tech_adden.txt

TECHNICAL ADDENDUM: METHODOLOGIES FOR THE BENEFIT ANALYSIS OF
THE CLEAR SKIES INITIATIVE
September 2002


I. INTRODUCTION
Background
The Need for Multi-pollutant Legislation
In the United States, power generation is responsible for 63% of
sulfur dioxide (SO2), 22% of nitrogen oxides (NOx), and 37% of
man-made mercury released to the environment. Once released, these
pollutants together with their atmospheric transformation products
(e.g. ozone and fine particles) can travel long distances before
being deposited. Environmental and public health problems resulting
from power generation emissions include:


•
Cardiovascular and respiratory conditions associated with
exposure to fine particles (PM) and ozone;


•
Visibility impairment associated with regional
haze;


•
Acidification of surface waters and forest
ecosystems;


•
Ecosystem and public health effects associated with the
accumulation of mercury in fish and other wildlife;


•
Acidic damage to cultural monuments and other
materials;


•
Ozone damage to forested ecosystems; and


•
Eutrophication in coastal areas.


While the current Clean Air Act has played a role in
significantly improving some of these issues, additional reductions
in the emissions of SO2, NOx, and mercury are necessary to address
persistent public health and environmental problems. Because of the
regional and global scale of these pollutants, individual states or
localities experiencing the environmental effects cannot always
control them. In addition, current law tends to address each
environmental problem independently, even if one pollutant
contributes to several problems. To more effectively address the
environmental problems caused by power generation, there is a need
for a national program that would take advantage of synergies of
controlling multiple pollutants at once.
The Clear Skies Act
On February 14, 2002, the President announced the Clear Skies
Initiative, a proposal to reduce emissions from electric power
generating sources. The proposal was embodied in legislative form
as the Clear Skies Act, which was introduced in the House of
Representatives as H.R.5266 and in the Senate as S.2815 in July
2002. For the purpose of the analyses presented here, the central
features examined in the Initiative are identical to those
contained in the Act.
The Clear Skies Act would reduce emissions of sulfur dioxide
(SO2), nitrogen oxides (NOx), and mercury from fossil fuel-fired
combustion units by approximately 70% from current levels.1 These
mandatory emission reductions would be achieved through a cap and
trade
1 The Clear Skies Act would cut sulfur dioxide (SO2) emissions
by 73 percent, from current emissions of 11 million tons to caps of
4.5 million tons in 2010 and 3 million tons in 2018. It would cut
emissions of nitrogen oxides (NOx) by 67 percent, from current
emissions of 5 million tons
program, modeled on the current Acid Rain Program for SO2.
Federally enforceable emissions limits, or national caps, for each
pollutant would be established. Sources would be allowed to
transfer these authorized emission limits among themselves to
achieve the required reductions for all three pollutants at the
lowest overall cost. This proposal would alleviate many of the
remaining environmental and health problems associated with power
generation.
This document reports the methods and results of an analysis of
the environmental and health benefits of the Clear Skies Act. It
presents quantitative estimates of the health improvements and
monetary benefits that would be achieved by this proposal.


Summary of the Benefits Analysis Methods and Results
The Clear Skies Act would provide significant benefits to public
health and the environment, whether expressed as health and
environmental improvements or as monetized benefits. These include
prolonging thousands of lives and reducing tens of thousands to
millions of cases in other indicators of adverse health effects,
such as work loss days, restricted activity days, and days with
asthma attacks. Environmental benefits include significant
increases in visibility and substantial improvements in chronically
acidic conditions in lakes in the Northeastern US. Based on
emissions reductions that would start well before 2010 and the
expected increase in benefits between 2010 and 2020, the cumulative
health benefits of the program across the next two decades would be
significant. The key results of this analysis of the Clear Skies
Act are summarized in Exhibit 1.
Section II (Analytical Approach) discusses the analytic
framework used in conducting this assessment, which includes
scenario development, emissions modeling, air quality modeling,
human health and visibility effects estimation, economic valuation,
and adjustments for income growth and benefits aggregation.
As depicted in Exhibit 1, we have used two approaches to provide
benefits in health and environmental effects and in monetary terms.
While there is a substantial difference in the specific estimates,
both approaches show that the monetary benefits of the Clear Skies
Act are well in excess of the estimated costs of $3.7 billion in
2010 and $6.5 billion in 2020.2
The first approach presented, the Base Estimate, is a
peer-reviewed method developed for previous risk and benefit-cost
assessments carried out by the Environmental Protection Agency. It
is the method used in the regulatory assessments of the Heavy Duty
Diesel and Tier II Rules and the Section 812 Report to Congress.
Following the approach of these earlier assessments, along with the
results of the Base Estimate, we present various sensitivity
analyses on the Base Estimate that alter select subsets of
variables; these sensitivity analyses yield results as much as 42
percent lower to over 180 percent higher. By far, the largest
component of these monetized benefits is related to premature
mortality from long-term exposure to particulate matter ($41
billion and $89 billion in 2010 and 2020, respectively), followed
by chronic bronchitis ($1.5 billion and $3.2 billion in 2010 and
2020, respectively).
to caps of 2.1 million tons in 2008 and 1.7 million tons in
2018. Mercury emissions would be reduced by 69 percent, from
current emissions of 48 tons to caps of 26 tons in 2010 and 15 tons
in 2018.
Detailed information on the costs of Clear Skies can be found in
the Clear Skies Act Analytical Support Package (2002).
In order to provide some insight into the potential importance
of the key elements underlying estimates of the benefits of
reducing SOx and NOx emissions, we developed an Alternative
Estimate using different choices of data, methods, and assumptions
that are detailed in Section II (Analytical Approach). As indicated
in Exhibit 1, the differences between the Alternative and Base
Estimates are found in the estimation of the impact of fine
particle reductions on premature mortality and the valuation of
reducing the risk of premature mortality and the risk of chronic
bronchitis. The Alternative Estimate of the impact of fine particle
reductions on premature mortality relies on recent scientific
studies finding an association between increased mortality and
short-term (days to weeks) exposure to particulate matter, while
the Base Estimate relies on a recent reanalysis of earlier studies
that found associations between long-term exposure to fine
particles and increased mortality. The Alternative approach also
uses different data to value reductions in the risk of premature
mortality and chronic bronchitis and makes adjustments relating to
the health status and potential longevity of the populations most
likely affected by PM. Even using the more conservative assumptions
of this Alternative, the benefits of Clear Skies still outweigh the
projected costs of the proposal.
All such benefit estimates are subject to a number of
assumptions and uncertainties, which are discussed in Section III
(Major Uncertainties in the Benefits Analysis) of this report. For
example key assumptions underlying the Base and Alternative
Estimates for the mortality category include the following: (1)
Inhalation of fine particles is causally associated with premature
death at concentrations near those experienced by most Americans on
a daily basis. While biological mechanisms for this effect have not
yet been definitively established, the weight of the available
epidemiological evidence supports an assumption of causality. (2)
All fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality. This is an important
assumption, because fine particles from power plant emissions are
chemically different from directly emitted fine particles from both
mobile sources and other industrial facilities, but no clear
scientific grounds exist for supporting differential effects
estimates by particle type. (3) The concentration-response function
for fine particles is approximately linear within the range of
ambient concentrations under consideration. Thus, the estimates
include health benefits from reducing fine particles in areas with
varied concentrations of particulate matter, including both regions
that are in attainment with fine particle standard and those that
do not meet the standard. (4) The forecasts for future emissions
and associated air quality modeling are valid. Although recognizing
the difficulties, assumptions and inherent uncertainties in the
overall enterprise, these analyses are based on peer-reviewed
scientific literature and up-to-date assessment tools, and we
believe the results are highly useful in assessing this
proposal.
In addition to the quantified and monetized benefits summarized
above, there are a number of additional categories are not
currently amenable to quantification or valuation. These include:
the health and environmental benefits of reducing mercury
accumulation in fish and other wildlife; reduced acid and
particulate deposition damage to cultural monuments and other
materials; reduced ozone effects on forested ecosystems; and
environmental benefits due to reductions of impacts of
acidification in lakes and streams and eutrophication in coastal
areas. Additionally, we have not quantified a number of known or
suspected health effects linked with PM and ozone for which
appropriate concentration-response functions are not available or
which do not provide easily interpretable outcomes (i.e. changes in
forced expiratory volume (FEV1)).
As a result, both the Base and Alternative monetized benefits
estimates underestimate the total benefits attributable to the
Clear Skies Act.
Exhibit 1 Summary of Results: The Estimated PM and Ozone-Related
Benefits of the Clear Skies Act in 2010 and 20203


* Results calculated using three percent discount rate as
recommended by EPA's Guidelines for Economic Analysis (US EPA,
2000a).
** Results calculated using seven percent discount rate as
recommended by OMB Circular A-94 (OMB, 1992).
The two sets of estimates depicted in this table reflect
alternative assumptions and analytical approaches regarding
quantifying and evaluating the effects of airborne particles on
public health. All estimates assume that particles are causally
associated with health effects, and that all components have the
same toxicity. Linear concentration-response relationships between
PM and all health effects are assumed, indicating that reductions
in PM have the same impact on health outcomes regardless of the
absolute level of PM in a given location. The Base Estimate relies
on estimates of the potential cumulative effect of long-term
exposure to particles, while the Alternative Estimate presumes that
PM effects are limited to those that accumulate over much shorter
time periods. The Alternative Estimate also uses different
approaches to value health effects damages. All such estimates are
subject to a number of assumptions and uncertainties. It is of note
that, based on recent preliminary findings from the Health Effects
Institute, the magnitude of mortality from short-tern exposure
(Alternative Estimate) and hospital/ER admissions estimates (both
estimates) may be under or overestimated.


II. ANALYTICAL APPROACH
The framework for the Clear Skies Act benefits analysis is the
same as that used in three recent state-of-the-art EPA regulatory
analyses: the Section 812 Prospective Analysis (U.S. EPA, 1999a);
the Tier II motor vehicle/gasoline sulfur rules Regulatory Impact
Analysis (RIA) (U.S. EPA, 1999b); and the Heavy-Duty Engine/Diesel
Fuel RIA (U.S. EPA, 2000b). The analysis uses the same health
effect and valuation functions employed in the most recent of these
analyses, the Heavy-Duty Engine/Diesel Fuel RIA. The analytical
approach can be described as a sequence of six steps, summarized
below and described in detail later in this report. These steps,
listed in order of completion, are:


1.
Scenario development


2.
Emissions modeling


3.
Air qua lity modeling


4.
Human health and visibility effects estimation


5.
Economic valuation


6.
Adjustments for income growth and benefits
aggregation


Exhibit 2 outlines the analytical framework used to study the
benefits of the Clear Skies Act. The first step in the benefits
analysis is the specification of the regulatory scenarios that will
be evaluated. Typically, an analysis will include a baseline
scenario that simulates future conditions in the absence of the
proposed regulation and one or more control scenarios that simulate
conditions under the regulations being evaluated. The benefits of a
proposed regulation are then estimated as the difference in benefit
outcomes (e.g., adverse health effects) between the control and
baseline scenarios. For this analysis, the baseline scenarios for
2010 and 2020 assume no additional emissions control regulation
beyond the continuing effects of Title IV of the Clean Air Act
Amendments, the NOx SIP Call, and other promulgated federal rules
issued under the Clean Air Act. For each year (2010 and 2020), our
analysis evaluates a single control scenario, as described
below.
After scenario development, the second step of the benefits
analysis is the estimation of the effect of the Clear Skies Act on
emissions sources. We generated emissions estimates for the
baseline by projecting changes in emissions under the baseline case
for 2010 and 2020. We generated emissions estimates for the Clear
Skies Act control scenario using the same set of economic activity
projections as the baseline but with additional emissions controls
consistent with the Clear Skies Act caps. Emissions inputs were
derived from the 1996 NTI and the 1996 NEI. In addition, emissions
inventories prepared for the Heavy-Duty Diesel Engine rulemaking
were the basis for future year emissions projections. The
Integrated Planning Model (IPM) was used to derive all future
projections of electricity generation source emissions.
After the emissions inventories are developed, they are
translated into estimates of futureyear air quality conditions
under each scenario. We employed two sophisticated computer models,
the Regulatory Modeling System for Aerosols and Deposition (REMSAD)
and the Comprehensive Air Quality Model with Extensions (CAMx) to
estimate changes to the concentration of particulate matter and
ozone, respectively, resulting from the Clear Skies Act. The REMSAD
model was also used to estimate changes in visibility associated
with those changes in particulate matter concentrations and to
estimate changes in deposition of sulfur, nitrogen, and
mercury.
The air quality modeling results serve as inputs to a modeling
system that translates air quality changes to changes in health
outcomes (e.g., premature mortality, emergency room visits) through
the use of concentration-response functions. Scientific literature
on the health effects of air pollutants provides the source of
these concentration-response functions. At this point, we derive
estimates of the differences between the two scenarios in terms of
incidences of a range of human health effects that are associated
with exposure to ambient particulate matter and ozone.
In the next step, we use economic valuation models or
coefficients to estimate a dollar value for the reduced incidence
of those adverse effects amenable to monetization. For example,
analysis of estimates derived from the economic literature provides
an estimate of the value of reductions in mortality risk. Finally,
we adjust the benefit values for expected income growth through
2010 and 2020 and aggregate the benefits to the appropriate
geographic level.
As noted in Section I (Introduction), we present Base and
Alternative estimates for mortality and chronic bronchitis
benefits. The different methodologies and assumptions for these
approaches are discussed in separate subsections in the effects
estimation and valuation sections below.
Exhibit 2 Analytic Sequence for Multi-Emissions Reduction
Proposal Benefits Analysis



Baseline and Regulatory Scenario Development
This analysis looks at the impacts of the multi-pollutant
reductions that are part of the Clear Skies Act for two future
target years, 2010 and 2020. Avoided health effects and visibility
improvements are quantified by comparing two scenarios:


(1)
A baseline scenario (Base Case) that reflects the
continuing effects of Title IV of the Clean Air Act Amendments (the
Acid Rain Program) as well as other promulgated federal rules
issued under Clean Air Act authority that are expected to affect
Electric Generating Units (EGUs) and other sources of emissions
(e.g. the NOx SIP call and the Tier II and Heavy Duty Diesel Rules
for mobile sources).


(2)
A scenario that reflects full implementation of the Clear
Skies Act in the target year.




Emissions Profile Development
Emissions Inventories
Emission inventories were developed to support the benefits
analysis fo r the Clear Skies Act. Emissions profiles were
generated for the following cases: 1996 Base Year, 2010 Base Case,
2010 Clear Skies, 2020 Base Case, and 2020 Clear Skies.
These national inventories were prepared for all 50 States at
the county level for mobile highway and non-road sources. They were
prepared for the 48 contiguous states at the countylevel for
electric generating unit (EGU), non-EGU point, and stationary area
sources. The approach used to create inventories was the same as
that used for the Heavy-Duty Engine (HDE) Rulemaking analysis (US
EPA, 2000d) with modifications to reflect emission and modeling
advances since that analysis.4
Power generation emissions of SOx and NOx for each of the
scenarios is presented in The Clear Skies Act: Technical Support
Package. Exhibit 3 presents total national emissions of NOx and SO2
from all sectors, including power.
This approach was documented and can be located at
http://www.epa.gov/otaq/hdmodels.htm.
Exhibit 3 National SOx and NOx Emissions Projections for Base
and Clear Skies Scenarios (million tons)

The 1996 Base Year inventory was used to project future
emissions under the Base Case and differences between the Base Case
and the Clear Skies Act. It was constructed using existing
emissions inventories created for various recent rulemaking
activities. For criteria pollutants, the 1996 National Emissions
Inventory (NEI) used for the Heavy Duty Diesel vehicle rulemaking
was used. For mercury, the 1996 National Toxics Inventory was
modified based on the 1999 information collection effort for coal
utilities and the 2002 MACT implementation for medical waste
incinerators, and the 2000 MACT implementation for municipal waste
combustors was used.
For the 2010 and 2020 Base Cases, emissions under current
regulations with economic and population growth were projected. The
electric utility portion was developed using the Integrated
Planning Model (IPM). IPM projects power sector emissions under
Title IV of the 1990 Clean Air Act Amendments (The Acid Rain
Program), which caps SO2 emissions at 8.95 million tons/year
beginning in 2010. In addition, IPM's projections for electric
utilities under the Base Case include the NOX SIP Call with a cap
on summertime NOX emissions in SIP Call states in 2004 (based on
0.15 lb/mmBtu from 2001) and state-imposed NOX caps in Texas,
Connecticut, and Missouri. This case also includes no controls on
mercury emissions from power generation. The emissions inventory
for the Base Case also includes Tier II and Heavy Duty Diesel Rules
for mobile sources. The uncertainty about how these mobile source
rules will be implemented in the future contributes to uncertainty
in both the Base Case and the Clear Skies Act profile.
The 2010 and 2020 Clear Skies Act profile includes a 4.5 million
ton/year cap on EGUs beginning in 2010 for SO2 emissions, which
will be lowered to a 3 million ton cap in 2018; a 2.1 million
ton/yr cap beginning in 2008 for NOX emissions, which will be
lowered to a 1.7 million ton cap in 2018; and a 26 ton/yr cap
beginning in 2010 for mercury emissions, which will be lowered to a
15 ton cap in 2018. Because sources can reduce emissions early,
earn allowances for these actions, and use the allowances later,
actual emissions are projected to be higher than the cap in the
first years of each cap.
The Integrated Planning Model (IPM)
The Integrated Planning Model (IPM) predicts future emissions
outputs from EGUs affected by the Clear Skies Act. These outputs
are used to develop the emissions inventories.
IPM is a linear programming model of the electricity sector that
finds the most efficient
(i.e. least cost) approach to operating the electric power
system over a given time period subjectto specific constraints
(e.g. pollution caps or transmission limitations). The model, which
was developed for EPA by ICF Resources, Inc., selects investment
strategies given the cost and performance characteristics of
available options, forecasts of customer demand for electricity,
and reliability criteria. System dispatch, which determines the
proper and most efficient use of the existing and new resources
available to utilities and their customers, is optimized given the
resource mix, unit operating characteristics, and fuel and other
costs. Unit and system operating constraints provide
system-specific realism to the outputs of the model.
The IPM is dynamic; it has the capability to use forecasts of
future conditions, requirements, and option characteristics to make
decisions for the present. This ability replicates, to the extent
possible, the perspective of utility managers, regulatory
personnel, and the public in reviewing important investment options
for the utility industry and electricity consumers. Decisions are
made based on minimizing the net present value of capital and
operating costs over the full planning horizon. IPM also models a
variety of environmental market mechanisms, such as emissions caps,
allowances, trading, and banking. 5


