using effect coefficients derived from the Pope et al. study and the
Laden et al study, as well as 12 effect coefficients derived from
EPA’s expert elicitation on PM mortality. The results shown are not
the direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies. These estimates do not include benefits from
reducing HAP emissions, but they do include the energy disbenefits. Due
to data, methodology, and resource limitations, we were unable to
monetize the benefits associated with several categories of benefits,
including exposure to HAPs, NO2, and SO2, ecosystem effects, and
visibility effects.

Calculation of PM2.5 Human Health Benefits

In addition to pollutants we cannot monetize, this rulemaking would
reduce emissions of PM2.5 and SO2.  Because SO2 is also a precursor to
PM2.5, reducing and SO2 emissions would also reduce PM2.5 formation,
human exposure, and the incidence of PM2.5-related health effects.  The
PM reductions are the result of emission limits on PM as well as
emission limits on other pollutants, including hazardous air pollutants
for the NESHAP and criteria pollutants for the NSPS.  The total PM2.5
reductions are the consequence of the technologies installed to meet
these multiple limits.

Methodology Improvements since Proposal

This benefits analysis incorporates an array of policy and technical
improvements since the proposal RIA in 2009 (U.S. EPA, 2009a),
including:

Cement sector-specific air quality modeling data. The benefits estimates
for this final analysis are based on air quality data modeled by CAMx
that reflect the emissions from the cement sector and the reductions
anticipated as a result of this rule. This data provides a superior
representation of the geographic distribution of the emission reductions
and resulting ambient concentrations than the national average
benefit-per-ton estimates used in the proposal. For more information
regarding the modeling inputs and assumptions, please see Section 5 of
this RIA.

Use of a revised Value of Statistical Life (VSL). The Agency continues
to update its guidance on valuing mortality risk reductions and until a
final report is available, EPA now uses a single, peer-reviewed mean VSL
estimate of $6.3 million (2000$). We discuss this issue in more detail
in Section 6.2.5.

Lowest Measured Level (LML) assessment. Consistent with the rationale
outlined in the proposal RIA, EPA now estimates PM-related mortality
without assuming an arbitrary threshold in the concentration-response
function.  Consistent with recent scientific advice, we are replacing
the previous threshold sensitivity analysis with a new LML assessment to
highlight the uncertainty associated with benefits estimated at low air
quality levels.  We discuss this issue in more detail in Section 6.2.4
and provide the results of this LML assessment in Section 6.3.

Qualitative benefits for ecosystems and HAPs.  Data, resource, and
methodological limitations prevented EPA from quantifying or monetizing
the benefits from several important benefit categories, including
benefits from reducing toxic air pollutant emissions, ecosystem effects,
and visibility impairment.  Instead, we provide a qualitative
description of the benefits anticipated as a result of the emission
reductions from this rule.  These unquantified benefits are described in
Section 6.5.

Mercury deposition. The air quality modeling data provide an estimate of
the reduction in mercury deposition associated with the mercury emission
reductions anticipated as a result of this rule.  We provide maps of the
reduced mercury deposition in Section <IP_ADDRESS>.  Due to time and resource
limitations, we were unable to model mercury methylation,
bioaccumulation in fish tissue, and human consumption of
mercury-contaminated fish that would be needed in order to estimate the
human health benefits from reducing mercury emissions.

Benefits Analysis Approach

We follow a “damage-function” approach in calculating total benefits
of the modeled changes in environmental quality.  This approach
estimates changes in individual health and welfare endpoints and assigns
values to those changes assuming independence of the individual values.
Total benefits are calculated simply as the sum of the values for all
non-overlapping health and welfare endpoints. The “damage-function”
approach is the standard method for assessing costs and benefits of
environmental quality programs and has been used in several recent
published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et
al., 2009).

To assess economic value in a damage-function framework, the changes in
environmental quality must be translated into effects on people or on
the things that people value.  For changes in PM, a health impact
analysis (HIA) must first be conducted to convert air quality changes
into effects that can be assigned dollar values.  For this RIA, the
health impacts analysis is limited to those health effects that are
directly linked to ambient levels of air pollution and specifically to
those linked to PM.  We also provide qualitative discussions of the
impact of changes in other environmental and ecological effects,
including the benefits associated with decreasing deposition of sulfur
to terrestrial and aquatic ecosystems, but we are unable to place an
economic value on these changes due to time and resource limitations.

We note at the outset that EPA rarely has the time or resources to
perform extensive new research to measure directly either the health
outcomes or their values for regulatory analyses.  Thus, similar to
Kunzli et al. (2001) and other recent health impact analyses, our
estimates are based on the best available methods of benefits transfer.
Benefits transfer is the science and art of adapting primary research
from similar contexts to obtain the most accurate measure of benefits
for the environmental quality change under analysis.  Adjustments are
made for the level of environmental quality change, the
socio-demographic and economic characteristics of the affected
population, and other factors to improve the accuracy and robustness of
benefits estimates.

Health Impact Analysis (HIA)

The HIA quantifies the changes in the incidence of adverse health
impacts resulting from changes in human exposure to PM2.5 air quality.
HIAs are a well-established approach for estimating the retrospective or
prospective change in adverse health impacts resulting from
population-level changes in exposure to pollutants (Levy et al. 2009).
Analysts have applied the HIA approach to estimate human health impacts
resulting from hypothetical changes in pollutant levels (Hubbell et al.
2005; Davidson et al. 2007, Tagaris et al. 2009).  For this analysis, we
used the environmental Benefits Mapping and Analysis Program (BenMAP),
which is a PC-based tool that can systematize health impact analyses by
applying a database of key input parameters, including health impact
functions and population projections.

The HIA approach used in this analysis involves three basic steps: (1)
utilizing CAMx-generated projections of PM2.5 air quality and estimating
the change in the spatial distribution of the ambient air quality; (2)
determining the subsequent change in population-level exposure; (3)
calculating health impacts by applying concentration-response
relationships drawn from the epidemiological literature (Hubbell et al.
2009) to this change in population exposure.

A typical health impact function might look as follows:

where y0 is the baseline incidence rate for the health endpoint being
quantified (for example, a health impact function quantifying changes in
mortality would use the baseline, or background, mortality rate for the
given population of interest); Pop is the population affected by the
change in air quality; (x is the change in air quality; and β is the
effect coefficient drawn from the epidemiological study. For this
analysis, we systematize the HIA calculation process using BenMAP’s
library of existing air quality monitoring data, population data and
health impact functions.  Figure 6-2 provides a simplified overview of
this approach, and Figure 6-3 identifies the data inputs and outputs for
the BenMAP model.

Figure 6-2. Illustration of BenMAP Approach

Figure 6-3. Data inputs and outputs for the BenMAP model

The benefits estimates in this analysis were derived using modified
versions of the health impact functions used in the PM NAAQS Regulatory
Impact Analysis (RIA) (U.S. EPA, 2006).  While many of the functions are
identical to those used in the PM NAAQS RIA, we have updated a few of
the underlying assumptions over the last few years.  For a detailed
description of the underlying functions, studies, baseline incidence
rates, and population data used in this analysis, please refer to
Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010a).
Table 6-1 identifies which human health and welfare endpoints are
included in the monetized benefits and which endpoints are unquantified.
 In summary, the monetized PM benefits include premature mortality and
11 morbidity endpoints.

Table 6-1.	Human Health and Welfare Effects of Pollutants Affected

Pollutant/ Effect	Quantified and monetized in primary estimate
Unquantified

PM: healtha	Premature mortality based on cohort study estimatesb	Low
birth weight

	Premature mortality based on expert elicitation estimates	Pulmonary
function

	Hospital admissions: respiratory and cardiovascular	Chronic respiratory
diseases other than chronic bronchitis

	Emergency room visits for asthma	Non-asthma respiratory emergency room
visits

	Nonfatal heart attacks (myocardial infarctions)	UVb exposure (+/-)c

	Lower and upper respiratory illness

Minor restricted activity days

Work loss days

Asthma exacerbations (among asthmatic populations

Respiratory symptoms (among asthmatic populations)

Infant mortality

	PM: welfare

Visibility in Class I areas in SE, SW, and CA regions

Visibility in residential areas

Visibility in non-class I areas and class 1 areas in NW, NE, and Central
regions

UVb exposure (+/-)c

Global climate impactsc

SO2: health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

SOX: welfare

Commercial fishing and forestry from acidic deposition effects

Recreation in terrestrial and aquatic ecosystems from acid deposition
effects

Increased mercury methylation

Mercury: health

Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Potential cardiovascular effects including:

--Altered blood pressure regulation

--Increased heart rate variability

--Incidences of Myocardial infarction

Potential reproductive effects

Mercury: welfare

Impact on birds and mammals (e.g. reproductive effects)

Impacts to commercial, subsistence and recreational fishing

a	In addition to primary economic endpoints, there are a number of
biological responses that have been associated with PM health effects
including morphological changes and altered host defense mechanisms.
The public health impact of these biological responses may be partly
represented by our quantified endpoints.

b	Cohort estimates are designed to examine the effects of long term
exposures to ambient pollution, but relative risk estimates may also
incorporate some effects due to shorter term exposures (see Kunzli et
al., 2001 xe "Kunzli, 2001"  for a discussion of this issue). While some
of the effects of short term exposure are likely to be captured by the
cohort estimates, there may be additional premature mortality from short
term PM exposure not captured in the cohort estimates included in the
primary analysis.

c	May result in benefits or disbenefits.

Estimating PM2.5-related premature mortality

Consistent with the proposal RIA for this rule (U.S. EPA, 2009a  XE
"U.S. EPA, 2009a"  ), the PM2.5 benefits estimates utilize the
concentration-response functions as reported in the epidemiology
literature, as well as the 12 functions obtained in EPA’s expert
elicitation study as a characterization of uncertainty.

One estimate is based on the concentration-response (C-R) function
developed from the extended analysis of American Cancer Society (ACS)
cohort, as reported in Pope et al. (2002) xe "Pope et al. (2002)" , a
study that EPA has previously used to generate its primary benefits
estimate.  When calculating the estimate, EPA applied the effect
coefficient as reported in the study without an adjustment for assumed
concentration threshold of 10 µg/m3 as was done in recent (2006-2009)
Office of Air and Radiation RIAs.

One estimate is based on the C-R function developed from the extended
analysis of the Harvard Six Cities cohort, as reported by Laden et al.
(2006).  This study, published after the completion of the Staff Paper
for the 2006 PM2.5 NAAQS, has been used as an alternative estimate in
the PM2.5 NAAQS RIA and PM2.5 benefits estimates in RIAs completed since
the PM2.5 NAAQS.  When calculating the estimate, EPA applied the effect
coefficient as reported in the study without an adjustment for assumed
concentration threshold of 10 µg/m3 as was done in recent (2006-2009)
RIAs.

