The Benefits and Costs of the 

Clean Air Act from 1990 to 2020 

Final Report 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

March 2011 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

ABSTRACT 

Section 812 of the 1990 Clean Air Act Amendments requires the U.S. Environmental 

Protection Agency to develop periodic reports that estimate the benefits and costs of the 

Clean Air Act. The main goal of these reports is to provide Congress and the public with 

comprehensive, up-to-date, peer-reviewed information on the Clean Air Act’s social 

benefits and costs, including improvements in human health, welfare, and ecological 

resources, as well as the impact of the Act’s provisions on the US economy. This report 

is the third in the Section 812 series, and is the result of EPA’s Second Prospective 

analysis of the 1990 Amendments. 

The Clean Air Act Amendments (CAAA) of 1990 augmented the significant progress made 

in improving the nation's air quality through the original Clean Air Act of 1970 and its 

1977 amendments. The amendments built off the existing structure of the original Clean 

Air Act, but went beyond those requirements to tighten and clarify implementation goals 

and timing, increase the stringency of some federal requirements, revamp the hazardous 

air pollutant regulatory program, refine and streamline permitting requirements, and 

introduce new programs for the control of acid rain and stratospheric ozone depleters. 

The main purpose of this report is to document the costs and benefits of the 1990 CAAA 

provisions incremental to those costs and benefits achieved from implementing the 

original 1970 Clean Air Act and the 1977 amendments. 

The analysis estimates the costs and benefits of reducing emissions of air pollutants by 

comparing a "with-CAAA" scenario that reflects expected or likely future measures 

implemented under the CAAA with a “without-CAAA” scenario that freezes the scope 

and stringency of emissions controls at the levels that existed prior to implementing the 

CAAA. There are six basic steps undertaken to complete this analysis: 1. air pollutant 

emissions modeling; 2. compliance cost estimation; 3. ambient air quality modeling; 4. 

health and environmental effects estimation; 5. economic valuation of these effects; and 

6. results aggregation and uncertainty characterization. 

The results of our analysis, summarized in the table below, make it abundantly clear that 

the benefits of the CAAA exceed its costs by a wide margin, making the CAAA a very 

good investment for the nation. We estimate that the annual dollar value of benefits of air 

quality improvements will be very large, and will grow over time as emissions control 

programs take their full effect, reaching a level of approximately $2.0 trillion in 2020. 

These benefits will be achieved as a result of CAAA-related programs and regulatory 

compliance actions estimated to cost approximately $65 billion in 2020. Most of these 

benefits (about 85 percent) are attributable to reductions in premature mortality 

associated with reductions in ambient particulate matter; as a result, we estimate that 

cleaner air will, by 2020, prevent 230,000 cases of premature mortality in that year. The 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

remaining benefits are roughly equally divided among three categories of human health 

and environmental improvement: preventing premature mortality associated with ozone 

exposure; preventing morbidity, including acute myocardial infarctions and chronic 

bronchitis; and improving the quality of ecological resources and other aspects of the 

environment, the largest component of which is improved visibility. 

The very wide margin between estimated benefits and costs, and the results of our 

uncertainty analysis, suggest that it is extremely unlikely that the monetized benefits of 

the CAAA over the 1990 to 2020 period reasonably could be less than its costs, under any 

alternative set of assumptions we can conceive. Our central benefits estimate exceeds 

costs by a factor of more than 30 to one, and the high benefits estimate exceeds costs by 

90 times. Even the low benefits estimate exceeds costs by about three to one. 

ESTIMATED MONETIZED BENEFITS AND COSTS OF THE 1990 CLEAN AIR ACT AMENDMENTS 

ANNUAL ESTIMATES 

PRESENT VALUE 

ESTIMATE 

2000 2010 2020 1990-2020 

Monetized Direct Compliance Costs (millions 2006$): 

Central a $20,000 $53,000 $65,000 $380,000 

Monetized Direct Benefits (millions 2006$): 

Lowb $90,000 $160,000 $250,000 $1,400,000 

Central $770,000 $1,300,000 $2,000,000 $12,000,000 

Highb $2,300,000 $3,800,000 $5,700,000 $35,000,000 

Net Benefits - Benefits minus Costs (millions 2006$): 

Low $70,000 $110,000 $190,000 $1,000,000 

Central $750,000 $1,200,000 $1,900,000 $12,000,000 

High $2,300,000 $3,700,000 $5,600,000 $35,000,000 

Benefit/Cost Ratio: 

Lowc 5/1 3/1 4/1 4/1 

Central 39/1 25/1 31/1 32/1 

Highc 115/1 72/1 88/1 92/1 

Compliance Costs per Premature Mortality Avoided (2006$): 

Central $180,000 $330,000 $280,000 Not estimated 

a The cost estimates for this analysis are based on assumptions about future changes in factors 

such as consumption patterns, input costs, and technological innovation, which introduce 

significant uncertainty. The degree of uncertainty associated with many of the key factors, 

however, cannot be reliably quantified. Thus, we are unable to present specific low and high 

cost estimates. 

b

 Low and high benefits estimates correspond to 5th and 95th percentile results from statistical 

uncertainty analysis, incorporating uncertainties in physical effects and valuation steps of 

benefits analysis. 

c The low benefit/cost ratio reflects the ratio of the low benefits estimate to the central cost 

estimate, while the high ratio reflects the ratio of the high benefits estimate to the central 

costs estimate. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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TABLE OF CONTENTS 

ACKNOWLEDGEMENTS 

CHAPTER 1 - INTRODUCTION 

Background and Purpose 1-1

Relationship of this Report to Other Analyses 1-2

Analytical Design and Review 1-5

Review Process 1-14 

Report Organization 1-14 

CHAPTER 2 - EMISSIONS 

Overview of Approach 2-3

Emissions Estimation Results 2-9

Comparison of Emissions Estimates with the First Prospective Analysis 2-14

Uncertainty in Emissions Estimates 2-16

CHAPTER 3 – DIRECT COSTS 

Overview of Approach 3-2

Direct Compliance Cost Results 3-7

Comparison of Cost Estimates with the First Prospective Analysis 3-9

Uncertainty in Direct Cost Estimates 3-11

CHAPTER 4 – AIR QUALITY BENEFITS 

Overview of Approach 4-1 

Air Quality Modeling Tools Deployed 4-3

Air Quality Results 4-13 

Uncertainty in Air Quality Estimates 4-22

CHAPTER 5 – ESTIMATION OF HUMAN HEALTH EFFECTS AND ECONOMIC 

BENEFITS 

Overview of Approach 5-2 

Health Effects Modeling Results 5-24

Avoided Health Effects of Air Toxics 5-28 

Comparison of Health Effects Modeling with First Prospective Analysis 5-34

Uncertainty in Health Benefits Estimates 5-36 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

