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Chapter 8 Comparison of Program Costs to Program Emission 

Reductions and Air Quality Benefits

EPA traditionally evaluates the effectiveness of a proposal in terms of net benefits and in 

terms of cost-effectiveness. Section 8.1 below presents the cost-benefit analysis of the proposal, 

while Section 8.2 presents the cost-effectiveness.

8.1 Cost-Benefit Analysis

The net benefits of the proposed Tier 3 program are determined by the effects of the 

program on the costs to comply with the vehicle and fuel aspects of the program along with the 

benefits of improved air quality on health and the environment. 

8.1.1 Program-Wide Costs

The costs that would be incurred from our proposed program fall into three categories -

for the Tier 3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of 

gasoline. While we present these three categories of costs separately in this section, for purposes 

of the calculation of cost-effectiveness we have summed them to represent the estimated costs of 

the proposed program. 

All costs represent the fleet-weighted average of light-duty vehicles and trucks. All costs 

are represented in 2010 dollars.

8.1.1.1 Vehicle Costs

The vehicle costs include the technology costs projected to meet the proposed exhaust 

and evaporative standards, as detailed in draft RIA Chapter 2 and shown in Table 8-1. The fleet 

mix of light-duty vehicles, light duty trucks, and medium-duty trucks represents the 2016 MY 

fleet used in the 2012-2016MY light-duty GHG final rulemaking.

Table 8-1: Annual Vehicle Technology Costs, 2010$

Year Vehicle Exhaust 

Emission Control 

Costs ($Million)

Vehicle Evaporative 

Emission Control 

Costs ($Million)

Facility Costs 

($Million)

Total Proposed 

Vehicle Program 

Costs ($Million)

2016 $ - $ - $ 22.5 $22.5

2017 $705 $79.4 $ 3.75 $788

2018 $1,300 $190 $ 3.75 $1,494

2019 $1,410 $180 $ 3.75 $1,594

2020 $1,530 $245 $ 3.75 $1,779

2021 $1,670 $237 $ 3.75 $1,911

2022 $1,810 $301 $ 3.75 $2,115

2023 $1,840 $288 $ 3.75 $2,132

2024 $1,960 $293 $ 3.75 $2,257
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Year Vehicle Exhaust 

Emission Control 

Costs ($Million)

Vehicle Evaporative 

Emission Control 

Costs ($Million)

Facility Costs 

($Million)

Total Proposed 

Vehicle Program 

Costs ($Million)

2025 $2,080 $281 $ 3.75 $2,365

2026 $2,070 $281 $ 3.75 $2,355

2027 $2,040 $281 $ 3.75 $2,325

2028 $2,040 $281 $ 3.75 $2,325

2029 $2,000 $281 $ 3.75 $2,285

2030 $1,990 $281 $ 3.75 $2,275

8.1.1.2 Fuel Costs

The fuel costs consist of the additional operating costs and capital costs to the refiners to 

meet the proposed sulfur average of 10 ppm. The sulfur control costs, as described in detail in 

draft RIA Chapter 5, assume a cost of 0.89 cents per gallon which includes the refinery operating 

and capital costs. The projected annual fuel consumption and annual fuel costs of the proposed 

program are listed in Table 8-2. 

Table 8-2: Annual Fuel Costs, 2010$

Year Annual Fuel 

Consumption 

(million gallons)

Fuel Sulfur 

Control Costs

($Million)

2016 36,208 $ 322 

2017 144,878 $ 1,289 

2018 144,710 $ 1,288 

2019 144,435 $ 1,285 

2020 144,324 $ 1,284 

2021 144,562 $ 1,287 

2022 144,838 $ 1,289 

2023 144,774 $ 1,288 

2024 144,812 $ 1,289 

2025 145,057 $ 1,291 

2026 145,476 $ 1,295 

2027 145,988 $ 1,299 

2028 146,761 $ 1,306 

2029 147,280 $ 1,311 

2030 148,295 $ 1,320 

8.1.1.3 Total Costs

The sum of the vehicle technology costs to control exhaust and evaporative emissions, in 

addition to the costs to control the sulfur level in the fuel, represent the total costs of the 

proposed program, as shown in Table 8-3.
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8-3

Table 8-3: Total Annual Vehicle and Fuel Control Costs, 2010$

Year Total Proposed 

Vehicle Program 

Costs ($Million)

Fuel Sulfur 

Control Costs

($Million)

Total Proposed 

Program Costs 

($Million)

2016 $22.5 $ 322 $345

2017 $788 $ 1,289 $2,078

2018 $1,494 $ 1,288 $2,782

2019 $1,594 $ 1,285 $2,879

2020 $1,779 $ 1,284 $3,063

2021 $1,911 $ 1,287 $3,197

2022 $2,115 $ 1,289 $3,404

2023 $2,132 $ 1,288 $3,420

2024 $2,257 $ 1,289 $3,546

2025 $2,365 $ 1,291 $3,656

2026 $2,355 $ 1,295 $3,649

2027 $2,325 $ 1,299 $3,624

2028 $2,325 $ 1,306 $3,631

2029 $2,285 $ 1,311 $3,596

2030 $2,275 $ 1,320 $3,595

8.1.2 Quantified and Monetized Health and Environmental Impacts

This section presents EPA’s analysis of the criteria pollutant-related health and 

environmental impacts that would occur as a result of the proposed Tier 3 standards. The 

vehicles and fuels subject to the proposed standards are significant sources of mobile source air 

pollution such as direct PM, NOX, SOX, VOCs and air toxics. The standards would affect 

exhaust and evaporative emissions of these pollutants from vehicles. Emissions of NOX (a 

precursor to ozone formation and secondarily-formed PM2.5), SOX (a precursor to secondarilyformed PM2.5), VOCs (a precursor to ozone formation and, to a lesser degree, secondarilyformed PM2.5) and directly-emitted PM2.5 contribute to ambient concentrations of PM2.5 and 

ozone. Exposure to ozone and PM2.5 is linked to adverse human health impacts such as 

premature deaths as well as other important public health and environmental effects.

The analysis in this section aims to characterize the benefits of the proposed standard by 

answering two key questions:

1. What are the health and welfare effects of changes in ambient particulate matter 

(PM2.5) and ozone air quality resulting from reductions in precursors including NOX and SO2?

2. What is the economic value of these effects?

For the proposal, we have quantified and monetized the health and environmental 

impacts in 2030, representing impacts associated with a year when the program is fully

implemented and most of the fleet is turned over. Overall, we estimate that the proposed 

standards would lead to a net decrease in PM2.5- and ozone-related health impacts in 2030. The 
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decrease in population-weighted national average PM2.5 exposure results in a net decrease in 

adverse PM-related human health impacts (the decrease in national population-weighted annual 

average PM2.5 is 0.05 μg/m3in 2030).A The decrease in population-weighted national average 

ozone exposure results in a net decrease in ozone-related health impacts (population-weighted 

maximum 8-hour average ozone decreases by 0.52 ppb in 2030).

Using the most conservative premature mortality estimates (Pope et al., 2002 for PM2.5

and Bell et al., 2004 for ozone),B,C we estimate that by 2030, implementation of the proposed 

standards would reduce approximately 970 premature mortalities annually and yield 

approximately $9.5 billion in total annual benefits. The upper end of the range of avoided 

premature mortality estimates associated with the proposed standards (based on Laden et al., 

2006 for PM2.5 and Levy et al., 2005 for ozone)D,Eresults in approximately 2,800 premature 

mortalities avoided in 2030 and yields approximately $27 billion in total benefits. Thus, even 

taking the most conservative premature mortality assumptions, the health impacts of the 

proposed standards presented in this rule are clearly substantial.

We note that of necessity decisions on the emissions and other elements used in the air 

quality modeling were made early in the analytical process for this proposal. For this reason, the 

Tier 3 emission control scenario used in the air quality and benefits modeling includes emission 

reductions from Tier 3 across the nation, assuming no reductions associated with California's 

LEV III program (as opposed to including California’s LEV III program and its associated 

emission reductions in the baseline scenario). This was because EPA had not granted California 

a waiver of preemption under CAA section 209 for the LEV III program at the time EPA 

conducted the air quality modeling. EPA did include California’s fuel program, which 

independent of LEV III was already resulting in average gasoline sulfur levels of 10 ppm, in the 

baseline scenario. Since then, EPA granted a waiver for California's LEV III program (78 FR 

2112, January 9, 2013). Based on this change in circumstances, we will conduct new air quality

modeling for the final rule that will include emission reductions from California’s LEV III 

program in the baseline scenario. 

Had we modeled the California LEV III emission impacts in the Tier 3 air quality 

baseline, we estimate that benefits would decrease by approximately 12-16 percent, depending 

 

A Note that the national, population-weighted PM2.5 and ozone air quality metrics presented in this Chapter represent 

an average for the entire, gridded U.S. CMAQ domain. These are different than the population-weighted PM2.5 and 

ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air quality 

monitor.

B

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

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the 

American Medical Association, 287, 1132-1141.

C Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities, 1987-2000.Journal of the 

American Medical Association, 292(19), 2372-2378.

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

Mortality. American Journal of Respiratory and Critical Care Medicine. 173, 667-672.

E Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4), 458-68.
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8-5

on the particular health impact functions used to characterize both PM- and ozone-related 

premature mortality.

F

 As a result, we estimate that in 2030, using the most conservative 

premature mortality estimates (Pope et al., 2002 for PM2.5 and Bell et al., 2004 for ozone),G,Hthe 

proposed standards would reduce approximately 820 premature mortalities annually and yield 

approximately $8.0 billion in total annual benefits. The upper end of the range of avoided 

premature mortality estimates associated with the proposed standards (based on Laden et al., 

2006 for PM2.5 and Levy et al., 2005 for ozone)I,Jresults in approximately 2,400 premature 

mortalities avoided in 2030 and yields approximately $23 billion in total benefits. These are 

rough estimates since, without new photochemical air quality modeling to reflect the revised 

baseline and control scenarios, we are unable to account for cross-state transport of pollution. 

However, we believe this is a reasonable characterization of the small reduction in benefits had 

we modeled California in the baseline; our overall cost-benefit conclusions do not materially

change with or without the inclusion of California emissions in our analysis. We will conduct

new air quality modeling for the final rule that will include emission reductions from California’s 

LEV III program in the baseline scenario. The rest of this Chapter presents benefits that include 

California LEV III emission reductions.

8.1.2.1 Overview

This analysis reflects the impacts of the proposed Tier 3 rule in 2030 compared to a 

future-year reference scenario without the program in place. Overall, we estimate that the 

proposed rule would lead to a net decrease in PM2.5-related health and environmental impacts 

(see Section 7.2.5 for more information about the air quality modeling results). The decrease in 

population-weighted national average PM2.5 exposure results in a net decrease in adverse PMrelated human health and environmental impacts (the decrease in national population weighted 

annual average PM2.5 is 0.05 μg/m3 in 2030). 