Air Quality and Deposition Modeling
Air quality modeling is a critical analytical step that provides
the link between emissions changes and the physical effects that
affect human health and the environment. This step of the analysis
employs complex computer models that simulate the transport and
transformation of emitted pollutants in the atmosphere. The results
of these model runs are predictions of pollutant concentrations
under each of the emission control scenarios specified above. These
predicted concentrations are then used as inputs to the human
health effect estimation model discussed in the next section.
Air quality modelers face two key challenges in attempting to
translate emission inventories into pollutant concentrations.
First, they must model the dispersion and transport of pollutants
through the atmosphere. Second, they must model pertinent
atmospheric chemistry and other pollutant transformation and
removal processes. These challenges are particularly difficult for
those pollutants that are not emitted directly but instead form
through secondary processes. Ozone is the best example; it forms in
the atmosphere through a series of complex, non-linear chemical
interactions of precursor pollutants, particularly certain classes
of volatile organic compounds (VOCs) and nitrogen oxides (NOx).
Modelers face similar challenges when
Complete documentation of the IPM model can be found
athttp://www.epa.gov/airmarkt/epa-ipm/index.html#documentation
.
estimating PM concentrations. Atmospheric transformation of
gaseous sulfur dioxide and nitrogen oxides to particulate sulfates
and nitrates, respectively, contributes significantly to ambient
concentrations of fine particulate matter. In addition to
recognizing the complex atmospheric chemistry relevant for some
pollutants, air quality modelers also must deal with uncertainties
associated with variable meteorology and the spatial and temporal
distribution of emissions.
Air quality modelers and researchers have responded to the need
for scientifically valid and reliable estimates of air quality
changes by developing a number of sophisticated atmospheric
dispersion and transformation models. Some of these models have
been employed in support of the development of federal clean air
programs, national assessment studies, State Implementation Plans
(SIPs), and individual air toxic source risk assessments. In this
analysis, we used two of these well-established models, the
Regional Modeling System for Aerosols and Deposition (REMSAD) and
the Comprehensive Air Quality Model with Extensions (CAMx), to
develop a picture of future changes in air quality resulting from
the implementation of the Clear Skies Act.
Regional Modeling System for Aerosols and Deposition
(REMSAD)
The change in PM concentrations due to the Clear Skies Act was
modeled using the Regional Modeling System for Aerosols and
Deposition (REMSAD). REMSAD was also used to estimate the changes
in visibility and deposition of mercury, nitrogen, and sulfur.
REMSAD is a three-dimensional, grid-based Eulerian air quality
model designed to simulate long-term (e.g., annual) concentrations
and deposition fluxes of atmospheric pollutants over large spatial
scales (e.g., over the contiguous U.S.). Air pollution issues meant
to be addressed by REMSAD include long-term PM2.5 ambient
concentrations; visibility; ambient concentrations and deposition
fluxes of several hazardous air pollutants, including mercury;
deposition fluxes of nutrient nitrogen; and deposition of acids
such as sulfuric acid and nitric acid.
REMSAD has been developed under funding from the U.S.
Environmental Protection Agency over the past five years. REMSAD
consists of three components: (1) a meteorological data
pre-processor; (2) the core aerosol and toxic deposition model
(ATDM); and (3) postprocessing programs. The horizontal grid size
can be on the order of a few kilometers (km) for an urban-scale
simulation up to about 100 km for a continental-scale simulation.
For large-scale simulations, one-way nesting of fine and coarse
grids can be performed to allow simulation of sensitive areas with
strong pollution spatial gradients using a fine grid resolution.
The vertical structure of REMSAD covers the whole troposphere from
the surface up to about 15 km. The physical and chemical processes
simulated by REMSAD include emissions of pollutants from surface
and elevated sources, advective transport, horizontal turbulent
diffusion, vertical mixing via turbulent diffusion and convective
transport, cloud processes, gas-phase and aqueous-phase chemistry,
PM2.5 formation, dry deposition and wet deposition.
Version 6.40 of REMSAD was employed for this analysis. Previous
versions of REMSAD have been used to estimate PM for EPA's Heavy
Duty Engine Diesel Fuel Rule and for the first Section 812
Prospective Analysis. REMSAD Version 6.40 includes improvements
that address comments EPA obtained during the 1999 peer review of
REMSAD Version 4.1
(Seigneur et al., 1999), including improved treatment of
ammonium/nitrate/sulfate equilibrium, inclusion of additional
aqueous sulfate formation pathways, and expanded treatment of
mercury chemistry (ICF Consulting, 2001).
The REMSAD modeling domain selected for the Clear Skies Act
consists of 36 km x 36 km grid cells covering the 48-contiguous
United States, and REMSAD can perform a full-year simulation,
generating predictions of hourly PM concentrations (including both
PM2.5 component species and PM10) at each grid cell. These hourly
predictions form the basis for direct calculation of daily and
annual PM air quality metrics (e.g., annual mean PM concentration)
as inputs to the health and welfare C-R functions of the benefits
analysis. REMSAD also gives visibility, which is used as an input
into the visibility damage function.
For this benefits analysis, we applied REMSAD to the entire U.S.
for four future-year scenarios: a 2010 Base Case, a 2020 Base Case,
a 2010 Clear Skies Act Case, and a 2020 Clear Skies Act Case. The
difference in REMSAD-modeled PM concentrations for these scenarios
represents the expected change in PM due to the emission controls
under the Clear Skies Act.
Comprehensive Air Quality Model with Extensions (CAMx)
We modeled changes in ozone in the eastern U.S. using the
Comprehensive Air Quality Model with Extensions (CAMx). CAMx is an
Eulerian photochemical dispersion model designed to assess both
gaseous and particulate air pollution over many scales, from urban
to super-regional. The model estimates concentrations of both inert
and chemically reactive pollutants by simulating the physical and
chemical processes in the atmosphere that affect ozone formation.
The latest version of the model, CAMx 3.10, provides full support
for parallel processing for increased computational performance, as
well as new algorithms for gas phase chemistry (CAMx v3.10 User's
Guide, April 2002).
The modeling domain for this analysis encompasses most of the
eastern U.S., bounded on the east by the 67 degrees west longitude
and on the west by the 99 degrees west longitude. Ozone modeling is
only done for the East because there is very little confidence in
the application of this model to the West. The horizontal
resolution for the outer grid is approximately 36 km. The
horizontal resolution for the inner grid is approximately 12 km.
The vertical resolution for both grids consists of nine layers. The
top of the modeling domain is 4000 meters above ground level.
Recognizing the relationship between grid cell resolution and the
certainty of results, we sought to estimate pollutant
concentrations in more populated areas using higher resolution
models. Similarly, we used an intermediate resolution grid (12 km x
12 km) to model ozone in "inner OTAG" states where population
density is high and ozone transport is a major problem.6 This
approach makes CAMx well suited to estimate effects based on a
range of ozone averaging times, an important capability for
benefits assessment applications.
This study extracted hourly, surface-layer ozone concentrations
for each grid-cell from the standard CAMx output file containing
hourly average ozone values. These model predictions are used in
conjunction with the observed concentrations obtained from the
Aerometric
6
The Ozone Transport Assessment Group (OTAG) consists of the 37
easternmost states and the District of Columbia. The "inner OTAG"
region is comprised of the more eastern (and more populated) states
within the OTAG domain.
Information Retrieval System (AIRS) to generate ozone
concentrations for the entire ozone season. 7,8 The predicted
changes in ozone concentrations from the Base Case to the Clear
Skies Act serve as inputs to the health and welfare C-R functions
of the benefits analysis, i.e., the Criteria Air Pollutant Modeling
System (CAPMS).
In order to estimate ozone-related health and welfare effects
for the eastern U.S., fullseason ozone data are required for every
CAPMS grid-cell. Given available ozone monitoring data, we
generated full-season ozone profiles for each location in the
modeling domain in two steps: (1) we combine monitored observations
and modeled ozone predictions to interpolate hourly ozone
concentrations to a grid of 8 km by 8 km population grid-cells, as
will be described in the Human Health and Environmental Effects
Modeling section, and (2) we converted these full-season hourly
ozone profiles to an ozone measure of interest, such as the daily
average. 9, 10 For the analysis of ozone impacts on agriculture, we
use a similar approach except air quality is interpolated to county
centroids as opposed to population grid-cells. We report ozone
concentrations as a cumulative index called the SUM06. The SUM06 is
the sum of the ozone concentrations for every hour that exceeds
0.06 parts per million (ppm) within a 12-hour period from 8 am to 8
pm in the months of May to September. These methods are described
in detail in the Heavy Duty Engine/Diesel Fuel RIA (USEPA,
2000b).


Human Health and Environmental Effects Modeling
As part of the evaluation of the effects of various scenarios
concerning SO2 and NOx emissions, we have identified and, where
possible, developed quantitative, monetized estimates of these
health benefits. This section describes the first step in this
process, the estimation of changes in the incidence of adverse
health effects. Our analysis also looked at several environmental
endpoints, including the benefits associated with visibility
improvements, ozone damage to agriculture, and changes in
acidification in lakes and streams in the East.
Exhibit 4 provides a list of the health effects for which we
estimate quantified benefits as part of our analysis plus a list of
the health effects for which we are unable to quantify benefits at
this time. The unquantified benefits for ozone and PM fall into two
categories: (1) those for which the scientific literature does not
provide an established Concentration-Response (C-R) function
capable of estimating health effects with reasonable certainty and
(2) those effects that may double-count benefits (e.g., hospital
admissions for specific cardiovascular illnesses). The direct
health effects of nitrogen oxide gases and sulfur dioxide gases are
also unquantified. Although C-R functions are available to estimate
health effects of exposure to nitrogen oxides
7
The ozone season for this analysis is defined as the 5-month
period from May to September; however, to estimate certain crop
yield benefits, the modeling results were extended to include
months outside the 5-month ozone season.
8
Based on AIRS, there were 949 ozone monitors with sufficient
data, i.e., at least 9 hourly observations per day (8 am to 8 pm)
in a given season.
9
The 8 km grid squares contain the population data used in the
health benefits analysis model, CAPMS. See Section C of this
chapter for a discussion of this model.
10
This approach is a generalization of planar interpolation that
is technically referred to as enhanced Voronoi Neighbor Averaging
(EVNA) spatial interpolation (See Abt Associates (2000) for a more
detailed description).
and sulfur dioxide gases, these effects were not estimated in
this analysis because of modeling and resource limitations. The
health and environmental effects of mercury exposure are also not
quantified. EPA is currently investigating methods to quantify and
monetize the human health related benefits of reductions in air
emissions of mercury. However, there are still major gaps in the
science of mercury fate, transport, and transformation that make
such an assessment difficult at time. Methods for mercury benefits
analyses are still under development and do not yet provide a means
to estimate the mercury-related benefits of the Clear Skies
Act.
Exhibit 4 Human Health Effects of Air Pollutants

Pollutant Quantified Health Effects Unquantified Health
Effects
Ozone Minor restricted activity days
Hospital admissions-Respiratory and Cardiovascular
Emergency room visits for asthma Asthma attacks Mortality
Increased airway responsiveness to stimuli Inflammation in the lung
Chronic respiratory damage / Premature aging
of the lungs Acute inflammation and respiratory cell damage
Increased susceptibility to respiratory infection Respiratory
symptoms Chronic asthma (new cases) Non-asthma respiratory
emergency room visits
Particulate Matter (PM10, PM2.5)
Chronic Premature Mortality* Acute Premature Mortality ‡*
Bronchitis - Chronic and Acute Hospital admissions -
Respiratory and
Cardiovascular Emergency room visits for asthma Lower and Upper
respiratory illness Asthma Attacks Respiratory symptoms Minor
restricted activity days** Days of work loss Changes in pulmonary
function Neonatal mortality Low birth weight Chronic respiratory
diseases other than
chronic bronchitis Morphological changes Altered host defense
mechanisms Moderate or worse asthma status
(asthmatics) Shortness of breath Lung cancer Acute myocardial
infarction Cardiac arrhythmias School absence days
Mercury
Neurological disorders Learning disabilities Retarded
development Cerebral palsy
Cardiovascular effects Altered blood pressure regulation
Increased heart rate variability Myocardial infarctions
Damage to the immune system Altered renal function and renal
hypertrophy Reproductive effects
Nitrogen Oxides Respiratory illness
Hospital Admissions -All Respiratory and All Cardiovascular
Non-asthma respiratory emergency room visits Increased airway
responsiveness to stimuli Chronic respiratory damage / Premature
aging of the
lungs Inflammation of the lung Increased susceptibility to
respiratory infection
Acute inflammation and respiratory cell damage
Sulfur Dioxide
Hospital Admissions -All Respiratory and All Cardiovascular
In exercising asthmatics: Chest tightness, Shortness of breath,
or Wheezing
Non-asthma respiratory emergency room visits Changes in
pulmonary function Respiratory symptoms in non-asthmatics
‡ Quantified as an alternative or supplemental calculation.
Current uncertainties in our understanding of these effects
and/or
concern about double counting of benefits do not support
including these quantitative estimates in the primary benefits
estimate. Moderate or Worse Asthma Status is not included in
Primary Estimate due to concerns of double-counting other asthma
endpoints.
* This analysis estimates avoided mortality using PM as an
indicator of the criteria air pollutant mix to which individuals
wereexposed.
** Minor restricted activity days are estimated excluding asthma
attacks to avoid double counting.
Exhibit 5 provides a list of the ecological effects associated
with the emissions targeted by Clear Skies. As stated earlier, most
of these effects have not been quantified as part of our analysis,
due to data or modeling limitations. We have, however, monetized
effects of changes in ambient ozone on some agricultural production
and changes in particulate matter on visibility.
Exhibit 5 Ecological Effects of Air Pollutants


Pollutant Quantified Effects Unquantified Effects
Particulate Matter Recreational visibility in Class I areas in
Recreational visibility for Class I areas in other (PM10, PM2.5)
California, the Southwest, and the parts of the U.S.
Southwest Residential visibility
Ozone Impacts to agriculture (e.g., reduced crop Impacts on
commercial timber sales yields) Ozone impacts on carbon
sequestration in commercial timber
Acidic Deposition
Impacts to recreational freshwater fishing
Impacts to commercial forests (e.g., timber, non-timber forest
products)
Impacts to commercial freshwater fishing
Watershed damages (water filtration flood control)
Impacts to recreation in terrestrial ecosystems (e.g. forest
aesthetics, nature study)
Reduced existence value and option values for non-acidified
ecosystems (e.g. biodiversity values)
Nitrogen Deposition
Impacts to commercial fishing, agriculture, and forests
Watershed damages (water filtration, flood control)
Impacts to recreation in estuarine ecosystems (e.g. recreational
fishing, aesthetics, nature study)
Reduced existence value and option values for non-eutrophied
ecosystems
(e.g. biodiversity values)
Mercury Deposition
Impacts on birds and mammals (e.g. reproductive effects)
Impacts to commercial, subsistence, and recreational fishing
Reduced existence value and option values for ecosystems without
accumulated mercury (e.g. biodiversity values)
To estimate health benefits from the Clear Skies Act, we used
the same general approach used in recent major OAR regulatory
analyses (U.S. EPA, 1999a, 1999b and 2000b). This approach takes
the estimates of changes in ambient pollutant concentrations
predicted by air quality modeling for each scenario (relative to
the baseline scenario) and converts them into estimates of changes
in the incidence of adverse health effects using
concentration-response (C-R) functions. The model we use to
generate these estimates is the Criteria Air Pollutant Modeling
System (CAPMS).
We calculated the benefits attributable to the Clear Skies Act
as the change in incidence of adverse health effects between the
control and baseline scenarios. CAPMS estimates incidence changes
for each health effect within 8 km x 8 km grid cells covering the
contiguous
U.S. and generates national health benefits estimates by summing
the annual incidence changefor each effect across all grid cells.
CAPMS uses C-R functions specific to each health effect to
calculate incidences in each grid cell. C-R functions are equations
that relate the change in the number of individuals in a population
exhibiting a "response" (in this case an adverse health effect such
as respiratory disease) to a change in pollutant concentration
experienced by that population. In general, the C-R functions used
in CAPMS require four input values: (1) the grid-cell-specific
change in pollutant concentration; (2) the grid-cell affected
population (i.e. asthmatic children); (3) the baseline incidence
rate of the health effects; and (4) an estimate of the change in
the number of individuals that suffer an adverse health effect per
unit change in air quality. Both the form of the C-R function and
the fourth input value are derived from epidemiological studies in
the scientific literature that link pollutant exposures with
adverse health effects.
In addition to our national benefits results, we generated
regional estimates of the benefits of the Clear Skies Act using the
same benefits estimation procedure used to generate the national
estimates. The REMSAD and CAMx air quality models provide
information on the improvements in ambient air concentrations
throughout the country within 36 km by 36 km gridboxes. This
information is used in subsequent exposure, dose-response, and
valuation steps, including location-specific baseline mortality and
morbidity risk data to generate locationspecific estimates of
health benefits. This "bottom-up" approach provides a more accurate
representation of regional benefits estimates than a comparable
"top-down", emissions-weighted approach might, particularly given
the importance of long-range transport for the major pollutants
controlled by the Clear Skies Act (SO2 and NOx, as well as
mercury).
Recreational visibility benefits can also be geographically
disaggregated, based on either the location of the recreational
Class I area where visibility is improved, or on the state of
origin of visitors to these sites. For this analysis, we
disaggregated benefits based on the state of origin of visitors,
reflecting the notion that many of the recreational sites with the
highest visitation rates are valued by individuals throughout the
country, not only by those individuals who live closest to the
site. The results of the regional analysis of visibility benefits
indicate that benefits are realized throughout the country, with a
higher concentration of benefits in those areas of higher
population density.
Exhibit 6 provides a list of the health effect endpoints we
quantified as part of our analysis of the Clear Skies Act, as well
as references to the studies that serve as the basis for the C-R
functions. As with emissions and air quality estimates, our
estimates of the effect of ambient pollution levels on all of these
endpoints represent the best science and analytical tools
available. With the exception of the short-term mortality endpoint,
the choice of C-R functions and the majority of the analytical
assumptions used to develop our estimates have been reviewed and
approved by EPA's Science Advisory Board (SAB). The C-R functions
in Exhibit 6 only capture effects related to exposures to
particulate matter and ozone; they do not include human health
effects related to exposures to SO2, NO2, or mercury. As a result,
for these exposures, we have underestimated the total health
benefits attributable to Clear Skies emissions reductions.
Air Quality Changes
As in the analysis of the Heavy-Duty Engine/Diesel Fuel Rule
(U.S. EPA, 2000b), the REMSAD PM and CAMx ozone results discussed
above served as direct inputs to the CAPMS model. To calculate
population exposure to PM, each 8 km by 8 km CAPMS grid cell was
assigned to the nearest REMSAD grid cell by calculating the
shortest distance between the center of the CAPMS grid cell to the
center of a REMSAD grid cell.
To develop baseline and control exposure predictions for ozone,
we used the results of the variable-grid Comprehensive Air Quality
Model with Extensions (CAMx) for each scenario and observed ozone
data for the baseline year (1996). At each ozone monitor, we
quantified the relationship between CAMx modeled levels of ozone at
the monitor for 1996 and the future year (2010 or 2020). These
adjustment ratios are applied to the actual monitoring data to
generate estimates of ozone levels at the monitor for the future
scenarios. Note that we do not use the modeling data directly to
estimate future-year ozone levels. Instead, we use them in a
relative sense to simply adjust actual, 1996 ozone monitor levels
to future Base Case or Clear Skies levels. This provides a better
estimate than the CAMx modeling data itself. To calculate
population exposure to ozone, each CAPMS grid cell was assigned a
distance-weighted average of adjusted ozone levels from nearby
ozone monitors.
Population
Health benefits are related to the change in air pollutant
exposure experienced by individuals; because the expected changes
in pollutant concentrations vary from location to location,
individuals in different parts of the country may not experience
the same level of health benefits. We apportioned benefits among
individuals by matching the change in air pollutant concentration
in each grid cell with the size of the population that experiences
that change. We extrapolated grid cell population estimates for
future years from 1990 U.S. Census Bureau data according to the
method described in U.S. EPA (2000b).