CHAPTER 6 – ECOLOGICAL AND OTHER WELFARE BENEFITS 

Overview of Approach 6-1

Qualitative Characterization of Effects 6-3

Distribution of Air Pollutants in Sensitive Ecosystems of the United States 6-11

Quantified Results: National Estimates 6-17 

Uncertainty in Ecological and Other Welfare Benefits 6-42 

CHAPTER 7 – COMPARISON OF BENEFITS AND COSTS 

Aggregating Benefit Estimates 7-1

Annual Benefits Estimates 7-3 

Aggregate Monetized Benefits 7-6 

Comparison of Benefits and Costs 7-7 

Overview of Uncertainty Analyses 7-10 

Quantifying Model, Parameter, and Scenario Uncertainty 7-13 

Lessons Learned and New Research Directions 7-16

CHAPTER 8 – COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS 

EMPAX-CGE 8-2

Development of Model Inputs 8-9

EMPAX-CGE Model Results 8-17

Analytic Limitations 8-23

REFERENCES 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

iii

LIST OF ACRONYMS 

ACS American Cancer Society 

AEO Annual Energy Outlook (from the US Department of Energy) 

AERMOD American Meteorological Society/Regulatory Model 

AIM Architectural and Industrial Maintenance 

AMI Acute myocardial infarction 

APEEP Air Pollution Emissions Experiments and Policy analysis model 

AQMS Air Quality Modeling Subcommittee (of the Council) 

AMET Atmospheric Model Evaluation Tool 

ANC Acid Neutralizing Capacity 

BenMAP Environmental Benefits Mapping and Analysis Program 

CAA Clean Air Act of 1970 

CAAA Clean Air Act Amendments of 1990 

CAIR Clean Air Interstate Rule 

CAMR Clean Air Mercury Rule 

CARB California Air Resources Board 

CAVR Clean Air Visibility Rule 

CDC Centers for Disease Control 

CGE Computable General Equilibrium 

CMAQ Community Multi-scale Air Quality [System] 

CO Carbon monoxide 

COI Cost of illness 

CONUS Continental United States (domain in CMAQ model) 

Council Advisory Council on Clean Air Compliance Analysis 

C-R Concentration-Response 

CTG Control Techniques Guideline 

CV Contingent valuation 

DDT Dichlorodiphenyl-trichloroethane 

DOE United States Department of Energy 

EC Elemental carbon 

EE Expert elicitation 

EES Ecological Effects Subcommittee (of the Council) 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

iv

EGU Electric Generating Unit 

EMPAX-CGE Economic Model for Policy Analysis – Computable General Equilibrium 

EPA United States Environmental Protection Agency 

EUS Eastern United States (domain in CMAQ model) 

EV [Hicksian] equivalent variation 

eVNA Enhanced Voronoi Neighbor Averaging 

FACA Federal Advisory Committee Act 

FASOM Forest and Agriculture Sector Optimization Model 

FRM Federal Reference Method 

GDP Gross Domestic Product 

GHG Greenhouse gas 

HAP Hazardous Air Pollutant 

HAPEM6 Hazardous Air Pollution Exposure Model, Version 6 

HDDV Heavy-Duty Diesel Vehicle 

HES Health Effects Subcommittee (of the Council) 

I&M Inspection and maintenance 

IC/BC Initial and boundary conditions 

IMPROVE Interagency Monitoring of Protected Visual Environments 

IPM Integrated Planning Model 

LEV Low-Emission Vehicle 

LML Lowest measured level 

MACT Maximum Available Control Technology 

MAGIC Model of Acidification of Groundwater in Catchments 

MATS Modeled Attainment Test Software 

MCIP Meteorology-Chemistry Interface Processor 

MM5 Fifth Generation Mesoscale Model 

MSA Metropolitan statistical area 

NAA Non-Attainment Area 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industry Classification System 

NAPAP National Acid Precipitation Assessment Program 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

v

NEI National Emissions Inventory 

NEMS National Energy Modeling System 

NESHAP National Emission Standard for Hazardous Air Pollutants 

NH3 Ammonia 

NH4 Ammonium 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO3 Nitrate 

NOx Nitrogen oxides 

NPV Net present value 

NSPS New Source Performance Standard 

O&M Operation and maintenance 

OC Organic carbon 

OTC Ozone Transport Commission 

Pb Lead 

PCB Polychlorinated biphenyl 

PM Particulate matter 

PM2.5 Particulate matter with an aerodynamic diameter less than 2.5 microns 

PM10 Particulate matter with an aerodynamic diameter less than 10 microns 

PPB Parts per billion 

PRB Powder River Basin 

PSU/NCAR Pennsylvania State University/National Center for Atmospheric Research 

RACT Reasonably Available Control Technology 

RADM/RPM Regional Acid Deposition Model/Regional Particulate Model 

REMSAD Regulatory Modeling System for Aerosols and Acid Deposition 

RfC Reference concentration 

RFP Rate of Further Progress 

RIA Regulatory Impact Analysis 

RSM Response Surface Model 

RUM Random Utility Model 

SAB Science Advisory Board 

SANDWICH Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous mass, 

and estimated aerosol acidity (H+)) process 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

vi

SCAQMD South Coast Air Quality Management District 

SIP State Implementation Plan 

SMAT Speciated Modeled Attainment Test 

SMOKE Sparse-Matrix Operator Kernel Emissions 

SO2 Sulfur dioxide 

SOx Sulfur oxides 

SOA Secondary organic aerosol 

STN Speciation Trends Network 

SUV Sport-Utility Vehicle 

TAC Total Annualized Cost 

TSP Total Suspended Particulates 

UVb or UVB Ultraviolet B radiation 

VMT Vehicle miles traveled 

VNA Voronoi Neighbor Averaging 

VOC Volatile organic compound 

VSL Value of statistical life 

WTAC Willingness-to-accept-compensation 

WTP Willingness-to-pay 

WUS Western United States (domain in CMAQ model) 

gm-3 or g/m3

 Micrograms per cubic meter (unit for PM2.5 measurement) 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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ACKNOWLEDGEMENTS 

The Project Team for the Second Prospective Study was comprised of EPA staff, and 

staff from a number of organizations working under contract to EPA. The project 

manager was Jim DeMocker, Senior Policy Analyst, EPA Office of Air and Radiation. 

Under EPA direction, Project Team members designed and implemented the study, and 

authored the study’s full report, summary report, and supporting technical reports and 

technical memoranda. In particular, the full report and summary report of the overall 

Second Prospective Study were authored by Jim DeMocker of EPA and Jim Neumann of 

Industrial Economics, Incorporated. Major contributions to the main reports and/or key 

supporting reports were made by Rob Brenner and Jeneva Craig of EPA; Henry Roman, 

Jason Price, Maura Flight, Tyra Walsh, Lindsay Ludwig, and Nadav Tanners of Industrial 

Economics, Incorporated; Leland Deck of Stratus Consulting; Jim Wilson and Frank 

Divita of E.H. Pechan and Associates; Sharon Douglas and Boddu Venkatesh of ICF 

International; Neil Wheeler of Sonoma Technologies; and Brooks Depro and Robert 

Beach of RTI International. 