The air quality modeling also projects decreases in ozone concentrations (see Section 

7.2.5). The overall decrease in population-weighted national average ozone exposure results in 

decreases in ozone-related health and environmental impacts (population weighted maximum 8-

hour average ozone decreases by 0.52 ppb in 2030).

 

F To conduct this sensitivity analysis, we simply assumed no air quality change in the California portion of the 

CMAQ domain. We then exported the reference and control air quality surfaces to be used as inputs to BenMAP. 

Note that this simple approach is unable to account for legitimate emissions impacts related to cross-state transport 

of pollution.

G

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

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the 

American Medical Association, 287, 1132-1141.

H Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 US urban communities, 1987-2000.Journal of the 

American Medical Association, 292(19), 2372-2378.

I Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and 

Mortality. American Journal of Respiratory and Critical Care Medicine. 173, 667-672.

J Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4), 458-68.
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8-6

We base our analysis of the program’s impact on human health and the environment on 

peer-reviewed studies of air quality and human health effects.1,2 Our benefits methods are also 

consistent with rulemaking analyses such as the final 2012-2016 MY Light-Duty Vehicle Rule, 3

the final Portland Cement National Emissions Standards for Hazardous Air Pollutants 

(NESHAP) RIA,

4

, and the final 2017-2025 MY Light-Duty Vehicle Rule.5To model the ozone 

and PM air quality impacts of the proposal, we used the Community Multiscale Air Quality

(CMAQ) model (see Section 7.2.2). The modeled ambient air quality data serves as an input to 

the Environmental Benefits Mapping and Analysis Program version 4.0 (BenMAP).K BenMAP 

is a computer program developed by the U.S. EPA that integrates a number of the modeling 

elements used in previous analyses (e.g., interpolation functions, population projections, health 

impact functions, valuation functions, analysis and pooling methods) to translate modeled air 

concentration estimates into health effects incidence estimates and monetized benefits estimates.

The range of total monetized ozone- and PM-related health impacts in 2030 is presented 

in Table 8-4. We present total benefits based on the PM- and ozone-related premature mortality

function used. The benefits ranges therefore reflect the addition of each estimate of ozonerelated premature mortality (each with its own row in) to estimates of PM-related premature 

mortality. The analysis of the proposed standards reflects EPA’s work to characterize benefits 

prior to the most recent PM NAAQS.L EPA will update its benefits analysis, and related 

uncertainty analysis, to be consistent with the final PM NAAQS for the final Tier 3 regulatory

impact analysis. 

Table 8-4: Estimated 2030 Monetized PM-and Ozone-Related Health Benefitsa,d

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function

Reference Total Benefits

(Billions, 2010$, 3% 

Discount Rate)b,c

Total Benefits

(Billions, 2010$, 7% 

Discount Rate) b,c

Multi-city analyses Bell et al., 2004 Total: $9.5 - $21

PM: $7.7 - $19

Ozone: $1.8

Total: $8.7 - $19

PM: $7.0 - $17

Ozone: $1.8

Huang et al., 2005 Total: $10 - $21

PM: $7.7 - $19

Ozone: $2.6

Total: $9.5 - $20

PM: $7.0 - $17

Ozone: $2.6

Schwartz, 2005 Total: $10 - $22

PM: $7.7 - $19

Ozone: $2.7

Total: $9.6 - $20

PM: $7.0 - $17

Ozone: $2.7

Meta-analyses Bell et al., 2005 Total: $13 - $24

PM: $7.7 - $19

Ozone: $5.5

Total: $12 - $23

PM: $7.0 - $17

Ozone: $5.5

Ito et al., 2005 Total: $15 - $26

PM: $7.7 - $19

Ozone: $7.5

Total: $15 - $25

PM: $7.0 - $17

Ozone: $7.5

 

K

Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html.

L US EPA (2012). National Ambient Air Quality Standards for Particulate Matter. 

http://www.epa.gov/PM/2012/finalrule.pdf
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Levy et al., 2005 Total: $15 - $27

PM: $7.7 - $19

Ozone: $7.7

Total: $15 - $25

PM: $7.0 - $17

Ozone: $7.7

Notes:

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was developed 

by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature 

mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health and welfare effects is provided in Table 8-5.

c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing 

Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation 

and computation. Totals may not sum due to rounding.

The benefits in Table 8-4 include all of the human health impacts we are able to quantify 

and monetize at this time. However, the full complement of human health and welfare effects 

associated with PM and ozone remain unquantified because of current limitations in methods or 

available data. We have not quantified a number of known or suspected health effects linked 

with ozone and PM for which appropriate health impact functions are not available or which do 

not provide easily interpretable outcomes (e.g., changes in heart rate variability). Additionally, 

we are unable to quantify a number of known welfare effects, including reduced acid and 

particulate deposition damage to cultural monuments and other materials, and environmental 

benefits due to reductions of impacts of eutrophication in coastal areas. These are listed in Table 

8-5. As a result, the health benefits quantified in this section are likely underestimates of the 

total benefits attributable to the proposed program.

Table 8-5: Human Health and Welfare Effects of Pollutants Affected by the Proposed

Tier 3 Program

Pollutant/ 

Effect

Quantified and monetized in primary estimate Unquantified

PM: healtha

Premature mortality based on cohort study 

estimatesb and expert elicitation estimates

Hospital admissions: respiratory and 

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial 

infarctions)

Lower and upper respiratory illness

Minor restricted activity days

Work loss days

Asthma exacerbations (among asthmatic 

populations

Respiratory symptoms (among asthmatic 

populations)

Infant mortality

Low birth weight, pre-term birth and other 

reproductive outcomes

Pulmonary function

Chronic respiratory diseases other than chronic 

bronchitis

Non-asthma respiratory emergency room visits

UVb exposure (+/-)

c

PM: welfare Visibility in Class I areas in SE, SW, and CA 

regions

Household soiling

Visibility in residential areas

Visibility in non-class I areas and class 1 areas in 

NW, NE, and Central regions

UVb exposure (+/-)

c

Global climate impactsc
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Pollutant/ 

Effect

Quantified and monetized in primary estimate Unquantified

Ozone: health Premature mortality based on short-term

study estimates

Hospital admissions: respiratory

Emergency room visits for asthma

Minor restricted activity days

School loss days

Chronic respiratory damage

Premature aging of the lungs

Non-asthma respiratory emergency room visits

UVb exposure (+/-)

c

Ozone: 

welfare

Decreased outdoor worker productivity

Yields for:

--Commercial forests

--Fruits and vegetables, and

--Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest 

aesthetics

Ecosystem functions

UVb exposure (+/-)

c

Climate impacts

CO: health Behavioral effects

Nitrate 

Deposition: 

welfare

Commercial fishing and forestry from acidic 

deposition effects

Commercial fishing, agriculture and forestry 

from nutrient deposition effects

Recreation in terrestrial and estuarine 

ecosystems from nutrient deposition effects 

Other ecosystem services and existence values 

for currently healthy ecosystems

Coastal eutrophication from nitrogen deposition 

effects

Sulfate 

Deposition: 

welfare

Commercial fishing and forestry from acidic 

deposition effects

Recreation in terrestrial and aquatic ecosystems 

from acid deposition effects

Increased mercury methylation

HC/Toxics: 

healthd

Cancer (benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde)

Anemia (benzene)

Disruption of production of blood components 

(benzene)

Reduction in the number of blood platelets 

(benzene)

Excessive bone marrow formation (benzene)

Depression of lymphocyte counts (benzene)

Reproductive and developmental effects (1,3-

butadiene)

Irritation of eyes and mucus membranes 

(formaldehyde)

Respiratory irritation (formaldehyde)

Asthma attacks in asthmatics (formaldehyde)

Asthma-like symptoms in non-asthmatics 

(formaldehyde)

Irritation of the eyes, skin, and respiratory tract 

(acetaldehyde)

Upper respiratory tract irritation and congestion 

(acrolein)
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Pollutant/ 

Effect

Quantified and monetized in primary estimate Unquantified

HC/Toxics: 

welfare

Direct toxic effects to animals

Bioaccumulation in the food chain

Damage to ecosystem function

Odor

Notes:

a

In addition to primary economic endpoints, there are a number of biological responses that have been associated 

with PM health effects including morphological changes and altered host defense mechanisms. The public health 

impact of these biological responses may be partly represented by our quantified endpoints.

b Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk 

estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion 

of this issue).6 While some of the effects of short term exposure are likely to be captured by the cohort estimates, 

there may be additional premature mortality from short term PM exposure not captured in the cohort estimates 

included in the primary analysis.

c May result in benefits or disbenefits.

d Many of the key hydrocarbons related to this action are also hazardous air pollutants listed in the CAA.

While there would be impacts associated with air toxic pollutant emission changes that 

result from the proposed program, we do not attempt to monetize those impacts. This is 

primarily because currently available tools and methods to assess air toxics risk from mobile 

sources at the national scale are not adequate for extrapolation to incidence estimations or 

benefits assessment. The best suite of tools and methods currently available for assessment at 

the national scale are those used in the National-Scale Air Toxics Assessment (NATA). The 

EPA Science Advisory Board specifically commented in their review of the 1996 NATA that 

these tools were not yet ready for use in a national-scale benefits analysis, because they did not 

consider the full distribution of exposure and risk, or address sub-chronic health effects.7 While 

EPA has since improved these tools, there remain critical limitations for estimating incidence 

and assessing benefits of reducing mobile source air toxics. 

As part of the second prospective analysis of the benefits and costs of the Clean Air Act,

8

EPA conducted a case study analysis of the health effects associated with reducing exposure to 

benzene in Houston from implementation of the Clean Air Act. While reviewing the draft report, 

EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the challenges for 

assessing progress in health improvement as a result of reductions in emissions of hazardous air 

pollutants (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in 

emissions inventories and background levels, the difficulty of extrapolating risk estimates to low 

doses and the challenges of tracking health progress for diseases, such as cancer, that have long 

latency periods.”9EPA continues to work to address these limitations; however, we did not have 

the methods and tools available for national-scale application in time for the analysis of the 

proposed program.

M

 

 

M

In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.
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8.1.2.1.1 Human Health Impacts

Table 8-6 and Table 8-7 present EPA’s preferred estimates of the annual PM2.5 and ozone 

health impacts in the 48 contiguous U.S. states associated with the proposed Tier 3 program. For 

each endpoint presented in Table 8-6 and Table 8-7, we provide both the point estimate and the 

90 percent confidence interval. Table 8-8 presents the sensitivity analysis.

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and SixCities studies and no threshold assumption in the model of mortality, we estimate that the 

proposed program would result in between 800 and 2,100 cases of avoided PM2.5-related 

premature deaths annually in 2030. A sensitivity analysis was conducted to understand the 

impact of alternative concentration response functions suggested by experts in the field. When 

the range of expert opinion is used, we estimate between 270 and 2,700 fewer premature 

mortalities in 2030.