Asthma Attacks PM and Whittemore and Korn (1980) Asthmatics, all
ages Ozone
Acute Bronchitis PM Dockery et al. (1996) Children, 8-12
years
Upper Respiratory Symptoms PM Pope et al. (1991) Asthmatic
children, 911
Lower Respiratory Symptoms PM Schwartz et al. (1994) Children,
7-14 years
Work Loss Days PM Ostro (1987) Adults, 18-65 years
Minor Restricted Activity Days PM and Ostro and Rothschild
(1989) Adults, 18-65 years (minus asthma attacks) Ozone
* For a discussion of the procedure for estimating these
endpoints see USEPA 2000b.
An epidemiological study typically focuses on a particular age
cohort (e.g., adults age 30 and older), and the C-R relationship
found in a particular study can not necessarily be generalized
across broader age categories. Therefore, to avoid overestimating
the benefits of reduced pollution levels, we applied C-R
relationships only to those age groups corresponding to the cohorts
studied. For outcomes where the study population reflects data
limitations and not the age-specificity of a health effect, this
assumption may lead us to underestimate the benefits of reductions
in pollutant exposures to the entire, exposed population.
Baseline Incidence Rate
Some C-R functions (those expressed as a change relative to
baseline conditions) require baseline incidence data associated
with ambient levels of pollutants. County mortality rates were used
in the estimation of PM-related mortality. For hospital admissions,
national level incidence rates were used. In cases where neither
county nor national-level incidence rates were available, the
baseline incidence from the C-R reference study was applied.
Sources for incidence rates are given in U.S. EPA (2000b).
Concentration-Response Functions
We relied on the most recently available, published scientific
literature to ascertain the relationship between particulate matter
exposure and adverse human health effects. We evaluated studies
using the nine selection criteria summarized in Exhibit 7. These
criteria include consideration of whether the study was
peer-reviewed, the study design and location, and characteristics
of the study population, among other considerations. The selection
of C-R functions for the benefits analysis is guided by the goal of
achieving a balance between comprehensiveness and scientific
defensibility. The C-R functions for PM exposure selected for the
Base Estimate are the same as those the Environmental Protection
Agency used in the Heavy-Duty Engine/Diesel Fuel RIA. The
Alternative Estimate uses alternative C-R functions to evaluate the
effect of short-term exposure to particulate matter on premature
mortality. We present information below on the selection of C-R
functions for the two most significant health effects evaluated (in
terms of monetized benefits), premature mortality and chronic
bronchitis. Detailed information on the selection and application
of C-R functions for other endpoints in Exhibit 4 is available in
the Heavy-Duty Engine/Diesel Fuel RIA (U.S. EPA, 2000b).


Exhibit 7 Summary of Considerations Used in Selecting C-R
Functions
Consideration Comments
Peer reviewed research Peer reviewed research is preferred to
research that has not undergone the peer review process.
Study type Among studies that consider chronic exposure (e.g.,
over a year or longer) prospective cohort studies are preferred
over cross-sectional studies (a.k.a. "ecological studies") because
they control for important confounding variables that cannot be
controlled for in cross-sectional studies. If the chronic effects
of a pollutant are considered more important than its acute
effects, prospective cohort studies may also be preferable to
longitudinal time series studies because the latter type of study
is typically designed to detect the effects of short-term (e.g.
daily) exposures, rather than chronic exposures. If short-term
effects are considered more important, distributed lag approaches,
which assume that mortality following a PM event will be
distributed over a number of days following the event, are
preferred over daily mortality studies. (Daily mortality studies
examine the impact of PM2.5 on mortality on a single day or over
the average of several days).
Study period Studies examining a relatively longer period of
time (and therefore having more data) are preferred, because they
have greater statistical power to detect effects. More recent
studies are also preferred because of possible changes in pollution
mixes, medical care, and life style over time.
Study population
Studies examining a relatively large sample are preferred.
Studies of narrow population groups are generally disfavored,
although this does not exclude the possibility of studying
populations that are potentially more sensitive to pollutants
(e.g., asthmatics, children, elderly). However, there are tradeoffs
to comprehensiveness of study population. Selecting a C-R function
from a study that considered all ages will avoid omitting the
benefits associated with any population age category. However, if
the age distribution of a study population from an "all population"
study is different from the age distribution in the assessment
population, and if pollutant effects vary by age, then bias can be
introduced into the benefits analysis.
Study location U.S. studies are more desirable than non-U.S.
studies because of potential differences in pollution
characteristics, exposure patterns, medical care system, and life
style.
Pollutants included in model Models with more pollutants are
generally preferred to models with fewer pollutants, though careful
attention must be paid to potential colinearity between pollutants.
Because PM has been acknowledged to be an important and pervasive
pollutant, models that include some measure of PM are highly
preferred to those that do not.
Measure of PM PM2.5 and PM10 are preferred to other measures of
particulate matter, such as total suspended particulate matter
(TSP), coefficient of haze (COH), or black smoke (BS) based on
evidence that PM2.5 and PM10 are more directly correlated with
adverse health effects than are these other measures of PM.
Economically valuable health Some health effects, such as forced
expiratory volume and other technical measurements of effects lung
function, are difficult to value in monetary terms. These health
effects are not quantified in this analysis.
Non-overlapping endpoints Although the benefits associated with
each individual health endpoint may be analyzed separately, care
must be exercised in selecting health endpoints to include in the
overall benefits analysis because of the possibility of double
counting of benefits. Including emergency room visits in a benefits
analysis that already considers hospital admissions, for example,
will result in double counting of some benefits if the category
"hospital admissions" includes emergency room visits.
Concentration-response relationships between a pollutant and a
given health endpoint are applied consistently across all locations
nationwide. This applies to both C-R relationships defined by a
single C-R function and those defined by a pooling of multiple C-R
functions. Although the C-R relationship may, in fact, vary from
one location to another (for example, due to differences in
population susceptibilities or differences in the composition of
PM), locationspecific C-R functions are generally not available. A
single function applied everywhere may result in overestimates of
incidence changes in some locations and underestimates elsewhere,
but these location-specific biases will negate each other, to some
extent, when the total incidence change is calculated. It is not
possible to know the extent or direction of the bias in the total
incidence change based on the general application of a single C-R
function everywhere.
C-R functions may also be estimated with or without explicit
thresholds. Air pollution levels below the threshold for each
health effect studied are assumed not to cause the effect. When no
threshold is assumed, as is often the case in epidemiological
studies, any exposure level is assumed to pose a non-zero risk of
response to at least one segment of the population. In the benefits
analyses for some recent RIAs (e.g., the Regional Haze RIA and the
NOx SIP Call RIA), the low-end estimate of benefits assumed a
threshold in PM health effects at 15 :g/m3. However, the SAB,
supported by recent literature addressing this issue (Rossi et al.,
1999; Schwartz, 2000), subsequently advised EPA that there is
currently no scientific basis for selecting a threshold of 15 :g/m3
or any other specific threshold for the PM-related health effects
considered in this analysis (EPA-SAB-Council-ADV-99-012, 1999).
Therefore, for our benefits analysis, we assume there are no
thresholds for modeling health effects. We do, however, present a
quantitative sensitivity analysis of this assumption in the results
section.
Recently, the Health Effects Institute (HEI) reported findings
by investigators at Johns Hopkins University and others that have
raised concerns about aspects of the statistical methods used in a
number of recent time-series studies of short-term exposures to air
pollution and health effects (Greenbaum, 2002a). Some of the
concentration-response functions used in this benefits analysis
were derived from such short-term studies. The estimates derived
from the long-term exposure studies, which account for a major
share of the benefits in the Base Estimate, are not affected. As
discussed in HEI materials provided to sponsors and to the Clean
Air Scientific Advisory Committee (Greenbaum, 2002a, 2002b), these
investigators found problems in the default "convergence criteria"
used in Generalized Additive Models (GAM) and a separate issue
first identified by Canadian investigators about the potential to
underestimate standard errors in the same statistical package.11
These and other investigators have begun to reanalyze the results
of several important time series studies with alternative
approaches that address these issues and have found a downward
revision of some results. For example, the mortality risk estimates
for short-term exposure to PM10 from NMMAPS were overestimated
(this study was not used in this benefits analysis of fine particle
effects).12 However, both the relative magnitude and the direction
of bias introduced by the convergence issue are case-specific. In
most cases, the concentration-response relationship may be
overestimated; in other cases, it may be underestimated. The
preliminary reanalyses of the mortality and morbidity components of
NMMAPS suggest that analyses reporting the lowest relative risks
appear to be affected more greatly by this error than studies
reporting higher relative risks (Dominici et al., 2002; Schwartz
and Zanobetti, 2002).
11Most of the studies used a statistical package known as
"S-plus." For further details, see
http://www.healtheffects.org/Pubs/NMMAPSletter.pdf.
12HEI sponsored the multi-city the National Morbidity,
Mortality, and Air Pollution Study (NMMAPS). See
http://biosun01.biostat.jhsph.edu/~fdominic/NMMAPS/nmmaps-revised.pdf
for revised mortality results.
Our examination of the original studies used in this analysis
finds that the health endpoints that are potentially affected by
the GAM issues include: reduced hospital admissions in both the
Base and Alternative Estimates; reduced lower respiratory symptoms
in the both the Base and Alternative Estimates; and reduced
premature mortality due to short-term PM exposures in the
Alternative Estimate. While resolution of these issues is likely to
take some time, the preliminary results from ongoing reanalyses of
some of the studies used in our Clear Skies analyses (Dominici et
al, 2002; Schwartz and Zanobetti, 2002; Schwartz, personal
communication 2002) suggest a more modest effect of the S-plus
error than reported for the NMMAPS PM10 mortality study. While we
wait for further clarification from the scientific community, we
have chosen not to remove these results from the Clear Skies
benefits estimates, nor have we elected to apply any interim
adjustment factor based on the preliminary reanalyses. EPA will
continue to monitor the progress of this concern, and make
appropriate adjustments as further information is made
available.
Premature Mortality (Particulate Matter)
Both long and short-term exposures to ambient levels of air
pollution have been associated with increased risk of premature
mortality. The size of the mortality risk estimates from these
epidemiological studies, the serious nature of the effect itself,
and the high monetary value ascribed to prolonging life make
mortality risk reduction the most important health endpoint
quantified in this analysis. Because of the importance of this
endpoint and the considerable uncertainty among economists and
policymakers as to the appropriate way to value reductions in
mortality risks, this section discusses some of the issues
surrounding the estimation of premature mortality. Additional
discussion is found in the section on uncertainties.
Health researchers have consistently linked air pollution,
especially PM, with excess mortality. Although a number of
uncertainties remain to be addressed by continued research (NRC,
1998), a substantial body of published scientific literature
recognizes a correlation between elevated PM concentrations and
increased mortality rates. Two types of community epidemiological
studies (involving measures of short-term and long-term exposures
and response) have been used to estimate PM/ mortality
relationships. Short-term studies relate shortterm (often
day-to-day) changes in PM concentrations and changes in daily
mortality rates up to several days after a period of elevated PM
concentrations. Long-term studies examine the potential
relationship between longer-term (e.g., one or more years) exposure
to PM and annual mortality rates. Researchers have found
significant associations using both types of studies.
1. Base Estimate
Over a dozen studies have found significant associations between
various measures of long-term exposure to PM and elevated rates of
annual mortality (e.g. Lave and Seskin, 1977; Ozkaynak and
Thurston, 1987). While most of the published studies found positive
(but not always significant) associations with available PM indices
such as total suspended particles (TSP), fine particles components
(i.e. sulfates), and fine particles, exploration of alternative
model specifications sometimes found inconsistencies (e.g. Lipfert,
1989). These early "crosssectional" studies were criticized for a
number of methodological limitations, particularly for inadequate
control at the individual level for variables that are potentially
important in causing mortality, such as wealth, smoking, and diet.
More recently, several new, long-term studies have been published
that use improved approaches and appear to be consistent with the
earlier body of literature. These new "prospective cohort" studies
reflect a significant improvement over the earlier work because
they include information on individual information with respect to
measures related to health status and residence. The most extensive
study and analyses has been based on data from two prospective
cohort groups, often referred to as the Harvard "Six-City study"
(Dockery et al., 1993) and the "American Cancer Society or ACS
study" (Pope et al., 1995); these studies have found consistent
relationships between fine particle indicators and mortality across
multiple locations in the U.S. A third major data set comes from
the California based 7th day Adventist study (e.g. Abbey et al,
1999), which reported associations between long-term PM exposure
and mortality in men. Results from this cohort, however, have been
inconsistent and the air quality results are not geographically
representative of most of the US. More recently, a cohort of adult
male veterans diagnosed with hypertension has been examined
(Lipfert et al., 2000). Unlike previous long-term analyses, this
study found some associations between mortality and ozone but found
inconsistent results for PM indicators.
Given their consistent results and broad applicability to
general US populations, the Six-City and ACS data have been of
particular importance in benefits analyses. The credibility of
these two studies is further enhanced by the fact that they were
subject to extensive reexamination and reanalysis by an independent
scientific analysis team of experts compiled by the Health Effects
Institute (Krewski et al., 2000). The final results of the
reanalysis were then independently peer reviewed by a Special Panel
of the HEI Health Review Committee. The results of these reanalyses
confirmed and expanded those of the original investigators. This
intensive independent reanalysis effort was occasioned both by the
importance of the original findings as well as concerns that the
underlying individual health effects information has never been
made publicly available. The HEI re-examination lends credibility
to the original studies but also found unexpected sensitivities
concerning (a) which pollutants are most important, (b) the role of
education in mediating the association between pollution and
mortality, and (c) the magnitude of the association depending on
how spatial correlation was handled. Further confirmation and
extension of the overall findings using more recent air quality and
ACS health information was recently published in the Journal of the
American Medical Association (Pope et al., 2002). In general, the
risk estimates based on the long-term mortality studies are
substantially greater than those derived from short-term
studies.
In developing and improving the methods for estimating and
valuing the potential reductions in mortality risk over the years,
EPA has consulted with a panel of the Science Advisory Board. That
panel recommended use of long-term prospective cohort studies in
estimating mortality risk reduction (EPA-SAB-COUNCIL-ADV-99-005,
1999). More specifically, the SAB recommended emphasis on Pope, et
al. (1995) because it includes a much larger sample size and longer
exposure interval, and covers more locations (e.g. 50 cities
compared to 6 cities examined in the Harvard data) than other
studies of its kind. As explained in the regulatory impact analysis
for the Heavy-Duty Engine/Diesel Fuel rule (U.S. EPA, 2000b), more
recent EPA benefits analyses have relied on an improved
specification from this data set that was developed in the HEI
reanalysis of this study (Krewski et al., 2000). The particular
specification estimated a C-R function based on changes in mean
levels of PM2.5, as opposed to the function in the original study,
which used median levels. This specification also includes a
broader geographic scope than the original study (63 cities versus
50). The SAB has recently agreed with EPA's selection of this
specification for use in analyzing mortality benefits of PM
reductions (EPA-SAB-COUNCIL-ADV-01-004, 2001). For these reasons,
the present analysis uses the same Concentration-Response function
in developing the Base Estimate of mortality benefits.
2. Alternative Estimate
To reflect concerns about the inherent limitations in the number
of studies supporting a causal association between long-term
exposure and mortality, an Alternative benefit estimate was derived
from the large number of time-series studies that have established
a likely causal relationship between short-term measures of PM and
daily mortality statistics. A particular strength of such studies
is the fact that potential confounding variables such as
socio-economic status, occupation, and smoking do not vary on a
day-to-day basis in an individual area. A number of multi-city and
other types of studies strongly suggest that these
effects-relationships cannot be explained by weather, statistical
approaches, or other pollutants. The risk estimates from the vast
majority of the short-term studies include the effects of only one
or two-day exposure to air pollution. More recently, several
studies have found that the practice of examining the effects on a
single day basis may significantly understate the risk of
short-term exposures (Schwartz, 2000; Zanobetti et al, 2002). These
studies suggest that the short-term risk can double when the
single-day effects are combined with the cumulative impact of
exposures over multiple days to weeks prior to a mortality
event.
The fact that the PM-mortality coefficients from the cohort
studies are far larger than the coefficients derived from the daily
time-series studies provides some evidence for an independent
chronic effect of PM pollution on health. Indeed, the Base Estimate
presumes that the larger coefficients represent a more complete
accounting of mortality effects, including both the cumulative
total of short-term mortality as well as an additional chronic
effect. This is, however, not the only possible interpretation of
the disparity. Various reviewers have argued that 1) the long-term
estimates may be biased high and/or 2) the short-term estimates may
be biased low. In this view, the two study types could be measuring
the same underlying relationship.
Reviewers have noted some possible sources of upward bias in the
long-term studies. Some have noted that the less robust estimates
based on the Six-Cities Study are significantly higher than those
based on the more broadly distributed ACS data sets. Some reviewers
have also noted that the observed mortality associations from the
1980's and 90's may reflect higher pollution exposures from the
1950's to 1960's. While this would bias estimates based on more
recent pollution levels upwards, it also would imply a truly
long-term chronic effect of pollution.
With regard to possible sources of downward bias, it is of note
that the recent studies suggest that the single day time series
studies may understate the short-term effect on the order of a
factor of two. These considerations provide a basis for considering
an Alternative Estimate using the most recent estimates from the
wealth of time-series studies, in addition to one based on the
long-term cohort studies.
In essence, the Alternative Estimate addresses the above noted
uncertainties about the relationship between premature mortality
and long-term exposures to ambient levels of fine particles by
assuming that there is no mortality effect of chronic exposures to
fine particles. Instead, it assumes that the full impact of fine
particles on premature mortality can be captured using a
concentration-response function relating daily mortality to
short-term fine particle levels. Specifically, a
concentration-response function based on Schwartz et al. (1996) is
employed, with an adjustment to account for recent evidence that
daily mortality is associated with particle levels from a number of
previous days (Schwartz, 2000). Previous daily mortality studies
(Schwartz et al., 1996) examined the impact of PM2.5 on mortality
on a single day or over the average of two or more days. Recent
analyses have found that impacts of elevated PM2.5 on a given day
can elevate mortality on a number of following days (Schwartz,
2000; Samet et al., 2000). Multi-day models are often referred to
as "distributed lag" models because they assume that mortality
following a PM event will be distributed over a number of days
following or "lagging" the PM event. 13
There are no PM2.5 daily mortality studies which report numeric
estimates of relative risks from distributed lag models; only PM10
studies are available. Daily mortality C-R functions for PM10 are
consistently lower in magnitude than PM2.5-mortality C-R functions,
because fine particles are believed to be more closely associated
with mortality than the coarse fraction of PM. Given that the
emissions reductions under the Clear Skies Act result primarily in
reduced ambient concentrations of PM2.5, use of a PM10 based C-R
function results in a significant downward bias in the estimated
reductions in mortality. To account for the full potential
multi-day mortality impact of acute PM2.5 events, we use the
distributed lag model for PM10 reported in Schwartz (2000) to
develop an adjustment factor which we then apply to the
PM2.5 based C-R function reported in Schwartz et al. (1996).
If most of the increase in mortality is expected to be
associated with the fine fraction of PM10, then it is reasonable to
assume that the same proportional increase in risk would be
observed if a distributed lag model were applied to the PM2.5 data.
The distributed lag adjustment factor is constructed as the ratio
of the estimated coefficient from the unconstrained distributed lag
model to the estimated coefficient from the single-lag model
reported in Schwartz (2000). The unconstrained distributed lag
model coefficient estimate is 0.0012818 and the single-lag model
coefficient estimate is 0.0006479. The ratio of these estimates is
1.9784. This adjustment factor is then multiplied by the estimated
coefficients from the Schwartz et al. (1996) study. There are two
relevant coefficients from the Schwartz et al. (1996) study, one
corresponding to all-cause mortality, and one corresponding to
chronic obstructive pulmonary disease (COPD) mortality (separation
by cause is necessary to implement the life years lost approach
detailed below). The adjusted estimates for these two C-R functions
are:
All cause mortality = 0.001489 * 1.9784 = 0.002946
COPD mortality = 0.003246 * 1.9784 = 0.006422
Note that these estimates, while approximating the full impact
of daily pollution levels on daily death counts, do not capture any
impacts of long-term exposure to air pollution. As discussed
earlier, EPA's Science Advisory Board, while acknowledging the
uncertainties in estimation of a PM-mortality relationship, has
repeatedly recommended the use of a study that
13 As discussed above, based on recent preliminary findings from
the Health Effects Institute, the magnitude of mortality from
shorttern exposure (Alternative Estimate) and hospital/ER
admissions estimates (both estimates) may be either under or
overestimated by an uncertain amount.
does reflect the impacts of long-term exposure. The omission of
long-term impacts accounts for approximately 40 percent reduction
in the estimate of avoided premature mortality in the Alternative
Estimate relative to the Base Estimate.
Chronic Bronchitis
Chronic bronchitis is characterized by mucus in the lungs and a
persistent wet cough for at least three months a year for several
years in a row. Chronic bronchitis affects an estimated five
percent of the U.S. population (American Lung Association, 1999). A
limited number of studies have estimated the impact of air
pollution on new incidences of chronic bronchitis. Schwartz (1993)
and Abbey, et al. (1995) provide evidence that long-term PM
exposure leads to the development of chronic bronchitis in the U.S.
Following the same approaches of the Heavy-Duty Engine/Diesel Fuel
RIA (U.S. EPA, 2000b) and the Section 812 Prospective Report (US
EPA, 1999a), this analysis pooled estimates from these two studies
to develop a C-R function linking PM to chronic bronchitis. The
Schwartz (1993) study examined the relationship between exposure to
PM10 and prevalence of chronic bronchitis. The Abbey, et al. (1995)
study examined the relationship between PM2.5 and new incidences of
chronic bronchitis. Both studies have strengths and weaknesses,
which suggest that pooling the effect estimates from each study,
may provide a better estimate of the expected change in incidences
of chronic bronchitis than using either study alone.
It should be noted that Schwartz used data on the prevalence of
chronic bronchitis, not its incidence. Following the approach of
the Section 812 Prospective Report, we estimated the percentage
change in the prevalence rate for chronic bronchitis using the
estimated coefficient from Schwartz's study in a C-R function, and
then applied this percentage change to a baseline incidence rate
obtained from another source. For example, if the prevalence
declines by 25 percent with a drop in PM, then baseline incidence
drops by 25 percent with the same drop in PM.
Visibility Benefits
As the name chosen for the Clear Skies Act implies, one of the
direct consequences of the reductions in fine particles that
accompany implementation of the SO2 and NOx emissions caps is an
improvement in atmospheric clarity and visibility. Changes in the
emissions of SO2 and NOx caused by the Clear Skies Act will change
the level of visibility in much of the U.S by reducing
concentrations of sulfate and nitrate particles. Fine particles
absorb and scatter light, impairing visibility. Visibility directly
affects people's enjoyment of a variety of daily activities both in
the places they live and work and in the places they travel to for
recreation. The Clean Air Act recognizes visibility as an important
public good in naming visibility as one of the aspects of public
welfare to be protected in setting secondary NAAQS. In Sections 165
and 169, the Act places particular value on protecting visibility
in 156 national parks and wilderness areas (e.g. Shenandoah,
Acadia, and Grand Canyon) that are termed class I Federal areas. As
noted above, the REMSAD modeling estimates regional and national
visibility improvements associated with Clear Skies. As discussed
in a subsequent section, this analysis also provides partial
estimates of the potential economic value of these visibility
improvements.
A number of related measures can be used to measure changes in
visibility associated with reduced fine particle concentrations. A
key such measure is light "extinction," a measure of the amount of
light scattered and absorbed by particles suspended in air. This
light scattering and absorption reduces atmospheric clarity and is
perceived as haze. Changes in fine particulate mass components are
used directly to estimate changes in extinction. Decreasing
extinction (in units of inverse distance) can in turn be used to
estimate quantitative measures more directly related to human
perception such as contrast of distant targets and visual range.
More recently, Sisler (1996) created a unitless measure of
visibility based directly on the degree of measured light
absorption called the deciview. Deciviews, like the analagous term
decibel, employ a logarithmic scale to evaluate relative changes in
visibility that is more directly related to human perception.
Sisler characterized a change in light extinction of one deciview
as "a small but perceptible scenic change under many
circumstances." For this analysis, REMSAD version 6.40 was used to
predict the change in visibility, measured in deciviews and
presented graphically, of the areas affected by the Clear Skies
Act.