Many current and former EPA and contractor staff also made helpful contributions to the 

development and/or review of the study. Those who made particularly significant 

contributions included EPA staff Bryan Hubbell, Neal Fann, Amy Lamson, Lisa Conner, 

Charles Fulcher, Rich Cook, Joe Touma, Chad Bailey, Ted Palma, Norm Possiel, Brian 

Timin, Marc Houyoux, Larry Sorrels, Ken Davidson, and Jason Lynch; and contractor 

staff Andrew Bollman, Maureen Mullen and Kirstin Thesing of E.H. Pechan and 

Associates; Belle Hudischewskyj, Tom Myers, Yi Hua Wei, and Jay Haney of ICF 

International; and Martin Ross and Lauren Davis of RTI International. 

During all phases of the study, from initial design to final report drafting, the Project 

Team and the Second Prospective Study benefitted immensely from the thoughtful, 

rigorous, and expert advice of the Advisory Council on Clean Air Compliance Analysis 

(Council) and its technical subcommittees. The Council was organized under the 

auspices of EPA’s Science Advisory Board, which provided staff support supervised by 

Vanessa Vu, Director of the SAB Staff Office. The Designated Federal Official for the 

final Council reviews was Stephanie Sanzone of the SAB Staff Office. Other SAB Staff 

Office personnel who assisted in the coordination of Council reviews included Holly 

Stallworth, Marc Rigas, Ellen Rubin, Angela Nugent, and Anthony Maciorowski. 

The Council panel providing final review of the study was chaired by Professor James K. 

Hammitt of Harvard University. Council members serving during the final review of this 

report include John Bailar (Chair of the Health Effects Subcommittee), Michelle Bell, 

Sylvia Brandt, Linda Bui, Dallas Burtraw, Ivan J. Fernandez (Chair of the Ecological 

Effects Subcommittee), Shelby Gerking, Wayne Gray, D. Alan Hansen, Nathaniel 

Keohane, Jonathan Levy, Richard L. Poirot, Arden Pope, Armistead (Ted) Russell (Chair 

of the Air Quality Modeling Subcommittee), and Michael Walsh. 

In addition to the Chairs listed above, members of the technical subcommittees serving 

during the final review of this report included David T. Allen, David Chock, Paulette 

Middleton, Ralph Morris, James Price, and Chris Walcek; Elizabeth Boyer, Charles T. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

viii

Driscoll, Jr., Christine Goodale, Keith G. Harrison, Allan Legge, Stephen Polasky, and 

Ralph Stahl, Jr.; John Fintan Hurley, Patrick Kinney, Michael T. Kleinman, Bart Ostro, 

and Rebecca Parkin. 

In addition, valuable advice and ideas in the early stages of project design and 

implementation, as well as review of interim products of the study, were provided by 

former Council members: Trudy Ann Cameron (former Council Chair), Maureen Cropper 

(former Council Chair), Lauraine Chestnut, Lawrence Goulder, F. Reed Johnson, 

Katherine Kiel, Charles Kolstad, Nino Kuenzli, Lester B. Lave, Virginia McConnell, 

David Popp, and V. Kerry Smith. Former subcommittee members include: Mark Castro, 

Harvey E. Jeffries, Morton Lippmann, and Scott Ollinger. The Council also consulted 

with a number of invited experts and past panel members, including Aaron Cohen, John 

Evans, Christopher Frey, Dale Hattis, D. Warner North, Thomas S. Wallsten, and Ronald 

Wyzga. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-1

CHAPTER 1 - INTRODUCTION 

BACKGROUND AND PURPOSE

Section 812 of the 1990 Clean Air Act Amendments established a requirement that EPA 

develop periodic reports that estimate the benefits and costs of the Clean Air Act (CAA). 

The main goal of these reports is to provide Congress and the public with comprehensive, 

up-to-date, peer-reviewed information on the Clean Air Act’s social benefits and costs, 

including improvements in human health, welfare, and ecological resources, as well as 

the impact of CAA provisions on the US economy. This report is the third in the Section 

812 series, and is the result of EPA’s Second Prospective analysis of the 1990 

Amendments. 

The first report EPA created under this authority, The Benefits and Costs of the Clean Air 

Act: 1970 to 1990, was published and conveyed to Congress in October 1997. This 

Retrospective analysis comprehensively assessed benefits and costs of requirements of 

the 1970 Clean Air Act and the 1977 Amendments, up to the passage of the Clean Air 

Act Amendments of 1990. The results of the Retrospective analysis showed that the 

nation's investment in clean air was more than justified by the substantial benefits that 

were gained in the form of increased health, environmental quality, and productivity. The 

aggregate benefits of the CAA during the 1970 to 1990 period exceeded costs by a factor 

of 10 to 100. 

A second Section 812 report, The Benefits and Costs of the Clean Air Act: 1990 to 2010, 

was completed in November of 1999 and addressed the incremental costs and benefits of 

the Clean Air Act Amendments (CAAA) enacted by Congress and signed by the 

President in November of 1990. This First Prospective analysis addressed 

implementation of the CAAA over the period 1990 to 2010, and found that aggregate 

benefits of the Amendments alone, excluding provisions in place prior to 1990, exceeded 

the costs by a factor of four. 

Similar to these prior analyses, this document has one primary and several secondary 

objectives. The main goal is to provide Congress and the public with comprehensive, upto-date, peer-reviewed information on the CAAA's social costs and benefits, including 

health, welfare, and ecological benefits. Data and methods derived from the 

Retrospective and First Prospective analysis have already been used to assist policymakers in refining clean air regulations over the last several years, and we hope the 

information continues to prove useful to Congress during future Clean Air Act 

reauthorizations. Beyond the statutory goals of Section 812, EPA also intends to use the 

results of this study to help support decisions on future investments in air pollution 

research. In addition, lessons learned in conducting this analysis will help better target 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-2

efforts to improve the accuracy and usefulness of future prospective analyses, generated 

either as part of this series or as part of EPA’s ongoing responsibility to estimate benefits 

and costs of major rulemakings. 

RELATIONSHIP OF THIS REPORT TO OTHER ANALYSES 

The Clean Air Act Amendments of 1990 augmented the significant progress made in 

improving the nation's air quality through the original Clean Air Act of 1970 and its 1977 

amendments. The amendments built off the existing structure of the original Clean Air 

Act, but went beyond those requirements to tighten and clarify implementation goals and 

timing, increase the stringency of some federal requirements, revamp the hazardous air 

pollutant regulatory program, refine and streamline permitting requirements, and 

introduce new programs for the control of acid rain and stratospheric ozone depleters. 