The range of ozone impacts is based on changes in risk estimated using several sources of 

ozone-related mortality effect estimates. This analysis presents six alternative estimates for the 

association based upon different functions reported in the scientific literature, derived from both 

the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (Bell et al., 2004; Huang 

et al., 2005; Schwartz, 2005) and from a series of meta-analyses (Bell et al., 2005, Ito et al., 

2005, and Levy et al., 2005). This approach is not inconsistent with recommendations provided 

by the NRC in their report (NRC, 2008) on the estimation of ozone-related mortality risk 

reductions, “The committee recommends that the greatest emphasis be placed on estimates from 

new systematic multicity analyses that use national databases of air pollution and mortality, such 

as in the NMMAPS, without excluding consideration of meta-analyses of previously published 

studies.”10 For ozone-related premature mortality in 2030, we estimate a range of between 170

to 770 fewer premature mortalities. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies and 

expert elicitation. 

Table 8-8 presents the distributions of the reduction in PM2.5-related premature mortality 

based on the C-R distributions provided by each expert, as well as that from the data-derived 

health impact functions, based on the statistical error associated with the ACS study (Pope et al., 

2002) and the Six-Cities study (Laden et al., 2006). The 90 percent confidence interval for each 

separate estimate of PM-related mortality is also provided. 

In 2030, the effect estimates of nine of the twelve experts included in the elicitation panel 

fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one 

expert falls below this range, while two of the experts are above this range. Although the overall 

range across experts is summarized in these tables, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the 

likely mean effect estimate are not evenly distributed across the range illustrated by arraying the 

highest and lowest expert means.
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Table 8-6: Estimated PM2.5-Related Health Impactsa

Health Effect 2030 Annual Reduction in 

Incidence (5th- 95th percentile)

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

 Adult, age 25+, Six-Cities Study (Laden et al., 2006)

 Infant, age <1 year (Woodruff et al., 1997)

800

(310 – 1,300)

2,100

(1,100 – 3,000)

3

(0 – 8)

Chronic bronchitis (adult, age 26 and over) 560

(100 – 1,000)

Non-fatal myocardial infarction (adult, age 18 and over) 980

(360 – 1,600)

Hospital admissions - respiratory (all ages)c160

(77 – 230)

Hospital admissions - cardiovascular (adults, age >18)d380

(270 – 440)

Emergency room visits for asthma (age 18 years and younger) 600

(350 – 850)

Acute bronchitis, (children, age 8-12) 1,300

(0 – 2,500)

Lower respiratory symptoms (children, age 7-14) 16,000

(7,700 – 24,000)

Upper respiratory symptoms (asthmatic children, age 9-18) 12,000

(3,800 – 20,000)

Asthma exacerbation (asthmatic children, age 6-18) 27,000

(3,000 – 74,000)

Work loss days 100,000

(88,000 – 110,000)

Minor restricted activity days (adults age 18-65) 600,000

(500,000 – 690,000)

Notes:

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United 

States. 

b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002)

and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and 

should not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, 

(1997).N

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), 

pneumonia and asthma.

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure.

 

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

Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health 

Perspectives 105(6):608-612.
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Table 8-7: Estimated Ozone-Related Health Impactsa

Health Effect 2030 Annual Reduction in Incidence

(5th- 95th percentile)

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental

 Huang et al. (2005) – Cardiopulmonary

 Schwartz (2005) – Non-accidental

Meta-analyses:

 Bell et al. (2005) – All cause

 Ito et al. (2005) – Non-accidental

 Levy et al. (2005) – All cause

170

(73 – 260)

250

(120 – 380)

260

(110 – 410)

540

(300 – 780)

750

(500 – 1,000)

770

(560 – 970)

Hospital admissions- respiratory causes (adult, 65 and older)c 1,200

(160 – 2,200)

Hospital admissions -respiratory causes (children, under 2) 550

(290 – 810)

Emergency room visit for asthma (all ages) 580

(0 – 1,500)

Minor restricted activity days (adults, age 18-65) 970,000

(490,000 – 1,500,000)

School absence days 350,000

(150,000 – 490,000)

Notes:

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative 

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). 

The estimates of ozone-related premature mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

Table 8-8: Results of Application of Expert Elicitation: Annual Reductions in Premature 

Mortality in 2030 Associated with the Proposed Program

Source of Mortality 

Estimate

2030 Tier 3 Control

5th Percentile Mean 95th Percentile

Pope et al. (2002) 310 800 1,300

Laden et al. (2006) 1,100 2,100 3,000

Expert A 400 2,200 4,000

Expert B 170 1,600 3,600

Expert C 300 1,700 3,600

Expert D 240 1,200 1,900

Expert E 1,400 2,700 4,100

Expert F 1,000 1,500 2,100

Expert G 0 960 1,800

Expert H 4 1,200 2,800

Expert I 260 1,600 2,900
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Source of Mortality 

Estimate

2030 Tier 3 Control

5th Percentile Mean 95th Percentile

Expert J 390 1,300 2,900

Expert K 0 270 1,300

Expert L 65 1,000 2,300

8.1.2.1.2 Monetized Estimates of Human Health and Environmental Impacts

Table 8-9 presents the estimated monetary value of changes in the incidence of ozone and 

PM2.5-related health and environmental effects. Total aggregate monetized benefits are 

presented in Table 8-10. All monetized estimates are presented in 2010$. Where appropriate,

estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 

2030.O The monetized value of PM2.5-related mortality also accounts for a twenty-year 

segmented cessation lag.P To discount the value of premature mortality that occurs at different 

points in the future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7

percent discount rate to value PM-related nonfatal heart attacks (myocardial infarctions).Q 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the final program is thus equal to 

the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus the 

sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits 

in 2030 for the proposed program, using the ACS and Six-Cities PM mortality studies and the 

range of ozone mortality assumptions, is between $9.5 and $27 billion, assuming a 3 percent 

discount rate, or between $8.7 and $25 billion, assuming a 7 percent discount rate. As the results

indicate, total benefits are driven primarily by the reduction in PM2.5- and ozone-related 

 

O Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most 

goods (such as environmental protection) will increase if real incomes increase. Benefits are therefore adjusted by 

multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. 

For growth between 2000 and 2030, this factor is 1.23 for long-term mortality, 1.27 for chronic health impacts, and 

1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the 

reader to the PM NAAQS regulatory impact analysis.9 Note that similar adjustments do not exist for cost-of-illnessbased unit values. For these, we apply the same unit value regardless of the future year of analysis.

P Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in 

pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, 

this term is often referred to as “cessation lag”. 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. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is 

consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science 

Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 

Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency 

Administrator, December.

Q Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost earnings 

and direct medical costs over a 5-year period following a nonfatal MI. 
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premature fatalities each year and represent the benefits of the Tier 3 program anticipated to 

occur annually when the program is fully implemented and most of the fleet turned over.

The next largest benefit is for reductions in chronic illness (chronic bronchitis and 

nonfatal heart attacks), although this value is more than an order of magnitude lower than for 

premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor 

restricted activity days, and work loss days account for the majority of the remaining benefits. 

The remaining categories each account for a small percentage of total benefit; however, they

represent a large number of avoided incidences affecting many individuals. A comparison of the 

incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, there are many more work loss days 

than PM-related premature mortalities, yet work loss days account for only a very small fraction 

of total monetized benefits. This reflects the fact that many of the less severe health effects, 

while more common, are valued at a lower level than the more severe health effects. Also, some 

effects, such as hospital admissions, are valued using a proxy measure of willingness-to-pay

(e.g., cost-of-illness). As such, the true value of these effects may be higher than that reported 

here. 

Table 8-9: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (millions of 2010$) a,b

2030

PM2.5-Related Health Effect (5th and 95th Percentile)

Premature Mortality –

Derived from Epidemiology 

Studiesc,d

Adult, age 30+ - ACS study 

(Pope et al., 2002)

 3% discount rate

 7% discount rate

$7,200

($920 – $18,000)

$6,500

($830 - $17,000)

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006)

 3% discount rate

 7% discount rate

$18,000

($2,600 - $45,000)

$17,000

($2,300 - $41,000)

Infant Mortality, <1 year –

(Woodruff et al. 1997)

$27

($0 - $100)

Chronic bronchitis (adults, 26 and over) $310

($25 - $1,000)

Non-fatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

$110

($24 - $260)

$90

($19 - $210)

Hospital admissions for respiratory causes $2.5

($1.2 – $3.6)

Hospital admissions for cardiovascular causes $5.5

($1.2 - $10)

Emergency room visits for asthma $0.24

($0.13 - $0.36)
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Acute bronchitis (children, age 8–12) $0.61

($0.00 - $1.5)

Lower respiratory symptoms (children, 7–14) $0.34

($0.13 - $0.63)

Upper respiratory symptoms (asthma, 9–11) $0.40

($0.12 - $0.89)

Asthma exacerbations $1.6

($0.17 - $4.4)

Work loss days $16

($14 - $19)

Minor restricted-activity days (MRADs) $41

($24 - $59)

Ozone-related Health Effect

Premature Mortality, All ages –

Derived from Multi-city analyses

Bell et al., 2004 $1,700

($220 - $4,200)

Huang et al., 2005 $2,500

($340 - $6,200)

Schwartz, 2005 $2,600

($330 - $6,500)

Premature Mortality, All ages –

Derived from Meta-analyses

Bell et al., 2005 $5,400

($760 - $13,000)

Ito et al., 2005 $7,400

($1,100 - $18,000)

Levy et al., 2005 $7,600

($1,100 - $18,000)

Hospital admissions- respiratory causes (adult, 65 and older) $32

($4.2 - $56)

Hospital admissions- respiratory causes (children, under 2) $6.0

($3.1 - $8.9)

Emergency room visit for asthma (all ages) $0.23

($0.0 - $0.57)

Minor restricted activity days (adults, age 18-65) $67

($31 - $110)

School absence days $34

($15 - $48)

Notes:

a Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and 

ozone benefits are nationwide. 

b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis 

year (2030).

c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses.
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Table 8-10: Total Monetized Ozone and PM-related Benefits Associated with the Proposed

Program in 2030

Total Ozone and PM Benefits (billions, 2010$) –

PM Mortality Derived from the ACS and Six-Cities Studies

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$9.5 - $21 Multi-city Bell et al., 

2004

$8.7 - $19

Huang et al., 

2005

$10 - $21 Huang et al., 

2005

$9.5 - $20

Schwartz, 

2005

$10 - $22 Schwartz, 

2005

$9.6 - $20

Meta-analysis Bell et al., 

2005

$13 - $24 Meta-analysis Bell et al., 

2005

$12 - $23

Ito et al., 

2005

$15 - $26 Ito et al., 

2005

$15 - $25

Levy et al., 

2005

$15 - $27 Levy et al., 

2005

$15 - $25

Total Ozone and PM Benefits (billions, 2010$) –

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$4.6 - $27 Multi-city Bell et al., 

2004

$4.4 - $24

Huang et al., 

2005

$5.4 - $27 Huang et al., 

2005

$5.2 - $25

Schwartz, 

2005

$5.5 - $28 Schwartz, 

2005

$5.3 - $25

Meta-analysis Bell et al., 

2005

$8.3 - $30 Meta-analysis Bell et al., 

2005

$8.1 - $28

Ito et al., 

2005

$10 - $32 Ito et al., 

2005

$10 - $30

Levy et al., 

2005

$11 - $33 Levy et al., 

2005

$10 - $30

8.1.2.1.3 Methodology

8.1.2.1.3.1 Human Health Impact Functions

Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 

components: (1) an effect estimate from a particular study; (2) a baseline incidence rate for the 

health effect (obtained from either the epidemiology study or a source of public health statistics 

such as the Centers for Disease Control); (3) the size of the potentially affected population; and 

(4) the estimated change in the relevant ozone or PM summary measures.
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A typical health impact function might look like: 

 1   0 

x

y y e



,

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), β is the effect estimate, and Δx is the estimated change in the 

summary pollutant measure. There are other functional forms, but the basic elements remain the 

same. The following subsections describe the sources for each of the first three elements: size 

of the potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence 

rates. We also describe the treatment of potential thresholds in PM-related health impact 

functions. Section 8.2 describes the ozone and PM air quality inputs to the health impact 

functions. 