Economic Valuation of Benefits
The overall approach applied in our estimates of the benefits of
the Clear Skies Act closely parallels that used in prior EPA
analyses, including the Section 812 series of Reports to Congress
(U.S. EPA, 1996 and 1999) and the recent Heavy-Duty Engine/Diesel
Fuel RIA (U.S. EPA, 2000b). As in those analyses, the EPA has not
conducted extensive new primary research to measure economic
benefits for individual rulemakings. As a result, our estimates are
based on the best available methods of benefits transfer. Benefits
transfer is the science and art of adapting primary benefits
research from similar contexts to obtain the most accurate measure
of benefits for the environmental quality change under analysis.
Where appropriate, we have made adjustments to existing primary
research for the level of environmental quality change, the
sociodemographic and economic characteristics of the affected
population, and other factors in order to improve the accuracy and
robustness of benefits estimates.
In general, economists tend to view an individual's
willingness-to-pay (WTP) for an improvement in environmental
quality as the most complete and appropriate measure of the value
of an environmental or health risk reduction. An individual's
willingness-to-accept (WTA) compensation for not receiving the
improvement is also a valid measure. Willingness to pay and
Willingness to accept are comparable measures when the change in
environmental quality is small and there are reasonably close
substitutes available. However, WTP is generally considered to be a
more readily measurable and conservative measure of benefits.
Adoption of WTP as the measure of value implies that the value of
environmental quality improvements is dependent on the individual
preferences of the affected population and that the existing
distribution of income (ability to pay) is appropriate.
Our analysis relies on up-to-date reviews of the relevant
resource economics literature that provides WTP values for health
risk reductions and visibility improvements similar to those that
will be provided by implementation of the Clear Skies Act. Exhibit
8 provides a summary of the base WTP values used to generate
estimates of the economic value of avoided health effects for this
analysis, adjusted to 1999 dollars, and a brief description of the
basis for these values. Exhibit 9 provides a summary of the
monetary values for the Alternative Estimate used for economic
valuation of mortality and chronic bronchitis. For these two
endpoints, the Alternative Estimate valuation differs from the Base
Estimate values.
In the sections that follow, we discuss in greater detail the
basis for generating WTP for premature mortality risk reductions
and WTP for reductions in the risk of contracting chronic
bronchitis and the basis for making adjustments to unit values to
make them more applicable to the air pollution reductions we
anticipate from the Clear Skies Act. The mortality and chronic
bronchitis health endpoints are the most influential in our
estimation of monetized benefits, because they account for over 95
percent of the total estimated monetized benefits of the Clear
Skies Act. In addition, we provide a brief summary of our approach
to valuing visibility and agricultural yield improvements. Detailed
descriptions of the basis for other economic valuation methods can
be found in Chapter VII of EPA's Heavy-Duty Engine/Diesel Fuel RIA
(U.S. EPA, 2000b).
Exhibit 8 Unit Values Used for Economic Valuation of Health
Endpoints
Estimated Value Health or Welfare Per Incidence
Endpoint (1999$) Derivation of Estimates Base Estimate
Premature Mortality

Value is the mean of a generated distribution of WTP to avoid a
Chronic Bronchitis (Base) $331,000 per case of pollution-related
CB. WTP to avoid a case of pollution
2
caserelated CB is derived by adjusting WTP (as described in
Viscusi et al., 1991) to avoid a severe case of CB for the
difference in severity and taking into account the elasticity of
WTP with respect to severity of CB.
Cost of Illness (COI) estimate is based on Cropper and Krupnick
Chronic Bronchitis (Alternative) $107,000 per case (1990).

Hospital Admissions
Chronic Obstructive Pulmonary Disease (COPD) $12,378 (ICD codes
490-492, 494-496)
Pneumonia
$14,693 Cost of Illness (COI) estimates are based on ICD-9 code
level(ICD codes 480-487) information (e.g., average hospital care
costs, average length of hospital stay, and weighted share of total
COPD category illnesses) Asthma admissions $6,633 reported in
Elixhauser (1993).
All Cardiovascular (ICD codes 390-429) $18,387
All Respiratory Variable
Dysrhythmia $12,441
Emergency room visits for $299 COI estimate based on data
reported by Smith, et al. (1997). asthma


Respiratory Ailments Not Requiring Hospitalization
Upper Respiratory Symptoms $24 per case3 (URS)
Combinations of the 3 symptoms for which WTP estimates are
available that closely match those listed by Pope, et al. result in
7 different "symptom clusters," each describing a "type" of URS. A
dollar value was derived for each type of URS, using mid-range
estimates of WTP (IEc, 1994) to avoid each symptom in the cluster
and assuming WTPs are additive. The dollar value for URS is the
average of the dollar values for the 7 different types of URS.
Lower Respiratory Symptoms $15 per case3 (LRS)
Combinations of the 4 symptoms for which WTP estimates are
available that closely match those listed by Schwartz, et al.
result in 11 different "symptom clusters," each describing a "type"
of LRS. A dollar value was derived for each type of LRS, using
mid-range estimates of WTP (IEc, 1994) to avoid each symptom in the
cluster and assuming WTPs are additive. The dollar value for LRS is
the average of the dollar values for the 11 different types of
LRS.
Acute Bronchitis $57 per case3 Average of low and high values
recommended for use in Section 812 analysis (Neumann, et al.
1994)
Exhibit 8 Unit Values Used for Economic Valuation of Health
Endpoints
Estimated Value Health or Welfare Per Incidence Derivation of
Estimates
Endpoint (1999$) Base Estimate