Twelve estimates are based on the C-R functions from EPA’s expert
elicitation study (IEc, 2006; Roman et al., 2008) on the PM2.5
-mortality relationship and interpreted for benefits analysis in EPA’s
final RIA for the PM2.5 NAAQS.  For that study, twelve experts (labeled
A through L) provided independent estimates of the PM2.5 -mortality
concentration-response function.  EPA practice has been to develop
independent estimates of PM2.5 -mortality estimates corresponding to the
concentration-response function provided by each of the twelve experts,
to better characterize the degree of variability in the expert
responses.

The effect coefficients are drawn from epidemiology studies examining
two large population cohorts: the American Cancer Society cohort (Pope
et al., 2002  XE "Pope et al., 2002"  ) and the Harvard Six Cities
cohort (Laden et al., 2006  XE "Laden et al., 2006"  ). These are
logical choices for anchor points in our presentation because, while
both studies are well designed and peer reviewed, there are strengths
and weaknesses inherent in each, which we believe argues for using both
studies to generate benefits estimates. Previously, EPA had calculated
benefits based on these two empirical studies, but derived the range of
benefits, including the minimum and maximum results, from an expert
elicitation of the relationship between exposure to PM2.5 and premature
mortality (Roman et al., 2006  XE "Roman et al., 2008"  ).  Within this
assessment, we include the benefits estimates derived from the
concentration-response function provided by each of the twelve experts
to better characterize the uncertainty in the concentration-response
function for mortality and the degree of variability in the expert
responses. Because the experts used these cohort studies to inform their
concentration-response functions, benefits estimates using these
functions generally fall between results using these epidemiology
studies (see Figure 6-1). In general, the expert elicitation results
support the conclusion that the benefits of PM2.5 control are very
likely to be substantial.

EPA strives to use the best available science to support our benefits
analyses, and we recognize that interpretation of the science regarding
air pollution and health is dynamic and evolving. This analysis
continues to use the updated assumptions first applied in the proposal
RIA for this rule (U.S. EPA, 2009a  XE "U.S. EPA, 2009a"  ), including
the updated population dataset in BenMAP 3.0 and the functions directly
from the epidemiology studies without an adjustment for an assumed
threshold.  Removing the threshold assumption is a key difference
between the method used in this analysis of PM benefits and the methods
used in RIAs prior to the proposal RIA for this rule, and we now
calculate incremental benefits down to the lowest modeled PM2.5 air
quality levels.    Prior to the proposal RIA for this rule, EPA
presented the results using an assumed threshold at 10 µg/m3 in the
PM-mortality health impact function as the primary PM-related benefits
results.  Using a threshold of 10 µg/m3 was an arbitrary choice, and we
could have assumed thresholds at other points in the lower end of the
observed range the analysis. Since the proposal RIA for this rule, EPA
included a sensitivity analysis with an assumed threshold at 10 µg/m3
to illustrate that the fraction of benefits that occur at lower air
pollution concentration levels are inherently more uncertain.

In the proposal RIA for this rule, EPA solicited comment on the use of
the no-threshold model for benefits analysis within the preamble. Based
on our review of the public comments as well as the current body of
scientific literature, EPA now estimates PM-related mortality without
applying an assumed concentration threshold.  EPA’s Integrated Science
Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently
reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S.
EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientific
literature consistently finds that a no-threshold log-linear model most
adequately portrays the PM-mortality concentration-response relationship
while recognizing potential uncertainty about the exact shape of the
concentration-response function. Since then, the Health Effects
Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The
HES fully supports EPA’s decision to use a no-threshold model to
estimate mortality reductions. This decision is supported by the data,
which are quite consistent in showing effects down to the lowest
measured levels. Analyses of cohorts using data from more recent years,
during which time PM concentrations have fallen, continue to report
strong associations with mortality. Therefore, there is no evidence to
support a truncation of the CRF.”  In conjunction with the underlying
scientific literature, this document provided a basis for reconsidering
the application of thresholds in PM2.5 concentration-response functions
used in EPA’s RIAs.  For a summary of these scientific review
statements and the panel members please consult the Technical Support
Document (TSD) entitled Summary of Expert Opinions on the Existence of a
Threshold in the Concentration-Response Function for PM-related
Mortality (U.S. EPA, 2010b), which is provided in Appendix D of this
RIA.

Consistent with recent scientific advice, we are replacing the previous
threshold sensitivity analysis with a new “Lowest Measured Level”
(LML) assessment.  This approach summarizes the distribution of avoided
PM mortality impacts according to the baseline PM2.5 levels experienced
by the population receiving the PM2.5 mortality benefit. In the results
section, we identify on the figures the lowest air quality levels
measured in each of the primary cohort studies that estimate PM-related
mortality. This information allows readers to determine the portion of
PM-related mortality benefits occurring above or below the LML of each
study; in general, our confidence in the estimated PM mortality
decreases as we consider air quality levels further below the LML in the
two epidemiological studies.

While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations.
Unlike an assumed threshold, which is a modeling assumption that reduces
the magnitude of the estimated health impacts, the LML is a
characterization of the fraction of benefits that are more uncertain.
It is important to emphasize that just because we have greater
confidence in the benefits above the LML, this does not mean that we
have no confidence that benefits occur below the LML.

Analyses of these cohorts using data from more recent years, during
which time PM concentrations have fallen, continue to report strong
associations with mortality.  As we model mortality impacts among
populations exposed to levels of PM2.5 that are successively lower than
the LML of each study, our confidence in the results diminishes. As air
pollution emissions continue to decrease over time, there will be more
people in areas where we do not have published epidemiology studies.
However, each successive cohort study has shown evidence of effects at
successively lower levels of PM2.5.  As more large cohort studies follow
populations over time, we will likely have more studies with lower LML
as air quality levels continue to improve.  Even in the absence of a
definable threshold, we have more confidence in the benefits estimates
above the LML of the large cohort studies.  To account for the
uncertainty in each of the studies that we base our mortality estimates
on, we provide the LML for each of the cohort studies.  However, the
finding of effects at the lowest LML from recent studies indicate that
confidence in PM2.5-related mortality effects down to at least 7.5
µg/m3 is high.

For these rules the SO2 reductions represent a large fraction of the
total benefits from reducing PM2.5 , but it is not possible to isolate
the portion if the total benefits attributable to the emission
reductions of SO2 resulting from the application of HCl controls.  The
benefits models assume that all fine particles, regardless of their
chemical composition, are equally potent in causing premature mortality
because there is no clear scientific evidence that would support the
development of differential effects estimates by particle type.

Economic valuation of health impacts

After quantifying the change in adverse health impacts, the final step
is to estimate the economic value of these avoided impacts. Please refer
to Table 5-11 in the recently proposed Transport Rule (U.S. EPA, 2010a)
for a detailed description of the underlying valuation functions and the
monetized unit values for each endpoint incorporated into this analysis.
 The monetized mortality benefits dominate the total benefits estimates.

As is the nature of RIAs, the assumptions and methods used to estimate
air quality benefits evolve over time to reflect the Agency’s most
current interpretation of the scientific and economic literature. For a
period of time (2004–2006), the Office of Air and Radiation (OAR)
valued mortality risk reductions using a value-of-a-statistical-life
(VSL) estimate derived from a limited analysis of some of the available
studies. OAR arrived at a VSL using a range of $1 million to $10 million
(2000$) consistent with two meta-analyses of the wage-risk literature.
The $1 million value represented the lower end of the interquartile
range from the Mrozek and Taylor (2002)  XE "Mrozek and Taylor (2002)"
meta-analysis of 33 studies. The $10 million value represented the upper
end of the interquartile range from the Viscusi and Aldy (2003)  XE
"Viscusi and Aldy (2003)"   meta-analysis of 43 studies. The mean
estimate of $5.5 million (2000$) was also consistent with the mean VSL
of $5.4 million estimated in the Kochi et al. (2006)  XE "Kochi et al.
(2006)"   meta-analysis. However, the Agency neither changed its
official guidance on the use of VSL in rule-makings nor subjected the
interim estimate to a scientific peer-review process through the Science
Advisory Board (SAB) or other peer-review group.

During this time, the Agency continued work to update its guidance on
valuing mortality risk reductions, including commissioning a report from
meta-analytic experts to evaluate methodological questions raised by EPA
and the SAB on combining estimates from the various data sources. In
addition, the Agency consulted several times with the Science Advisory
Board Environmental Economics Advisory Committee (SAB-EEAC) on the
issue. With input from the meta-analytic experts, the SAB-EEAC advised
the Agency to update its guidance using specific, appropriate
meta-analytic techniques to combine estimates from unique data sources
and different studies, including those using different methodologies
(i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007  XE "U.S.
EPA-SAB, 2007"  ).

Until updated guidance is available, the Agency determined that a
single, peer-reviewed estimate applied consistently best reflects the
SAB-EEAC advice it has received. Therefore, the Agency has decided to
apply the VSL that was vetted and endorsed by the SAB in the Guidelines
for Preparing Economic Analyses (U.S. EPA, 2000  XE "U.S. EPA, 2000"  )
while the Agency continues its efforts to update its guidance on this
issue. This approach calculates a mean value across VSL estimates
derived from 26 labor market and contingent valuation studies published
between 1974 and 1991. The mean VSL across these studies is $6.3 million
(2000$). The Agency is committed to using scientifically sound,
appropriately reviewed evidence in valuing mortality risk reductions and
has made significant progress in responding to the SAB-EEAC’s specific
recommendations.

Health Benefits Results

Table 6-2 provides a summary of the monetized PM2.5 benefits for the
final Portland Cement NESHAP and NSPS using the anchor points of Pope et
al. and Laden et al. as well as the results from the expert elicitation
on PM mortality at discount rates of 3% and 7%. Table 6-3 provides a
summary of the reductions in health incidences as a result of the
pollution reductions for the final Portland Cement NESHAP and NSPS.
Table 6-4 compares the monetized PM2.5 benefits attributable to the
final NSPS only, the final NESHAP only, and the more stringent NSPS and
final NESHAP.  Figure 6-4 illustrates the relative breakdown of the
monetized PM2.5 health benefits. Figure 6-5 provides a graphical
representation of all 14 of the PM2.5 benefits, at both a 3 percent and
7% discount rate.

The very large proportion of the avoided PM-related impacts we estimate
in this analysis occur among populations exposed at or above the lowest
LML of the cohort studies (Figures 6-6 and 6-7), increasing our
confidence in the PM mortality analysis. Figure 6-6 shows a bar chart of
the percentage of the estimated mortalities at each PM2.5 level.  Figure
6-7 shows a cumulative distribution function of the same data.  Both
figures identify the LML for each of the major cohort studies.

Using the Pope et al. (2002) study, approximately 94% of the mortality
impacts occur among populations with baseline exposure to annual mean
PM2.5 levels at or above the LML of 7.5 µg/m3.  Using the Laden et al.
(2006) study, 40% of the mortality impacts occur at or above the LML of
10 µg/m3.  As we model mortality impacts among populations exposed to
levels of PM2.5 that are successively lower than the LML of the lowest
cohort study, our confidence in the results diminishes. However, the
analysis above confirms that the great majority of the impacts occur at
or above the lowest cohort study’s LML. It is important to emphasize
that we have high confidence in PM2.5-related effects down to the lowest
LML of the major cohort studies.