8.1.2.1.3.2 Potentially Affected Populations

The starting point for estimating the size of potentially affected populations is the 2000 

U.S. Census block level dataset.11 Benefits Modeling and Analysis Program (BenMAP) 

incorporates 250 age/gender/race categories to match specific populations potentially affected by

ozone and other air pollutants. The software constructs specific populations matching the 

populations in each epidemiological study by accessing the appropriate age-specific populations 

from the overall population database. BenMAP projects populations to 2030 using growth 

factors based on economic projections.12

8.1.2.1.3.3 Effect Estimate Sources

The most significant quantifiable benefits of reducing ambient concentrations of ozone 

and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents13,14 and the World Health Organization’s 2003 and 200415,16 reports outline 

numerous human health effects known or suspected to be linked to exposure to ambient ozone 

and PM. EPA evaluated the ozone and PM literature for use in the benefits analysis for the final 

2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this 

analysis; for more information on the studies that underlie the health impacts quantified in this 

RIA, please refer to those documents.

It is important to note that we are unable to separately quantify all of the possible PM and 

ozone health effects that have been reported in the literature for three reasons: (1) the possibility

of double counting (such as hospital admissions for specific respiratory diseases versus hospital 

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect 

relationships that are based on clinical studies to the potentially affected population; or (3) the 

lack of an established concentration-response (CR) relationship. Table 8-11 lists the health 

endpoints included in this analysis.
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Table 8-11: Health Impact Functions Used in BenMAP to Estimate Impacts of PM2.5 and 

Ozone Reductions

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Premature Mortality

Premature mortality –

daily time series

O3 Multi-city

Bell et al (2004) (NMMAPS study)17– Nonaccidental

Huang et al (2005)18- Cardiopulmonary

Schwartz (2005)19– Non-accidental

Meta-analyses:

Bell et al (2005)20– All cause

Ito et al (2005)21– Non-accidental

Levy et al (2005)22– All cause

All ages

Premature mortality 

—cohort study, allcause

PM2.5 Pope et al. (2002)23

Laden et al. (2006)24

>29 years

>25 years

Premature mortality, 

total exposures

PM2.5 Expert Elicitation (IEc, 2006)25 >24 years

Premature mortality 

— all-cause

PM2.5 Woodruff et al. (1997)26 Infant (<1 year)

Chronic Illness

Chronic bronchitis PM2.5 Abbey et al. (1995)27 >26 years

Nonfatal heart attacks PM2.5 Peters et al. (2001)28 Adults (>18 years)

Hospital Admissions 

Respiratory

O3

Pooled estimate:

Schwartz (1995) - ICD 460-519 (all resp)29

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)30,31

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)

32

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD)

Moolgavkar et al. (1997) – ICD 490-496 

(COPD)

>64 years

Burnett et al. (2001)33 <2 years

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)34

Ito (2003)—ICD 490-496 (COPD)35

>64 years

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)36 20–64 years

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM2.5 Sheppard (2003)—ICD 493 (asthma)37 <65 years

Cardiovascular PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular)

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure)

>64 years

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular)

20–64 years
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ENDPOINT POLLUTANT STUDY STUDY POPULATION

Asthma-related ER 

visits

O3 Pooled estimate:

Peel et al (2005)38

Wilson et al (2005)39

All ages

All ages

Asthma-related ER 

visits (cont’d)

PM2.5 Norris et al. (1999)40 0–18 years

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996)41 8–12 years

Upper respiratory 

symptoms

PM2.5 Pope et al. (1991)42 Asthmatics, 9–11 

years

Lower respiratory 

symptoms

PM2.5 Schwartz and Neas (2000)43 7–14 years

Asthma exacerbations PM2.5 Pooled estimate:

Ostro et al. (2001)44 (cough, wheeze and 

shortness of breath)

Vedal et al. (1998)45 (cough)

6–18 yearsa

Work loss days PM2.5 Ostro (1987)46 18–65 years

School absence days

O3 

Pooled estimate:

Gilliland et al. (2001)47

Chen et al. (2000)48

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs)

O3 Ostro and Rothschild (1989)49 18–65 years

PM2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) 

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended 

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan 

for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits 

of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice 

from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for 

all school-aged children based on the biological similarity between children aged 5 to 17.

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 

U.S. To account for the potential impacts of different health care systems or underlying health 

status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due 

to the potential for confounding by co-pollutants, we give preference to effect estimates from 

models including both ozone and PM over effect estimates from single-pollutant models.50,51

8.1.2.1.3.4 Baseline Incidence Rates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 100 ppb decrease in daily ozone levels might, in turn, decrease 

hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to 
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convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per 100,000 people, that number must be 

multiplied by the number of 100,000s in the population.

Table 8-12 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. Table 8-13 presents the asthma 

prevalence rates used in this analysis. For both baseline incidence and prevalence data, we used 

age-specific rates where available. We applied concentration-response functions to individual 

age groups and then summed over the relevant age range to provide an estimate of total 

population benefits. In most cases, we used a single national incidence rate, due to a lack of 

more spatially disaggregated data. Whenever possible, the national rates used are national 

averages, because these data are most applicable to a national assessment of benefits. For some 

studies, however, the only available incidence information comes from the studies themselves; in 

these cases, incidence in the study population is assumed to represent typical incidence at the 

national level. Regional incidence rates are available for hospital admissions, and county-level 

data are available for premature mortality. We have projected mortality rates such that future 

mortality rates are consistent with our projections of population growth.52

Table 8-12: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter Rates

Value Source

Mortality Daily or annual mortality 

rate projected to 2020

Age-, cause-, and 

county-specific rate

CDC Wonder (2006–2008)53

U.S. Census bureau

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa,54

Asthma ER Visits Daily asthma ER visit rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa

Chronic Bronchitis Annual prevalence rate per 

person

Aged 18–44

Aged 45–64

Aged 65 and older

0.0367

0.0505

0.0587

1999 NHIS (American Lung 

Association, 2002, Table 4)55

Annual incidence rate per 

person

0.00378 Abbey et al. (1993, Table 3)

Nonfatal Myocardial 

Infarction (heart 

attacks)

Daily nonfatal myocardial 

infarction incidence rate 

per person, 18+

Age-, region-, state-, 

and county- specific 

rate

2007 HCUP data filesa; adjusted by 

0.93 for probability of surviving after 

28 days (Rosamond et al., 1999)

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily dyspnea

0.076

0.067

0.037 

Ostro et al. (2001)

Acute Bronchitis Annual bronchitis 

incidence rate, children

0.043 American Lung Association (2002, 

Table 11)56
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Endpoint Parameter Rates

Value Source

Lower Respiratory 

Symptoms

Daily lower respiratory 

symptom incidence among 

childrenb

0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory 

symptom incidence among 

asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate 

per person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41);57 U.S. 

Bureau of the Census (2000)58

School Loss Days Rate per person per year, 

assuming 180 school days 

per year

9.9 National Center for Education 

Statistics (1996)59 and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence 

rate per person

0.02137 Ostro and Rothschild (1989, p. 243)

Notes:

a Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes.

b Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

Table 8-13: Asthma Prevalence Rates Used for this Analysis

Population Group Asthma Prevalence Rates

Value Source

All Ages 0.0780

American Lung Association (2010, Table 7)

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5 to 17 0.1776 American Lung Association (2010, Table 9)

African American, <18 0.1553 American Lung Associationb

Notes:

a

See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.

b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).60

8.1.2.1.3.5 PM2.5-Related Premature Mortality “Lowest Measured Level” (LML) 

Assessment

Based on our review of the current body of scientific literature, EPA estimated PMrelated mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009),61 which was reviewed by EPA’s 

Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009; U.S. EPA-SAB, 2009),62,63

concluded that the scientific literature consistently finds that a no-threshold log-linear model 

most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function. 

Consistent with this finding, we have conformed the threshold sensitivity analysis to the current 

state of the PM science and improved upon our previous approach for estimating the sensitivity
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of the benefits estimates to the presence of an assumed threshold by incorporating a new 

“Lowest Measured Level” (LML) assessment.

This approach summarizes the distribution of avoided PM mortality impacts according to 

the baseline (i.e. pre-Tier 3 Program) PM2.5 levels experienced by the population receiving the 

PM2.5 mortality benefit (see Figure 8-1). We identify on this figure the lowest air quality levels 

measured in each of the two primary epidemiological studies EPA uses to quantify PM-related 

mortality. This information allows readers to determine the portion of PM-related mortality 

benefits occurring above or below the LML of each study; in general, our confidence in the 

estimated PM mortality decreases as we consider air quality levels further below the LML in the 

two epidemiological studies. While the LML analysis provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold 

and continues to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations.

The large proportion of the avoided PM-related impacts we estimate in this analysis 

occur among populations exposed at or above the LML of each study (Figure 8-1), increasing 

our confidence in the PM mortality analysis. Approximately 25 percent of the avoided impacts 

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

study); about 81 percent occur at or above an annual mean PM2.5 level of 7.5 μg/m3 (the LML of 

the Pope et al. 2002 study). As we model mortality impacts among populations exposed to levels 

of PM2.5 that are successively lower than the LML of each study our confidence in the results 

diminishes. However, the analysis above confirms that the great majority of the impacts occur at 

or above the Pope et al. LML.

