutions for compliance. Industry will
employ an array of responses, some of which regulators may not fully
anticipate and will generally lead to lower costs associated with the