Because the 1990 Amendments represented an additional improvement to the nation's 

existing clean air program, the analysis summarized in this report was designed to 

estimate the costs and benefits of the 1990 CAAA incremental to those costs and benefits 

assessed in the Retrospective analysis. In economic terminology, this report addresses 

the marginal costs and benefits of the 1990 CAAA. Figure 1-1 below outlines this 

relationship among the section 812 Retrospective, the First Prospective, and the Second 

Prospective. 

As illustrated in Figure 1-1, this report effectively updates and augments the First 

Prospective. This report addresses essentially the same scenario and target variables as 

the First Prospective, but incorporates a number of significant enhancements. First, this 

report extends the time period of analysis an additional ten years relative to the First 

Prospective, covering the period from the signing of the amendments in 1990 through 

2020. Second, this report reflects updated cost and emissions estimation methods, 

including use of a new model suited to nonroad engine regulation and incorporation of 

the effects of learning-by-doing on projections of direct costs. Third, this report 

incorporates new information on the benefits of air pollutant regulation, including use of 

an integrated national-scale air quality model, more comprehensive characterization of 

ecological benefits, and an air toxics case study. Fourth, the report reflects investments in 

more comprehensive uncertainty analysis, including quantitative analyses where feasible. 

Finally, this report incorporates a sophisticated economy-wide model to estimate effects 

of the CAAA on such measures as GDP, prices, and consumer welfare. The 

Retrospective analysis employed a similar model for assessing the direct costs of 

compliance, but for the first time in this study the Agency has explored the economywide implications of both the direct costs and the health benefits of the CAAA on 

economic productivity, providing a much more complete picture of the full implications 

of CAAA regulations. 

The scope of this analysis is to estimate the costs and benefits of reducing emissions of 

criteria pollutants under two scenarios, depicted in schematic form in Figure 1-1 below: 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-3

FIGURE 1-1. CLEAN AIR ACT SECTION 812 SCENARIOS: CONCEPTUAL SCHEMATIC 

1. An historical, "with-CAAA" scenario control case that reflects expected or likely 

future measures implemented since 1990 to comply with rules promulgated 

through September 20051

; and 

2. A counterfactual “without CAAA” scenario baseline case that freezes the scope 

and stringency of emissions controls at their 1990 levels, while allowing for 

changes in population and economic activity and, therefore, in emissions 

attributable to economic and population growth. 

The Second Prospective analysis required locking in a set of emissions reductions to be 

used in subsequent analyses at a relatively early date (late 2005), and as a result we were 

compelled to forecast the implementation outcome of several pending programs. The 

most important of these was the then-promulgated Clean Air Interstate Rule (CAIR), 

which took major steps to further reduce SOx and NOx emissions from electric 

generating units. The rule has subsequently been vacated, and then remanded; EPA is 

currently considering a proposed rule to modify areas identified by the court as 

 

1

 The lone exception is the Coke Ovens Residual Risk rulemaking, promulgated under Title III of the Act in March 2005. We 

omitted this rule because it has a very small impact on criteria pollutant emissions (less than 10 tons per year VOCs) 

relative to the overall impact of the CAAA. The primary MACT rule for coke oven emissions, however, involves much larger 

reductions and therefore is included in the with-CAAA scenario. 

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA

1970 1990 2000 2010 2020

A

B

Pre-CAA

Post-CAA

Without-CAAA

Time

Emissions

Retrospective First Prospective

Second Prospective

C

With-CAAA
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-4

problematic. As a result, the emissions forecasts for electric generating units 

incorporated in the with-CAAA scenario may not reflect the controls that are ultimately 

implemented in a modified program. We acknowledge and discuss these types of 

discrepancies and their impact on the outcome of our analysis in the document. 

In addition, despite our efforts to comprehensively evaluate the costs and benefits of all 

provisions of the Clean Air Act and its Amendments, there remain a few categories of 

effects that are not addressed by the Retrospective or either prospective analysis. For 

example, this Second Prospective analysis does not assess the effect of CAAA provisions 

on lead exposures, primarily because the 1990 Amendments did not include major new 

provisions for the control of lead emissions until the NAAQS for lead was recently 

revisited and made significantly more stringent; the NAAQS revision was finalized after 

our emissions inventory development had been completed, too late for inclusion in our 

analysis. In addition, persistent data and model limitations preclude a full quantitative 

treatment of some costs and many benefits of other clean air programs. Therefore, while 

we considered all potentially relevant effects of the Clean Air Act and related programs, 

the quantitative results we present are not fully comprehensive, even for programs 

included in our assessment. Other, more modest omissions are acknowledged in the 

supporting documentation for this effort.2

REQUIREMENTS OF THE 1990 CLEAN AIR ACT AMENDMENTS 

This Second Prospective analysis, within the limitations discussed above, presents a 

comprehensive estimate of costs and benefits of the key regulatory titles of the 1990 

Clean Air Act Amendments. The 1990 Amendments consist of the following eleven 

titles: 

Title I. Establishes a detailed and graduated program for the attainment and maintenance 

of the National Ambient Air Quality Standards. 

Title II. Regulates mobile sources and establishes requirements for reformulated gasoline 

and clean fuel vehicles. 

Title III. Expands and modifies regulations of hazardous air pollutant emissions; and 

establishes a list of 189 hazardous air pollutants to be regulated. 

Title IV. Establishes control programs for reducing acid rain precursors. 

Title V. Requires a new permitting system for primary sources of air pollution. 

Title VI. Limits emissions of chemicals that deplete stratospheric ozone. 

Title VII. Presents new provisions for enforcement. 

Titles VIII through XI. Establish miscellaneous provisions for issues such as 

disadvantaged business concerns, research, training, new regulation of outer continental 

 

2

 See www.epa.gov/oar/sect812 for a complete list and opportunity to download supporting documentation for this Second 

Prospective analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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shelf sources, and assistance for people whose employment opportunities shift as a result 

of the Clean Air Act Amendments. 

As part of the requirements under Title VIII, section 812 of the Clean Air Act 

Amendments of 1990 established a requirement that EPA analyze the costs and benefits 

to human health and the environment that are attributable to the Clean Air Act. In 

addition, section 812 directed EPA to measure the effects of this statute on economic 

growth, employment, productivity, cost of living, and the overall economy of the United 

States. 

This analysis does not provide updated information on the costs and benefits of CAAA 

Title V regulations, which were thoroughly assessed in the First Prospective. Although 

Title V is believed to have yielded benefits in the efficiency of air permitting, those 

benefits are largely unquantified – as a result, the main effect of including Title V in the 

First Prospective was to increase the cost estimate by about $300 million. Similarly, we 

omit further consideration of Title VI regulation of the emissions of stratospheric ozone 

depleting substances, which was also assessed in the First Prospective. Although 

regulations under Title VI are continually updated and refined, the major components of 

Title VI were in place prior to the First Prospective and were thoroughly analyzed as part 

of that effort, resulting in the finding that the benefits of Title VI vastly exceeded its cost. 