As an example, when considering mortality impacts among populations living in areas 

with an annual mean PM level of 8 μg/m3, we would place greater confidence in estimates drawn 

from the Pope et al. 2002 study, as this air quality level is above the LML of this study. 

Conversely, we would place equal confidence when estimating mortality impacts among 

populations living in locations where the annual mean PM levels are above 10 μg/m3because 

this value is at or above the LML of each study. 

While the LML of each study is important to consider when characterizing and 

interpreting the overall level PM-related benefits, EPA believes that both cohort-based mortality 

estimates are suitable for use in air pollution health impact analyses. When estimating PM 

mortality impacts using risk coefficients drawn from the Laden et al. analysis of the Harvard Six 

Cities and the Pope et al. analysis of the American Cancer Society cohorts there are innumerable 

other attributes that may affect the size of the reported risk estimates—including differences in 

population demographics, the size of the cohort, activity patterns and particle composition 

among others. The LML assessment presented here provides a limited representation of one key

difference between the two studies.
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Figure 8-1 Percentage of Total PM-related Mortalities Avoided by Baseline Air Quality 

Level

8.1.2.1.3.6 Economic Values for Health Outcomes

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993).64 Epidemiological studies generally

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk. For example, suppose a 

pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 

to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk).

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but 

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).65,66 
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We provide unit values for health endpoints (along with information on the distribution of the 

unit value) in Table 8-14. All values are in constant year 2010 dollars, adjusted for growth in 

real income out to 2030 using projections provided by Standard and Poor’s. Economic theory

argues that WTP for most goods (such as environmental protection) will increase if real income 

increases. Many of the valuation studies used in this analysis were conducted in the late 1980s 

and early 1990s. Because real income has grown since the studies were conducted, people’s 

willingness to pay for reductions in the risk of premature death and disease likely has grown as 

well. We did not adjust cost of illness-based values because they are based on current costs. 

Similarly, we did not adjust the value of school absences, because that value is based on current 

wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 PM 

NAAQS RIA.67 For details on valuation estimates for ozone-related endpoints, see the 2008 

Ozone NAAQS RIA.68

Table 8-14: Unit Values for Economic Valuation of Health Endpoints (2010$)

Health Endpoint Central Estimate of 

Value Per Statistical 

Incidence

2000 

Income 

Level

2030 

Income 

Level

Derivation of Distributions of Estimates

Premature Mortality 

(Value of a Statistical 

Life)

$8,000,000 $9,900,000 EPA currently recommends a central VSL of $6.3m (2000$) based on a 

Weibull distribution fitted to 26 published VSL estimates (5 contingent 

valuation and 21 labor market studies). The underlying studies, the 

distribution parameters, and other useful information are available in 

Appendix B of EPA's current Guidelines for Preparing Economic 

Analyses (U.S. EPA, 2000). 

Chronic Bronchitis 

(CB)

$450,000 $550,000 The WTP to avoid a case of pollution-related CB is calculated as where 

x is the severity of an average CB case, WTP13 is the WTP for a severe 

case of CB, and $ is the parameter relating WTP to severity, based on 

the regression results reported in Krupnick and Cropper (1992). The 

distribution of WTP for an average severity-level case of CB was 

generated by Monte Carlo methods, drawing from each of three 

distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 

probability of being each of the first nine deciles of the distribution of 

WTP responses in Viscusi et al. (1991); (2) the severity of a pollutionrelated case of CB (relative to the case described in the Viscusi study) 

is assumed to have a triangular distribution, with the most likely value 

at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant 

in the elasticity of WTP with respect to severity is normally distributed 

with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and 

Cropper [1992]). This process and the rationale for choosing it is 

described in detail in the Costs and Benefits of the Clean Air Act, 1990 

to 2010 (U.S. EPA, 1999). 
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Nonfatal Myocardial 

Infarction (heart 

attack)

3% discount rate

Age 0–24

Age 25–44

 Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$89,373

$100,690$1

06,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548

$89,373

$100,690

$106,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548

No distributional information available. Age-specific cost-of-illness 

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs are based on 

simple average of estimates from Russell et al. (1998) and Wittels et al. 

(1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of 

lost earnings:

age of onset: at 3% at 7%

25–44 $8,774 $7,855

45–54 $12,932 $11,578

55–65 $74,746 $66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no discounting)

2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate; 

$21,113 at 7% discount rate)

Hospital Admissions

Chronic Obstructive 

Pulmonary Disease 

(COPD)

$17,996 $17,996 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and Quality (2000) 

(www.ahrq.gov). 

Asthma Admissions $11,957 $11,957 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of hospital 

stay, and weighted share of total asthma category illnesses) reported in 

Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). 

All Cardiovascular $30,256 $30,256 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of hospital 

stay, and weighted share of total cardiovascular category illnesses) 

reported in Agency for Healthcare Research and Quality (2000) 

(www.ahrq.gov). 

All respiratory (ages 

65+)

$25,413 $25,413 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) reported in Agency 

for Healthcare Research and Quality, 2000 (www.ahrq.gov).

All respiratory (ages 

0–2)

$10,943 $10,943 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) reported in Agency 

for Healthcare Research and Quality, 2000 (www.ahrq.gov).

Emergency Room 

Visits for Asthma

$405 $405 No distributional information available. Simple average of two unit 

COI values:

(1) $311.55, from Smith et al. (1997) and

(2) $260.67, from Stanford et al. (1999).
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Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory 

Symptoms (URS)

$32 $34 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

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 additivity of WTPs. In the absence of information 

surrounding the frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we assumed a uniform 

distribution between $9.2 and $43.1.

Lower Respiratory 

Symptoms (LRS)

$20 $21 Combinations of the four 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 additivity of WTPs. The dollar value for LRS is the average 

of the dollar values for the 11 different types of LRS. In the absence of 

information surrounding the frequency with which each of the 11 types 

of LRS occurs within the LRS symptom complex, we assumed a 

uniform distribution between $6.9 and $24.46.

Asthma 

Exacerbations

$55 $57 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986). This study 

surveyed asthmatics to estimate WTP for avoidance of a “bad asthma 

day,” as defined by the subjects. For purposes of valuation, an asthma 

exacerbation is assumed to be equivalent to a day in which asthma is 

moderate or worse as reported in the Rowe and Chestnut (1986) study. 

The value is assumed have a uniform distribution between $15.6 and 

$70.8.

Acute Bronchitis $452 $494 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc 1994 for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110. 

Work Loss Days 

(WLDs)

Variable 

(U.S. 

median = 

$137)

Variable 

(U.S. 

median = 

$137)

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 50 (assuming 2 weeks of vacation) 

and then by 5—to get median daily wage. U.S. Year 2000 Census, 

compiled by Geolytics, Inc.

Minor Restricted 

Activity Days 

(MRADs)

$64 $69 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $22 and a 

maximum of $83, with a most likely value of $52. Range is based on 

assumption that value should exceed WTP for a single mild symptom 

(the highest estimate for a single symptom—for eye irritation—is 

$16.00) and be less than that for a WLD. The triangular distribution 

acknowledges that the actual value is likely to be closer to the point 

estimate than either extreme.

School Absence Days $95 $95 No distribution available
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8.1.2.1.3.7 Manipulating Air Quality Modeling Data for Health Impacts Analysis

In Section 7.2, we summarized the methods for and results of estimating air quality for 

the program. These air quality results are in turn associated with human populations to estimate 

changes in health effects. For the purposes of this analysis, we focus on the health effects that

have been linked to ambient changes in ozone and PM2.5 related to emission reductions estimated 

to occur due to the implementation of the program. We estimate ambient PM2.5 and ozone 

concentrations using the Community Multiscale Air Quality model (CMAQ). This section 

describes how we converted the CMAQ modeling output into full-season profiles suitable for the 

health impacts analysis. 

General Methodology

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell 

from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.R,S The 

predicted changes in ozone concentrations from the future-year base case to future-year control 

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., 

BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we 

generated full-season ozone profiles for each location in two steps: (1) we combined monitored 

observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid 

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily 8-hour 

maximum.T,U

For PM2.5, we also use the model predictions in conjunction with observed monitor data. 

CMAQ generates predictions of hourly PM species concentrations for every grid. The species 

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a 

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several 

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the 

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5

were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5 

 

R The ozone season for this analysis is defined as the 5-month period from May to September.

S Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at least 

nine hourly observations per day [8 am to 8 pm] during the ozone season).

T The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

U This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap.
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8-28

species concentrations were created by interpolating EPA speciation network (ESPN) ambient 

data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance for 

modeling the PM2.5 standard (EPA, 2001).69 The guidance recommends that model predictions 

be used in a relative sense to estimate changes expected to occur in each major PM2.5 species. 

The procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled 

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the final 

program. 

Table 8-15 provides those ozone and PM2.5 metrics for grid cells in the modeled domain 

that enter the health impact functions for health benefits endpoints. The population-weighted 

average reflects the baseline levels and predicted changes for more populated areas of the nation. 

This measure better reflects the potential benefits through exposure changes to these populations.

Table 8-15: Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air 

Quality Metrics for Health Benefits Endpoints Associated with the Proposed Tier 3

Program

2030

Statistica Baseline Changeb

Ozone Metric: National Population-Weighted Average (ppb)c

Daily Maximum 8-Hour Average 

Concentration 

42.8652 0.5235

PM2.5 Metric: National Population-Weighted Average (μg/m3)

Annual Average Concentration 8.3941 0.0479

Notes:

a Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects 

estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of 

the “ozone season” (i.e., May through September). Note that the national, population-weighted 

PM2.5 and ozone air quality metrics presented in this chapter represent an average for the entire, 

gridded U.S. CMAQ domain. These are different than the population-weighted PM2.5 and ozone 

design value metrics presented in Chapter 7, which represent the average for areas with a current 

air quality monitor.

b The change is defined as the base-case value minus the control-case value. 

c Calculated by summing the product of the projected CMAQ grid-cell population and the 

estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used in the air quality and benefits modeling are 

slightly different than the final emission inventories estimated for the proposed program. Please 

refer to Section 7.2.1 for more information about the inventories used in the air quality modeling 

that supports the health impacts analysis. 

8.1.2.1.4 Methods for Describing Uncertainty

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty and this analysis is no exception. As outlined 

both in this and preceding chapters, many inputs were used to derive the estimate of benefits for 
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the proposal, including emission inventories, air quality models (with their associated parameters 

and inputs), epidemiological health effect estimates, estimates of values (both from WTP and 

COI studies), population estimates, income estimates, and estimates of the future state of the 

world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain 

and, depending on its role in the benefits analysis, may have a disproportionately large impact on 

estimates of total benefits. For example, emissions estimates are used in the first stage of the 

analysis. As such, any uncertainty in emissions estimates will be propagated through the entire 

analysis. When compounded with uncertainty in later stages, small uncertainties in emission 

levels can lead to large impacts on total benefits.