Table 6-2.	Summary of Monetized Benefits Estimates for Final Cement
NESHAP and NSPS in 2013 (millions of 2005$)a

 	3%	7%

Based on Epidemiology Literature

Pope et al.	$7,600	$6,900

	($620--$23,000)	($560--$21,000)

Laden et al.	$19,000	$17,000

	($1,600--$55,000)	($1,500--$49,000)

Based on Expert Elicitation

Expert A	$20,000	$18,000

	($1,100--$65,000)	($1,000--$59,000)

Expert B	$15,000	$13,000

	($550--$61,000)	($500--$55,000)

Expert C	$15,000	$14,000

	($870--$57,000)	($790--$52,000)

Expert D	$11,000	$9,700

	($690--$34,000)	($620--$31,000)

Expert E	$24,000	$22,000

	($2,100--$73,000)	($1,900--$66,000)

Expert F	$14,000	$12,000

	($1,300--$41,000)	($1,200--$37,000)

Expert G	$9,000	$8,200

	($56--$33,000)	($53--$30,000)

Expert H	$11,000	$10,000

	($75--$44,000)	($71--$40,000)

Expert I	$15,000	$13,000

	($820--$50,000)	($740--$45,000)

Expert J	$12,000	$11,000

	($900--$47,000)	($810--$42,000)

Expert K	$2,900	$2,700

	($56--$19,000)	($53--$17,000)

Expert L	$10,000	$9,100

	($370--$39,000)	($330--$35,000)

a	All estimates are for the implementation year (2013), and are rounded
to two significant figures so numbers may not sum across columns. All
fine particles are assumed to have equivalent health effects. These
estimates do not include benefits from reducing HAP emissions, and they
do not include the energy disbenefits described in the next section.

Table 6-3.	Summary of Reductions in Health Incidences and Monetized
Benefits from PM2.5 Benefits for the Final Cement NESHAP and NSPS in
2013 (95th percentile confidence interval)a

Health Endpoint	Incidence	3% Discount

(millions of 2005$)	7% Discount (millions of 2005$)

Avoided Premature Mortality

	Pope et al. (ACS cohort)	960	$7,000	$6,300

	(320--1,600)	($0,560--$21,000)	($0,500--$19,000)

Laden et al. (H6C cohort)	2,500	$18,000	$16,000

	(1,200--3,700)	($1,600--$53,000)	($1,400--$47,000)

Woodruff et al. (Infant Mortality)	4	$35	$35

	(-4--13)	(-$38--$160)	(-$38--$160)

Avoided Morbidity

	Chronic Bronchitis	650	$19	$19

	(70--1,200)	($1.1--$90)	($1.10--$90.00)

Acute Myocardial Infarction	1,500	$11	$11

	(470--2,600)	($2.0--$27)	($1.8--$26)

Hospital Admissions, Respiratory	240	$0.21	$0.21

	(100--360)	($0.10--$0.31)	($0.10--$0.31)

Hospital Admissions, Cardiovascular	500	$0.90	$0.90

	(360--590)	($0.47--$1.20)	($0.47--$1.2)

Emergency Room Visits, Respiratory	1,000	$0.03	$0.03

	(550--1,500)	($0.01--$0.04)	($0.01--$0.04)

Acute Bronchitis	1,500	$0.01	$0.01

	(-200--3,200)	($0.00--$0.02)	($0.00--$0.02)

Work Loss Days	130,000	$1.2	$1.2

	(110,000--140,000)	($1.1--$1.4)	($1.1--$1.4)

Asthma Exacerbation	17,000	$0.06	$0.06

	(1,200--52,000)	($0.00--$0.21)	($0.00--$0.21)

Minor Restricted Activity Days	750,000	$3.0	$3.0

	(620,000--880,000)	($1.6--$4.6)	($1.6--$4.6)

Lower Respiratory Symptoms	18,000	$0.02	$0.02

	(7,800--28,000)	($0.01--$0.05)	($0.01--$0.05)

Upper Respiratory Symptoms	14,000	$0.03	$0.03

	(3,400--24,000)	($0.01--$0.07)	($0.01--$0.07)

a	All estimates are for the analysis year (2013) and are rounded to
whole numbers with two significant figures. All fine particles are
assumed to have equivalent health effects. These estimates do not
include benefits from reducing HAP emissions, and they do not include
the energy disbenefits described in the next section.

Figure 6-4.	Breakdown of Monetized PM2.5 Health Benefits using Mortality
Function from Pope et al. (2002)a

a	This pie chart breakdown is illustrative, using the results based on
Pope et al. (2002)  XE "Pope et al. (2002)"   as an example. Using the
Laden et al. (2006)  XE "Laden et al. (2006)"   function for premature
mortality, the percentage of total monetized benefits due to adult
mortality would be 97%. This chart shows the breakdown using a 3%
discount rate, and the results would be similar if a 7% discount rate
was used. The monetized estimates do not include benefits from reducing
HAP emissions or NOx, and they do not include the energy disbenefits
described in the next section.

Table 6-4.	Comparison of Monetized Benefits and Emission Reductions for
Final Cement NESHAP and NSPS in 2013 (2005$)a

 	 	Final NESHAP and NSPS	Final NSPS only	Final NESHAP only	Final
NESHAP and Stringent NSPS

3%	Pope	$7,600	$510	$7,600	$7,600

	Laden	$19,000	$1,300	$19,000	$19,000

7%	Pope 	$6,900	$460	$6,900	$6,900

	Laden	$17,000	$1,100	$17,000	$17,000

Emission

Reductions 	PM (tpy)	11,000	590	11,000	11,000

	SO2 (tpy)	124,000	9,000	124,000	124,000

	NOx (tpy)	6,600	6,600	0	11,000

	HCl (tpy)	5,900	520	5,900	5,900

	Organic HAPs (tpy)	5,200	0	5,200	5,200

	Hg (pounds)	16,400	0	16,400	16,400

a	All estimates are for the analysis year (2013) and are rounded to
whole numbers with two significant figures. All fine particles are
assumed to have equivalent health effects. The monetized estimates do
not include benefits from reducing HAP emissions or NOx, and they do not
include the energy disbenefits described in the next section.

Figure 6-5.	Total Monetized PM2.5 Benefits for the Final Cement NESHAP
and NSPS in 2013a

a	This graph shows the estimated benefits at discount rates of 3% and 7%
using effect coefficients derived from the Pope et al. study and the
Laden et al study, as well as 12 effect coefficients derived from
EPA’s expert elicitation on PM mortality. The results shown are not
the direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies. These estimates do not include benefits from
reducing HAP emissions, and they do not include the energy disbenefits
described in the next section.

Figure 6-6.	Percentage of Total PM-Related Mortalities Avoided by
Baseline Air Quality Level for Final Portland Cement NESHAP and NSPS a

a	Approximately 94% of the mortality impacts occur among populations
with baseline exposure to annual mean PM2.5 levels at or above 7.5
µg/m3, which is the lowest air quality level considered in the ACS
cohort study by Pope et al. (2002).

Figure 6-7.	Cumulative Percentage of Total PM-related Mortalities
Avoided by Baseline Air Quality Level for Final Portland Cement NESHAP
and NSPS a

a	Approximately 94% of the mortality impacts occur among populations
with baseline exposure to annual mean PM2.5 levels at or above 7.5
µg/m3, which is the lowest air quality level considered in the ACS
cohort study by Pope et al. (2002).

Energy Disbenefits

Electricity usage associated with the operation of control devices is
anticipated to increase emissions of criteria pollutants from utility
boilers that supply electricity to the Portland cement facilities. We
estimate increased energy demand associated with the installation of
scrubbers, ACI systems, and RTO. The increases for kilns subject to
existing source standards are estimated to be 2,000 tpy of NOx, 1,000
tpy of CO, 3,500 tpy of SO2 and about 100 tpy of PM. For kilns subject
to new source standards increases in secondary air pollutants are
estimated to be 200 tpy of NOX, 100 tpy of CO, 400 tpy of SO2 and 10 tpy
of PM.  We also estimated increases of CO2 to be 1.1 million tpy for
kilns subject to existing source standards and 4,000 tpy for kilns
subject to new source standards.  The increase in electricity usage for
the pumps used in the SNCR system to deliver reagent to the kiln is
negligible.

6.4.1	PM2.5 Disbenefits

The additional energy usage required for the emission control devices
would increase emissions of PM, NOx, SO2.  Because NOx and SO2 are also
precursors to PM2.5, increasing these emissions would also increase
PM2.5 formation, human exposure, and the incidence of PM2.5-related
health effects.  Due to time and resource limitations, it was not
possible to provide a comprehensive estimate of the PM2.5-related
disbenefits using air quality modeling.  Instead, we used the
“benefit-per-ton” approach to estimate these disbenefits based on
the methodology described in Fann, Fulcher, and Hubbell (2009). These
PM2.5 benefit-per-ton estimates provide the total monetized human health
benefits (the sum of premature mortality and premature morbidity) of
reducing one ton of PM2.5 from a specified source. EPA has used the
benefit per-ton technique in several previous RIAs, including the
proposal for this rule (U.S. EPA, 2009a).  For this analysis, we use the
benefit-per-ton estimates associated with the EGU sector.  It is
important to note that the disbenefits associated with directly emitted
PM are overestimated in this analysis because we assume that all of the
increased PM tons are in the PM2.5 fraction.  Table 6-5 summarizes the
benefit-per-ton estimates and the monetized PM2.5 disbenefits at
discount rates of 3% and 7%.

Table 6-5.	Summary of Monetized PM2.5 Energy Disbenefits for the Final
Portland Cement NSPS and NESHAP in 2013 (2005$)

Pollutant	Emissions Reductions (tons)	Benefit per ton (Pope, 3%)	Benefit

per ton (Laden, 3%)	Benefit per ton (Pope, 7%)	Benefit

 per ton (Laden, 7%)	Monetized PM2.5 Disbenefits (millions, 3%)
Monetized PM2.5 Disbenefits (millions, 7%)

Direct PM2.5 	110	$210,000	$510,000	$190,000	$460,000	$23	to	$56	$21	to
$50

PM2.5 Precursors

	SO2	3,900	$37,000	$91,000	$34,000	$82,000	$150	to	$360	$130	to	$320

NOX	2,200	$6,800	$17,000	$6,100	$15,000	$15	to	$36	$13	to	$33

 	 	 	 	 	Total	$180	to	$450	$170	to	$400

a	All estimates are for the implementation year (2013), and are rounded
to two significant figures so numbers may not sum across columns. All
fine particles are assumed to have equivalent health effects, but the
benefit per ton estimates vary because each ton of precursor reduced has
a different propensity to become PM2.5. The monetized disbenefits
incorporate the conversion from precursor emissions to ambient fine
particles. Confidence intervals are unavailable for this analysis
because of the benefit-per-ton methodology. The disbenefits associated
with directly emitted PM are overestimated in this analysis because we
assume that all of the increased PM tons are in the PM2.5 fraction.