As a result, EPA chose to focus resources in the Second Prospective on other areas and 

refinements. Because Titles V and VI have been previously assessed, and because Titles 

VII through XI are largely procedural and have mostly modest effects on air pollutant 

emissions and costs, this Second Prospective analysis is focused on the major emissions 

regulatory programs of the CAAA, which make up Titles I through IV of the statutory 

language.3

ANALYTICAL DESIGN AND REVIEW

TARGET VARIABLE

The Second Prospective analysis compares the overall health, welfare, ecological and 

economic benefits of the 1990 Clean Air Act Amendment programs to the costs of these 

programs. By examining the overall effects of the Clean Air Act, this analysis 

complements the Regulatory Impact Analyses (RIAs) developed by EPA over the years 

to evaluate individual regulations. We relied on information about the costs and benefits 

of specific rules provided by these RIAs, as well as other EPA analyses, in order to use 

resources efficiently. For this analysis, although costs can be reliably attributed to 

individual programs, the broad-scale approach adopted in this prospective study largely 

precludes reliable re-estimation of the benefits on a per-standard or per-program level. 

Similar to the Retrospective and First Prospective benefits analysis, this study calculates 

 

3

 Note that some elements of Title VII enforcement efforts, such as settlements for historical violations of CAA provisions, 

particularly in the electric utility and petroleum refining sectors, are included in the emissions inventories of the with-CAAA

scenario. For more information, see EPA’s detailed emissions report supporting this study at www.epa.gov/oar/sect812 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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the change in incidences of adverse effects implied by changes in ambient concentrations 

of air pollutants. However, pollutant emissions reductions achieved contribute to changes 

in ambient concentrations of those, or secondarily formed, pollutants in ways that are 

highly complex, interactive, and often nonlinear. Although it would be possible to design 

specific scenarios that focused analyses only on a subset of regulations (for example, all 

of Title IV), those policy scenarios are not realistic. For example, exclusion of major 

components of the Federal rules required under the CAAA would then trigger a much 

greater need for reductions at the local level, in order to achieve NAAQS standards which 

apply at the metropolitan area scale. Further, emissions reductions achieved by the 

provisions of each Title, or more broadly by regulations across the CAAA provisions that 

apply to a specific category of emitting sources, interact with other regulations to affect 

the benefits implications of any emissions reduction. Therefore, benefits cannot be 

reliably isolated or matched to provision-specific changes in emissions or costs. 

Focusing on the broader target variables of overall costs and overall benefits of the Clean 

Air Act, the EPA Project Team adopted an approach based on construction and 

comparison of two distinct scenarios, briefly mentioned above: a “without-CAAA” and a 

“with-CAAA" scenario. The without-CAAA scenario essentially freezes federal, state, and 

local air pollution controls at the levels of stringency and effectiveness which prevailed in 

1990. The with-CAAA scenario assumes that all federal, state, and local rules promulgated 

pursuant to, or in support of, the 1990 CAAA were implemented. This analysis then 

estimates the differences between the economic and environmental outcomes associated 

with these two scenarios. For more information on the specific construction of the 

scenarios and their relationship to historical trends, see Chapter 2 of this document. 

KEY ASSUMPTIONS

Similar to the Retrospective and First Prospective analyses, we made two key 

assumptions during the scenario design process to avoid miring the analytical process in 

endless speculation. First, as stated above, we froze air pollution controls at 1990 levels 

throughout the “without-CAAA” scenario. Second, we assumed that the geographic 

distributions of population and economic activity remain the same between the two 

scenarios, although these distributions could be expected to change over time under both 

scenarios in response to differences across scenarios in income and air quality. 

The first assumption is an obvious simplification. In the absence of the 1990 CAAA, one 

would expect to see some air pollution abatement activity, either voluntary or due to state 

or local regulation. It is conceivable that state and local regulation would have required 

air pollution abatement equal to – or even greater than – that required by the 1990 

CAAA, particularly since some states, most notably California, have in the past done so. 

If one were to assume that state and local regulations would have been equivalent to 1990 

CAAA standards, then a cost-benefit analysis of the 1990 CAAA would be a meaningless 

exercise since both costs and benefits would equal zero. Any attempt to predict how 

states’ and localities’ regulations would have differed from the 1990 CAAA would be too 

speculative to support the credibility of the ensuing analysis. Instead, the without-CAAA

scenario has been structured to reflect the assumption that states and localities would not 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-7

have invested further in air pollution control programs after 1990 in the absence of the 

federal CAAA. Thus, this analysis accounts for all costs and benefits of air pollution 

control from 1990 to 2020 and does not speculate about the fraction of costs and benefits 

attributable exclusively to the federal CAAA. Nevertheless, it is important to note that 

state and local governments and private initiatives are responsible for a significant portion 

of these total costs and total benefits. In the end, the benefits of air pollution controls 

result from partnerships among all levels of government and with the active participation 

and cooperation of private entities and individuals. 

The second assumption concerns changing demographic patterns in response to air 

pollution. In the hypothetical without-CAAA scenario, air quality is worse than the actual 

1990 conditions and the projected air quality in the with-CAAA scenario. It is possible 

that under the without-CAAA scenario more people, relative to the with-CAAA case, 

would move away from the most heavily polluted areas. Rather than speculate on the 

scale of population movement, the analysis assumes no differences in demographic 

patterns between the two scenarios. Similarly, the analysis assumes no differences 

between the two scenarios with respect to the level or spatial pattern of overall economic 

activity. Both scenarios do, however, reflect recent Census Bureau projections of 

population growth and the distribution of population across the country. 

ANALYTIC SEQUENCE

The analysis comprises a sequence of six basic steps, summarized below and described in 

detail later in this report. These six steps, listed in order of completion, are: 

1. emissions modeling 

2. direct cost estimation 

3. air quality modeling 

4. health and environmental effects estimation 

5. economic valuation 

6. results aggregation and uncertainty characterization 

Figure 1-2 summarizes the analytical sequence used to develop the prospective results; 

we describe the analytic process in greater detail below. 

The first step of the analysis is the estimation of the effect of the 1990 CAAA on 

emissions sources. We generated emissions estimates through a three step process: (1) 

construction of an emissions inventory for the base year (1990); (2) projection of 

emissions for the without-CAAA case for three target years -- 2000, 2010, and 2020 -- 

assuming a freeze on emissions control regulation at 1990 levels and continued economic 

progress, consistent with sector-specific Department of Energy Annual Energy Outlook 

economic activity projections; and (3) construction of with-CAAA estimates for the same 

three target years, using the same set of economic activity projections used in the withoutCAAA case but with regulatory stringency, scope, and timing consistent with EPA's 

CAAA implementation plan (as of late 2005). The analysis reflects application of utility 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-8

and other sector-specific emissions models developed and used in various offices of 

EPA's Office of Air and Radiation. These emissions models provide estimates of 

emissions of five criteria air pollutants2

 from each of several key emitting sectors. We 

provide more details in Chapter 2. 

