Document ID: EPA-HQ-OAR-2011-0135-2840
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2013-05-17T04:00Z

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.
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.  
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.
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
                                     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
                                                                         $2,275
Fuel Costs
The fuel costs consist of the additional operating costs and capital costs to the refiners to meet the proposed sulfur average of 10 ppm.  The sulfur control costs, as described in detail in draft RIA Chapter 5, assume a cost of 0.89 cents per gallon which includes the refinery operating and capital costs.  The projected annual fuel consumption and annual fuel costs of the proposed program are listed in Table 8-2. 
Table 8-2: Annual Fuel Costs, 2010$

                                     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 
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.
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
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 secondarily-formed PM2.5), VOCs (a precursor to ozone formation and, to a lesser degree, secondarily-formed 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 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/m[3] in 2030).  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),[,] 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)[,] results 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 on the particular health impact functions used to characterize both PM- and ozone-related premature mortality.  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),[,] the 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)[,] results 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.
         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 PM-related 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).
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.[,]  Our benefits methods are also consistent with rulemaking analyses such as the final 2012-2016 MY Light-Duty Vehicle Rule,  the final Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA,, and the final 2017-2025 MY Light-Duty Vehicle Rule.  To 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).  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 ozone-related 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.  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 Benefits[a][,d]
2030 Total Ozone and PM Benefits  -  PM Mortality Derived from American Cancer Society Analysis and Six-Cities Analysis[a]
                      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

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: health[a]
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 impacts[c]
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: health[d]

 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)
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). 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.  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, 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."  EPA 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.  
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 Six-Cities 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.  As shown in Table 8-8, 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."  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.
Table 8-6:  Estimated PM2.5-Related Health Impacts[a]
                                 Health Effect
        2030 Annual Reduction in Incidence  (5[th] - 95[th] percentile)
 Premature Mortality  -  Derived from epidemiology literature[b]
   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)[c]
                                      160
                                  (77  -  230)
 Hospital admissions - cardiovascular (adults, age >18)[d] 
                                      380
                                 (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).
 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.

Table 8-7:  Estimated Ozone-Related Health Impacts[a]
                                 Health Effect
                       2030 Annual Reduction in Incidence
                          (5[th] - 95[th] percentile)
 Premature Mortality, All ages[b]
 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
Expert J
                                      390
                                     1,300
                                     2,900
Expert K
                                       0
                                      270
                                     1,300
Expert L
                                      65
                                     1,000
                                     2,300

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.  The monetized value of PM2.5-related mortality also accounts for a twenty-year segmented cessation lag.  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).  
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 $25 billion, assuming a 7 percent discount rate.  As the results indicate, total benefits are driven primarily by the reduction in PM2.5- and ozone-related premature fatalities each year and represent the benefits of the Tier 3 program anticipated to occur annually when the program is fully implemented and most of the fleet turned over.
The next largest benefit is for reductions in chronic illness (chronic bronchitis and nonfatal heart attacks), although this value is more than an order of magnitude lower than for premature mortality.  Hospital admissions for respiratory and cardiovascular causes, minor restricted activity days, and work loss days account for the majority of the remaining benefits.  The remaining categories each account for a small percentage of total benefit; however, they represent a large number of avoided incidences affecting many individuals.  A comparison of the incidence table to the monetary benefits table reveals that there is not always a close correspondence between the number of incidences avoided for a given endpoint and the monetary value associated with that endpoint.  For example, there are many more work loss days than PM-related premature mortalities, yet work loss days account for only a very small fraction of total monetized benefits.  This reflects the fact that many of the less severe health effects, while more common, are valued at a lower level than the more severe health effects.  Also, some effects, such as hospital admissions, are valued using a proxy measure of willingness-to-pay (e.g., cost-of-illness).  As such, the true value of these effects may be higher than that reported here. 
Table 8-9:  Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (millions of 2010$) a,b
 
                                      2030
 PM2.5-Related Health Effect
                         (5[th] and 95[th] 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)
 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.