6.4.2	Social Cost of Carbon and Greenhouse Gas Disbenefits

EPA has assigned a dollar value to reductions in carbon dioxide (CO2)
emissions using recent estimates of the “social cost of carbon”
(SCC).  The SCC is an estimate of the monetized damages associated with
an incremental increase in carbon emissions in a given year.  It is
intended to include (but is not limited to) changes in net agricultural
productivity, human health, property damages from increased flood risk,
and the value of ecosystem services due to climate change.  The SCC
estimates used in this analysis were developed through an interagency
process that included EPA and other executive branch entities, and
concluded in February 2010.  EPA first used these SCC estimates in the
benefits analysis for the final joint EPA/DOT Rulemaking to establish
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards; see the rule’s preamble for discussion
about application of SCC (75 FR 25324; 5/7/10).  The SCC Technical
Support Document (SCC TSD) provides a complete discussion of the methods
used to develop these SCC estimates.

The interagency group selected four SCC values for use in regulatory
analyses, which we have applied in this analysis: $5, $21, $35, and $65
per metric ton of CO2 emissions in 2010, in 2007 dollars.  The first
three values are based on the average SCC from three integrated
assessment models, at discount rates of 2.5, 3, and 5 percent,
respectively.  SCCs at several discount rates are included because the
literature shows that the SCC is quite sensitive to assumptions about
the discount rate, and because no consensus exists on the appropriate
rate to use in an intergenerational context. The fourth value is the
95th percentile of the SCC from all three models at a 3 percent discount
rate.  It is included to represent higher-than-expected impacts from
temperature change further out in the tails of the SCC distribution. Low
probability, high impact events are incorporated into all of the SCC
values through explicit consideration of their effects in two of the
three models as well as the use of a probability density function for
equilibrium climate sensitivity.  Treating climate sensitivity
probabilistically results in more high temperature outcomes, which in
turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to
produce larger incremental damages as physical and economic systems
become more stressed in response to greater climatic change.  Note that
the interagency group estimated the growth rate of the SCC directly
using the three integrated assessment models rather than assuming a
constant annual growth rate. This helps to ensure that the estimates are
internally consistent with other modeling assumptions.   The SCC
estimates for the analysis years of 2013, in 2005 dollars are provided
in Table 6-6.

When attempting to assess the incremental economic impacts of carbon
dioxide emissions, the analyst faces a number of serious challenges.  A
recent report from the National Academies of Science (NRC, 2008) points
out that any assessment will suffer from uncertainty, speculation, and
lack of information about (1) future emissions of greenhouse gases, (2)
the effects of past and future emissions on the climate system, (3) the
impact of changes in climate on the physical and biological environment,
and (4) the translation of these environmental impacts into economic
damages.  As a result, any effort to quantify and monetize the harms
associated with climate change will raise serious questions of science,
economics, and ethics and should be viewed as provisional.

The interagency group noted a number of limitations to the SCC analysis,
including the incomplete way in which the integrated assessment models
capture catastrophic and non-catastrophic impacts, their incomplete
treatment of adaptation and technological change, uncertainty in the
extrapolation of damages to high temperatures, and assumptions regarding
risk aversion.  The limited amount of research linking climate impacts
to economic damages makes the interagency modeling exercise even more
difficult.  The interagency group hopes that over time researchers and
modelers will work to fill these gaps and that the SCC estimates used
for regulatory analysis by the Federal government will continue to
evolve with improvements in modeling. Additional details on these
limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to
updating the current estimates as the science and economic understanding
of climate change and its impacts on society improves over time.
Specifically, the interagency group has set a preliminary goal of
revisiting the SCC values within two years or at such time as
substantially updated models become available, and to continue to
support research in this area.

Applying the global SCC estimates to the estimated increases in CO2
emissions for the range of policy scenarios, we estimate the dollar
value of the climate-related disbenefits captured by the models for each
analysis year.  For internal consistency, the annual disbenefits are
discounted back to NPV terms using the same discount rate as each SCC
estimate (i.e. 5%, 3%, and 2.5%) rather than 3% and 7%. These estimates
are provided in Table 6-7.

Table 6-6.  Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for
2013 a

Discount Rate and Statistic	SCC estimate (2005$)

5%     Average	$5.0

3%     Average	$21.5

2.5%  Average 	$34.9

3%     95%ile	$65.6

a The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

Table 6-7.  Monetized Disbenefits of CO2 Emission Increases in 2013 a

Discount Rate and Statistic	SCC-derived disbenefits

(millions of 2005$)

5%     Average	$5.1

3%     Average	$22

2.5%  Average 	$36

3%     95%ile	$67

a The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

6.4.3	Total Monetized Disbenefits

The additional energy usage required for the emission control devices
would increase emissions of several pollutants.  In this analysis, we
were able to monetize the disbenefits associated with the increased
emissions of PM, NOX, SO2, and CO2, but we were unable to monetize the
disbenefits associated with the increased emissions of CO.  We estimate
that the total monetized disbenefits at a 3% discount rate are $210 to
$470 million.  Therefore, these disbenefits reduce the total monetized
benefits to $7.4 billion to $18 billion and $6.7 billion to $17 billion,
at discount rates of 3% and 7% respectively.

In addition, we were unable to quantify the emission increases or
monetize the disbenefits associated with “leakage” of emissions to
other counties.  This benefits analysis only incorporates the domestic
emission changes, but this regulation could lead to increased imports
and production in other countries.  For this analysis, because we do not
have sufficient information on origin of these imports, the specific
location of the additional emissions, or the level of control on those
facilities, we are unable to estimate the potential disbenefits
associated with increased emissions in other countries that might occur
as a result of this regulation.  However, the monetized benefits
estimates do not account for the decrease in domestic emissions
associated with the decrease in domestic production and transportation.
The economic analysis estimates that domestic production would decrease
by 10 million tons, but imports would increase by only 3 million tons.
The net effect on global pollutants like CO2 and mercury is difficult to
determine because it depends on many factors, and quantifying the
benefits associated with either omission is beyond the scope of this
analysis.

Unquantified or Nonmonetized Benefits

The monetized benefits estimated in this RIA only reflect the portion of
benefits attributable to the health impacts associated with exposure to
ambient fine particles. Data, resource, and methodological limitations
prevented EPA from quantifying or monetizing the benefits from several
important benefit categories, including benefits from reducing toxic
emissions, ecosystem effects, and visibility impairment. The health
benefits from reducing hazardous air pollutants (HAPs) have not been
monetized in this analysis. In addition to being a PM2.5 precursor, SO2
emissions also contribute to adverse effects from acidic deposition in
aquatic and terrestrial ecosystems, increase mercury methylation, as
well as visibility impairment.

Other SO2 and PM Benefits

In addition to being a precursor to PM2.5, SO2 emissions are also
associated with a variety of respiratory health effects. Unfortunately,
we were unable to estimate the health benefits associated with reduced
SO2 exposure in this analysis because we do not have air quality
modeling data available. Without knowing the location of the emission
reductions and the resulting ambient concentrations, we were unable to
estimate the exposure to SO2 for nearby populations. Therefore, this
analysis only quantifies and monetizes the PM2.5 benefits associated
with the reductions in SO2 emissions.

Following an extensive evaluation of health evidence from epidemiologic
and laboratory studies, the Integrated Science Assessment (ISA) for
Sulfur Dioxide concluded that there is a causal relationship between
respiratory health effects and short-term exposure to SO2 (U.S. EPA,
2008b). According to summary of the ISA in EPA’s risk and exposure
assessment (REA) for the SO2 NAAQS“the immediate effect of SO2 on the
respiratory system in humans is bronchoconstriction” (U.S. EPA,
2009c). In addition, the REA summarized from the ISA that “asthmatics
are more sensitive to the effects of SO2 likely resulting from
preexisting inflammation associated with this disease.” A clear
concentration-response relationship has been demonstrated in laboratory
studies following exposures to SO2 at concentrations between 20 and 100
ppb, both in terms of increasing severity of effect and percentage of
asthmatics adversely affected (U.S. EPA, 2009c).  Based on our review of
this information, we identified four short-term morbidity endpoints that
the SO2 ISA identified as a “causal relationship”: asthma
exacerbation, respiratory-related emergency department visits, and
respiratory-related hospitalizations. The differing evidence and
associated strength of the evidence for these different effects is
described in detail in the SO2 ISA. The SO2 ISA also concluded that the
relationship between short-term SO2 exposure and premature mortality was
“suggestive of a causal relationship” because it is difficult to
attribute the mortality risk effects to SO2 alone. Although the SO2 ISA
stated that studies are generally consistent in reporting a relationship
between SO2 exposure and mortality, there was a lack of robustness of
the observed associations to adjustment for pollutants.

SO2 emissions also contribute to adverse welfare effects from acidic
deposition, visibility impairment, and enhanced mercury methylation.
Deposition of sulfur causes acidification, which can cause a loss of
biodiversity of fishes, zooplankton, and macro invertebrates in aquatic
ecosystems, as well as a decline in sensitive tree species, such as red
spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial
ecosystems. In the northeastern United States, the surface waters
affected by acidification are a source of food for some recreational and
subsistence fishermen and for other consumers and support several
cultural services, including aesthetic and educational services and
recreational fishing. Biological effects of acidification in terrestrial
ecosystems are generally linked to aluminum toxicity, which can cause
reduced root growth, which restricts the ability of the plant to take up
water and nutrients. These direct effects can, in turn, increase the
sensitivity of these plants to stresses, such as droughts, cold
temperatures, insect pests, and disease leading to increased mortality
of canopy trees. Terrestrial acidification affects several important
ecological services, including declines in habitat for threatened and
endangered species (cultural), declines in forest aesthetics (cultural),
declines in forest productivity (provisioning), and increases in forest
soil erosion and reductions in water retention (cultural and
regulating). (U.S. EPA, 2008c)

Reducing SO2 and PM emissions would improve the level of visibility
throughout the United States. Fine particles with significant
light-extinction efficiencies include sulfates, nitrates, organic
carbon, elemental carbon, and soil (Sisler, 1996). These suspended
particles and gases degrade visibility by scattering and absorbing
light. Higher visibility impairment levels in the East are due to
generally higher concentrations of fine particles, particularly
sulfates, and higher average relative humidity levels. In fact,
particulate sulfate is the largest contributor to regional haze in the
eastern U.S. (i.e., 40% or more annually and 75% during summer). In the
western U.S., particulate sulfate contributes to 20-50% of regional haze
(U.S. EPA, 2009b). Visibility has direct significance to people’s
enjoyment of daily activities and their overall sense of wellbeing. Good
visibility increases the quality of life where individuals live and
work, and where they engage in recreational activities.  Due to time and
resource limitations, we were unable to estimate the monetized benefits
associated with visibility improvements.  Previous analyses (U.S. EPA,
2006; U.S. EPA, 2010c) show that visibility benefits are a significant
welfare benefit category.