The National Research Council (NRC) (2002, 2008)70,71 highlighted the need for EPA to 

conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In general, the NRC concluded that EPA’s general methodology for calculating the 

benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. 

Since the publication of these reports, EPA’s Office of Air and Radiation (OAR) continues to 

make progress toward the goal of characterizing the aggregate impact of uncertainty in key 

modeling elements on both health incidence and benefits estimates in two key ways: Monte 

Carlo analysis and expert-derived concentration-response functions. In this analysis, we use both 

of these two methods to assess uncertainty quantitatively, as well as provide a qualitative 

assessment for those aspects that we are unable to address quantitatively. 

First, we used Monte Carlo methods for characterizing random sampling error associated 

with the concentration response functions from epidemiological studies and random effects 

modeling to characterize both sampling error and variability across the economic valuation 

functions. Monte Carlo simulation uses random sampling from distributions of parameters to 

characterize the effects of uncertainty on output variables, such as incidence of premature 

mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around 

the estimated health impact and dollar benefits. The reported standard errors in the 

epidemiological studies determined the distributions for individual effect estimates.

Second, because characterization of random statistical error omits important sources of 

uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may

exist), we also incorporate the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008).72 Use of the expert 

elicitation and incorporation of the standard errors approaches provide insights into the 

likelihood of different outcomes and about the state of knowledge regarding the benefits 

estimates. However, there are significant unquantified uncertainties present in upstream inputs 

including emission and air quality. Both approaches have different strengths and weaknesses, 

which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85 to 95 percent of total monetized benefits. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated with 

reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 
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studies. In our results, we report credible intervals based on these standard errors, reflecting the 

uncertainty in the estimated change in incidence of avoided premature deaths. We also provide 

multiple estimates, to reflect model uncertainty between alternative study designs. 

For premature mortality associated with exposure to PM, we follow the same approach 

used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation 

study. Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. Furthermore, 

the approach presented here does not yet include methods for addressing correlation between 

input parameters and the identification of reasonable upper and lower bounds for input 

distributions characterizing uncertainty in additional model elements. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the estimates. This information should be interpreted within the context of the 

larger uncertainty surrounding the entire analysis.

In 2006 the EPA requested an NAS study to evaluate the extent to which the 

epidemiological literature to that point improved the understanding of ozone-related mortality.

The NAS found that short-term ozone exposure was likely to contribute to ozone-related 

mortality (NRC, 2008) and issued a series of recommendations to EPA, including that the 

Agency should:

1. Present multiple short-term ozone mortality estimates, including those based on multicity analyses such as the National Morbidity, Mortality and Air Pollution Study

(NMMAPS) as well as meta-analytic studies.

2. Report additional risk metrics, including the percentage of baseline mortality attributable 

to short-term exposure.

3. Remove reference to a no-causal relationship between ozone exposure and premature 

mortality.

The quantification and presentation of ozone-related premature mortality in this chapter 

is responsive to these NRC recommendations.

Some key sources of uncertainty in each stage of both the PM and ozone health impact 

assessment are the following:

 gaps in scientific data and inquiry;

 variability in estimated relationships, such as epidemiological effect estimates, 

introduced through differences in study design and statistical modeling;

 errors in measurement and projection for variables such as population growth rates;
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 errors due to misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; and

 biases due to omissions or other research limitations.

In Table 8-16 we summarize some of the key uncertainties in the benefits analysis. 

Table 8-16: Primary Sources of Uncertainty in the Benefits Analysis

1. Uncertainties Associated with Impact Functions

- The value of the ozone or PM effect estimate in each impact function.

- Application of a single impact function to pollutant changes and populations in all locations.

- Similarity of future-year impact functions to current impact functions. 

- Correct functional form of each impact function. 

- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source 

epidemiological study. 

- Application of impact functions only to those subpopulations matching the original study population.

2. Uncertainties Associated with CMAQ-Modeled Ozone and PM Concentrations 

- Responsiveness of the models to changes in precursor emissions from the control policy.

- Projections of future levels of precursor emissions, especially ammonia and crustal materials.

- Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from urban to 

rural areas.

3. Uncertainties Associated with PM Mortality Risk

- Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.

- Direct causal agents within the complex mixture of PM have not been identified.

- The extent to which adverse health effects are associated with low-level exposures that occur many times in the 

year versus peak exposures.

- The extent to which effects reported in the long-term exposure studies are associated with historically higher 

levels of PM rather than the levels occurring during the period of study.

- Reliability of the PM2.5 monitoring data in reflecting actual PM2.5 exposures.

4. Uncertainties Associated with Possible Lagged Effects

- The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels 

that would occur in a single year is uncertain as well as the portion that might occur in subsequent years.

5. Uncertainties Associated with Baseline Incidence Rates

- Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not 

accurately represent the actual location-specific rates.

- Current baseline incidence rates may not approximate well baseline incidence rates in 2030.

- Projected population and demographics may not represent well future-year population and demographics.

6. Uncertainties Associated with Economic Valuation

- Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore

have uncertainty surrounding them.

- Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of 

differences in income or other factors.

7. Uncertainties Associated with Aggregation of Monetized Benefits

- Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or 

unmonetized benefits are not included.

8.1.3 Comparison of Costs and Benefits

This section presents the cost-benefit comparison related to the expected impacts of the 

proposed Tier 3 program. In estimating the net benefits of the program, the appropriate cost 
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8-32

measure is ‘social costs.’ Social costs represent the welfare costs of a rule to society and do not 

consider transfer payments (such as taxes) that are simply redistributions of wealth. For this 

analysis, we estimate that the social costs of the program are equivalent to the estimated vehicle 

and fuel compliance costs of the program. While vehicle manufacturers and fuel producers

would see their costs increase by the amount of those compliance costs, they are expected to pass 

them on in their entirety to vehicle and fuel consumers in the form of increased prices. 

Ultimately, these costs will be borne by the final consumers of these goods. The social benefits 

of the program are represented by the monetized value of health and welfare improvements 

experienced by the U.S. population. Table 8-17 contains the estimated social costs and the 

estimated monetized benefits of the program.

The results in Table 8-17 suggest that the 2030 monetized benefits of the proposed 

standards are greater than the expected costs. Specifically, the annual benefits of the total 

program will range between $9.5 to $27 billion annually in 2030 using a three percent discount 

rate, or between $8.7 to $25 billion assuming a 7 percent discount rate, compared to estimated 

social costs of approximately $3.6 billion in that same year. Though there are a number of health 

and environmental effects associated with the proposed standards that we are unable to quantify

or monetize (see Table 8-5), the benefits of the proposed standards outweigh the projected costs. 

Using a conservative benefits estimate, the 2030 benefits outweigh the costs by a factor 

of 2.4. Using the upper end of the benefits range, the benefits could outweigh the costs by a 

factor of 7.5. Thus, even taking the most conservative benefits assumptions, benefits of the 

proposed standards clearly outweigh the costs.

Table 8-17: Summary of Annual Benefts and Costs Associated with the Proposed Tier 3 

Program (Billions, 2010$)a

Description 2030

Vehicle Program Costs

Fuels Program Costs

Total Estimated Costsb

$2.3

$1.3

$3.6

Total Estimated Health Benefitsc,d,e,f

 3 percent discount rate

 7 percent discount rate

$9.5 - $27

$8.7 - $25

Annual Net Benefits (Total Benefits – Total Costs)

 3 percent discount rate

 7 percent discount rate

$5.9 - $23

$5.1 - $21

Notes:

a All estimates represent annual benefits and costs anticipated for the year 2030. Totals are rounded to two 

significant digits and may not sum due to rounding.

b

 The calculation of annual costs does not require amortization of costs over time. Therefore, the estimates of annual 

cost do not include a discount rate or rate of return assumption (see Chapter 2 of the draft RIA for more information 

on vehicle costs, Chapter 5 for fuel costs, and Section 8.1.1 for a summary of total program costs). 

c Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the Bell et al., 2004

ozone premature mortality function to PM2.5-related premature mortality derived from the American Cancer Society 

cohort study (Pope et al., 2002) for the low estimate and ozone premature mortality derived from the Levy et al., 

2005 study to PM2.5-related premature mortality derived from the Six-Cities (Laden et al., 2006) study for the high 

estimate.
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d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing 

economic analyses. 

e Valuation of premature mortality based on long-term PM exposure assumes discounting over the SAB 

recommended 20-year segmented lag structure described in the Regulatory Impact Analysis for the 2006 PM 

National Ambient Air Quality Standards (September, 2006). 

f Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories 

that have not been quantified and monetized are listed in Table 8-5.

8.1.4 Illustrative Analysis of Quantified and Monetized Impacts Associated with the Proposal 

in 2017

For illustrative purposes, this section presents the quantified and monetized impacts 

associated with the proposed standards in 2017. As presented in Section 7.1.5, the emissions 

impacts of the proposed standards in 2017 are primarily due to the effects of sulfur on the 

existing (pre-Tier 3) fleet. For reasons explained in Section 7.1.3.2.2, our analysis of the air 

quality impacts in 2017 reflects an increase in direct PM emissions from vehicles (along with 

reductions in NOX, VOCs and other pollutants). This emissions increase results from a series of 

conservative assumptions and uncertainties related to fuel parameters in 2017, and is not

expected to occur in reality. Because our air quality modeling assumes this increase, as well as 

increased direct PM emissions due to an emissions processing error (see Section 7.2.1.1.2), our 

illustrative benefits analysis in 2017 overestimates ambient concentrations of PM and 

underestimates the benefits of the proposed Tier 3 standards. 

This analysis reflects the impacts of the proposed Tier 3 rule in 2017 compared to a 

future-year reference scenario without the program in place. Overall, we estimate that the 

proposed rule would lead to a net decrease in PM2.5-related health and environmental impacts in 

2017 (see Section 7.2.5 for more information about the air quality modeling results). The 

decrease in population-weighted national average PM2.5 exposure results in a net decrease in 

adverse PM2.5-related human health and environmental impacts (the decrease in national 

population-weighted annual average PM2.5 is 0.003 μg/m3in 2017). The air quality modeling 

also projects decreases in ozone concentrations. The overall decrease in population-weighted 

national average ozone exposure results in decreases in ozone-related health and environmental

impacts (population-weighted maximum 8-hour average ozone decreases by 0.17 ppb in 2017).

Table 8-18 and Table 8-19 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the proposed Tier 3 program. For each endpoint 

presented in Table 8-18 and Table 8-19, we provide both the point estimate and the 90 percent

confidence interval. Using EPA’s preferred estimates, based on the American Cancer Society 

(ACS) and Six-Cities studies and no threshold assumption in the model of mortality, we estimate 

that the proposed standards would result in between 57 and 150 cases of avoided PM2.5-related 

premature mortalities annually in 2017. For ozone-related premature mortality in 2017, we 

estimate a range of between 49 to 230 fewer premature mortalities. 