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

Methodology
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.
A typical health impact function might look like:  
				,
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.  
Potentially Affected Populations
The starting point for estimating the size of potentially affected populations is the 2000 U.S. Census block level dataset.  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.
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 Documents[,] and the World Health Organization's 2003 and 2004[,] 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.
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)  -  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
All ages
  Premature mortality  -- cohort study, all-cause
PM2.5 
Pope et al. (2002)
Laden et al. (2006)
>29 years
>25 years
  Premature mortality, total exposures
PM2.5 
Expert Elicitation (IEc, 2006)
>24 years
  Premature mortality  --  all-cause
PM2.5 
Woodruff et al. (1997)
Infant (<1 year)
Chronic Illness
  Chronic bronchitis
PM2.5
Abbey et al. (1995)
>26 years
  Nonfatal heart attacks
PM2.5 
Peters et al. (2001)
Adults (>18 years)
Hospital Admissions 
  Respiratory

O3 
Pooled estimate:
Schwartz (1995) - ICD 460-519 (all resp)
Schwartz (1994a; 1994b) - ICD 480-486 (pneumonia)[,]
Moolgavkar et al. (1997) - ICD 480-487 (pneumonia)
Schwartz (1994b) - ICD 491-492, 494-496 (COPD)
Moolgavkar et al. (1997)  -  ICD 490-496 (COPD)
>64 years
  

Burnett et al. (2001)
<2 years
  
PM2.5 
Pooled estimate:
Moolgavkar (2003) -- ICD 490-496 (COPD)
Ito (2003) -- ICD 490-496 (COPD)
>64 years
  
PM2.5
Moolgavkar (2000) -- ICD 490-496 (COPD)
20 - 64 years
  
PM2.5
Ito (2003) -- ICD 480-486 (pneumonia)
>64 years
  
PM2.5 
Sheppard (2003) -- ICD 493 (asthma)
<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
  Asthma-related ER visits
O3 
Pooled estimate:
Peel et al (2005)
Wilson et al (2005)

All ages
All ages
  Asthma-related ER visits (cont'd)
PM2.5 
Norris et al. (1999)
0 - 18 years
Other Health Endpoints
  Acute bronchitis
PM2.5 
Dockery et al. (1996)
8 - 12 years
  Upper respiratory symptoms
PM2.5
Pope et al. (1991)
Asthmatics, 9 - 11 years
  Lower respiratory symptoms
PM2.5 
Schwartz and Neas (2000)
7 - 14 years
  Asthma exacerbations
PM2.5 
Pooled estimate:
Ostro et al. (2001) (cough, wheeze and shortness of breath)
Vedal et al. (1998) (cough)
6 - 18 years[a]
  Work loss days
PM2.5 
Ostro (1987)
18 - 65 years
  School absence days

O3 
Pooled estimate:
Gilliland et al. (2001)
Chen et al. (2000)

5 - 17 years[b]
  Minor Restricted Activity Days (MRADs)
O3
Ostro and Rothschild (1989)
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-SAB-COUNCIL-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.[,] 
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 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.
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)
U.S. Census bureau
Hospitalizations
Daily hospitalization rate
Age-, region-, state-, county- and cause- specific rate
2007 HCUP data files[a,]
Asthma ER Visits
Daily asthma ER visit rate
Age-, region-, state-, county- and cause- specific rate
2007 HCUP data files[a]
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) 

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 files[a]; 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)
Lower Respiratory Symptoms
Daily lower respiratory symptom incidence among children[b]
                                    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); U.S. Bureau of the Census (2000)
School Loss Days
Rate per person per year, assuming 180 school days per year
                                      9.9
National Center for Education Statistics (1996) and 1996 HIS (Adams et al., 1999, Table 47); 
Minor Restricted-Activity Days
Daily 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 Association[b]
  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).
PM2.5-Related Premature Mortality "Lowest Measured Level" (LML) Assessment
Based on our review of the current body of scientific literature, EPA estimated PM-related mortality without applying an assumed concentration threshold. EPA's Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009), which was reviewed by EPA's Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009; U.S. EPA-SAB, 2009),[,] 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 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 ug/m[3] (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 ug/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 ug/m[3], 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 ug/m[3] because 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.