HAP Benefits

Americans are exposed to ambient concentrations of air toxics at levels
which have the potential to cause adverse health effects. The levels of
air toxics to which people are exposed vary depending on where people
live and work and the kinds of activities in which they engage. In order
to identify and prioritize air toxics, emission source types and
locations which are of greatest potential concern, U.S. EPA conducts the
National-Scale Air Toxics Assessment (NATA). The most recent NATA was
conducted for calendar year 2002, and was released in June 2009. NATA
for 2002 includes four steps:

1) Compiling a national emissions inventory of air toxics emissions from
outdoor sources

2) Estimating ambient concentrations of air toxics across the United
States

3) Estimating population exposures across the United States

4) Characterizing potential public health risk due to inhalation of air
toxics including both cancer and noncancer effects

Noncancer health effects can result from chronic, subchronic, or acute
inhalation exposures to air toxics, and include neurological,
cardiovascular, liver, kidney, and respiratory effects as well as
effects on the immune and reproductive systems. According to the 2002
NATA, nearly the entire U.S. population was exposed to an average
concentration of air toxics that has the potential for adverse noncancer
respiratory health effects.  Figures 6-8 and 6-9 depict estimated
county-level carcinogenic risk and noncancer respiratory hazard from the
assessment. The respiratory hazard is dominated by a single pollutant,
acrolein.

This rule is anticipated to reduce 16,400 pounds of mercury, 5,800 tons
of HCl, and 5,200 tons of organic HAPs each year.  Due to data,
resource, and methodology limitations, we were unable to estimate the
benefits associated with the thousands tons of hazardous air pollutants
that would be reduced as a result of this rule. Available emissions data
show that several different HAPs are emitted from Portland cement
manufacturing plants, either released from kilns systems, raw material
dryers, clinker coolers, raw mills, finish mills, storage bins,
conveying system transfer points, bagging systems, or bulk loading and
unloading systems.

Figure 6-8.	Estimated County Level Carcinogenic Risk from HAP exposure
from outdoor sources (NATA, 2002)

Figure 6-9.	Estimated County Level Noncancer (Respiratory) Risk from HAP
exposure from outdoor sources (NATA, 2002)

Mercury

Mercury is a highly neurotoxic contaminant that enters the food web as a
methylated compound, methylmercury (U.S. EPA, 2008c). The contaminant is
concentrated in higher trophic levels, including fish eaten by humans.
Experimental evidence has established that only inconsequential amounts
of methylmercury can be produced in the absence of sulfate (U.S. EPA,
2008c). Current evidence indicates that in watersheds where mercury is
present, increased sulfate deposition very likely results in
methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al,
2007). The SO2 ISA concluded that evidence is sufficient to infer a
casual relationship between sulfur deposition and increased mercury
methylation in wetlands and aquatic environments (U.S. EPA, 2008c).

In addition to the role of sulfate deposition on methylation, these
rules would also reduce mercury emissions. Mercury is emitted to the air
from various man-made and natural sources. These emissions transport
through the atmosphere and eventually deposit to land or water bodies.
This deposition can occur locally, regionally, or globally, depending on
the form of mercury emitted and other factors such as the weather. The
form of mercury emitted varies depending on the source type and other
factors. Available data indicate that the mercury emissions from these
sources are a mixture of gaseous elemental mercury (35%), inorganic
divalent mercury (reactive gas phase mercury) (58%), and particulate
bound mercury (7%) (U.S. EPA, 2010c). Gaseous elemental mercury can be
transported very long distances, even globally, to regions far from the
emissions source (becoming part of the global “pool”) before
deposition occurs. Inorganic divalent and particulate bound mercury have
a shorter atmospheric lifetime and can deposit to land or water bodies
closer to the emissions source. Furthermore, elemental mercury in the
atmosphere can undergo transformation into divalent mercury, providing a
significant pathway for deposition of emitted elemental mercury.

Potential exposure routes to mercury emissions include both direct
inhalation and consumption of fish containing methylmercury. The primary
route of human exposure to mercury emissions from industrial sources is
generally indirectly through the consumption of fish containing
methylmercury. As described above, mercury that has been emitted to the
air eventually settles into water bodies or onto land where it can
either move directly or be leached into waterbodies. Once deposited,
certain microorganisms can change it into methylmercury, a highly toxic
form that builds up in fish, shellfish and animals that eat fish.
Consumption of fish and shellfish are the main sources of methylmercury
exposure to humans. Methylmercury builds up more in some types of fish
and shellfish than in others. The levels of methylmercury in fish and
shellfish vary widely depending on what they eat, how long they live,
and how high they are in the food chain. Most fish, including ocean
species and local freshwater fish, contain some methylmercury. For
example, in recent studies by EPA and the U.S. Geological Survey (USGS)
of fish tissues, every fish sampled contained some methylmercury
(Scudder, 2009).

The majority of fish consumed in the U.S. are ocean species. The
methylmercury concentrations in ocean fish species are primarily
influenced by the global mercury pool. However, the methylmercury found
in local fish can be due, at least partly, to mercury emissions from
local sources. Research shows that most people’s fish consumption does
not cause a mercury-related health concern. However, certain people may
be at higher risk because of their routinely high consumption of fish
(e.g., tribal and other subsistence fishers and their families who rely
heavily on fish for a substantial part of their diet). It has been
demonstrated that high levels of methylmercury in the bloodstream of
unborn babies and young children may harm the developing nervous system,
making the child less able to think and learn. Moreover, mercury
exposure at high levels can harm the brain, heart, kidneys, lungs, and
immune system of people of all ages.

Several studies suggest that the methylmercury content of fish may
reduce these cardio-protective effects of fish consumption.  Some of
these studies also suggest that methylmercury may cause adverse effects
to the cardiovascular system.  For example, the NRC (2000) review of the
literature concerning methylmercury health effects took note of two
epidemiological studies that found an association between dietary
exposure to methylmercury and adverse cardiovascular effects.  Moreover,
in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al.
(1995) observed a relationship between methylmercury exposure via fish
consumption and acute myocardial infarction (AMI or heart attacks),
coronary heart disease, cardiovascular disease, and all-cause mortality.
 The NRC also noted a study of 917 seven year old children in the Faroe
Islands, whose initial exposure to methylmercury was in utero although
post natal exposures may have occurred as well.  At seven years of age,
these children exhibited an increase in blood pressure and a decrease in
heart rate variability. Based on these and other studies, NRC concluded
in 2000 that, while “the data base is not as extensive for
cardiovascular effects as it is for other end points (i.e. neurologic
effects) the cardiovascular system appears to be a target for
methylmercury toxicity.”

Since publication of the NRC report there have been some 30 published
papers presenting the findings of studies that have examined the
possible cardiovascular effects of methylmercury exposure.  These
studies include epidemiological, toxicological, and toxicokinetic
investigations.  Over a dozen review papers have also been published.
If there is a causal relationship between methylmercury exposure and
adverse cardiovascular effects, then reducing exposure to methylmercury
would result in public health benefits from reduced cardiovascular
effects.

In early 2010, EPA sponsored a workshop in which a group of experts were
asked to assess the plausibility of a causal relationship between
methylmercury exposure and cardiovascular health effects and to advise
EPA on methodologies for estimating population level cardiovascular
health impacts of reduced methylmercury exposure. The report from that
workshop is in preparation.

Portland cement manufacturing plants emitted about 16 tons of mercury in
the air in 2006 in the U.S. Based on the EPA’s National Emission
Inventory, and about 103 tons of mercury were emitted from all
anthropogenic sources in the U.S. in 2005. Moreover, the United Nations
has estimated that about 2,100 tons of mercury were emitted worldwide by
anthropogenic sources in 2005. We believe that total mercury emissions
in the U.S. and globally in 2006 were about the same magnitude in 2005.
Therefore, we estimate that in 2006, these sources emitted about 16% of
the total anthropogenic mercury emissions in the U.S. and about 0.8% of
the global emissions in 2005.

Using 2008 inventory estimates, the mercury emissions from Portland
cement kilns only were approximately 9.1 tons.  Overall, the NESHAP and
NSPS would reduce mercury emissions by about 8.2 tons (90%) per year
from current levels, and therefore, contribute to reductions in mercury
exposures and health effects.  Due to time and resource limitations, we
were unable to model mercury methylation, bioaccumulation in fish
tissue, and human consumption of mercury-contaminated fish that would be
needed in order to estimate the human health benefits from reducing
mercury emissions.  However, we were able to model the change in mercury
deposition using CAMx for the final Portland Cement NESHAP and NSPS.
These modeling results indicate significantly reduced total mercury
deposition (wet and dry forms), including reducing deposition by up to
30% in the West and up to 17% in the East in 2013.  This modeling
indicates that mercury deposition reductions tend to be greatest nearest
the sources. Figure 6-10 shows the change in mercury deposition as a
result of the final Portland Cement NESHAP and NSPS in the Eastern U.S.,
and Figure 6.11 shows the change in mercury deposition in the Western
U.S.

Figure 6-10.	Reductions in Total Mercury Deposition (µg/m2) in the
Eastern U.S.

Figure 6-11.	Reductions in Total Mercury Deposition (µg/m2) in the
Western U.S.

Hydrogen Chloride (HCl)

Hydrogen chloride gas is intensely irritating to the mucous membranes of
the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes
throat irritation, and levels of 50 to 100 ppm are barely tolerable for
1 hour. The greatest impact is on the upper respiratory tract; exposure
to high concentrations can rapidly lead to swelling and spasm of the
throat and suffocation. Most seriously exposed persons have immediate
onset of rapid breathing, blue coloring of the skin, and narrowing of
the bronchioles. Patients who have massive exposures may develop an
accumulation of fluid in the lungs. Exposure to hydrogen chloride can
lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or
irritant-induced type of asthma. Children may be more vulnerable to
corrosive agents than adults because of the relatively smaller diameter
of their airways. Children may also be more vulnerable to gas exposure
because of increased minute ventilation per kg and failure to evacuate
an area promptly when exposed. Hydrogen chloride has not been classified
for carcinogenic effects.

Toluene

Toluene is found in evaporative as well as exhaust emissions from motor
vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment,
there is inadequate information to assess the carcinogenic potential of
toluene because studies of humans chronically exposed to toluene are
inconclusive, toluene was not carcinogenic in adequate inhalation cancer
bioassays of rats and mice exposed for life, and increased incidences of
mammary cancer and leukemia were reported in a lifetime rat oral
bioassay.