Restricted Activity and Work Loss Days
Work Loss Days (WLDs) $105.83 per case4 Regionally adjusted
median weekly wage for 1990 divided by 5 (adjusted to 1999$) (US
Bureau of the Census, 1992).
Minor Restricted Activity Days $48 per case3 Median WTP estimate
to avoid one MRAD from Tolley, et al. (1986). (MRADs)
1 This value does not reflect the 5-year lag adjustment and the
adjustment for changes in real income over time that are included
in the mortality valuation in our national benefits summaries. The
lag adjustment distributes the mortality incidence over five years
(25 percent in each of the first two years, and 17 percent for each
of the remaining years) and discounts mortality benefits over this
period at a rate of three percent. The adjustment to the mortality
unit valuation for growth in real income in 2020 is achieved using
an adjustment factor of 1.278.2 This value does not reflect the
adjustment for changes in real income over time that is included in
the chronic bronchitis valuation in our national benefits
summaries. The adjustment to the chronic bronchitis unit valuation
for growth in real income in 2020 is achieved using an adjustment
factor of 1.319. 3 These values do not reflect the adjustment for
changes in real income over time that is included in the benefit
valuations in our national benefits summaries. The adjustment to
the unit valuations of these endpoints for growth in real income in
2020 is achieved using an adjustment factor of 1.089. 4 The value
of a Work Loss Day presented here represents the national median.
The valuation of Work Loss Days presented in our national benefits
summaries, however, incorporates county-specific adjustment factors
to account for variations in regional income.
Valuation of Premature Mortality
1. Base Estimate
The monetary benefit of reducing premature mortality risk was
estimated using the "value of statistical lives saved" (VSL)
approach, although the actual valuation is of small changes in
mortality risk experienced by a large number of people. The VSL
approach applies information from several published value-of-life
studies, which themselves examine tradeoffs of monetary
compensation for small additional mortality risks, to determine a
reasonable benefit of preventing premature mortality. The mean
value of avoiding one statistical death (i.e., the statistical
incidence of a single death, equivalent to a product of a
population risk times a population size that equals one) is
estimated to be $6 million in 1999 dollars. This represents an
intermediate value from a range of estimates that appear in the
economics literature, and it is a value the EPA uses in rulemaking
support analyses and in the Section 812 Reports to Congress.
This estimate is the mean of a distribution fitted to the
estimates from 26 value-of-life studies identified in the Section
812 reports as "applicable to policy analysis." The approach and
set of selected studies mirrors that of Viscusi (1992) (with the
addition of two studies), and uses the same criteria as Viscusi in
his review of value-of-life studies. The $6 million estimate is
consistent with Viscusi's conclusion (updated to 1999$) that "most
of the reasonable estimates of the value of life are clustered in
the $3.7 to $8.6 million range." Five of the 26 studies are
contingent valuation (CV) studies, which directly solicit WTP
information from subjects; the rest are wage-risk studies, which
base WTP estimates on estimates of the additional compensation
demanded in the labor market for riskier jobs, controlling for
other job and employee characteristics such as education and
experience. As indicated in the previous section on quantification
of premature mortality benefits, we assume for this analysis that
some of the incidences of premature mortality related to PM
exposures occur in a distributed fashion over the five years
following exposure. To take this into account in the valuation of
reductions in premature mortality, we apply an annual three percent
discount rate to the value of premature mortality occurring in
future years.14
The economics literature concerning the appropriate method for
valuing reductions in premature mortality risk is still developing.
The adoption of a value for the projected reduction in the risk of
premature mortality is the subject of continuing discussion within
the economic and public policy analysis community. Regardless of
the theoretical economic considerations, distinctions in the
monetary value assigned to the lives saved were not drawn, even if
populations differed in age, health status, socioeconomic status,
gender or other characteristics.
Following the advice of the EEAC of the SAB, the VSL approach
was used to calculate the Base Estimate of mortality benefits
(EPA-SAB-EEAC-00-013). While there are several differences between
the risk context implicit in labor market studies we use to derive
a VSL estimate and the particulate matter air pollution context
addressed here, those differences in the affected populations and
the nature of the risks imply both upward and downward adjustments.
For example, adjusting for age differences between subjects in the
economic studies and those affected by air pollution may imply the
need to adjust the $6 million VSL downward, but the involuntary
nature of air pollution-related risks and the lower level of
risk-aversion of the manual laborers in the labor market studies
may imply the need for upward adjustments. In certain cases, labor
market studies have not adequately controlled for non-fatal injury
risks and other unfavorable job attributes (e.g. dirt and noise).
These factors may increase the estimated risk premium for
reductions in premature mortality risk.
Some economists emphasize that the value of a statistical life
is not a single number relevant for all situations. Indeed, the VSL
estimate of $6 million (1999 dollars) is itself the central
tendency of a number of estimates of the VSL for some rather
narrowly defined populations. When there are significant
differences between the population affected by a particular health
risk and the populations used in the labor market studies, as is
the case here, some economists prefer to adjust the VSL estimate to
reflect those differences. The CV-based estimates of VSL
collectively may better represent the population affected by
pollution than the labor market studies.
There is general agreement that the value to an individual of a
reduction in mortality risk can vary based on several factors,
including the age of the individual, the type of risk, the level of
control the individual has over the risk, the individual's
attitudes towards risk, and the health status of the individual.
While the empirical basis for adjusting the $6 million VSL for many
of these factors does not yet exist, a thorough discussion of these
uncertainties is included in EPA's Guidelines for Preparing
Economic Analyses (U.S. EPA, 2000a). The EPA recognizes the need
for investigation by the scientific community to develop additional
empirical support for adjustments to VSL for the factors mentioned
above.
14 The choice of a discount rate, and its associated conceptual
basis, is a topic of ongoing discussion within the federal
government. We adopted a 3 percent discount rate for our Base
analysis in this case to reflect reliance on a "social rate of time
preference" discounting concept. We have also calculated benefits
using a 7 percent rate consistent with an "opportunity cost of
capital" concept to reflect the time value of resources directed to
meet regulatory requirements. In this analysis, the benefit
estimates were not significantly affected by the choice of discount
rate. Further discussion of this topic appears in EPA's Guidelines
for Preparing Economic Analyses, EPA 240-R-00-003, September
2000.
As further support for the Base Estimate, the SAB-EEAC advised
in their recent report that the EPA "continue to use a
wage-risk-based VSL as its Base Estimate, including appropriate
sensitivity analyses to reflect the uncertainty of these
estimates," and that "the only risk characteristic for which
adjustments to the VSL can be made is the timing of the
risk"(EPA-SAB-EEAC-00-013). In developing the Base Estimate of the
benefits of premature mortality reductions, we have discounted over
the lag period between exposure and premature mortality. However,
in accordance with the SAB advice, we use the VSL in the Base
Estimate and present age adjusted values in the tables of
alternative calculations, Exhibit 12 and 13.
2. Alternative Estimate
The Alternative Estimate reflects the impact of changes to key
assumptions associated with the valuation of mortality. These
include: 1) the impact of using wage-risk and contingent
valuation-based value of statistical life estimates in valuing risk
reductions from air pollution as opposed to contingent
valuation-based estimates alone, 2) the relationship between age
and willingness-to-pay for fatal risk reductions, and 3) the degree
of prematurity in mortalities from air pollution.
The Alternative Estimate addresses this issue by using an
estimate of the value of statistical life that is based only on the
set of five contingent valuation studies included in the larger set
of 26 studies recommended by Viscusi (1992) as applicable to policy
analysis. The mean of the five contingent valuation based VSL
estimates is $3.7 million (1999$), which is approximately 60
percent of the mean value of the full set of 26 studies.
The second issue is addressed by assuming that the relationship
between age and willingness-to-pay for fatal risk reductions can be
approximated using an adjustment factor derived from Jones-Lee
(1989). The SAB has advised the EPA that the appropriate way to
account for age differences is to obtain the values for risk
reductions from the age groups affected by the risk reduction.
Several studies have found a significant effect of age on the value
of mortality risk reductions expressed by citizens in the United
Kingdom (Jones-Lee et al., 1985; Jones-Lee, 1989; Jones-Lee,
1993).
Two of these studies provide the basis to form ratios of the WTP
of different age cohorts to a base age cohort of 40 years. These
ratios can be used to provide Alternative age-adjusted estimates of
the value of avoided premature mortalities. One problem with both
of the Jones-Lee studies is that they examine VSL for a limited age
range. They then fit VSL as a function of age and extrapolate
outside the range of the data to obtain ratios for the very old.
Unfortunately, because VSL is specified as quadratic in age,
extrapolation beyond the range of the data can lead to a very
severe decline in VSL at ages beyond 75.
A simpler and potentially less biased approach is to simply
apply a single age adjustment based on whether the individual was
over or under 65 years of age at the time of death. This is
consistent with the range of observed ages in the Jones-Lee studies
and also agrees with the findings of more recent studies by
Krupnick et al. (2000) that the only significant difference in WTP
is between the over 70 and under 70 age groups. To correct for the
potential extrapolation error for ages beyond 70, the adjustment
factor is selected as the ratio of a 70 year old individual's WTP
to a 40 year old individual's WTP, which is 0.63, based on the
Jones-Lee (1989) results and 0.92 based on the Jones-Lee (1993)
results. To show the maximum impact of the age adjustment, the
Alternative Estimate is based on the Jones-Lee (1989) adjustment
factor of 0.63, which yields a VSL of $2.3 million for populations
over the age of 70. Deaths of individuals under the age of 70 are
valued using the unadjusted mean VSL value of $3.7 million (1999$).
Since these are acute mortalities, it is assumed that there is no
lag between reduced exposure and reduced risk of mortality.
Jones-Lee and Krupnick may understate the effect of age because
they only control for income and do not control for wealth. While
there is no empirical evidence to support or reject hypotheses
regarding wealth and observed WTP, WTP for additional life years by
the elderly may in part reflect their wealth position vis a vis
middle age respondents.
The third issue is addressed by assuming that deaths from
chronic obstructive pulmonary disease (COPD) are advanced by 6
months, and deaths from all other causes are advanced by 5 years.
These reductions in life years lost are applied regardless of the
age at death. Actuarial evidence suggests that individuals with
serious preexisting cardiovascular conditions have a remaining life
expectancy of around 5 years. While many deaths from daily exposure
to PM may occur in individuals with cardiovascular disease, studies
have shown relationships between all cause mortality and PM, and
between PM and mortality from pneumonia (Schwartz, 2000). In
addition, recent studies have shown a relationship between PM and
non-fatal heart attacks, which suggests that some of the deaths due
to PM may be due to fatal heart attacks (Peters et al., 2001). And,
a recent meta-analysis has shown little effect of age on the
relative risk from PM exposure (Stieb et al. 2002), which suggests
that the number of deaths in non-elderly populations (and thus the
potential for greater loss of life years) may be significant.
Indeed, this analysis estimates that 21 percent of non-COPD
premature deaths avoided are in populations under 65. Thus, while
the assumption of 5 years of life lost may be appropriate for a
subset of total avoided premature mortalities, it may over or
underestimate the degree of life shortening attributable to PM for
the remaining deaths."
In order to value the expected life years lost for COPD and
non-COPD deaths, we need to construct estimates of the value of a
statistical life year. The value of a life year varies based on the
age at death, due to the differences in the base VSL between the 65
and older population and the under 65 population. The valuation
approach used is a value of statistical life years (VSLY) approach,
based on amortizing the base VSL for each age cohort. Previous
applications have arrived at a single value per life year based on
the discounted stream of values that correspond to the VSL for a 40
year old worker (U.S. EPA, 1999a). This assumes 35 years of life
lost is the base value associated with the mean VSL value of $3.7
million (1999$). The VSLY associated with the $3.7 million VSL is
$163,000, annualized assuming EPA's guideline value of a 3 percent
discount rate, or $270,000, annualized assuming OMB's guideline
value of a 7 percent discount rate. The VSL applied in this
analysis is then built up from that VSLY by taking the present
value of the stream of life years, again assuming a 3% discount
rate. Thus, if you assume that a 40 year-old dying from pneumonia
would lose 5 years of life, the VSL applied to that death would be
$0.79 million. For populations over age 65, we then develop a VSLY
from the age-adjusted base VSL of $2.3 million. Given an assumed
remaining life expectancy of 10 years, this gives a VSLY of
$258,000, assuming a 3 percent discount rate. Again, the VSL is
built based on the present value of 5 years of lost life, so in
this case, we have a 70 year old individual dying from pneumonia
losing 5 years of life, implying an estimated VSL of $1.25 million.
As a final step, these estimated VSL values are multiplied by the
appropriate adjustment factors to account for changes in WTP over
time, as outlined above.
Applying the VSLY approach to the four categories of acute
mortality results in four separate sets of values for an avoided
premature mortality based on age and cause of death. Non-COPD
deaths for populations aged 65 and older are valued at $1.4 million
per incidence in 2010, and $1.6 million in 2020. Non-COPD deaths
for populations aged 64 and younger are valued at $0.88 million per
incidence in 2010, and $1.0 million in 2020. COPD deaths for
populations aged 65 and older are valued at $0.15 million per
incidence in 2010, and $0.17 million in 2020. Finally, COPD deaths
for populations aged 64 and younger are valued at $0.096 million
per incidence in 2010, and $0.11 million in 2020. The implied VSL
for younger populations is less than that for older populations
because the value per life year is higher for older populations.
Since we assume that there is a 5-year loss in life years for a PM
related mortality, regardless of the age of person dying, this
necessarily leads to a lower VSL for younger populations.
Valuation of Avoided Cases of Chronic Bronchitis
1. Base Estimate
The best available estimate of WTP to avoid a case of chronic
bronchitis (CB) comes from Viscusi, et al. (1991). The Viscusi, et
al. study, however, describes a severe case of CB to the survey
respondents. We therefore employ an estimate of WTP to avoid a
pollution-related case of CB, based on adjusting the Viscusi, et
al. (1991) estimate of the WTP to avoid a severe case. This is done
to account for the likelihood that an average case of
pollution-related CB is not as severe. The adjustment is made by
applying the elasticity of WTP with respect to severity reported in
the Krupnick and Cropper (1992) study. Details of this adjustment
procedure can be found in the Heavy-Duty Engine/Diesel Fuel RIA and
its supporting documentation, and in the most recent Section 812
study (EPA 1999).
We use the mean of a distribution of WTP estimates as the
central tendency estimate of WTP to avoid a pollution-related case
of CB in this analysis. The distribution incorporates uncertainty
from three sources: (1) the WTP to avoid a case of severe CB, as
described by Viscusi, et al.; (2) the severity level of an average
pollution-related case of CB (relative to that of the case
described by Viscusi, et al.); and (3) the elasticity of WTP with
respect to severity of the illness. Based on assumptions about the
distributions of each of these three uncertain components, we
derive a distribution of WTP to avoid a pollution-related case of
CB by statistical uncertainty analysis techniques. The expected
value (i.e., mean) of this distribution, which is about $331,000
(1999$), is taken as the central tendency estimate of WTP to avoid
a PM-related case of CB.
2. Alternative Estimate
For the Alternative Estimate, a cost-of illness value is used in
place of willingness-to-pay to reflect uncertainty about the value
of reductions in incidences of chronic bronchitis. In the Base
Estimate, the willingness-to-pay estimate was derived from two
contingent valuation studies (Viscusi et al., 1991; Krupnick and
Cropper, 1992). These studies were experimental studies intended to
examine new methodologies for eliciting values for morbidity
endpoints. Although these studies were not specifically designed
for policy analysis, the SAB (EPA-SAB-COUNCIL-ADV-00-002, 1999) has
indicated that the severity-adjusted values from this study provide
reasonable estimates of the WTP for avoidance of chronic
bronchitis. As with other contingent valuation studies, the
reliability of the WTP estimates depends on the methods used to
obtain the WTP values. In order to investigate the impact of using
the CV based WTP estimates, the Alternative Estimate relies on a
value for incidence of chronic bronchitis using a cost-of-illness
estimate based Cropper and Krupnick (1990) which calculates the
present value of the lifetime expected costs associated with the
illness. The current cost-of-illness (COI) estimate for chronic
bronchitis is around $107,000 per case, compared with the current
WTP estimate of $330,000.
Valuation of Changes in Visibility
Estimating benefits for visibility is a more difficult and less
precise exercise than estimating health benefits because the
endpoints are not directly or indirectly valued in markets. The
contingent valuation (CV) method has been employed in the economics
literature to value endpoint changes for visibility (Chestnut and
Rowe, 1990a, 1990b; Chestnut and Dennis, 1997). The CV method
values endpoints by using carefully structured surveys to ask a
sample of people what amount of compensation is equivalent to a
given change in environmental quality. There is an extensive
scientific literature and body of practice on both the theory and
technique of CV. The EPA believes that well-designed and
well-executed CV studies are valid for estimating the benefits of
air quality regulation. 15
Individuals value visibility both in the places they live and
work (referred to as residential visibility), and in the places
they travel to for recreational purposes (referred to as
recreational visibility). Although CV studies that address both
types of visibility exist, in our analysis we rely only on
recreational visibility studies, as explained further below.
We considered benefits from two categories of visibility
changes: residential visibility and recreational visibility.
Residential visibility benefits are those that occur from
visibility changes in urban, suburban, and rural areas, and also in
recreational areas not listed as federal Class I areas.16 For the
purposes of this analysis, recreational visibility improvements are
defined as those that occur specifically in federal Class I areas.
A key distinction between recreational and residential benefits is
that only those people living in residential areas are assumed to
receive benefits from residential visibility, while all households
in the U.S. are assumed to derive some benefit from improvements in
Class I areas.
Only two existing studies provide defensible monetary estimates
of the value of visibility
15Concerns about the reliability of value estimates from CV
studies arose because research has shown that bias can be
introduced easily into these studies if they are not carefully
conducted. Accurately measuring WTP for avoided health and welfare
losses depends on the reliability and validity of the data
collected. There are several issues to consider when evaluating
study quality, including but not limited to 1) whether the sample
estimates of WTP are representative of the population WTP; 2)
whether the good to be valued is comprehended and accepted by the
respondent; 3) whether the WTP elicitation format is designed to
minimize strategic responses; 4) whether WTP is sensitive to
respondent familiarity with the good, to the size of the change in
the good, and to income; 5) whether the estimates of WTP are
broadly consistent with other estimates of WTP for similar goods;
and 6) the extent to which WTP responses are consistent with
established economic principles.
16
The Clean Air Act designates 156 national parks and wilderness
areas as Class I areas for visibility protection.
changes. One is a study on residential visibility conducted in
1990 (McClelland, et. al., 1993) and the other is a 1988 survey on
recreational visibility value (Chestnut and Rowe, 1990a; 1990b).
Both utilize the contingent valuation method. There has been a
great deal of controversy and significant development of both
theoretical and empirical knowledge about how to conduct CV surveys
in the past decade. In EPA's judgment, the Chestnut and Rowe study
contains many of the elements of a valid CV study and is
sufficiently reliable to serve as the basis for monetary estimates
of the benefits of visibility changes in recreational areas.17 This
study serves as an essential input to our estimates of the benefits
of recreational visibility improvements. Consistent with SAB
advice, the EPA has designated the McClelland, et al. study as
significantly less reliable for regulatory benefit-cost analysis,
although it does provide useful estimates on the order of magnitude
of residential visibility benefits (EPA-SAB-COUNCIL-ADV-00-002,
1999). Residential visibility benefits are therefore only included
as part of our sensitivity tests. The methods for this calculation
are similar to the procedure for recreational benefits.
The Chestnut and Rowe study measured the demand for visibility
in Class I areas managed by the National Park Service (NPS) in
three broad regions of the country: California, the Southwest, and
the Southeast. Respondents in five states were asked about their
willingness to pay to protect national parks or NPS-managed
wilderness areas within a particular region. The survey used
photographs reflecting different visibility levels in the specified
recreational areas. The visibility levels in these photographs were
later converted to deciviews for the current analysis. The survey
data collected were used to estimate a WTP equation for improved
visibility. In addition to the visibility change variable, the
estimating equation also included household income as an
explanatory variable.
The Chestnut and Rowe study did not measure values for
visibility improvement in Class I areas outside the three regions.
Their study covered 86 of the 156 Class I areas in the U.S. We can
infer the value of visibility changes in the other Class I areas by
transferring values of visibility changes at Class I areas in the
study regions. However, these values are not as defensible and are
thus presented only as a sensitivity calculation.
The estimated relationship from the Che stnut and Rowe study is
only directly applicable to the populations represented by survey
respondents. We used benefits transfer methods to extrapolate these
results to the population affected by the Clear Skies Act. A
general willingness
17
An SAB advisory letter indicates that "many members of the
Council believe that the Chestnut and Rowe study is the best
available." (EPA-SAB-COUNCIL-ADV-00-002, 1999) However, the
committee did not formally approve use of these estimates because
of concerns about the peer-reviewed status of the study. EPA
believes the study has received adequate review and has been cited
in numerous peerreviewed publications (Chestnut and Dennis,
1997).
to pay equation for improved visibility (measured in deciviews)
was developed as a function of the baseline level of visibility,
the magnitude of the visibility improvement, and household income.
The behavioral parameters of this equation were taken from analysis
of the Chestnut and Rowe data. These parameters were used to
calibrate WTP for the visibility changes resulting from the Clear
Skies Act. The method for developing calibrated WTP functions is
based on the approach developed by Smith, et al. (1999). Available
evidence indicates that households are willing to pay more for a
given visibility improvement as their income increases (Chestnut,
1997). The benefits estimates here incorporate Chestnut's estimate
that a one percent increase in income is associated with a 0.9
percent increase in WTP for a given change in visibility.
For the sensitivity test calculation for residential visibility,
the McClelland, et al. study's results were used to calculate the
parameter to measure the effect of deciview changes on WTP. The WTP
equation was then run for the population affected by the Clear
Skies Act.
Agricultural Benefits
The Ozone Criteria Document notes that "ozone affects vegetation
throughout the United States, impairing crops, native vegetation,
and ecosystems more than any other air pollutant" (US EPA, 1996).
Reduced levels of ground-level ozone resulting from the final Clear
Skies Act will have generally beneficial results on agricultural
crop yields and commercial forest growth. Welldeveloped techniques
exist to provide monetary estimates of these benefits to
agricultural producers and consumers. These techniques use models
of planting decisions, yield response functions, and agricultural
product supply and demand. The resulting welfare measures are based
on predicted changes in market prices and production costs.
Laboratory and field experiments have shown reductions in yields
for agronomic crops exposed to ozone, including vegetables (e.g.,
lettuce) and field crops (e.g., cotton and wheat). The most
extensive field experiments, conducted under the National Crop Loss
Assessment Network (NCLAN), examined 15 species and numerous
cultivars. The NCLAN results show that "several economically
important crop species are sensitive to ozone levels typical of
those found in the U.S." (US EPA, 1996). In addition, economic
studies have shown a relationship between observed ozone levels and
crop yields (Garcia, et al., 1986).
To estimate changes in crop yields, we used biological
exposure-response information derived from controlled experiments
conducted by the NCLAN (NCLAN, 1996). For the purpose of our
analysis, we analyze changes for the six most economically
significant crops for which C-R functions are available: corn,
cotton, peanuts, sorghum, soybean, and winter wheat.18 For some
crops there are multiple C-R functions, some more sensitive to
ozone and some less. Our estimate assumes that crops are evenly
mixed between relatively sensitive and relatively insensitive
varieties.
We analyzed the economic value associated with varying levels of
yield loss for ozonesensitive commodity crops using the AGSIM©
agricultural benefits model (Taylor, et al., 1993). AGSIM© is an
econometric-simulation model that is based on a large set of
statistically
18 The total value for these crops in 1998 was $47 billion.
40
estimated demand and supply equations for agricultural
commodities produced in the United States. The model is capable of
analyzing the effects of changes in policies that affect commodity
crop yields or production costs.19
The measure of benefits calculated by the model is the net
change in consumer and producer surplus from baseline ozone
concentrations to the ozone concentrations resulting from
attainment of particular standards. Using the baseline and
post-control equilibria, the model calculates the change in net
consumer and producer surplus on a crop-by-crop basis.20 Dollar
values are aggregated across crops for each standard. The total
dollar value represents a measure of the change in social welfare
associated with implementation of the Clear Skies Act.
Adjustments for Changes in Income Over Time
Recent SAB deliberations on mortality and morbidity valuation
approaches suggest that some adjustments to unit values are
appropriate to reflect economic theory (EPA-SAB-EEAC-00-013, 2000).
As noted above, we apply one adjustment by discounting lagged
mortality incidence effects. A second adjustment is conducted as
part of the mortality, morbidity, and visibility valuation
procedures to incorporate the effect of changes in income over time
on WTP. To estimate the effects of changes in income over time we
use a procedure originally outlined in Appendix H of the Section
812 Prospective Report to Congress (EPA 1999). That procedure uses
per capita income estimates generated from Federal Government
projections of income and population growth, and applies three
different income elasticities for mortality, severe morbidity, and
light symptom effects.21
Benefits for each of the categories - minor health effects,
severe and chronic health effects (which include chronic bronchitis
and premature mortality), and visibility - were adjusted by
multiplying the unadjusted benefits by the appropriate adjustment
factor, listed in Exhibit 10 below.
19AGSIM© is designed to forecast agricultural supply and demand
out to 2010. We were not able to adapt the model to forecast out to
2020. Instead, we apply percentage increases in yields from
decreased ambient ozone levels in 2020 to 2010 yield levels, and
input these into an agricultural sector model held at 2010 levels
of demand and supply. It is uncertain what impact this assumption
will have on net changes in surplus.
20 Agricultural benefits differ from other health and welfare
endpoints in the length of the assumed ozone season. For
agriculture, the ozone season is assumed to extend from April to
September. This assumption is made to ensure proper calculation of
the ozone statistic used in the exposure-response functions. The
only crop affected by changes in ozone during April is winter
wheat.
21 Note that the Environmental Economics Advisory Committee
(EEAC) of the SAB advised EPA to adjust WTP for increases in real
income over time, but not to adjust WTP to account for
cross-sectional income differences "because of the sensitivity of
making such distinctions, and because of insufficient evidence
available at present" (EPA-SAB-EEAC-00-013).
Exhibit 10 Adjustment Factors Used to Account for Projected Real
Income Growth through 2010 and 2020
Benefit Adjustment Factor Adjustment Factor Category (2010)
(2020)
Minor Health Effect 1.038 1.089
Severe and Chronic Health Effects 1.127 1.319
Premature Mortality 1.112 1.278
Visibility 1.272 1.758
The procedure used to develop these adjustment factors is
described in more detail in the Heavy-Duty Engine/Diesel Fuel RIA
(U.S. EPA, 2000b). Also note that no adjustments were made to
benefits based on the cost-of-illness approach or to work loss
days. This assumption will also lead us to underpredict benefits
since it is likely that increases in real U.S. income would also
result in increased cost-of-illness (due, for example, to increases
in wages paid to medical workers) and increased cost of work loss
days (reflecting that if worker incomes are higher, the losses
resulting from reduced worker production would also be higher). The
result of applying these adjustment factors is an updated set of
unit economic values used in the valuation step. We summarize these
adjusted values in Exhibit 11.
42
Endpoint Pollutant Valuation per case Valuation per case (2010
mean est.) (2020 mean est.)