Figure 8-1 Percentage of Total PM-related Mortalities Avoided by Baseline Air Quality Level
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).  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 ($100/0.0001 change in risk).
WTP estimates generally are not available for some health effects, such as hospital admissions.  In these cases, we used the cost of treating or mitigating the effect as a primary estimate.  These cost-of-illness (COI) estimates generally understate the true value of reducing the risk of a health effect, because they reflect the direct expenditures related to treatment, but not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).[,]  We provide unit values for health endpoints (along with information on the distribution of the unit value) in Table 8-14.  All values are in constant year 2010 dollars, adjusted for growth in real income out to 2030 using projections provided by Standard and Poor's.  Economic theory argues that WTP for most goods (such as environmental protection) will increase if real income increases.  Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s.  Because real income has grown since the studies were conducted, people's willingness to pay for reductions in the risk of premature death and disease likely has grown as well.  We did not adjust cost of illness-based values because they are based on current costs.  Similarly, we did not adjust the value of school absences, because that value is based on current wage rates.  For details on valuation estimates for PM-related endpoints, see the 2006 PM NAAQS RIA.  For details on valuation estimates for ozone-related endpoints, see the 2008 Ozone NAAQS RIA.
Table 8-14: Unit Values for Economic Valuation of Health Endpoints (2010$)
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 pollution-related 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). 
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$106,053
$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).
              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
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.,  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., 
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 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).  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
Statistic[a]
Baseline
Change[b]
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 (ug/m[3])
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.  
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 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)[,] 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).  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 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 multi-city 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;
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.
      
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 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.
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.
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/m[3] in 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 2017.  The monetized value of PM2.5-related mortality also accounts for a twenty-year segmented cessation lag.  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).  
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.
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.
Table 8-18:  Estimated PM2.5-Related Health Impacts[a]
                                 Health Effect
         2017 Annual Reduction in Incidence (5[th] - 95[th] percentile)
 Premature Mortality  -  Derived from epidemiology literature[b]
   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)[c]
                                       10
                                   (3  -  17)
 Hospital admissions - cardiovascular (adults, age >18)[d] 
                                       23
                                   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).
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.

Table 8-19:  Estimated Ozone-Related Health Impacts[a]
                                 Health Effect
        2017 Annual Reduction in Incidence  (5[th] - 95[th] percentile)
 Premature Mortality, All ages[b]
 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
                         (5[th] and 95[th] 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)
 
 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.  
      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

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.   
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.
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 $/ton values represent the full range of control from the last applicable standard to our proposed standards.  
Since today's program includes both proposed fuel standards and proposed vehicle standards, it was necessary for us to define a baseline for both fuels and vehicles from which to calculate reductions in emissions and increases in costs.  For sulfur content of fuel and vehicle emissions, the previous standards were set under the Tier 2 program.  The baseline sulfur level in the fuel is therefore 30 ppm and the baseline vehicle exhaust standard is 0.07 g/mi NOX.  The baseline vehicle evaporative standards are listed below in Table 8-22.
Table 8-22:  Light-Duty Vehicle, Light-Duty Truck, and Medium-Duty Passenger Vehicle Evaporative Standards[a]  
                                 Vehicle Type
                       3 Day Diurnal + Hot Soak (g/test)
                       2 Day Diurnal + Hot Soak (g/test)
                                      LDV
                                     0.50
                                     0.65
                                     LLDT 
                                     0.65
                                     0.85
                                     MDPV
                                     1.00
                                     1.25
a 72 Federal Register at 8471 (February 26, 2007)
Costs
Costs that would be incurred from our proposed program would be due to the proposed Tier 3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of gasoline, as discussed above in Section 8.1.1.  The sum of the vehicle technology costs to control exhaust and evaporative emissions, in addition to the costs to control the sulfur level in the fuel, are as shown in Table 8-23.  All costs represent the fleet-weighted average of light-duty vehicles and trucks.  All costs are represented in 2010 dollars.
Table 8-23: Total Annual Vehicle and Fuel Control Costs, 2010$

                     Total Vehicle and Fuel Control Costs
                                  ($Million)
2017
                                    $2,078

2030
                                    $3,595
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 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.
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
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

References