Table 8-20 presents the estimated monetary value of changes in the incidence of ozone 

and PM2.5-related health and environmental effects. Total aggregate monetized benefits are 

presented in Table 8-21. All monetized estimates are presented in 2010$. Where appropriate,

estimates account for growth in real gross domestic product (GDP) per capita between 2000 and
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2017. The monetized value of PM2.5-related mortality also accounts for a twenty-year segmented 

cessation lag.V To discount the value of premature mortality that occurs at different points in the 

future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7 percent

discount rate to value PM2.5-related nonfatal heart attacks (myocardial infarctions).W 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the proposed program is thus 

equal to the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify

plus the sum of the nonmonetized health and welfare benefits. Our estimate of total monetized 

benefits associated with the proposed standards in 2017, using the ACS and Six-Cities PM 

mortality studies and the range of ozone mortality assumptions, is between $1.0 and $3.4 billion, 

assuming a 3 percent discount rate, or between $1.0 and $3.3 billion, assuming a 7 percent 

discount rate. Had our ambient air quality modeling of PM2.5 not included the increase in direct 

PM emissions, we estimate that benefits would increase by a range of $400 to $970 million 

(assuming a 3 percent discount rate) or increase by a range of $360 to $880 million (assuming a 

7 percent discount rate), using current EPA benefit-per-ton estimates for direct PM.X

The results in Table 8-21 demonstrate that the gasoline sulfur standards provide large 

immediate benefits in the program’s first year, related to emission reductions from existing 

gasoline vehicles. Accounting for the removal of the increase in direct PM emissions in the 2017 

air quality modeling, total monetized benefits increase even more, to between $1.4 and $4.3 

billion, assuming a 3 percent discount rate, or between $1.3 and $4.2 billion, assuming a 7 

percent discount rate. The benefits increase substantially after 2017, as the vehicle standards 

phase in after 2017 and as the fleet turns over.

 

V Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in 

pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, 

this term is often referred to as “cessation lag”. 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. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is 

consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science 

Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 

Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency 

Administrator, December.

W Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost earnings 

and direct medical costs over a 5-year period following a nonfatal MI. 

X The PM2.5-related benefit-per-ton estimates provide the total monetized human health benefits (the sum of 

premature mortality and premature morbidity) of reducing one ton of directly emitted PM2.5. The benefit per-ton 

technique has been used in previous analyses, including the 2012-2016 Light-Duty Greenhouse Gas Rule (U.S. 

EPA, 2010. Regulatory Impact Analysis, Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards. Office of Transportation and Air Quality. 

April. Available at http://www.epa.gov/otaq/climate/regulations/420r10009.pdf. EPA-420-R-10-009). The benefitsper-ton values are available at http://www.epa.gov/oaqps001/benmap/bpt.html. Note that the values on the website 

are presented in year 2006$; the values underlying the estimates here have been adjusted to 2010$ using the CPI-U 

“all” index.
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Table 8-18: Estimated PM2.5-Related Health Impactsa

Health Effect 2017 Annual Reduction in 

Incidence (5

th

- 95th percentile)

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

 Adult, age 25+, Six-Cities Study (Laden et al., 2006)

 Infant, age <1 year (Woodruff et al., 1997)

57

(9 – 110)

150

(54 – 240)

0

(0 – 1)

Chronic bronchitis (adult, age 26 and over) 38

(-7 – 84)

Non-fatal myocardial infarction (adult, age 18 and over) 64

(7 – 120)

Hospital admissions - respiratory (all ages)c10

(3 – 17)

Hospital admissions - cardiovascular (adults, age >18)d23

14 – 28)

Emergency room visits for asthma (age 18 years and younger) 40

(15 – 64)

Acute bronchitis, (children, age 8-12) 87

(-45 – 220)

Lower respiratory symptoms (children, age 7-14) 1,100

(270 – 1,900)

Upper respiratory symptoms (asthmatic children, age 9-18) 830

(0 – 1,700)

Asthma exacerbation (asthmatic children, age 6-18) 1,800

(-360 – 5,100)

Work loss days 7,300

(5,900 – 8,700)

Minor restricted activity days (adults age 18-65) 43,000

(33,000 – 53,000)

Notes:

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United 

States. 

b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and 

the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should 

not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997).Y

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), 

pneumonia and asthma.

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure.

 

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

Postneonatal Infant Mortality and Particulate Air Pollution in the United States.” Environmental Health 

Perspectives 105(6):608-612.
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Table 8-19: Estimated Ozone-Related Health Impactsa

Health Effect 2017 Annual Reduction in 

Incidence (5th- 95th

percentile)

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental

 Huang et al. (2005) – Cardiopulmonary

 Schwartz (2005) – Non-accidental

Meta-analyses:

 Bell et al. (2005) – All cause

 Ito et al. (2005) – Non-accidental

 Levy et al. (2005) – All cause

49

(21 – 77)

71

(33 – 110)

75

(31 – 120)

160

(88 – 230)

220

(140 – 290)

230

(160 – 290)

Hospital admissions- respiratory causes (adult, 65 and older)c 290

(37 – 520)

Hospital admissions -respiratory causes (children, under 2) 170

(85 – 250)

Emergency room visit for asthma (all ages) 170

(-3 – 441)

Minor restricted activity days (adults, age 18-65) 300,000

(150,000 – 440,000)

School absence days 98,000

(43,000 – 140,000)

Notes:

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative 

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). 

The estimates of ozone-related premature mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

Table 8-20: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (millions of 2010$) a,b

2017

PM2.5-Related Health Effect (5th and 95th Percentile)

Premature Mortality –

Derived from Epidemiology 

Studiesc,d

Adult, age 30+ - ACS study 

(Pope et al., 2002)

 3% discount rate

 7% discount rate

$490

($41 - $1,300)

$440

($37 - $1,200)

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006)

 3% discount rate

 7% discount rate

$1,300

($160 - $3,200)

$1,100

($140 - $2,900)
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8-37

Infant Mortality, <1 year –

(Woodruff et al. 1997)

$2.5

(-$3.3 - $11)

Chronic bronchitis (adults, 26 and over) $20

(-$2.6 - $70)

Non-fatal acute myocardial infarctions 

 3% discount rate

 7% discount rate

$7.4

($0.80 - $20)

$5.9

($0.60 - $15)

Hospital admissions for respiratory causes $0.16

($0.05 - $0.23)

Hospital admissions for cardiovascular causes $0.38

($0.01 - $0.74)

Emergency room visits for asthma $0.016

($0.006 - $0.026)

Acute bronchitis (children, age 8–12) $0.041

(-$0.021 - $0.12)

Lower respiratory symptoms (children, 7–14) $0.023

($0.005 - $0.047)

Upper respiratory symptoms (asthma, 9–11) $0.028

($0 - $0.070)

Asthma exacerbations $0.10

(-$0.02 - $0.30)

Work loss days $1.2

($0.96 - $1.4)

Minor restricted-activity days (MRADs) $2.9

($1.7 - $4.3)

Ozone-related Health Effect

Premature Mortality, All ages –

Derived from Multi-city analyses

Bell et al., 2004 $460

($60 - $1,200)

Huang et al., 2005 $670

($90 - $1,700)

Schwartz, 2005 $700

($91 - $1,800

Premature Mortality, All ages –

Derived from Meta-analyses

Bell et al., 2005 $1,500

($210 - $3,700)

Ito et al., 2005 $2,100

($300 - $4,900)

Levy et al., 2005 $2,100

($320 - $5,000)

Hospital admissions- respiratory causes (adult, 65 and older) $7.6

($0.97 - $14)

Hospital admissions- respiratory causes (children, under 2) $1.8

($0.93 - $2.7)

Emergency room visit for asthma (all ages) $0.07

($0 - $0.17)

Minor restricted activity days (adults, age 18-65) $20

($9.0 - $34)

School absence days $9.4

($4.1 - $13)

Notes:

a Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and 

ozone benefits are nationwide. 
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b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis 

year (2017).

c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses.

Table 8-21: Total Monetized Ozone and PM-related Benefits Associated with the Proposed

Program in 2017

Total Ozone and PM Benefits (billions, 2010$) –

PM Mortality Derived from the ACS and Six-Cities Studies

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$1.0 - $1.8 Multi-city Bell et al., 

2004

$0.96 - $1.7

Huang et al., 

2005

$1.2 - $1.9 Huang et al., 

2005

$1.2 - $1.9

Schwartz, 

2005

$1.3 - $2.0 Schwartz, 

2005

$1.2 - $1.9

Meta-analysis Bell et al., 

2005

$2.1 - $2.8 Meta-analysis Bell et al., 

2005

$2.0 - $2.7

Ito et al., 

2005

$2.6 - $3.4 Ito et al., 

2005

$2.6 - $3.3

Levy et al., 

2005

$2.7 - $3.4 Levy et al., 

2005

$2.6 - $3.3

8.2 Cost-Effectiveness

This section will present the cost-effectiveness analysis we completed for the proposed 

combined Tier 3 vehicle and fuel standards. This analysis relies in part on cost information from 

draft RIA Chapters 2 and 5 and emissions information from draft RIA Chapter 8 to estimate the 

dollars per ton ($/ton) of total NOX + NMOG emission reductions after the proposed Tier 3 

standards would have been fully implemented. We have calculated the cost-effectiveness on an 

aggregate basis to provide a comprehensive means for capturing the effectiveness of the 

proposed program on vehicles and fuels. This chapter also compares the cost-effectiveness of 

the proposed provisions with the cost-effectiveness of other NOX and NMOG control strategies 

from previous and potential future EPA emission control programs. 

8.2.1 Overview

We have calculated the aggregate cost-effectiveness which uses the costs and emission 

reductions for calendar years 2017 and 2030, consistent with the years that we evaluated for air 

quality and the cost-benefit analysis. All of our results are presented and discussed in Section 

8.2.5 below.
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8.2.2 Baselines

An average approach to cost-effectiveness requires that we compare the costs and 

emission reductions associated with the proposed standards to those for the previous set of 

standards that are being met by manufacturers. In this case, the $/ton values represent the full 

range of control from the last applicable standard to our proposed standards. 

Since today's program includes both proposed fuel standards and proposed vehicle 

standards, it was necessary for us to define a baseline for both fuels and vehicles from which to 

calculate reductions in emissions and increases in costs. For sulfur content of fuel and vehicle

emissions, the previous standards were set under the Tier 2 program. The baseline sulfur level in 

the fuel is therefore 30 ppm and the baseline vehicle exhaust standard is 0.07 g/mi NOX. The 

baseline vehicle evaporative standards are listed below in Table 8-22.