The central nervous system (CNS) is the primary target for toluene
toxicity in both humans and animals for acute and chronic exposures. CNS
dysfunction (which is often reversible) and narcosis have been
frequently observed in humans acutely exposed to low or moderate levels
of toluene by inhalation; symptoms include fatigue, sleepiness,
headaches, and nausea. Central nervous system depression has been
reported to occur in chronic abusers exposed to high levels of toluene.
Symptoms include ataxia, tremors, cerebral atrophy, nystagmus
(involuntary eye movements), and impaired speech, hearing, and vision.
Chronic inhalation exposure of humans to toluene also causes irritation
of the upper respiratory tract, eye irritation, dizziness, headaches,
and difficulty with sleep.

Human studies have also reported developmental effects, such as CNS
dysfunction, attention deficits, and minor craniofacial and limb
anomalies, in the children of women who abused toluene during pregnancy.
A substantial database examining the effects of toluene in subchronic
and chronic occupationally exposed humans exists. The weight of evidence
from these studies indicates neurological effects (i.e., impaired color
vision, impaired hearing, decreased performance in neurobehavioral
analysis, changes in motor and sensory nerve conduction velocity,
headache, dizziness) as the most sensitive endpoint.

Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human
carcinogen based on evidence in humans and in rats, mice, hamsters, and
monkeys. EPA is currently reviewing recently published epidemiological
data. For instance, research conducted by the National Cancer Institute
(NCI) found an increased risk of nasopharyngeal cancer and
lymphohematopoietic malignancies such as leukemia among workers exposed
to formaldehyde., In an analysis of the lymphohematopoietic cancer
mortality from an extended follow-up of these workers, NCI confirmed an
association between lymphohematopoietic cancer risk and peak exposures.
A recent National Institute of Occupational Safety and Health (NIOSH)
study of garment workers also found increased risk of death due to
leukemia among workers exposed to formaldehyde. Extended follow-up of a
cohort of British chemical workers did not find evidence of an increase
in nasopharyngeal or lymphohematopoietic cancers, but a continuing
statistically significant excess in lung cancers was reported.

In the past 15 years there has been substantial research on the
inhalation dosimetry for formaldehyde in rodents and primates by the
CIIT Centers for Health Research (formerly the Chemical Industry
Institute of Toxicology), with a focus on use of rodent data for
refinement of the quantitative cancer dose-response assessment.,,
CIIT’s risk assessment of formaldehyde incorporated mechanistic and
dosimetric information on formaldehyde. However, it should be noted that
recent research published by EPA indicates that when two-stage modeling
assumptions are varied, resulting dose-response estimates can vary by
several orders of magnitude.,,, These findings are not supportive of
interpreting the CIIT model results as providing a conservative (health
protective) estimate of human risk. EPA research also examined the
contribution of the two-stage modeling for formaldehyde towards
characterizing the relative weights of key events in the mode-of-action
of a carcinogen. For example, the model-based inference in the published
CIIT study that formaldehyde’s direct mutagenic action is not relevant
to the compound’s tumorigenicity was found not to hold under
variations of modeling assumptions.

Based on the developments of the last decade, in 2004, the working group
of the IARC concluded that formaldehyde is carcinogenic to humans (Group
1), on the basis of sufficient evidence in humans and sufficient
evidence in experimental animals - a higher classification than previous
IARC evaluations. After reviewing the currently available
epidemiological evidence, the IARC (2006) characterized the human
evidence for formaldehyde carcinogenicity as “sufficient,” based
upon the data on nasopharyngeal cancers; the epidemiologic evidence on
leukemia was characterized as “strong.” EPA is reviewing the recent
work cited above from the NCI and NIOSH, as well as the analysis by the
CIIT Centers for Health Research and other studies, as part of a
reassessment of the human hazard and dose-response associated with
formaldehyde.

Formaldehyde exposure also causes a range of noncancer health effects,
including irritation of the eyes (burning and watering of the eyes),
nose and throat. Effects from repeated exposure in humans include
respiratory tract irritation, chronic bronchitis and nasal epithelial
lesions such as metaplasia and loss of cilia. Animal studies suggest
that formaldehyde may also cause airway inflammation – including
eosinophil infiltration into the airways. There are several studies that
suggest that formaldehyde may increase the risk of asthma –
particularly in the young.,

Dioxins (Chlorinated dibenzodioxins (CDDs)

A number of effects have been observed in people exposed to 2,3,7,8-TCDD
levels that are at least 10 times higher than background levels. The
most obvious health effect in people exposure to relatively large
amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin disease
with acne-like lesions that occur mainly on the face and upper body.
Other skin effects noted in people exposed to high doses of 2,3,7,8-TCDD
include skin rashes, discoloration, and excessive body hair. Changes in
blood and urine that may indicate liver damage also are seen in people.
Alterations in the ability of the liver to metabolize (or breakdown)
hemoglobin, lipids, sugar, and protein have been reported in people
exposed to relatively high concentrations of 2,3,7,8-TCDD. Most of the
effects are considered mild and were reversible. However, in some people
these effects may last for many years. Slight increases in the risk of
diabetes and abnormal glucose tolerance have been observed in some
studies of people exposed to 2,3,7,8-TCDD. We do not have enough
information to know if exposure to 2,3,7,8-TCDD would result in
reproductive or developmental effects in people, but animal studies
suggest that this is a potential health concern.

In certain animal species, 2,3,7,8-TCDD is especially harmful and can
cause death after a single exposure. Exposure to lower levels can cause
a variety of effects in animals, such as weight loss, liver damage, and
disruption of the endocrine system. In many species of animals,
2,3,7,8-TCDD weakens the immune system and causes a decrease in the
system's ability to fight bacteria and viruses at relatively low levels
(approximately 10 times higher than human background body burdens). In
other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive
damage and birth defects. Some animal species exposed to CDDs during
pregnancy had miscarriages and the offspring of animals exposed to
2,3,7,8-TCDD during pregnancy often had severe birth defects including
skeletal deformities, kidney defects, and weakened immune responses. In
some studies, effects were observed at body burdens 10 times higher than
human background levels.

Furans (Chlorinated dibenzofurans (CDFs))

Most of the information on the adverse health effects comes from studies
in people who were accidentally exposed to food contaminated with CDFs.
The amounts that these people were exposed to were much higher than are
likely from environmental exposures or from a normal diet. Skin and eye
irritations, especially severe acne, darkened skin color, and swollen
eyelids with discharge, were the most obvious health effects of the CDF
poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more
frequent lung infections, numbness, effects on the nervous system, and
mild changes in the liver. Children born to exposed mothers had skin
irritation and more difficulty learning, but it is unknown if this
effect was permanent or caused by CDFs alone or CDFs and polychlorinated
biphenyls in combination.

Many of the same effects that occurred in people accidentally exposed
also occurred in laboratory animals that ate CDFs. Animals also had
severe weight loss, and their stomachs, livers, kidneys, and immune
systems were seriously injured. Some animals had birth defects and
testicular damage, and in severe cases, some animals died. These effects
in animals were seen when they were fed large amounts of CDFs over a
short time, or small amounts over several weeks or months. Nothing is
known about the possible health effects in animals from eating CDFs over
a lifetime.

Benzene

According to NATA for 2002, benzene is the largest contributor to cancer
risk of all 124 pollutants quantitatively assessed in the 2002 NATA.
The EPA’s IRIS database lists benzene as a known human carcinogen
(causing leukemia) by all routes of exposure, and concludes that
exposure is associated with additional health effects, including genetic
changes in both humans and animals and increased proliferation of bone
marrow cells in mice. EPA states in its IRIS database that data indicate
a causal relationship between benzene exposure and acute lymphocytic
leukemia and suggest a relationship between benzene exposure and chronic
non-lymphocytic leukemia and chronic lymphocytic leukemia. The
International Agency for Research on Carcinogens (IARC) has determined
that benzene is a human carcinogen and the U.S. Department of Health and
Human Services (DHHS) has characterized benzene as a known human
carcinogen.   A number of adverse noncancer health effects including
blood disorders, such as preleukemia and aplastic anemia, have also been
associated with long-term exposure to benzene. The most sensitive
noncancer effect observed in humans, based on current data, is the
depression of the absolute lymphocyte count in blood. In addition,
recent work, including studies sponsored by the Health Effects Institute
(HEI), provides evidence that biochemical responses are occurring at
lower levels of benzene exposure than previously known. EPA’s IRIS
program has not yet evaluated these new data.

Other Air Toxics

In addition to the compounds described above, other compounds in gaseous
hydrocarbon and PM emissions would be affected by this rule, including
metal and organic HAPs. Information regarding the health effects of
these compounds can be found in EPA’s IRIS database.

Limitations and Uncertainties

The National Research Council (NRC) (2002) concluded that EPA’s
general methodology for calculating the benefits of reducing air
pollution is reasonable and informative in spite of inherent
uncertainties.  To address these inherent uncertainties, NRC highlighted
the need to conduct rigorous quantitative analysis of uncertainty and to
present benefits estimates to decisionmakers in ways that foster an
appropriate appreciation of their inherent uncertainty.  In response to
these comments, EPA’s Office of Air and Radiation (OAR) is developing
a comprehensive strategy for characterizing the aggregate impact of
uncertainty in key modeling elements on both health incidence and
benefits estimates.  Components of that strategy include emissions
modeling, air quality modeling, health effects incidence estimation, and
valuation.

In this analysis, we use three methods to assess uncertainty
quantitatively: Monte Carlo analysis, alternate concentration-response
functions for PM mortality, and LML assessment.  We also provide a
qualitative assessment for those aspects that we are unable to address
quantitatively in this analysis.  Each of these analyses is described in
detail in the following sections.

This analysis includes many data sources as inputs, including emission
inventories, air quality data from models (with their associated
parameters and inputs), population data, health effect estimates from
epidemiology studies, and economic data for monetizing benefits.  Each
of these inputs may be uncertain and would affect the benefits estimate.
 When the uncertainties from each stage of the analysis are compounded,
small uncertainties can have large effects on the total quantified
benefits.  In this analysis, we are unable to quantify the cumulative
effect of all of these uncertainties, but we provide the following
analyses to characterize many of the largest sources of uncertainty.

Monte Carlo analysis

Similar to other recent RIAs, we used Monte Carlo methods for
characterizing random sampling error associated with the concentration
response functions and economic valuation functions.  Monte Carlo
simulation uses random sampling from distributions of parameters to
characterize the effects of uncertainty on output variables, such as
incidence of morbidity.  Specifically, we used Monte Carlo methods to
generate confidence intervals around the estimated health impact and
dollar benefits.  The reported standard errors in the epidemiological
studies determined the distributions for individual effect estimates, as
shown in Tables 6-2 and 6-3.