1 This value reflects both the 5-year lag adjustment and the
adjustments for changes in real income over time that are included
in the mortality valuation in our national benefits summaries. The
lag adjustment distributes the mortality incidence over five years
(25 percent in each of the first two years, and 17 percent for each
of the remaining years) and discounts mortality benefits over this
period at a rate of three percent. The adjustment to the mortality
unit valuation for growth in real income in 2010 is achieved using
an adjustment factor of 1.112. For 2020, the adjustment factor is
1.278. 2 This value reflects the adjustment for changes in real
income over time that is included in the chronic bronchitis
valuation in our national benefits summaries. The adjustment to the
chronic bronchitis unit valuation for growth in real income in 2010
is achieved using an adjustment factor of 1.127. For 2020, the
adjustment factor is 1.319. 3 These values reflect the adjustment
for changes in real income over time that is included in the
benefit valuations in our national benefits summaries. The
adjustment to the unit valuations of these endpoints for growth in
real income in 2010 is achieved using an adjustment factor of
1.038. For 2020, the adjustment factor is 1.089. 4 The value of a
Work Loss Day presented here represents the national median. The
valuation of Work Loss Days presented in our national benefits
summaries, however, incorporates county-specific adjustment factors
to account for variations in regional income.
Totals may not sum due to rounding.



III. MAJOR UNCERTAINTIES IN BENEFITS ANALYSIS
The estimates of avoided health effects, improved visibility,
and monetary benefits of the Clear Skies Act are based on a method
that reflects peer-reviewed data, models, and approaches that are
applied to support EPA rulemakings and generate Reports to Congress
on the benefits of air pollution regulation. Although EPA has made
a concerted effort to apply well-accepted methods, there remain
significant uncertainties in the estimation of these benefits.
There are three types of uncertainty that affect these
estimates:

In the remainder of this section, we discuss the major sources
of each of these three categories of uncertainty related to the
estimate of avoided health effects, avoided ecological effects, and
monetary valuation of these benefits. Our analysis of the Clear
Skies Act has not included formal uncertainty analyses, although we
have conducted several sensitivity tests and have analyzed a full
Alternative Estimate.


Uncertainties Associated with Health Benefit Estimates
Within-Study Variation
Within-study variation refers to the precision with which a
given study estimates the relationship between air quality changes
and health effects. Health effects studies provide both a "best
estimate" of this relationship plus a measure of the statistical
uncertainty of the relationship. This size of this uncertainty
depends on factors such as the number of subjects studied and the
size of the effect being measured. The results of even the most
well designed epidemiological studies are characterized by this
type of uncertainty, though well-designed studies typically report
narrower uncertainty bounds around the best estimate than do
studies of lesser quality. In selecting health endpoints, we
generally focus on endpoints where a statistically significant
relationship has been observed, which by definition assures a
reasonably
44
tight confidence interval around the best estimate of the mean
concentration-response relationship.
Across-study Variation
Across-study variation refers to the fact that different
published studies of the same pollutant/health effect relationship
typically do not report identical findings; in some instances the
differences are substantial. These differences can exist even
between equally reputable studies and may result in health effect
estimates that vary considerably. Across-study variation can result
from two possible causes. One possibility is that studies report
different estimates of the single true relationship between a given
pollutant and a health effect due to differences in study design,
random chance, or other factors. For example, a hypothetical study
conducted in New York and one conducted in Seattle may report
different C-R functions for the relationship between PM and
mortality, in part because of differences between these two study
populations (e.g., demographics, activity patterns). Alternatively,
study results may differ because these two studies are in fact
estimating different relationships; that is, the same reduction in
PM in New York and Seattle may result in different reductions in
premature mortality. This may result from a number of factors, such
as differences in the relative sensitivity of these two populations
to PM pollution and differences in the composition of PM in these
two locations.22 In either case, where we identified multiple
studies that are appropriate for estimating a given health effect,
we generated a pooled estimate of results from each of those
studies.
Application of C-R Relationship Nationwide
Whether this analysis estimated the C-R relationship between a
pollutant and a given health endpoint using a single function from
a single study or using multiple C-R functions from several
studies, each C-R relationship was applied uniformly throughout the
U.S. to generate health benefit estimates. However, to the extent
that pollutant/health effect relationships are region-specific,
applying a location-specific C-R function at all locations in the
U.S. may result in overestimates of health effect changes in some
locations and underestimates of health effect changes in other
locations. It is not possible, however, to know the extent or
direction of the overall effect on health benefit estimates
introduced by application of a single C-R function to the entire
U.S. This may be a significant uncertainty in the analysis, but the
current state of the scientific literature does not allow for a
region-specific estimation of health benefits.
Uncertainties in the PM Mortality Relationship
Health researchers have consistently linked air pollution,
especially PM, with excess mortality. A substantial body of
published scientific literature recognizes a correlation between
elevated PM concentrations and increased mortality rates. However,
there is much about this relationship that is still uncertain.23
These uncertainties include:
22 PM is a mix of particles of varying size and chemical
properties. The composition of PM can vary considerably from one
region to another depending on the sources of particulate emissions
in each region.
23The morbidity studies used in the Clear Skies Act benefits
analysis may also be subject to many of the uncertainties listed in
this section.

24 Much of this literature is summarized in the 1996 PM Criteria
Document (US EPA, 1996a). There is much about this relationship
that is still uncertain. As stated in preamble to the 1997 PM
National Ambient Air Quality Standards (40 CFR 50, 1997), "the
consistency of the results of the epidemiological studies from a
large number of different locations and the coherent nature of the
observed effects are suggestive of a likely causal role of ambient
PM in contributing to the reported effects," which include
premature mortality. The National Academy of Sciences, in their
report on research priorities for PM (NAS, 1998), indicates that
"there is a great deal of uncertainty about the implications of the
findings [of an association between PM and premature mortality] for
risk management, due to the limited scientific information about
the specific types of particles that might cause adverse health
effects, the contributions of particles of outdoor origin to actual
human exposures, the toxicological mechanisms by which the
particles might cause adverse health effects, and other important
questions." EPA acknowledges these uncertainties; however, for this
analysis, we assume a causal relationship between exposure to
elevated PM and premature mortality, based on the consistent
evidence of a correlation between PM and mortality reported in the
scientific literature.
46
relationship independent of that for PM. However, most of the
studies examined by Ito and Thurston only controlled for PM10 or
broader measures of particles and did not directly control for
PM2.5. As such, there may still be potential for confounding of
PM2.5 and ozone mortality effects, as ozone and PM2.5 are highly
correlated during summer months in some areas.25 In its September
2001 advisory on the draft analytical blueprint for the second
Section 812 prospective analysis, the SAB cited the Thurston and
Ito study as a significant advance in understanding the effects of
ozone on daily mortality and recommended re-evaluation of the ozone
mortality endpoint for inclusion in the next prospective study
(EPA-SAB-COUNCIL-ADV-01-004, 2001). Thus, recent evidence suggests
that by not including an estimate of reductions in short-term
mortality due to changes in ambient ozone, both the Base and
Alternative Estimates may underestimate the benefits of
implementation of the Clear Skies Act.
C Shape of the C-R Function. The shape of the true PM mortality
C-R function is uncertain, but this analysis assumes the C-R
function to have a log-linear form (as derived from the literature)
throughout the relevant range of exposures. If this is not the
correct form of the C-R function, or if certain scenarios predict
concentrations well above the range of values for which the C-R
function was fitted, avoided mortality may be misestimated.
C Regional Differences. As discussed above, significant
variability exists in the results of different PM/mortality
studies. This variability may reflect regionally specific C-R
functions resulting from regional differences in factors such as
the physical and chemical composition of PM. If true regional
differences exist, applying the PM/Mortality C-R function to
regions outside the study location could result in mis-estimation
of effects in these regions.
C Exposure/Mortality Lags. It is currently unknown whether there
is a time lag -- a delay between changes in PM exposures and
changes in mortality rates -- in the chronic PM/mortality
relationship. The existence of such a lag is important for the
valuation of premature mortality incidence because economic theory
suggests that benefits occurring in the future should be
discounted. There is no specific scientific evidence of the
existence or structure of a PM effects lag. However, current
scientific literature on adverse health effects similar to those
associated with PM (e.g., smoking-related disease) and the
difference in the effect size between chronic exposure studies and
daily mortality studies suggest that all incidences of premature
mortality reduction associated with a given incremental change in
PM exposure probably would not occur in the same year as the
exposure reduction. The smoking-related literature also implies
that lags of up to a few years are plausible. Adopting the lag
structure used in the Tier 2/Gasoline Sulfur and Heavy-Duty
Engine/Diesel Fuel RIAs and endorsed by the SAB
(EPA-SAB-COUNCIL-ADV-00-001, 1999), we assume a five-year lag
structure. This approach assumes that 25 percent of PM-related
premature deaths occur in each of the first two years after the
exposure and the rest occur in equal parts (approximately 17%) in
each of the ensuing three years.
C Cumulative Effects. As a general point, we attribute the
PM/mortality relationship in the
25 Short-term ozone mortality risk estimates may also be
affected by the statistical issue discovered by the Health Effects
Institute (Greenbaum, 2002a). See page 24 for a more detailed
discussion of this issue.
underlying epidemiological studies to cumulative exposure to PM.
However, the relative roles of PM exposure duration and PM exposure
level in inducing premature mortality remain unknown at this
time.


Uncertainties Associated with Environmental and Ecosystem
Effects Estimation
Our analysis of the Clear Skies Act includes a quantitative
estimate of only two environmental effects: recreational visibility
and ozone effects on agriculture. Scientific studies, however, have
reliably linked atmospheric emissions of sulfur, nitrogen, and
mercury to a much wider range of other environmental and ecological
effects. Some of these effects are acute in nature, and some are
longer-term and could take many years to manifest. The effects
include the following:

These effects are left unquantified for a variety of reasons,
but mostly because of the complexity of modeling these effects and
the major uncertainties in reliably quantifying the incremental
effects of atmospheric emissions reductions on ecological
endpoints.
Individually, many of these environmental effects may be
relatively small in terms of their overall ecosystem and monetary
importance, particularly in the near-term. Their cumulative and
longer term effects, however, some of which may be largely unknown
at this time, may be substantial. As a result, the omission of this
broad class of benefits from our quantitative results likely causes
our estimates to substantially understate the total benefits of the
Clear Skies Act.
48


Uncertainties Associated with Economic Valuation of
Benefits
Economic valuation of benefits often involves estimation of the
willingness-to-pay of individuals to avoid harmful health or
environmental effects. In most cases, there are no markets in which
to directly observe WTP for these types of commodities. In some
cases, we can rely on indirect market transactions, such as the
implicit tradeoff of wages for on-the-job mortality risk among the
working population, to estimate WTP. In other cases, we must rely
on survey approaches to estimate WTP, usually through a variant of
the contingent valuation approach, which generally involves
directly questioning respondents for their WTP in hypothetical
market situations. Regardless of the method used to estimate WTP,
there are measurement errors, data inadequacies, and ongoing
debates about the best practices for each method that contribute to
the overall uncertainty of economic estimates.
General Benefits Transfer Considerations
For the Clear Skies benefits analysis, we do not have the time
or resources to conduct primary economic research targeted at the
specific air pollution-related benefits provided. As a result, we
rely on the transfer of benefits estimates from existing studies.
The conduct of "benefits transfer" exercises necessarily involves
some uncertainties. These uncertainties can be reduced by careful
consideration of the differences in the health risk or air
pollution commodity and the study populations in the underlying
economic literature versus the context of benefits conferred by the
Clear Skies Act. For example, we make adjustments to the mortality
valuation estimates to account for the estimated lag between
exposure and manifestation of the effect, reflecting the basic
economic tenet that individuals prefer benefits that occur sooner
to those that occur later. We also make adjustments to account for
expected changes in WTP over time as per capita income increases.
We cannot adjust for all benefits transfer considerations, however,
thus introducing additional uncertainty into our estimates.
Lack of Adequate Data or Methods
The lack of adequate data or methods to characterize WTP results
in our inability to present monetized benefits of some categories
of effects. For example, while studies exist that estimate the
benefits of visibility improvements to individuals in the places
they reside, these residential visibility studies are considered by
some in the resource economics community to be less reliable
because of the methods applied. In the case of residential
visibility, we conduct sensitivity analyses to estimate the impact
of this uncertainty in the reliability of methods. To the extent
effects such as these represent categories of benefits that are
truly valuable to the U.S. population, we have underestimated the
total benefits of the Clear Skies Act.
Uncertainties Specific to Premature Mortality Valuation
The economic benefits associated with premature mortality are
the largest category of monetized benefits of the Clear Skies
Act.26 In addition, in prior analyses EPA has identified valuation
of mortality benefits as the largest contributor to the range of
uncertainty in monetized
26As noted in the methods section, it is actually reductions in
mortality risk that are valued in a monetized benefit analysis.
Individual WTPs for small reductions in mortality risk are summed
over enough individuals to infer the value of a statistical life
saved. This is different from the value of a particular, identified
life saved. The "value of a premature death avoided," then, should
be understood as shorthand for "the value of a statistical
premature death avoided."
benefits (see USEPA 1999a). Because of the uncertainty in
estimates of the value of premature mortality avoidance, it is
important to adequately characterize and understand the various
types of economic approaches available for mortality valuation.
Such an assessment also requires an understanding of how
alternative valuation approaches reflect that some individuals may
be more susceptible to air pollution-induced mortality, or reflect
differences in the nature of the risk presented by air pollution
relative to the risks studied in the relevant economic
literature.
The health science literature on air pollution indicates that
several human characteristics affect the degree to which mortality
risk affects an individual. For example, some age groups appear to
be more susceptible to air pollution than others (e.g., the elderly
and children). Health status prior to exposure also affects
susceptibility. At risk individuals include those who have suffered
strokes or are suffering from cardiovascular disease and angina
(Rowlatt, et al. 1998). An ideal benefits estimate of mortality
risk reduction would reflect these human characteristics, in
addition to an individual's willingness to pay (WTP) to improve
one's own chances of survival plus WTP to improve other
individuals' survival rates.27 The ideal measure would also take
into account the specific nature of the risk reduction commodity
that is provided to individuals, as well as the context in which
risk is reduced. To measure this value, it is important to assess
how reductions in air pollution reduce the risk of dying from the
time that reductions take effect onward, and how individuals value
these changes. Each individual's survival curve, or the probability
of surviving beyond a given age, should shift as a result of an
environmental quality improvement. For example, changing the
current probability of survival for an individual also shifts
future probabilities of that individual's survival. This
probability shift will differ across individuals because survival
curves are dependent on such characteristics as age, health state,
and the current age to which the individual is likely to
survive.
Although a survival curve approach provides a theoretically
preferred method for valuing the benefits of reduced risk of
premature mortality associated with reducing air pollution, the
approach requires a great deal of data to implement. The economic
valuation literature does not yet include good estimates of the
value of this risk reduction commodity. As a result, in this study
we value avoided premature mortality risk using the value of
statistical life approach in the Base Estimate, supplemented by
valuation based on an age-adjusted value of statistical life
estimate in the Alternative Estimate.
Other uncertainties specific to premature mortality valuation
include the following:
Across-study Variation: The analytical procedure used in the
main analysis to estimate the monetary benefits of avoided
premature mortality assumes that the appropriate economic value for
each incidence is a value from the currently accepted range of the
value of a statistical life. This estimate is based on 26 studies
of the value of mortal risks. There is considerable uncertainty as
to whether the 26 studies on the value of a statistical life
provide adequate estimates of the value of a statistical life saved
by air pollution reduction. Although there is considerable
variation in the analytical designs and data used in the 26
underlying studies, the majority of the studies involve the value
of risks to a middle-aged working population. Most of the studies
examine differences in wages of risky occupations, using a
wage-hedonic approach. Certain characteristics of both the
27 For a more detailed discussion of altruistic values related
to the value of life, see Jones-Lee (1992).
50
population affected and the mortality risk facing that
population are believed to affect the average willingness to pay
(WTP) to reduce the risk. The appropriateness of a distribution of
WTP estimates from the 26 studies for valuing the mortality-related
benefits of reductions in air pollution concentrations therefore
depends not only on the quality of the studies (i.e., how well they
measure what they are trying to measure), but also on (1) the
extent to which the risks being valued are similar, and (2) the
extent to which the subjects in the studies are similar to the
population affected by changes in pollution concentrations.
C Level of risk reduction. The transferability of estimates of
the value of a statistical life from the 26 studies to the Clear
Skies Act analysis rests on the assumption that, within a
reasonable range, WTP for reductions in mortality risk is linear in
risk reduction. For example, suppose a study estimates that the
average WTP for a reduction in mortality risk of 1/100,000 is $50,
but that the actual mortality risk reduction resulting from a given
pollutant reduction is 1/10,000. If WTP for reductions in mortality
risk is linear in risk reduction, then a WTP of $50 for a reduction
of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000
(which is ten times the risk reduction valued in the study). Under
the assumption of linearity, the estimate of the value of a
statistical life does not depend on the particular amount of risk
reduction being valued. This assumption has been shown to be
reasonable provided the change in the risk being valued is within
the range of risks evaluated in the underlying studies (Rowlatt et
al. 1998).
C Voluntariness of risks evaluated. Although there may be
several ways in which jobrelated mortality risks differ from air
pollution-related mortality risks, the most important difference
may be that job-related risks are incurred voluntarily, or
generally assumed to be, whereas air pollution-related risks are
incurred involuntarily. There is some evidence28 that people will
pay more to reduce involuntarily incurred risks than risks incurred
voluntarily. If this is the case, WTP estimates based on wage-risk
studies may understate WTP to reduce involuntarily incurred air
pollution-related mortality risks.
C Sudden versus protracted death. A final important difference
related to the nature of the risk may be that some workplace
mortality risks tend to involve sudden, catastrophic events,
whereas air pollution-related risks tend to involve longer periods
of disease and suffering prior to death. Some evidence suggests
that WTP to avoid a risk of a protracted death involving prolonged
suffering and loss of dignity and personal control is greater than
the WTP to avoid a risk (of identical magnitude) of sudden death.
To the extent that the mortality risks addressed in this assessment
are associated with longer periods of illness or greater pain and
suffering than are the risks addressed in the valuation literature,
the WTP measurements employed in the present analysis would reflect
a downward bias.