FIGURE 1-2. ANALYTIC SEQUENCE FOR THE SECOND PROSPECTIVE ANALYSIS 

The emissions modeling step is a critical component of the analysis, because it establishes 

consistency between the subsequent cost and benefit estimates that we develop. 

Estimates of direct compliance costs to achieve the emissions reductions estimated in the 

first step are generated as either an integral or subsequent output from the emissions 

estimation models, depending on the model used. For example, the Integrated Planning 

Model used to analyze the utility sector reflects a financially optimal allocation of 

reductions of sulfur and nitrogen oxides – taking into account the regulatory flexibility 

 

2

 The five pollutants are particulate matter (separate estimates for each of PM10 and PM 2.5), sulfur dioxide (SO2), nitrogen 

oxides (NOx), carbon monoxide (CO), and volatile organic compounds (VOCs). One of the CAA criteria pollutants, ozone 

(O3), is formed in the atmosphere through the interaction of sunlight and ozone precursor pollutants such as NOx and VOCs. 

We also develop estimates for ammonia (NH3) emissions. Ammonia is not a criteria pollutant, but is an important input to 

the air quality modeling step because it affects secondary particulate formation. A sixth criteria pollutant, lead (Pb), is not 

included in this analysis since airborne emissions of lead were mostly eliminated by pre-1990 Clean Air Act programs – the 

recent tightening of the Pb NAAQS, necessitated by an enhanced understanding of the effects of even small exposures to 

airborne lead, was finalized too late to include in our scenarios. However, available estimates of the benefits and costs of 

the updated Pb NAAQS could be viewed as approximately additive to the results presented here. 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Supplemental Analyses:

Air Toxic case study

Ecological lit review

Ecological case study

Uncertainty Analyses

Macroeconomic 

modeling

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Supplemental Analyses:

Air Toxic case study

Ecological lit review

Ecological case study

Uncertainty Analyses

Macroeconomic 

modeling
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-9

inherent in the Title IV trading programs – thereby estimating emissions reductions and 

compliance costs simultaneously. Direct costs are addressed in Chapter 3. 

Emissions estimates also form the first step in estimating benefits. After the emissions 

inventories are developed, they are translated into estimates of air quality conditions 

under each scenario. For secondary particulate matter, ozone, and other air quality 

conditions that involve substantial non-linear formation processes and/or long-range 

atmospheric transport and transformation, the EPA Project Team employed EPA’s 

Community Multi-scale Air Quality (CMAQ) system. This modeling system, for the first 

time in the series of Section 812 studies, provides a fully national, integrated analysis of 

multiple emissions and their interactions. The result is a consistent estimate of air quality 

for both primary and secondarily formed pollutants, as well as deposition and visibility 

outcomes that represent the core of the subsequent benefit analyses. Air quality modeling 

is covered in Chapter 4. 

Up to this point of the analysis, modeled conditions and outcomes establish the withoutCAAA and with-CAAA scenarios. However, at the air quality modeling step, the analysis 

returns to a foundation based on actual historical conditions and data, providing a form of 

“ground-truthing” of the results. Specifically, actual 2000 historical air quality 

monitoring data are used to define the baseline conditions from which the without-CAAA

and with-CAAA scenario air quality projections are constructed. We derive air quality 

conditions under each of the projected years of the with-CAAA scenario by scaling the 

historical data adopted for the base year (2000) by the ratio of the modeled with-CAAA

and base year air quality. We use the same approach to estimate future year air quality 

for the without-CAAA scenario. This method takes advantage of the richness of the 

monitoring data on air quality, provides a realistic grounding for the benefit measures, 

and yet retains analytical consistency by using the same modeling process for both 

scenarios. The outputs of this step of the analysis are profiles for each pollutant 

characterizing air quality conditions at each monitoring site in the lower 48 states. This 

procedure also provided a means for calibrating model results in those grid cells where no 

monitors exist, combining model results with nearby monitor data to yield a “surface” of 

air quality that avoids the problems with direct extrapolation of results from monitors not 

located within a grid cell boundary. 

The without-CAAA and with-CAAA scenario air quality profiles serve as inputs to a 

modeling system that translates air quality to physical outcomes (e.g., mortality, 

emergency room visits, or crop yield losses) through the use of concentration-response 

functions. Scientific literature on the health and ecological effects of air pollutants 

provides the source of these concentration-response functions. At this point, we derive 

estimates of the differences between the two scenarios in terms of incidence rates for a 

broad range of human health and other effects of air pollution by year, by pollutant, and 

by geographic area. 

In the next step, we use economic valuation models or coefficients to estimate the 

economic value of the reduction in incidence of those adverse effects amenable to 

monetization. For example, a distribution of unit values derived from the economic 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-10

literature provides estimates of the value of reductions in mortality risk. In addition, we 

compile and present benefits that cannot be expressed in economic terms. In some cases, 

we calculate quantitative estimates of scenario differences in the incidence of a 

nonmonetized effect. In many other cases, available data and techniques are insufficient 

to support anything more than a qualitative characterization of the change in effects. 

Health effects estimation and valuation are addressed in Chapter 5, and welfare effects, 

including ecological impacts, visibility, and agriculture and forest productivity effects, 

and their valuation, are addressed in Chapter 6. 

Next, we compare costs and monetized benefits to provide our primary estimate of the net 

economic benefits of the 1990 CAAA and associated programs, and a range of estimates 

around that primary estimate reflecting quantified uncertainties associated with the 

physical effects and economic valuation steps. The monetized benefits used in the net 

benefit calculations reflect only a portion of the total benefits due to limitations in 

analytical resources, available data and models, and the state of the science. For example, 

in many cases we are unable to quantify or monetize the potentially large benefits of air 

pollution controls that result from protection of the health, structure, and function of 

ecosystems. In addition, although available scientific studies demonstrate clear links 

between air quality changes and changes in many human health effects, the available 

studies do not always provide the data needed to quantify and/or monetize some of these 

effects. Details are provided in Chapter 7. 