Alternate concentration-response functions for PM mortality

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis.  To better understand the
concentration-response relationship between PM2.5 exposure and premature
mortality, EPA conducted an expert elicitation in 2006 (Roman et al.,
2008 xe "Roman et al., 2008" ; IEc, 2006 xe "IEc, 2006" ).  In general,
the results of the expert elicitation support the conclusion that the
benefits of PM2.5 control are very likely to be substantial.  In
previous RIAs, EPA presented benefits estimates using concentration
response functions derived from the PM2.5 Expert Elicitation as a range
from the lowest expert value (Expert K) to the highest expert value
(Expert E).  However, this approach did not indicate the agency’s
judgment on what the best estimate of PM benefits may be, and EPA’s
Science Advisory Board described this presentation as misleading.
Therefore, we began to present the cohort-based studies (Pope et al,
2002; and Laden et al., 2006) as our core estimates in the proposal RIA
for this rule (U.S. EPA, 2009a).  Using alternate relationships between
PM2.5 and premature mortality supplied by experts, higher and lower
benefits estimates are plausible, but most of the expert-based estimates
fall between the two epidemiology-based estimates (Roman et al., 2008 xe
"Roman et al., 2008" ).

In this analysis, we present the results derived from the expert
elicitation as indicative of the uncertainty associated with a major
component of the health impact functions, and we provide the independent
estimates derived from each of the twelve experts to better characterize
the degree of variability in the expert responses.  In this section, we
provide the results using the concentration-response functions derived
from the expert elicitation in both tabular (Tables 6-2 and 6-3) and
graphical form (Figure 6-5).  Please note that these results are not the
direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies.

LML assessment

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis.  To better characterize the uncertainty
associated with mortality impacts that are estimated to occur in areas
with low baseline levels of PM2.5, we included the LML assessment.  We
have more confidence in the mortality impacts among populations exposed
to levels of PM2.5 above the lowest LML of the large cohort studies, and
our confidence in the results diminish as we model that are lower than
the LML. While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations.  It is
important to emphasize that just because we have greater confidence in
the benefits above the LML, this does not mean that we have no
confidence that benefits occur below the LML.  In section 6.3, we
provide the results of the LML assessment in Figures 6-6 and 6-7.

Qualitative assessment of uncertainty and other analysis limitations

Although we strive to incorporate as many quantitative assessments of
uncertainty, there are several aspects for which we are only able to
address qualitatively.  These aspects are important factors to consider
when evaluating the relative benefits of the attainment strategies for
each of the alternative standards:

Above we present the estimates of the total monetized benefits, based on
our interpretation of the best available scientific literature and
methods and supported by the SAB-HES and the NAS (NRC, 2002  XE "NRC,
2002"  ). The benefits estimates are subject to a number of assumptions
and uncertainties. For example, the key assumptions underlying the
estimates for premature mortality, which typically account for at least
90% of the total monetized benefits, include the following:

We assume that all fine particles, regardless of their chemical
composition, are equally potent in causing premature mortality. This is
an important assumption, because PM2.5 produced via transported
precursors emitted from EGUs may differ significantly from direct PM2.5
released from diesel engines and other industrial sources, but no clear
scientific grounds exist for supporting differential effects estimates
by particle type.

We assume that the health impact function for fine particles is linear
down to the lowest air quality levels modeled in this analysis. Thus,
the estimates include health benefits from reducing fine particles in
areas with varied concentrations of PM2.5, including both regions that
are in attainment with fine particle standard and those that do not meet
the standard down to the lowest modeled concentrations.

To characterize the uncertainty in the relationship between PM2.5 and
premature mortality (which typically accounts for 85% to 95% of total
monetized benefits), we include a set of twelve estimates based on
results of the expert elicitation study in addition to our core
estimates. Even these multiple characterizations omit the uncertainty in
air quality estimates, baseline incidence rates, populations exposed and
transferability of the effect estimate to diverse locations. As a
result, the reported confidence intervals and range of estimates give an
incomplete picture about the overall uncertainty in the PM2.5 estimates.
This information should be interpreted within the context of the larger
uncertainty surrounding the entire analysis. For more information on the
uncertainties associated with PM2.5 benefits, please consult the PM2.5
NAAQS RIA (Table 5.5).

In addition, there is some uncertainty associated with the specificity
of the air quality inputs to benefits model for this particular
regulatory scenario.  By assuming that each kiln proportionately reduces
their emissions by the same percentage as the national percentage
reduction, we may be slightly under or overestimating the air quality
impacts at specific locations and the associated monetized benefits.  By
including the hazardous waste kilns in the emissions inventory, we may
be slightly overestimating the air quality impacts and monetized
benefits.  By omitting the decrease in domestic cement production and
transportation, we are underestimating the air quality impacts and
monetized benefits.  By omitting the increase in cement imports, we may
be overestimating the monetized benefits by not accounting for
additional global pollutants.  By using national average benefit-per-ton
estimates to calculate the energy disbenefits, we may be under or
overestimating these monetized disbenefits.  Despite our inability to
fully characterize and quantify these relatively small effects, we
believe that, on net, the air quality impacts and associated monetized
benefits are representative of the magnitude of benefits anticipated
from this regulation.

As previously described, we strive to monetize as many of the benefits
anticipated from this rule as possible, but the monetized benefits
estimated in this RIA inevitably only reflect the portion of benefits.
Specifically, only the benefits attributable to the health impacts
associated with exposure to ambient fine particles have been monetized
in this analysis. Data, resource, and methodological limitations
prevented EPA from quantifying or monetizing the benefits from several
important benefit categories, including benefits from reducing toxic
emissions, ecosystem effects, and visibility impairment. Data
limitations include limited monitoring for HAPs, incomplete emissions
inventories for HAPs, and limited photochemical air quality modeling for
non-mercury HAPs.  Resource limitations include limited staff and
extramural funding in conjunction with a heavy regulatory workload.
Methodological limitations include an absence of concentration-response
functions for many HAP health effects, with issues such as exposure
misclassification, small number of cases, confounding, and extrapolation
of toxicological effects down to ambient levels (IEc, 2008).  Despite
our inability to monetize all of the benefit categories, the monetized
benefits still exceed the costs by a substantial margin.

This RIA does not include the type of detailed uncertainty assessment
found in the PM NAAQS RIA.  However, the results of the Monte Carlo
analyses of the health and welfare benefits presented in Chapter 5 of
the PM RIA can provide some evidence of the uncertainty surrounding the
benefits results presented in this analysis.

Comparison of Benefits and Costs

Using a 3% discount rate, we estimate the total monetized benefits of
the final Portland Cement NESHAP and NSPS to be $7.4 billion to $18
billion in the implementation year (2013).  Using a 7% discount rate, we
estimate the total monetized benefits of the final Portland Cement
NESHAP and NSPS to be $6.7 billion to $16 billion.  These estimates
include the energy disbenefits associated with increased electricity
usage by the control devices.  The annualized social costs of the final
NESHAP and NSPS are $880 million.  Thus, net benefits are $6.5 billion
to $17 billion at a 3% discount rate for the benefits and $5.8 billion
to $16 billion at a 7% discount rate.  In addition, the benefits from
reducing 16,400 pounds of mercury, 4,400 tons of NOx, 5,800 tons of HCl,
and 5,200 tons of organic HAPs each year have not been included in these
estimates. All estimates are in 2005$.

Table 6-5 shows a summary of the monetized benefits, social costs, and
net benefits for the final Portland Cement NESHAP and NSPS, the final
NSPS only, the final NESHAP only, and the more stringent NSPS and final
NESHAP.  Figures 6-12 and 6-13 show the full range of net benefits
estimates (i.e., annual benefits minus annualized costs) utilizing the
14 different PM2.5 mortality functions at discount rates of 3% and 7%.
Data, resource, and methodological limitations prevented EPA from
monetizing the benefits from several important benefit categories,
including benefits from reducing hazardous air pollutants, ecosystem
effects, and visibility impairment.  EPA believes that the benefits are
likely to exceed the costs under this rulemaking even when taking into
account uncertainties in the cost and benefit estimates.

Table 6-8.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the final Portland Cement NESHAP in 2013 (millions of
2005$)1

Final NESHAP and NSPS

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $926           to                 $950 	   $926
        to                 $950

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Non-monetized Benefitsd	4,400 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NSPS only

 	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$510	to	$1,300	$460	to	$1,100

Total Social Costs3	$72	$40

Net Benefits	$470	to	$1,300	$420	to	$1,100

Non-monetized Benefitsd	6,600 tons of NOx

	520 tons of HCl

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

Final NESHAP only

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $904           to                 $930	   $904
       to                 $930

Net Benefits	$6,500	to	$17,000	$5,800	to	$16,000

Non-monetized Benefitsd	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury

	Health effects from HAPs, SO2 exposure

	Ecosystem effects

	Visibility impairment

Alternative: More Stringent NSPS and Final NESHAP

	3% Discount Rate	7% Discount Rate

Total Monetized Benefits2	$7,400	to	$18,000	$6,700	to	$16,000

Total Social Costs3	   $955           to                 $979	   $955
       to                 $979

Net Benefits	$6,500	to	$17,000	$5,700	to	$15,000

Non-monetized Benefits4	7,800 tons of NOx (includes energy disbenefits)

	5,200 tons of organic HAPs

	5,900 tons of HCl

	16,400 pounds of mercury

	Health effects from HAPs, NO2, and SO2 exposure

	Ecosystem effects

	Visibility impairment

1	All estimates are for the implementation year (2013), and are rounded
to two significant figures.

2	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002) to Laden et al. (2006). These models assume that
all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality because there is no clear
scientific evidence that would support the development of differential
effects estimates by particle type. The total monetized benefits include
the energy disbenefits.

3	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

4	Due to data, methodology, and resource limitations, we were unable to
monetize the benefits associated with these categories of benefits.

Figure 6-12.	Net Benefits for the Final Portland Cement NESHAP and NSPS
at 3% Discount Rate a

a	Net Benefits are quantified in terms of PM2.5 benefits for
implementation year (2013). This graph shows 14 benefits estimates
combined with the cost estimate. All combinations are treated as
independent and equally probable. All fine particles are assumed to have
equivalent health effects, but the benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These net benefits
include the energy disbenefits. Due to data, methodology, and resource
limitations, we were unable to monetize the benefits associated with
several categories of benefits, including exposure to HAPs, NO2, and
SO2, ecosystem effects, and visibility effects.

Figure 6-13.	Net Benefits for the Final Portland Cement NESHAP and NSPS
at 7% Discount Rate a

a	Net Benefits are quantified in terms of PM2.5 benefits for
implementation year (2013). This graph shows 14 benefits estimates
combined with the cost estimate. All combinations are treated as
independent and equally probable. All fine particles are assumed to have
equivalent health effects, but the benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These net benefits
include the energy disbenefits. Due to data, methodology, and resource
limitations, we were unable to monetize the benefits associated with
several categories of benefits, including exposure to HAPs, NO2, and
SO2, ecosystem effects, and visibility effects.