IV. RESULTS
Base Estimate
28See, for example, Violette and Chestnut, 1983.
Exhibits 12 and 13 present a summary of health effects benefits
resulting from improvements in air quality between the Base Case
and the Clear Skies Act scenarios. Exhibit 12 presents the mean
estimate of avoided health effects in 2010 and 2020 for each health
endpoint included in the Base analysis. We estimate that reductions
in exposure to fine PM and ozone due to the Clear Skies Act will
result in over 6,000 fewer deaths in 2010 and nearly 12,000 fewer
deaths in 2020, as well as nearly 4,000 fewer cases of chronic
bronchitis in 2010 and over 7,000 fewer cases in 2020. In addition,
193,000 fewer asthma attacks are estimated to occur in 2010 and
373,000 fewer in 2020. Exhibit 13 summarizes the mean monetized
health and visibility benefits due to the Clear Skies Act. As that
exhibit shows, we estimate the monetized benefits of the Clear
Skies Act in the continental United States will be $44 billion in
2010, including $43 billion in health benefits and $1 billion in
recreational visibility benefits. In 2020, total benefits increase
to $96 billion, with $93 billion in health benefits and $3 billion
in recreational visibility benefits.
The results of our regional benefits analysis indicate that the
vast majority of the health benefits of the Clear Skies Act are
realized in the easternmost 39 states, including the states of
North Dakota, South Dakota, Nebraska, Kansas, Oklahoma, and Texas.
We estimate total benefits of $44 billion in these 39 states in
2010, and $95 billion in 2020.
In addition to calculating the physical effects and monetary
impacts of the Clear Skies Act, we also estimated the distribution
of particulate matter air quality improvements that will be
experienced by the US population. Exhibit 14 illustrates the
numbers of individuals and the percent of the US population that
they represent that will experience changes in ambient particulate
matter concentrations in 2010 and 2020. As indicated in the table,
the Clear Skies Act yields relatively modest air quality
improvements for about one-fourth of the US population (i.e.,
changes in PM concentrations of less than 0.25 µg/m3), in both 2010
and 2020, but more substantial improvements for a large percentage
of the population, including improvements in excess of 2 µg/m3 for
more than 24 million individuals by 2020.
52
Exhibit 12 Change in Incidence of Adverse Health Effects
Associated with Reductions in Particulate Matter and Ozone Due to
the Clear Skies Act - 48 State U.S. Population (avoided cases per
year)
2010 2020 Endpoint Pollutant mean mean
Mortality
Chronic Exposure, Ages 30 and Older PM2.5 6,400 11,900

Chronic Illness
Chronic Bronchitis PM10, PM2.5 3,900 7,400
Hospitalization / ER Visits
COPD Admissions Pneumonia Admissions Cardiovascular Admissions
Asthma AdmissionsAll Respiratory Admissions Dysrhythmia Admissions
Emergency Room Visits for Asthma Hospitalization / ER Visits
Subtotal Minor Respiratory Illness and Symptoms
PM10 700 1,300
PM10 800 1,500
PM10 2,000 3,700
PM2.5 600 1,200
Ozone 500 1,000 Ozone 100 300 PM10 and 1,600 2,900 Ozone 6,300
11,900
Acute Bronchitis PM2.5 Upper Respiratory Symptoms PM10 Lower
Respiratory Symptoms PM2.5 Asthma Attacks PM10 and
Ozone Work Loss Days PM2.5 Minor Restricted Activity Days PM2.5
and (minus asthma attacks)Ozone Minor Respiratory Illness and
Symptoms Subtotal
12,900 23,800 141,000 262,000 141,000 260,000 195,000
373,000
1,100,000 2,060,000 6,400,000 12,100,000
8,000,000 15,100,000
Totals may not sum due to rounding.
Exhibit 13 Results of Human Health and Welfare Benefits
Valuation for the Clear Skies Act (Particulate Matter and Ozone
Reductions Only)


Mortality
Chronic Exposure, Ages 30 and older PM2.5 $41,400* $88,900*
$38,900** $83,500**
Chronic Illness
Chronic Bronchitis PM10 PM2.5 $1,500 $3,200
Hospitalization
COPD Admissions PM10 Pneumonia Admissions PM10 Cardiovascular
Admissions PM10 Asthma Admissions PM2.5
All Respiratory Admissions Ozone Dysrhythmia Admissions Ozone
Emergency Room Visits for Asthma PM10 and
Ozone
$8 $16 $12 $23 $37 $69 $4 $8 $6 $14 $1 $3 $0.4 $1
$69 $130
Hospitalization / ER Visits Subtotal Minor Respiratory Illness
and Symptoms
Acute Bronchitis Upper Respiratory Symptoms Lower Respiratory
Symptoms Work Loss Days Minor Restricted Activity Days (minus
asthma attacks)
PM2.5 PM10 PM2.5 PM2.5 PM2.5 and Ozone $1 $1 $4 $7 $2 $4 $120
$220 $325 $630
$450 $860


Minor Respiratory Illness and Symptoms Subtotal
Total Health Benefits in 2020 $43,400* $93,000* $40,900**
$87,600**
Welfare
Recreational Visibility; CA, SW, and SE park regions Agriculture
Worker Productivity
PM $900 $2,800
Ozone $47 $56 Ozone $55 $130
Total Benefits in 2020 $44,000* $96,000* $41,500** $90,600**
Totals may not sum due to rounding.
* Results calculated using three percent discount rate as
recommended by EPA's Guidelines for Economic Analysis (US EPA,
2000a).
** Results calculated using seven percent discount rate as
recommended by OMB Circular A-94 (OMB, 1992). Total benefit numbers
reflect use of three percent discount rate.
54


* Totals may not sum due to rounding.




Alternative Estimate
Exhibits 15 and 16 present the results of the Alternative
calculations. Exhibit 15 presents the mean estimate of avoided
health effects in 2010 and 2020 for each health endpoint included
in the Base analysis. Under the Alternative Estimate, the number of
avoided cases of chronic bronchitis, hospital and ER visits, and
minor respiratory illnesses and symptoms is the same as the Base.
The Alternative projects that reductions in exposure to fine PM and
ozone due to the Clear Skies Act will result in 3,800 avoided
premature deaths in 2010 and nearly 7,200 avoided premature deaths
in 2020. The omission of long-term impacts of particulate matter on
mortality accounts for approximately 40 percent reduction in the
estimate of avoided premature mortality in the Alternative Estimate
relative to the Base Estimate.
Exhibit 16 summarizes the mean monetized health and visibility
benefits of the Alternative Estimate, which will be $6.3 billion in
2010 and $14.1 billion in 2020. The 40 percent reduction in
mortality under the Alternative Estimate and the difference in
valuation of premature mortality and chronic bronchitis explain the
difference in benefits between these two approaches. Even using the
more conservative Alternative Estimate benefit projections,
however, the benefits of Clear Skies still outweigh the costs of
$3.7 billion in 2010 and $6.5 billion in 2020. It is also important
to note that both the Alternative and Base Estimate are likely to
underestimate the benefits of this proposal because of the many
environmental and health effects that we were unable to quantify in
this analysis.
55
Exhibit 15 Alternative Estimate of the Change in Incidence of
Adverse Health Effects Associated with Reductions in Particulate
Matter and Ozone Due to the Clear Skies Act in 2010 - 48 State U.S.
Population (avoided cases per year)
2010 2020

Endpoint Pollutant mean mean
Mortality
Short-Term Exposure, Non-COPD Related, Ages 65 PM2.5 2,600 4,900
and Over
Short-Term Exposure, Non-COPD Related, Ages 64 PM2.5 800 1,500
and Under
Short-Term Exposure, COPD Related, Ages 65 and PM2.5 360 670
Over
Short-Term Exposure, COPD Related, Ages 64 and PM2.5 57 110
Under
Short-Term Mortality Subtotal 3,800 7,200

Totals may not sum due to rounding.
56



* Results calculated using three percent discount rate as
recommended by EPA's Guidelines for Economic Analysis (US EPA,
2000a).
** Results calculated using seven percent discount rate as
recommended by OMB Circular A-94 (OMB, 1992).Totals may not sum due
to rounding.
57



Sensitivity Analyses
The Base Estimate is based on our current interpretation of the
scientific and economic literature; its judgments regarding the
best available data, models, and modeling methodologies; and the
assumptions it considers most appropriate to adopt in the face of
important uncertainties. The majority of the analytical assumptions
used to develop the Base Estimate have been reviewed and approved
by EPA's Science Advisory Board (SAB). However, we recognize that
data and modeling limitations as well as simplifying assumptions
can introduce significant uncertainty into the benefit results and
that reasonable alternative assumptions exist for some inputs to
the analysis, such as the mortality C-R functions.
To address these concerns, we supplement our Base Estimate of
benefits with a series of sensitivity calculations that make use of
other sources of concentration-response and valuation data for key
benefits categories. These sensitivity calculations are conducted
only for the Base Estimate and not for the Alternative Estimate.
First we applied three alternative concentrationresponse (C-R)
functions to estimate premature mortality incidence. Although we
used the Krewski, et al. (2000) mean-based ("PM2.5(DC), All
Causes") model exclusively to derive our Base Estimate of avoided
premature mortality, this analysis also examined the sensitivity of
the benefit results to the selection of alternative C-R functions
for premature mortality. We used three sources of alternative C-R
functions for this sensitivity analysis: (1) an alternative
specification of the Pope/ACS model from Krewski, et al. (2000)
that adjusted for spatial correlation in the dataset; (2) the
original Pope/ACS model; and (3) the Krewski et al. "Harvard Six
Cities" estimate. Exhibits 15 and 16 present the results of these
sensitivity analyses for 2010 and 2020, respectively.
The first alternative C-R function is based on the relative risk
of 1.16 from the "Fine Particles Alone, Regional Adjustment Random
Effects" model reported in Table 46 of the HEI report. Commentary
by an independent review panel noted that "a major contribution of
the [HEI] Reanalysis Project is the recognition that both pollutant
variables and mortality appear to be spatially correlated in the
ACS data set. If not identified and modeled correctly, spatial
correlation could cause substantial errors in both the regression
coefficients and their standard errors (HEI, 2000)." This C-R
function is a reasonable specification to explore the impact of
adjustments for broad regional correlations. However, the HEI
report noted that the spatial adjustment methods "may have over
adjusted the estimated effect for regional pollutants such as fine
particles and sulfate compared with the effect estimates for more
local pollutants such as sulfur dioxide." Thus, the estimates of
avoided incidences of premature mortality based on this C-R
function may underestimate the true effect. (Note that this C-R
function is based on the original air quality dataset used in the
ACS study, covering 50 cities, and used the median PM2.5 levels
rather than mean PM2.5 as the indicator of exposure.)
For comparison with earlier benefits analyses, such as the first
Section 812 Prospective Analysis, we also include estimates of
avoided incidences of premature mortality based on the
58
original ACS/Pope et al. (1995) analysis in the second row of
Exhibit 15 and 16. The third row of Exhibit 17 shows the Krewski,
et al. "Harvard Six Cities" estimate of mortality. The
Krewski-Harvard Six Cities study used a smaller sample of
individuals from fewer cities than the study by Pope, et al.;
however, it features improved exposure estimates, a slightly
broader study population (adults aged 25 and older), and a
follow-up period nearly twice as long as that of Pope, et al. The
SAB has noted that "the [Harvard Six Cities] study had better
monitoring with less measurement error than did most other studies"
(EPA-SAB-COUNCIL-ADV-99-012, 1999).
Second, we use an alternative valuation procedure to estimate
the value of avoided premature mortality, with explicit
consideration of the expected age of mortality incidence associated
with air pollution exposure. Age-specific VSL adjustment factors
can be derived from a series of contingent valuation studies
conducted in the United Kingdom to evaluate WTP for road safety
improvements that reduce mortality risk. The two available sources,
both authored by Michael Jones-Lee, derive significantly differing
adjustment factors, and reflect reflecting the overall uncertainty
within the literature about age-specific VSL adjustments. The
results of this alternative calculation reduce the overall Base
Estimate for the Clear Skies Act by 43 percent for the more extreme
adjustment derived from Jones-Lee (1989), and by 9 percent for the
less extreme adjustment derived from Jones-Lee (1993), as
summarized in Exhibits 15 and 16 below. The specific adjustment
procedure applied is described in more detail in the Heavy-Duty
Engine/Diesel Fuel RIA (U.S. EPA, 2000b).
Third, as noted in the section above on visibility valuation, we
chose not to include in our Base Estimate the valuation of
residential visibility or valuation of recreational visibility at
Class I areas outside of the study regions examined in the Chestnut
and Rowe (1990a, 1990b) study. The last three rows of Exhibits 17
and 18 summarize the impact of applying the existing visibility
valuation literature more broadly than in our Base Estimate.
59
Exhibit 17 Key Sensitivity Analyses for the Clear Skies Act in
2010A
Impact on Base Benefits Description of Basis for Analysis
Avoided Incidences Estimate Adjusted for Growth in Real Income
(billion 1999$)
Concentration-Response Functions for PM-related Premature
Mortality
Krewski/ACS Study Regional 7,300 +$5.8 (+13%) Adjustment
ModelB
Pope/ACS StudyC 7,700 +$8.5 (+20%)
Krewski/Harvard Six-City StudyD 18,800 +$80 (+182%)
Methods for Valuing Reductions in Incidences of PM-related
Premature Mortality


A These results indicate the sensitivity of the primary benefits
estimate to alternative assumptions; results reflect the use of a
three percent discount rate, where appropriate.
B
This C-R function is included as a reasonable specification to
explore the impact of adjustments for broad regional correlations,
which have been identified as important factors in correctly
specifying the PM mortality C-R function. C The Pope et al. C-R
function was used to estimate reductions in premature mortality for
the Tier 2/Gasoline Sulfur benefits analysis. It is included here
to provide a comparable estimate for the Clear Skies Act. D The
Krewski et al. "Harvard Six-cities Study" estimate is included
because the Harvard Six-cities Study featured improved exposure
estimates, a slightly broader study population (adults aged 25 and
older), and a follow-up period nearly twice as long as that of
Pope, et al. and as such provides a reasonable alternative to the
Base Estimate. E Jones-Lee (1989) provides an estimate of
age-adjusted VSL based on a finding that older people place a much
lower value on mortality risk reductions than middle-age people.
Jones-Lee (1993) provides an estimate of age-adjusted VSL based on
a finding that older people value mortality risk reductions only
somewhat less than middle-aged people.
60
Exhibit 18 Key Sensitivity Analyses for the Clear Skies Act in
2020A
Impact on Base Benefits Description of Basis for Analysis
Avoided Incidences Estimate Adjusted for Growth in Real Income
(billion 1999$)
Concentration-Response Functions for PM-related Premature
Mortality
Krewski/ACS Study Regional 13,400 +$11 (+11%) Adjustment
ModelB
Pope/ACS StudyC 14,200 +$17 (+17%)
Krewski/Harvard Six-City StudyD 35,000 +$171 (+179%)
Methods for Valuing Reductions in Incidences of PM-related
Premature Mortality


A These results indicate the sensitivity of the primary benefits
estimate to alternative assumptions; results reflect the use of a
three percent discount rate, where appropriate.
B
This C-R function is included as a reasonable specification to
explore the impact of adjustments for broad regional correlations,
which have been identified as important factors in correctly
specifying the PM mortality C-R function. C The Pope et al. C-R
function was used to estimate reductions in premature mortality for
the Tier 2/Gasoline Sulfur benefits analysis. It is included here
to provide a comparable estimate for the Clear Skies Act.D The
Krewski et al. "Harvard Six-cities Study" estimate is included
because the Harvard Six-cities Study featured improved exposure
estimates, a slightly broader study population (adults aged 25 and
older), and a follow-up period nearly twice as long as that of
Pope, et al. and as such provides a reasonable alternative to the
Base Estimate. E Jones-Lee (1989) provides an estimate of
age-adjusted VSL based on a finding that older people place a much
lower value on mortality risk reductions than middle-age people.
Jones-Lee (1993) provides an estimate of age-adjusted VSL based on
a finding that older people value mortality risk reductions only
somewhat less than middle-aged people.
61
Fourth, we conducted a quantitative sensitivity test on one
aspect of the PM-mortality dose-response function. Although the
consistent advice from EPA's Science Advisory Board has been to
model premature mortality associated with PM exposure as a
non-threshold effect, that is, with harmful effects to exposed
populations regardless of the absolute level of ambient PM
concentrations, some analysts have hypothesized the presence of a
threshold relationship. The nature of the hypothesized relationship
is that there might exist a PM concentration level below which
further reductions no longer yield premature mortality reduction
benefits. EPA does not necessarily endorse any particular
threshold. Nonetheless, Exhibit 19 illustrates how our estimates of
the number of premature mortalities in the Base Estimate might
change under a range of alternative assumptions for a PM mortality
threshold. If, for example, there were no benefits of reducing PM
concentrations below the proposed PM2.5 standard of 15 µg/m3, our
estimate of the total number of premature mortalities in 2020 would
be reduced by approximately 80 percent, from approximately 12,000
annually to approximately 2,200 annually.
One important assumption that we adopted for the threshold
sensitivity analysis is that no adjustments are made to the shape
of the concentration-response function above the assumed threshold.
Instead, thresholds were applied by simply assuming that any
changes in ambient concentrations below the assumed threshold would
have no impacts on the incidence of premature mortality. If there
were actually a threshold, then the shape of the C-R function above
the threshold would likely change.
62
Exhibit 18 Sensitivity Analysis: Effect of Thresholds on
Estimated 2010 and 2020 Clear Skies Analysis PM-Related
Mortality