In addition to the sequence of analyses outlined in Figure 1-2, which are focused on 

generating the key target variable of national net monetized benefits, a number of 

supplemental analyses were also conducted to provide further insights on the impacts of 

CAAA provisions for natural resources, health, and economic output. The first of these 

supplemental analyses uses the Second Prospective’s national direct cost, health 

incidence, and health benefits valuation results to conduct further national-scale 

economy-wide modeling using what is known as a Computable General Equilibrium 

(CGE) model. The CGE model simulates, in a simplified way, shifts in markets and 

transactions throughout the economy that might result from CAAA provisions. It is 

therefore useful in assessing impacts on Gross Domestic Product (GDP), prices, and 

sector shifts in production (e.g., from “dirty” to “clean” industries). Most past 

applications of CGEs have focused on the economy-wide implications of the costs of 

complying with regulations – as a result, many prior applications, including the use of 

CGE in the Retrospective study, tell only half the story. Air pollution regulations not 

only impose direct costs, but also yield benefits, and at least some of these benefits (e.g., 

reduced medical expenditures, improved labor productivity owing to better health) affect 

market transactions in ways that can be assessed in the CGE framework. Not all benefits 

are amenable to analysis in a CGE, however – for example, nonmarket effects such as 

willingness-to-pay to avoid pain and suffering of air pollutant-linked disease cannot be 

incorporated. Nonetheless, this study represents one of the first broad applications of a 

CGE tool to regulatory costs and benefits. More details are provided in Chapter 8. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-11

Two other supplemental analyses represent local-scale case studies of difficult-toquantify benefits of air pollution regulation. One is a case study of health benefits 

associated with air toxics control. In prior section 812 studies, benefits of air toxics 

programs have been largely limited to their effects on criteria pollutant outcomes. For 

example, many air toxics are also volatile organic compounds, and so contribute to ozone 

formation, an effect which can be fairly readily quantified. The direct effects of air toxics 

on health, however, have been more difficult to quantify, partly because of data 

constraints, and partly because the highly localized effects of air toxics require a level of 

emissions and air quality modeling resolution that is currently infeasible for a national 

analysis. The air toxics case study, the results of which are presented in Chapter 5, 

provides an example of the benefits of air toxics control for a pollutant (benzene) and 

geographic scope (Houston area) that is both relatively data rich and computationally 

manageable. 

A second case study involves ecological effects, focused on the Adirondack region of 

New York State. This region was carefully chosen, based on the recommendation of the 

Advisory Council on Clean Air Compliance Analysis Ecological Effects Subcommittee 

(Council EES), because of its relatively high sensitivity to the effects of deposited air 

pollutants, because those same effects are relatively well-studied, and because methods 

exist to quantify and, in many cases, monetize the benefits of air pollution controls. 

Using the same emissions and air quality scenarios as in the overall national study, the 

ecological case study assesses the impact of sulfur and nitrogen deposition in the 

Adirondack region on aquatic resources, particularly lakes and ponds that support 

recreational fishing, and on commercial timber resources. 

Uncertainty analyses are also conducted at each phase of the analyses. Where applicable, 

we present the results of a series of quantitative uncertainty analyses that test the effect of 

alternative methods, models, or assumptions that differ from those we used to derive the 

primary net benefit estimate. The primary estimate of net benefits and the range around 

this estimate, however, reflect our current interpretation of the available literature; our 

judgments regarding the best available data, models, and modeling methodologies; and 

the assumptions we consider most appropriate to adopt in the face of important 

uncertainties. 

Finally, throughout the report, at the end of each chapter, we discuss the major sources of 

uncertainty for each analytic step. Although the impact of many of these uncertainties 

cannot be quantified, we qualitatively characterize the magnitude of effect on our net 

benefit results by assigning one of two classifications to each source of uncertainty: 

potentially major factors could, in our estimation, have effects of greater than five percent 

of the total net benefits; and probably minor factors likely have effects less than five 

percent of total net benefits. 

The Second Prospective involved a much greater effort in uncertainty analyses than prior 

reports in this series. Figure 1-3 illustrates the Project Team’s approach to uncertainty 

analysis in the Second Prospective, superimposed on the overall analytic chain for the 

study presented above. The grey box in Figure 1-3 represents the extent of uncertainty 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-12

analysis in the first section 812 prospective analysis, which was largely limited to 

analysis of parameter uncertainty in the concentration-response and valuation steps of the 

benefits analyses. Those parameter uncertainty analyses have become standard practice 

in EPA analyses of air pollution program benefits, and are an integral part of the 

BenMAP benefits assessment tool. The results of the probabilistic modeling of these 

uncertainties constitute the “primary low” and “primary high” estimates presented in 

Table 5-7 in Chapter 5 as well as in Chapter 7. 

Enhancements employed in the current analysis include both “online” analyses (shown in 

color), that feed information on uncertainty into the analytical chain at various points and 

propagate it through the remaining steps in the chain, and separate “offline” analyses and 

research that provide insights into the uncertainty, sensitivity, and robustness of results to 

alternative assumptions that are currently most easily modeled outside the main analytical 

process. 

The online analyses consist of the selection of alternative inputs for mortality 

concentration-response and valuation in BenMAP, as well as an analysis of the effect on 

benefits of sector specific, marginal changes in PM-related emissions from the core 

scenarios. This online analysis substitutes EPA’s Response Surface Model (RSM) for 

CMAQ. RSM is a less resource intensive meta-model of CMAQ used to rapidly 

approximate PM concentrations from alternative emissions inputs. Those analyses are 

described in much greater detail in the supporting uncertainty analysis report, referenced 

at the end of this chapter. 

The bottom box in Figure 1-3 lists additional offline research and analysis we 

incorporated into the current study. As with the online analyses, these analyses were 

chosen because they address uncertainty in key analytical elements or choices that may 

significantly influence benefit or cost estimates. Most of these are described in this 

integrated report, some only briefly, but full descriptions of the data, models, and 

methods applied in these analyses are included in the underlying uncertainty analysis 

report. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-13

FIGURE 1-3. SCHEMATIC OF UNCERTAINTY ANALYSES 

Analytic Design

Scenario 

Development

Emissions

Profile 

Development

Air Quality

Modeling –

Criteria Pollutants

Physical

Effects

Valuation

Comparison of

Benefits and

Costs

Direct Cost

Estimation*

Scenario Uncertainty

Emissions by Sector 

(Using RSM)

PM/Mortality

C-R Uncertainty

from Expert Elicitation 

& other functions

Benefits

Analysis

Baseline

Uncertainty Analysis

(BenMap)

- C-R and Valuation

Simulation Modeling

Cost

Analysis

Offline Analyses

1. Dynamic versus Static Population Modeling (Benefits)

2. Cessation lag (Benefits)

3. Differential Toxicity of PM Components (Benefits)

4. Emissions and Air Quality Modeling Uncertainty Literature Review and 

Qualitative Analysis (Benefits)

5. Unidentified Controls (Costs)

6. Fleet Composition, I&M Failure Rates (Costs)

7. Learning Curve Assumptions (Costs)

Ozone/Mortality

C-R Uncertainty

Single vs. Pooled 

functions

VSL Uncertainty

Alternative

Distributions

* In addition, we perform a computable general equilibrium (CGE) analysis of 

costs alone and of costs and benefits, but we omit this step from the diagram 

because we do not conduct uncertainty analyses on the CGE modeling.