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Appendix A

Short-Run Regional Portland Cement Economic Model

The Office of Air Quality Planning and Standards (OAQPS) has adopted
the standard-industry level analysis described in the Office’s
resource manual (EPA, 1999a  XE "EPA, 1999a"  ). This approach is
consistent with previous EPA analyses of the Portland cement industry
(EPA, 1998; EPA, 1999b  XE "EPA, 1998"    XE "EPA, 1999b"  ) and uses a
single-period static partial-equilibrium model to compare prepolicy
cement market baselines with expected postpolicy outcomes in these
markets. The benchmark time horizon for the analysis is the intermediate
run where producers have some constraints on their flexibility to adjust
factors of production. This time horizon allows us to capture important
transitory impacts of the program on existing producers. Key measures in
this analysis include

market-level effects (market prices, changes in domestic production and
consumption, and international trade),

industry-level effects (changes in operating profits and employment),

facility-level effects (plant utilization changes), and

social costs (changes in producer and consumer surplus).

In this appendix, we provide additional details about economic model
updates, model equations and parameters.

A.1	Economic Impact Model Updates Since Proposal

The need for a complete set of statistics makes the use of a 2005
baseline the best choice for a typical year. At the time of proposal
model development, it was the latest year for which the PCA had
published their plant information summary and complete statistics for
updating variable cost functions were available. Details of model
development are provided in EPA (2009  XE "EPA (2009"  ), Appendix A.
Since proposal, EPA identified several plants where operations had
changed (see Table A-1). As a result, EPA modified the baseline U.S.
production quantities to approximate these changes and maintain
consistency with 2005 market conditions (Table A-2).

EPA also recognizes that the demand for cement is a derived demand
because it depends on demand for sectors such as housing and
construction. As a result, business cycles also significantly influence
the cement industry (see Table A-3). If 2013 is more or less favorable
for the cement industry than 2005, then impacts would be expected to
change accordingly.

Table A-1.	Economic Model Population Updates: 2005

Market	Approximate Clinker Capacity Removed

(thousand metric tons)	Description	Approximate Clinker Capacity Added

(thousand metric tons)	Net Change in Market Plant Population

Atlanta	300	Closure 	0	−1

Baltimore/Philadelphia	500	Closure 	400	−1

Chicago	600	Replacement 	1,100	0

Dallas	800	Replacement	800	0

Detroit	900	Closure	0	-1

Kansas City	300	Closure 	0	−1

Los Angeles	1,100	Replacement 	2,200	0

Phoenix	600	Replacement 	700	+1

San Antonio	300	 Closure	0	0

St. Louis	500	Replacement	1,200	0

Market	U.S. Production

Proposal	U.S. Production Revised	Difference

Atlanta	6.1	5.8	−0.3

Baltimore/Philadelphia	8.0	7.8	−0.2

Birmingham	5.9	5.9	0.0

Chicago	4.3	4.7	0.4

Cincinnati	3.7	3.7	0.0

Dallas	8.2	8.1	−0.1

Denver	3.4	3.4	0.0

Detroit	4.8	3.8	−1.0

Florida	5.6	5.5	−0.1

Kansas City	5.3	5.0	−0.3

Los Angeles	9.6	10.6	1.0

Minneapolis	1.7	1.7	0.0

New York/Boston	3.2	3.2	0.0

Phoenix	4.1	4.3	0.2

Pittsburgh	1.5	1.5	0.0

St. Louis	5.4	6.0	0.6

Salt Lake City	2.4	2.4	0.0

San Antonio	5.7	5.5	−0.2

San Francisco	3.4	3.4	0.0

Seattle	1.1	1.1	0.0

Total, Grey	93.6	93.6	0.0

Source: EPA calculations.

Table A-3.	Recent Market Trends

Economic Variable	2005	2006	2007	2008	2009a

Clinker production (million metric tons)	87	89	86	78	58

Price, average mill value ($/metric ton)	$91	$102	$104	$103	$100

Employment (thousand)	16	16	16	15	14

Share of consumption provided by imports (percent)	25	27	19	11	8

aestimated.

Source: USGS, 2010  XE "USGS, 2010"  . Mineral Commodity Survey 2010.
http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2010-cemen.p
df

A.2	Partial Equilibrium Model

The partial equilibrium analysis performed for this rule uses the cement
market model developed during proposal (U.S. EPA, 2009  XE "U.S. EPA,
2009"  ). The model simulates how stakeholders (consumers and firms) may
respond to the additional regulatory program costs. In the near term,
the regional cement markets are assumed to have few sellers that offer
similar/identical products. As a result, EPA used an oligopoly market
structure. As described in Section 3, this market structure assumption
suggests that the observed baseline market price will be higher than
marginal production costs (i.e., there may be a preexisting market
distortion prior to regulation). To provide some intuition about factors
that influence the size of the existing distortion, we express a
seller’s “best” supply decision as a function of the market price,
the seller’s market share, the market demand elasticity, and the
seller’s marginal costs (see Varian [1992], pp. 289–290  XE "Varian
[1992], pp. 289–290"  ):

Price × (1 + Market Sharei/Demand Elasticity) = Marginal Costi.

This equation shows the relationship between the oligopoly model and
perfect competition. The market distortion will typically be higher when
market sharei is high (there are few sellers) and in markets where the
quantity demanded is less sensitive to price (i.e., the demand
elasticity is inelastic).

A.2.1	 Model Equations

To estimate the economic impacts of the regulation, EPA used four linear
equations to calculate the following unknown variables:

change in domestic plant production (dqi),

change in imports (dqimports),

change in cement market quantity (dQ), and

change in cement price (dP).

Equation 1: Domestic Supply. For each plant, we describe its response to
the regulatory program as follows. The total compliance cost per ton
(ci) is applied to each kiln, and the difference in the highest cost
kiln with-regulation and the highest cost kiln in the baseline
approximates the plant’s change in the marginal cost of production
(dMCi). In with-regulation equilibrium, the change in marginal revenue
(dMRi) must equal the change in the marginal cost (dMCi) for each plant.

dmarginal Revenuei = dmarginal Costi

or

Equation 2: Supply of Imports. If applicable to the market, an equation
describing the supply of cement from other countries was included:

dimports = import supply elasticity × (dprice/baseline price) ×
baseline imports.

For import supply, EPA used the latest empirical work on how other
countries who export (i.e., supply imports) to the United States respond
to price changes. Broda et al. (2008  XE "Broda et al. (2008"  ) report
that the export supply elasticity for commodities imported by the United
States was approximately two. This implies that a 1% increase in prices
results in a 2% increase in the volume of exports for a typical good.

Equation 3: Market Supply. Market supply of Portland cement equals the
change in domestic production and imports:

dmarket Q = dtotal domestic production + dimports.

This condition ensures that the market quantity is consistent with the
individual supply decisions of domestic plants and imports in the new
with-regulation equilibrium for each regional market.

Equation 4: Market Demand. The demand for Portland cement is derived
from the demand for concrete products, which, in turn, is derived
largely from the demand for construction. Based on a linear demand
equation, the market demand condition for Portland cement must hold
based on the projected change in market price, that is,

dMarketQ = demand elasticity (dprice/baseline price) × baseline
consumption.

estimate of −0.88 (EPA, 1998  XE "EPA 1998"  ). This value suggests
that a 1% increase in the cement price would lead to a 0.88% reduction
in cement consumption.

Appendix B

The Cement Plant’s Production Decision:

A Mathematical Representation

This appendix provides additional detail about the cement ’s
production decision used in the economic model (see Equation 1 in
Section 3 of the RIA). Table B-1 identifies and describes the key
variables of the cement plant’s profit function.

Table B-1.	Variable Descriptions

 	Market output

qi	Domestic plant i’s output

FCi	Plant fixed costs

VCi	Plant variable costs

Step 1: First, we assume the plant’s goal is to maximize profits:

  .

Step 2: The first-order conditions for a profit maximum are:

  .

Step 3: Apply two key assumptions in the Cournot price model:

Plant’s (i) recognizes its own production decisions influence the
market price:

Plant (i) output decisions do not affect those of any other plant (j)
(e.g., there is no strategic action among cement plants):

Step 4: Next, multiply second term by

  .

Step 5: Rearranging terms:

  .

Step 6: Use and apply the following definitions:

  .

We derive the following expression:

  .

Step 7: The total differential of this equation is determined and gives
us the optimal decision rule for the plant:

  .

Appendix C

Social Cost Methodology

The Office of Air Quality Planning and Standards (OAQPS) has adopted
the standard industry-level analysis described in the Office’s
resource manual (EPA, 1999a  XE “U.S. EPA, 1999a”  ). This approach
is consistent with previous EPA analyses of the Portland cement industry
(EPA, 1998  XE “U.S. EPA, 1998”  ; EPA, 1999b  XE “U.S. EPA,
1999b”  ) and uses a single-period static partial-equilibrium model to
compare prepolicy cement market baselines with expected postpolicy
outcomes in these markets. The benchmark time horizon for the analysis
is the intermediate run where producers have some constraints on their
flexibility to adjust factors of production. This time horizon allows us
to capture important transitory impacts of the program on existing
producers. The model provides an estimate of the social costs (changes
in producer and consumer surplus) associated with controls applying to
existing kilns (see Section 4). Since the social cost methodology is
identical to the approach used in previous cement analysis (EPA, 1998,
Appendix C), we have included elements of the previous report’s
Appendix C in this RIA.

Figure C-1 illustrates the conceptual framework for evaluating the
social cost and distributive impacts under the imperfectly competitive
structure of U.S. cement markets. The baseline equilibrium is given by
the price, P0, and the quantity, Q0. Without the regulation, the total
benefits of consuming cement are given by the area under the demand
curve up to the market output, Q0. This equals the area filled by the
letters ABCDEFGHIJ. The total variable cost to society of producing Q0
equals the area under the MC function, given by the area IJ. Thus, the
total surplus value to society from the production and consumption of
output level Q0 equals the total benefits minus the total costs, or the
area filled by the letters ABCDEFGH.

This total surplus value to society can be further divided into producer
surplus and consumer surplus. Producer surplus accrues to the suppliers
of cement and reflects the value they receive in the market for
producing Q0 units of cement less their costs of production, i.e., their
profits. As shown in Figure C-1, producer surplus is given by the area
DEFGH, which is the difference between cement revenues (i.e., area
DEFGHIJ) and production costs (area IJ). Consumer surplus accrues to the
consumers of cement and reflects the value they place on consumption
(total benefits of consumption) less what they must pay on the market,
i.e., P0. Consumer surplus is thereby given by the area ABC.

Figure C-1.	Social Cost of Regulation Under Imperfect Competition

The final rule will increase the marginal cost of producing cement and
thereby shift this curve upward by the amount of the incremental
compliance costs. As shown in Figure C-1, this results in a new market
equilibrium that occurs at a higher market price for cement, P1, and a
lower level of output, Q1. In this scenario, the total benefits of
consumption are equal to area ABDFI and the total variable costs of
production are equal to area FI. This yields a with-regulation social
surplus equal to area ABD with area BD representing the new producer
surplus and area A being the new consumer surplus. The social cost of
the regulation equals the total change in social surplus caused by the
regulation. Therefore, the social cost of the regulation is represented
by the area FGHEC in Figure C-1.