63


V. REFERENCES
Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K.
Mills. 1995a. "Estimated Long-Term Ambient Concentrations of PM(10)
and Development of Respiratory Symptoms in a Nonsmoking
Population." Archives of Environmental Health 50(2): 139-152.
Abbey, D.E., N. Nishino, W.F. McDonnell, R.J. Burchette, S.F.
Knutsen, W.L. Beeson, and J.X. Yang. 1999. "Long-Term Inhalable
Particles and Other Air Pollutants Related to Mortality in
Nonsmokers." American Journal of Respiratory and Critical Care
Medicine. 159: 373-382.
Abt Associates, 2000. Final Heavy-Duty Engine / Diesel Fuel
Rule: Air Quality Estimation, Selected Health and Welfare Benefits
Methods, and Benefit Analysis Results. Prepared for the
U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, ResearchTriangle Park, NC. December.
American Lung Association, 1999. Chronic Bronchitis. Web site
available at: http://www.lungusa.org/diseases/lungchronic.html.
Blumenschein, K. and M. Johannesson. 1998. "Relationship Between
Quality of Life Instruments, Health State Utilities, and
Willingness to Pay in Patients with Asthma." Annals of Allergy,
Asthma, and Immunology 80:189-194.
Comprehensive Air Quality Model with Extensions (CAMx) Overview;
Accessed June 5, 2002 via http://www.camx.com/overview.html. CAMx
Version 3.1 User's Guide; Accessed June 5, 2002 via
http://www.camx.com/pdf/CAMx3.UsersGuide.020410.pdf.
Chestnut, L.G. 1997. Draft Memorandum: Methodology for
Estimating Values for Changes in Visibility at National Parks.
April 15.
Chestnut, L.G. and R.L. Dennis. 1997. Economic Benefits of
Improvements in Visibility: Acid Rain Provisions of the 1990 Clean
Air Act Amendments. Journal of Air and Waste Management Association
47:395-402.
Chestnut, L.G. and R.D. Rowe. 1990a. Preservation Values for
Visibility Protection at the National Parks: Draft Final Report.
Prepared for Office of Air Quality Planning and Standards, US
Environmental Protection Agency, Research Triangle Park, NC and Air
Quality Management Division, National Park Service, Denver, CO.
Chestnut, L.G., and R.D. Rowe. 1990b. A New National Park
Visibility Value Estimates. In Visibility and Fine Particles,
Transactions of an AWMA/EPA International Specialty Conference,
C.V. Mathai, ed. Air and Waste Management Association,
Pittsburgh.
Chestnut, L.G., and R.D. Rowe. 1990b. A New National Park
Visibility Value Estimates. In Visibility and Fine Particles,
Transactions of an AWMA/EPA International Specialty
64
Conference, C.V. Mathai, ed. Air and Waste Management
Association, Pittsburgh.
Cropper, M.L. and A.J. Krupnick. 1990. "The Social Costs of
Chronic Heart and Lung Disease," Resources for the Future
Discussion Paper QE 89-16-REV. Dickie, M. et al. 1991. Reconciling
Averting Behavior and Contingent Valuation Benefit Estimates of
Reducing Symptoms of Ozone Exposure (draft), as cited in Neumann,
J.E., Dickie, M.T., and R.E. Unsworth. 1994. Industrial Economics,
Incorporated. Memorandum to Jim DeMocker, US EPA, Office of Air and
Radiation. March 31.
Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware,
M.E. Fay, B.G. Ferris and F.E. Speizer. 1993. "An association
between air pollution and mortality in six U.S. cities." New
England Journal of Medicine 329(24): 1753-1759.
Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D.
Spengler, P. Koutrakis, J.H. Ware, M. Raizenne and F.E. Speizer.
1996. "Health Effects of Acid Aerosols On North American
Children-Respiratory Symptoms." Environmental Health Perspectives.
104(5): 500505.
Dominici, F., A. McSermott, S. Zeger, and J.M. Samet. 2002. "On
the Use of Generalized Additive Models in Time-Series Studies of
Air Pollution and Health." American Journal of Epidemiology.
156(9): 193-203.
Elixhauser, A., R.M. Andrews, and S. Fox. 1993. Clinical
Classifications for Health Policy Research: Discharge Statistics by
Principal Diagnosis and Procedure. Agency for Health Care Policy
and Research (AHCPR), Center for General Health Services Intramural
Research, US Department of Health and Human Services.
EPA-SAB, Health and Ecological Effects Subcommittee of the
Advisory Council on Clean Air Act Compliance Analysis. 1999.
Summary Minutes of Public Meeting, April 21-22, 1999.
EPA-SAB-COUNCIL-ADV-99-005, 1999. An SAB Advisory on the Health
and Ecological Effects Initial Studies of the Section 812
Prospective Study: Report to Congress: Advisory by the Health and
Ecological Effects Subcommittee, February.
EPA-SAB-COUNCIL-ADV-98-003, 1998. Advisory Council on Clean Air
Compliance Analysis Advisory on the Clean Air Act Amendments (CAAA)
of 1990 Section 812 Prospective Study: Overview of Air Quality and
Emissions Estimates: Modeling, Health and Ecological Valuation
Issues Initial Studies.
EPA-SAB-COUNCIL-ADV-99-012, 1999. The Clean Air Act Amendments
(CAAA) Section 812 Prospective Study of Costs and Benefits (1999):
Advisory by the Health and Ecological Effects Subcommittee on
Initial Assessments of Health and Ecological Effects: Part 1.
July.
EPA-SAB-COUNCIL-ADV-00-001, 1999. The Clean Air Act Amendments
(CAAA) Section
65
812 Prospective Study of Costs and Benefits (1999): Advisory by
the Health and Ecological Effects Subcommittee on Initial
Assessments of Health and Ecological Effects: Part 2. October
1999.
EPA-SAB-COUNCIL-ADV-00-002, 1999. The Clean Air Act Amendments
(CAAA) Section 812 Prospective Study of Costs and Benefits (1999):
Advisory by the Advisory Council on Clean Air Compliance Analysis:
Costs and Benefits of the CAAA. Effects Subcommittee on Initial
Assessments of Health and Ecological Effects: Part 2. October
1999.
EPA-SAB-COUNCIL-ADV-01-004, 2001. Review of the Draft Analytical
Plan for EPA's Second Prospective Analysis - Benefits and Costs of
the Clean Air Act 1990-2020: Advisory by a Special Panel of the
Advisory Council on Clean Air Compliance Analysis. September
2001.
Greenbaum, D. 2002a. Memorandum. May 30, 2002. Health Effects
Institute, Cambridge, MA.
Greenbaum D. 2002b. Main Points of the Preliminary Review and
Critique of the Revised Analyses by the HEI Peer Review Panel.
Presentation to the Clean Air Science Advisory Committee. July 18,
2002. Health Effects Institute, Cambridge, MA.
Hoskins, G., C. McCowan, R.G. Neville, G.E. Thomas, B. Smith,
and S. Silverman. 2000. "Risk Factors and Costs Associated with and
Asthma Attack." Thorax. 55: 19-24.
Hoskins, G., C. McCowan, R.G. Neville, G.E. Thomas, B. Smith, S.
Silverman. 2000. Risk Factors and Costs Associated with an Asthma
Attack. Thorax, 55: 19-24.
ICF Consulting. 2001. Memorandum to Jim DeMocker, Office of Air
and Radiation, Office of Policy Analysis and Review, US
Environmental Protection Agency, June 27.
Jones-Lee, M.W. 1989. The Economics of Safety and Physical Risk.
Oxford: Basil Blackwell.
Jones-Lee, M.W., G. Loomes, D. O'Reilly, and P.R. Phillips.
1993. The Value of Preventing Non-fatal Road Injuries: Findings of
a Willingness-to-pay National Sample Survey. TRY Working Paper, WP
SRC2.
Krewski D, Burnett RT, Goldbert MS, Hoover K, Siemiatycki J,
Jerrett M, Abrahamowicz M, White WH. 2000. Reanalysis of the
Harvard Six Cities Study and the American Cancer Society Study of
Particulate Air Pollution and Mortality. Special Report to the
Health Effects Institute, Cambridge MA, July 2000
Krupnick, A.J. and M.L. Cropper. 1992. "The Effect of
Information on Health Risk Valuations." Journal of Risk and
Uncertainty 5(2): 29-48.
Krupnick, A.J., A. Alberini, M. Cropper, N. Simon, B. O'Brien,
R. Goeree, and M. Heintzelman. 2000. Age, Health, and the
Willingness to Pay for Mortality Risk Reductions: A Contingent
66
Valuation Survey of Ontario Residents. Resources for the Future
Discussion Paper 00-37.
Kunzli N, Medina S, Kaiser R, Quenel P, Horak F Jr, Studnicka M
2001. Assessment of deaths attributable to air pollution: should we
use risk estimates based on time series or on cohort studies?
American Journal of Epidemiology. 153:1050_5.
Lang, C., G. Yarwood, F. Lalonde, and R. Bloxam. 1995.
Environmental and Health Benefits of Cleaner Vehicles and Fuels.
Prepared for: Canadian Council of Ministers of the Environment Task
Force on Cleaner Vehicles and Fuels, Winnipeg, Manitoba.
October.
Lave, L.B. and E.P. Seskin, 1977. Air Pollution and Public
Health. Johns Hopkins Univeristy Press, Baltimore.
Loehman, E.T., S.V. Berg, A.A. Arroyo, R.A. Hedinger, J.M.
Schwartz, M.E. Shaw, R.W. Fahien, V.H. De, R.P. Fishe, D.E. Rio,
W.F. Rossley, and A.E.S. Green. 1979. "Distributional Analysis of
Regional Benefits and Cost of Air Quality Control." Journal of
Environmental Economics and Management 6: 222-243.
Lipfert F.W. 1989. Sulfur oxides, particulates, and human
mortality: synopsis of statistical correlations. JAPCA.
30:366-371.
Lipfert F.W., H.M. Perry, Jr., J.P. Miller, J.D. Baty, R. Wyzga,
S.E. Carmody. 2000. The Washington University - EPRI Veterans'
Cohort Mortality Study: Preliminary Results. Inhalation Toxicology.
12:41-73.
McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T.
Stewart, L. Deck, and M. Thayer. 1993. Valuing Eastern Visibility:
A Field Test of the Contingent Valuation Method. Prepared for
Office of Policy, Planning and Evaluation, US Environmental
Protection Agency. September.
National Research Council (1998). Research Priorities for
Airborne Particulate Matter: Immediate Priorities and a Long-Range
Research Portfolio. National Academy Press. Washington, DC.
Neumann, J.E., R.E. Unsworth, W.E. Brown. 1994. Memorandum to
Jim DeMocker, Office of Air and Radiation, Office of Policy
Analysis and Review, US Environmental Protection Agency, March
31.
OMB Circular A-94, "Guidelines and Discount Rates for
Benefit-Cost Analysis of Federal Programs," October 29, 1992.
Ostro, B.D. 1987. Air Pollution and Morbidity Revisited: a
Specification Test. Journal of Environmental Economics Management.
14: 87-98.
67
Ostro B.D. and S. Rothschild. 1989. "Air Pollution and Acute
Respiratory Morbidity: An Observational Study of Multiple
Pollutants." Environmental Research 50:238-247.
Ozkaynak, H. And G.D. Thurston. 1987. Associations between 1980
U.S. mortality rates and alternative measures of airborne particle
concentration. Risk Anal. 7: 449-461.
Peters et al. 2001. Increased Particulate Air Pollution and the
Triggering of Myocardial Infarction. Circulation 103:
2810-2815.
Pew Environmental Health Commission. 2000. Attack Asthma: Why
America Needs A Public Health Defense System to Battle
Environmental Threats. Johns Hopkins School of Public Health,
Baltimore, MD.
Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D.
Krewski, K. Ito, and G.D. Thurston. 2002. "Lung Cancer
Cardiopulmonary Mortality, and Long-term Exposure to Fine
Particulate Air Pollution." Journal of the American Medical
Association. 287(9):1132-41.
Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S.
Evans, F.E. Speizer, and
C.W. Heath, Jr. 1995. "Particulate Air Pollution as a Predictor
of Mortality in a Prospective Study of U.S. Adults." American
Journal of Respiratory Critical Care Medicine 151: 669-674.
Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne.
1991. "Respiratory Health and PM10 Pollution: a Daily Time Series
Analysis" American Review of Respiratory Diseases 144: 668-674.
Rossi, G., M.A. Vigotti, A. Zanobetti, F. Repetto, V. Gianelle,
and J. Schwartz. 1999. "Air Pollution and Cause-Specific Mortality
in Milan, Italy, 1980-1989" Arch. Environ. Health. 54:158-164.
Rowe, R.D. and L.G. Chestnut. 1986. "Oxidants and Asthmatics in
Los Angeles: A Benefits Analysis--Executive Summary." Prepared by
Energy and Resource Consultants, Inc. Report to the US EPA, Office
of Policy Analysis. EPA-230-09-86-018. Washington, DC March.

Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery
DW, Schwartz J, Zanobetti A. 2000. The National Morbidity,
Mortality and Air Pollution Study: Part II: Morbidity, Mortality
and Air Pollution in the United States. Research Report No. 94,
Part II. Health Effects Institute, Cambridge MA, June 2000.
Schwartz, J., D.W. Dockery and L.M. Neas. 1996. "Is Daily
Mortality Associated Specifically With Fine Particles" Journal of
the Air & Waste Managment Association. 46: 927-939.
Schwartz, J., Dockery, D.W., Neas, L.M., Wypij, D., Ware, J.H.,
Spengler, J.D., Koutrakis, P., Speizer, F.E., and Ferris, Jr., B.G.
1994. "Acute Effects of Summer Air Pollution on Respiratory Symptom
Reporting in Children" American Journal of Respiratory Critical
Care
68
Medicine 150: 1234-1242.
Schwartz, J. and A. Zanobetti. 2002. "Re-Analysis of NMMAPS
Morbidity Data and Further Exploration of the GAM-Time Series
Issue." A Report on Revised NMMAPS Morbidity by Analysts at Harvard
University.
Schwartz, J. 1993. "Particulate Air Pollution and Chronic
Respiratory Disease" Environmental Research 62: 7-13.
Schwartz, J. 2000. "Assessing Confounding, Effect Modification,
and Thresholds in the Association Between Ambient Particles and
Daily Deaths" Environ. Health Perspectives 108:563-568.
Schwartz, J. 2000. "The Distributed Lag Between Air Pollution
and Daily Deaths" Epidemiology 11:320-326.
Schwartz, J. 2000. Harvesting and Long Term Exposure Effects in
the Relation between Air Pollution and Mortality. American Journal
of Epidemiology 151: 440-448.
Seigneur, C., G. Hidy, I. Tombach, J. Vimont, and P. Amar. 1999.
Scientific Peer Review of the Regulatory Modeling System for
Aerosols and Deposition (REMSAD).
Sheppard, L., D. Levy, G. Norris, T.V. Larson, and J.Q. Koenig.
1999. "Effects of ambient air pollution on nonelderly asthma
hospital admissions in Seattle, Washington" 1987-1994.
Epidemiology. Vol. 10(1):23-30.
Sisler, J.F. 1996. Spatial and Seasonal Patterns and Long Term
Variability of the Composition of the Haze in the United States: An
Analysis of Data from the IMPROVE Network. Cooperative Institute
for Research in the Atmosphere, Colorado State University; Fort
Collins, CO July.
Smith, D.H., D.C. Malone, K.A. Lawson, L. J. Okamoto, C.
Battista, and W.B. Saunders, 1997. "A National Estimate of the
Economic Costs of Asthma." American Journal of Respiratory Critical
Care Medicine 156: 787-793.
Smith, V.K., G.Van Houtven, and S. Pattanayak. 1999. "Benefit
Transfer as Preference Calibration" Resources for the Future
Discussion Paper 99-36.
Stieb, D.M., S. Judek, and R.T. Burnett. 2002. Meta-analysis of
Time-series Suties of Air Pollution and Mortality: Effects of Gases
and Particles and the Influence of Cause of Death, Age, and Season.
Journal of the Air and Waste Management Association 52:
470-484.
Tolley, G.S. et al. 1986. Valuation of Reductions in Human
Health Symptoms and Risks. University of Chicago. Final Report for
the US Environmental Protection Agency. January.
69
Thurston, G.D., and K. Ito. 2001. Epidemiological Studies of
Acute Ozone Exposures and Mortality. Journal of Exposure Analysis
and Environmental Epidemiology. 11:286-294.
US Department of Commerce, Economics and Statistics
Administration. 1992. Statistical Abstract of the United States,
1992: The National Data Book. 112th Edition, Washington, DC US
Department of Commerce, Bureau of Economic Analysis. BEA Regional
Projections to 2045: Vol. 1, States. Washington, DC US Govt.
Printing Office, July 1995.
US Department of Health and Human Services, Centers for Disease
Control and Prevention, National Center for Health Statistics.
1994. Vital Statistics of the United States, 1990. Volume
II-Mortality. Hyattsville, MD.
US Department of Health and Human Services, Centers for Disease
Control and Prevention, National Center for Health Statistics.
1999. National Vital Statistics Reports, 47(19).
US Environmental Protection Agency, 1997. The Benefits and Costs
of the Clean Air Act, 1970 to 1990. Prepared for US Congress by US
EPA, Office of Air and Radiation/Office of Policy Analysis and
Review, Washington, DC
US Environmental Protection Agency, 1997b. Regulatory Impact
Analysis for the Particulate Matter and Ozone National Ambient Air
Quality Standards and Proposed Regional Haze Rule. Appendix J.
Table 3. Prepared by: Office of Air Quality Planning and Standards,
Research Triangle Park, NC. July.
US Environmental Protection Agency, 1999a. Benefits and Costs of
the Clean Air Act: 19902010; EPA Report to Congress. US EPA, Office
of Air and Radiation and Office of Policy. Washington, DC. Document
No. EPA-410-R-99-001. November.
US Environmental Protection Agency, 1999b. Regulatory Impact
Analysis: Control of Air Pollution from New Motor Vehicles: Tier 2
Motor Vehicle Emissions Standards and Gasoline Sulfur Control
Requirements. Prepared by: Office of Mobile Sources, Office of Air
and Radiation, December.
US Environmental Protection Agency, 1999c. Regulatory Impact
Analysis for the Final Regional Haze Rule. Prepared by: Office of
Air Quality Planning and Standards, Office of Air and Radiation,
April.
US Environmental Protection Agency, 2000a. Guidelines for
Preparing Economic Analyses. Document Number EPA 240-R-00-003.
Prepared by: Office of the Administrator, September.
US Environmental Protection Agency, 2000b. Regulatory Impact
Analysis for the Heavy-Duty Standards/Diesel Fuel Rulemaking.
Prepared by: Innovative Strategies and Economics Group, Office of
Air Quality Planning and Standards, Research Triangle Park, NC
December.
70
US Environmental Protection Agency, 2000c. Valuing Fatal Cancer
Risk Reductions. White Paper for Review by the EPA Science Advisory
Board.
US Environmental Protection Agency, 2000d. Procedures for
Developing Base Year and Future Year Mass and Modeling Inventories
for the Heavy-Duty Engine and Vehicle Standards and Highway Diesel
Fuel (HDD) Rulemaking. EPA420-R-00-020. October 2000.
Viscusi, W.K. 1992. Fatal Tradeoffs: Public and Private
Responsibilities for Risk. (New York: Oxford University Press).
Viscusi, W.K., W.A. Magat, and J. Huber. 1991. "Pricing
Environmental Health Risks: Survey Assessments of Risk-Risk and
Risk-Dollar Trade-Offs for Chronic Bronchitis" Journal of
Environmental Economics and Management, 21: 32-51.
Whittemore, A.S. and E.L. Korn. 1980. "Asthma and Air Pollution
in the Los Angeles Area" Am J Public Health. Vol. 70:687-696.
Zanobetti A, Schwartz J, Samoli E, Gryparis A, Touloumi G,
Atkinson R, Le Tertre A, Bobros J, Celko M, Goren A, Forsberg B,
Michelozzi P, Rabczenko D, Aranguez Ruiz E, Katsouyanni K. 2002.
The temporal pattern of mortality responses to air pollution: a
multicity assessment of mortality displacement. Epidemiology.
Jan;13(1):87_93.
71