Table 8-22: Light-Duty Vehicle, Light-Duty Truck, and Medium-Duty Passenger Vehicle 

Evaporative Standardsa

Vehicle Type 3 Day Diurnal + Hot Soak 

(g/test)

2 Day Diurnal + Hot 

Soak (g/test)

LDV 0.50 0.65

LLDT 0.65 0.85

MDPV 1.00 1.25

a 72 Federal Register at 8471 (February 26, 2007)

8.2.3 Costs

Costs that would be incurred from our proposed program would be due to the proposed

Tier 3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of 

gasoline, as discussed above in Section 8.1.1. The sum of the vehicle technology costs to control 

exhaust and evaporative emissions, in addition to the costs to control the sulfur level in the fuel, 

are as shown in Table 8-23. All costs represent the fleet-weighted average of light-duty vehicles 

and trucks. All costs are represented in 2010 dollars.

Table 8-23: Total Annual Vehicle and Fuel Control Costs, 2010$

Total Vehicle and Fuel Control Costs

($Million)

2017 $2,078

2030 $3,595

8.2.4 Emission Reductions

In order to determine the overall cost-effectiveness of the standards we are proposing, it 

was necessary to calculate the tons of each pollutant reduced on an aggregate basis. Our

proposed standards are intended primarily to reduce emissions of NOX and NMOG. As a result, 

we have determined that the cost-effectiveness of our standards should be determined for both 

NOX and NMOG. It is true that our program does include new proposed standards for PM. 

However, as discussed in Chapter 2, we believe that the efforts manufacturers make to meet the 
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8-40

NOX+NMOG standards will also result in sufficient PM reductions to meet our proposed PM 

standards. Thus we estimate that manufacturers would incur no additional costs to comply with 

the Tier 3 PM standard and a cost-effectiveness analysis of the PM standards is therefore 

unnecessary.

8.2.4.1 NOX and NMOG

Several past rulemakings which produced reductions in both NOX and NMOG have taken 

an approach to cost-effectiveness that sums the NOX and NMOG emission reductions. This 

approach leads to $/ton NOX+NMOG. In addition, many standards for mobile sources have been 

established in terms of NOX+NMOG caps, including the previous Tier 2 vehicle standards. Thus 

we believe that this approach to cost-effectiveness is appropriate for our Tier 3 standards as well. 

The projected annual reductions in NOX and NMOG in 2017 and 2030 are included in 

Table 8-24.

Table 8-24: Annual NOX and VOC Reductions (tons) in 2017 and 2030

NOX Reductions 

(tons)

VOC Reductions (tons) Total NOX+VOC 

Reductions (tons

2017 284,381 44,782 329,162

2030 524,790 226,028 750,818

8.2.5 Results

The results of our cost-effectiveness analysis are provided in Table 8-25. Costs are 

provided above in Table 8-23. The tons reduced are from the values in Table 8-24 as the 

difference between our Tier 2 baseline at our baseline fuel sulfur level of 30 ppm, and our Tier 3 

standards at our fuel sulfur standard of 10 ppm. 

The costs of the proposed program would be higher immediately after it is implemented 

than they would be after several years, since both vehicle manufacturers and refiners can take 

advantage of decreasing capital and operating costs over time. In addition, the reductions in 

NOX and VOC emissions will become greater as a greater percentage of the fleet contains the 

technologies required to meet the proposed standards.

Table 8-25 Cost-Effectiveness of the Proposed Vehicle and Fuel Standards

Total Proposed 

Program Cost 

($million, 2010$)

Total NOX + VOC 

Reductions (tons)

Cost Effectiveness ($/ton)

2017 $2,078 329,162 $6,312

2030 $3,595 750,818 $4,788
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8-41

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to 2020. Office of Air and Radiation, Washington, DC. March. Available on the Internet at 

<http://www.epa.gov/air/sect812/feb11/fullreport.pdf>.

9 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing 

Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. July. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPA-COUNCIL08-001-unsigned.pdf>.

10 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 

11 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 1.0. GeoLytics, Inc. 

East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 September 2004]

12 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM. Woods & Poole 

Economics, Inc. Washington, D.C. EPA-HQ-OAR-2009-0472-0011

13 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related photochemical oxidants 

(second external review draft). Research Triangle Park, NC: National Center for Environmental Assessment; report 

no. EPA/600R-05/004aB-cB, 3v. Available: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 

2006] EPA-HQ-OAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, EPA-HQ-OAR-2009-0472-0101

14 U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter Volume II of II.

National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental 

Protection Agency, Research Triangle Park, NC EPA/600/P-99/002bF. EPA-HQ-OAR-2009-0472-0097
*** Working Comments on Draft Language under E.O. 13563 and 12866 Interagency Review. Subject 

to Further Policy Review.E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release 

During Review ***

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15 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with Particulate Matter, Ozone and 

Nitrogen Dioxide: Report on a WHO Working Group. World Health Organization. Bonn, Germany. 

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16 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Meta-analysis of time-series 

studies and panel studies of Particulate Matter (PM) and Ozone (O3): Report of a WHO task group. Copenhagen, 

Denmark: World Health Organization. 

17 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 U.S. urban communities, 1987-2000. JAMA, 2004. 

292(19): p. 2372-8. EPA-HQ-OAR-2009-0472-1662

18 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for summer ozone 

exposure and cardio-respiratory mortality. Environmetrics. 16: 547-562. EPA-HQ-OAR-2009-0472-0233

19 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control for temperature? 

Am. J. Respir. Crit. Care Med. 171: 627-631. EPA-HQ-OAR-2009-0472-1678

20 Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of time-series studies of ozone and mortality 

with comparison to the national morbidity, mortality, and air pollution study. Epidemiology. 16(4): p. 436-45. EPAHQ-OAR-2009-0472-0222

21 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between ozone and daily mortality: analysis and 

meta-analysis. Epidemiology. 16(4): p. 446-57. EPA-HQ-OAR-2009-0472-0231

22 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4): p. 458-68. EPA-HQ-OAR-2009-0472-0236

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

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141. EPA-HQ-OAR-2009-0472-0263

24 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and 

Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667-672. EPA-HQ-OAR-2009-

0472-1661

25 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM2.5 Exposure and Mortality. Peer Review Draft. Prepared for: Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. August. EPAHQ-OAR-2009-0472-0242

26 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental Health 

Perspectives. 105(6):608-612. EPA-HQ-OAR-2009-0472-0382

27 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. (1995). Estimated Long-Term Ambient 

Concentrations of PM(10) and Development of Respiratory Symptoms in a Nonsmoking Population. Archives of 

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28 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. (2001). Increased Particulate Air Pollution and the 

Triggering of Myocardial Infarction. Circulation. 103:2810-2815. EPA-HQ-OAR-2009-0472-0239

29 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory 

disease. Thorax. 50(5):531-538.

30 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, 

Minnesota. Arch Environ Health. 49(5):366-374. EPA-HQ-OAR-2009-0472-1673

31 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J Respir 

Crit Care Med. 150(3):648-655. EPA-HQ-OAR-2009-0472-1674

32 Moolgavkar SH, Luebeck EG, Anderson EL. (1997). Air pollution and hospital admissions for respiratory causes

in Minneapolis St. Paul and Birmingham. Epidemiology. 8(4):364-370. EPA-HQ-OAR-2009-0472-1673 
*** Working Comments on Draft Language under E.O. 13563 and 12866 Interagency Review. Subject 

to Further Policy Review.E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release 

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33 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). Association between 

ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. Am J Epidemiol. 

153(5):444-452. EPA-HQ-OAR-2009-0472-0223

34 Moolgavkar, S.H. (2003). “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles and Cook 

Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Boston, MA: 

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35 Ito, K. (2003). “Associations of Particulate Matter Components with Daily Mortality and Morbidity in Detroit, 

Michigan.” In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. Health Effects 

Institute, Boston, MA. EPA-HQ-OAR-2009-0472-1674

36 Moolgavkar, S.H. (2000). Air Pollution and Hospital Admissions for Diseases of the Circulatory System in 

Three U.S. Metropolitan Areas. Journal of the Air and Waste Management Association 50:1199-1206. EPA-HQOAR-2009-0472-1664

37 Sheppard, L. (2003). Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in Seattle, 

Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air Pollution and Health. Special Report. 

Boston, MA: Health Effects Institute. EPA-HQ-OAR-2009-0472-0318

38 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory emergency department 

visits. Epidemiology. Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472-1663

39 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution, weather, and respiratory emergency room visits 

in two northern New England cities: an ecological time-series study. Environ Res. Vol. 97 (3): 312-21. EPA-HQOAR-2009-0472-0246

40 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. (1999). An Association 

between Fine Particles and Asthma Emergency Department Visits for Children in Seattle. Environmental Health 

Perspectives 107(6):489-493. EPA-HQ-OAR-2009-0472-0318

41 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):500-505. EPA-HQ-OAR-2009-0472-0225

42 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. EPA-HQ-OAR2009-0472-1672

43 Schwartz, J., and L.M. Neas. (2000). Fine Particles are More Strongly Associated than Coarse Particles with 

Acute Respiratory Health Effects in Schoolchildren. Epidemiology 11:6-10. 

44 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. (2001). Air Pollution and Exacerbation of 

Asthma in African-American Children in Los Angeles. Epidemiology 12(2):200-208. 

45 Vedal, S., J. Petkau, R. White, and J. Blair. (1998). Acute Effects of Ambient Inhalable Particles in Asthmatic 

and Nonasthmatic Children. American Journal of Respiratory and Critical Care Medicine 157(4):1034-1043. EPAHQ-OAR-2009-0472-1671

46 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental 

Economics Management 14:87-98. EPA-HQ-OAR-2009-0472-1670

47 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). The effects of 

ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology 12(1):43-54. EPA-HQOAR-2009-0472-1675

48 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air pollution. Inhal 

Toxicol 12(11):997-1016. EPA-HQ-OAR-2009-0472-0224

49 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An Observational Study 

of Multiple Pollutants. Environmental Research 50:238-247. EPA-HQ-OAR-2009-0472-0364
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50 U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the Analytical Plan for 

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51 National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed Air Pollution 

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52 Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate Projections. Memorandum to 

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53 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from 

years 1996-1998), Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human 

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54 Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project.

55 American Lung Association. 1999. Chronic Bronchitis. Available on the Internet at 

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56 American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American Lung Association, 

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57 Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health Interview Survey, 

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58 U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race, Hispanic Origin and 

Nativity: 1999 to 2100. Population Projections Program, Population Division, U.S. Census Bureau, Washington, 

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59 National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, Indicator 42: Student 

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60 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic Research (CDC Wonder) (data from 

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61 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter 

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62 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. Consultation on 

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63 U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. Review of EPA’s 

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64 Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. 

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65 Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health and Safety Regulation. Journal of Urban 

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66 Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing Changes in Health Risks: A 

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67 U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient 

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68 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient 

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69 U.S. EPA, (2001), “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze”, 

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70 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

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71 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

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72 Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, 

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