Analytic Design

Scenario 

Development

Emissions

Profile 

Development

Air Quality

Modeling –

Criteria Pollutants

Physical

Effects

Valuation

Comparison of

Benefits and

Costs

Direct Cost

Estimation*

Scenario Uncertainty

Emissions by Sector 

(Using RSM)

PM/Mortality

C-R Uncertainty

from Expert Elicitation 

& other functions

Benefits

Analysis

Baseline

Uncertainty Analysis

(BenMap)

- C-R and Valuation

Simulation Modeling

Cost

Analysis

Offline Analyses

1. Dynamic versus Static Population Modeling (Benefits)

2. Cessation lag (Benefits)

3. Differential Toxicity of PM Components (Benefits)

4. Emissions and Air Quality Modeling Uncertainty Literature Review and 

Qualitative Analysis (Benefits)

5. Unidentified Controls (Costs)

6. Fleet Composition, I&M Failure Rates (Costs)

7. Learning Curve Assumptions (Costs)

Ozone/Mortality

C-R Uncertainty

Single vs. Pooled 

functions

VSL Uncertainty

Alternative

Distributions

* In addition, we perform a computable general equilibrium (CGE) analysis of 

costs alone and of costs and benefits, but we omit this step from the diagram 

because we do not conduct uncertainty analyses on the CGE modeling.
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-14

REVIEW PROCESS 

The 1990 CAA Amendments established a requirement that EPA consult with an outside 

panel of experts during the development and interpretation of the 812 studies. This panel 

of experts was originally organized in 1991 under the auspices of EPA’s Science 

Advisory Board (SAB) as the Advisory Council on Clean Air Compliance Analysis 

(hereafter, the Council). Organizing the review committee under the SAB ensured that 

highly qualified experts would review the section 812 studies in an objective, rigorous, 

and publicly open manner consistent with the requirements and procedures of the Federal 

Advisory Committee Act (FACA). Council review of the present study began in 2003 

with a review of the analytical design plan. Since the initial meetings, the Council and its 

subcommittees have met many times to review proposed data, proposed methodologies, 

and interim results. While the full Council retains overall review responsibility for the 

section 812 studies, some specific issues concerning physical effects and air quality 

modeling were referred to subcommittees comprised of both Council members and 

members of other SAB committees. The Council's Health Effects Subcommittee (HES), 

Air Quality Modeling Subcommittee (AQMS), and Ecological Effects Subcommittee 

(EES) held both in-person and teleconference meetings to review methodology proposals 

and modeling results and conveyed their findings and recommendations to the parent 

Council. 

REPORT ORGANIZATION

The remainder of the main text of this report summarizes the key methodologies and 

findings of our prospective study. 

Chapter 2 summarizes emissions modeling and provides important additional detail 

on design of the regulatory scenarios. 

Chapter 3 discusses the direct cost estimation. 

Chapter 4 presents the air quality modeling methodology and results. 

Chapter 5 describes the approaches used and principal results obtained through the 

human health effects estimation and valuation processes. 

Chapter 6 summarizes the ecological and other welfare effects analyses, including 

assessments of commercial timber, agriculture, visibility, and other categories of 

effects. 

Chapter 7 presents aggregated results of the cost and benefit estimates and describes 

and evaluates important uncertainties in the results. 

Chapter 8 presents estimates of the effect of the Clean Air Act Amendments on 

economic growth, productivity, prices, household economic welfare, and the overall 

economy of the United States, through the application of an economy-wide 

economic simulation model. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

1-15

Note that additional details regarding the methodologies and results of this study can be 

found in a series of supporting reports, available at EPA’s Section 812 website 

(www.epa.gov/oar/sect812). These reports include the following: 

Emission Projections for the Clean Air Act Second Section 812 Prospective 

Analysis. 

Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective 

Analysis. 

Memorandum to the Files Re Documentation of Second Prospective Study Air 

Quality Modeling. 

Health and Welfare Benefits Analyses to Support the Second Section 812 BenefitCost Analysis of the Clean Air Act. 

Effects of Air Pollutants on Ecological Resources: Literature Review and Case 

Studies. 

Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air 

Toxics Case Study – Health Benefits of Benzene Reductions in Houston, 1990-2020. 

Uncertainty Analyses to Support the Second Section 812 Benefit-Cost Analysis of the 

Clean Air Act. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-1 

CHAPTER 2 – EMISSIONS 

Estimation of pollutant 

emissions, a key component of 

this prospective analysis, serves 

as the starting point for 

subsequent benefit and cost 

estimates. We focused the 

emissions analysis on six major 

pollutants that are regulated by 

the Clean Air Act Amendments: 

volatile organic compounds 

(VOCs), nitrogen oxides (NOx), 

sulfur dioxide (SO2), carbon 

monoxide (CO), particulate 

matter with an aerodynamic 

diameter of 10 microns or less 

(PM10), and fine particulate 

matter (PM2.5). Estimates of 

current and future year ammonia 

(NH3) emissions are also 

included in this study because of 

their importance in the 

atmospheric formation of fine particles in the ambient air. For each of these pollutants 

we projected emissions to the years 2010 and 2020 under two different scenarios: 

1. An historical "with-CAAA" scenario control case that reflects expected or 

likely future measures implemented since 1990 to comply with rules 

promulgated through September 2005; and 

2. A counterfactual “without-CAAA” scenario baseline case that freezes the 

scope and stringency of emissions controls at their 1990 levels, while 

allowing for changes in emissions attributable to economic and population 

growth.4

 

4

 Implementing this approach has occasionally required some difficult decisions on what constitutes 1990 levels of emissions 

controls. In general, we have interpreted any rules that were promulgated as final prior to 1990 to be part of the withoutCAAA scenario baseline. The residential wood stove New Source Performance Standard, however, was promulgated in 1988, 

but is not part of the without-CAAA scenario, because EPA did not certify NSPS compliant wood stoves until 1992. In this 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-2 

We projected emissions for five major source categories: utilities, or electricity 

generating units (EGUs); non-EGU industrial point sources; onroad motor vehicles; 

nonroad engines/vehicles; and area sources, which are smaller, more diffuse sources of 

pollutants that derive from many sources.5

 Table 2-1 gives examples of emissions 

sources for each of the five categories examined in this analysis and indicates which 

major pollutants are targeted by CAAA requirements in each category. The primary 

purpose of emissions analysis in this study is to estimate how emissions change over time 

and across our scenarios, so we can estimate costs of reducing emissions and the benefits 

of those emissions reductions for each of our target years. 

TABLE 2-1. MAJOR EMISSIONS SOURCE CATEGORIES 

SOURCE CATEGORY EXAMPLES 

POLLUTANTS WITH 

SUBSTANTIAL EMISSIONS 

REDUCTIONS FROM CAAA 

COMPLIANCE 

Electricity Generating Units 

(EGUs) 

electricity producing utilities NOx, SO2

Non-EGU Industrial Point 

Sources 

boilers, cement kilns, process 

heaters, turbines 

NOx, VOC, SO2, PM10 PM2.5

Onroad Motor Vehicles buses, cars, trucks (sources 

that usually operate on roads 

and highways) 

NOx, VOC, CO 

Nonroad Engines/Vehicles aircraft, construction 

equipment, lawn and garden 

equipment, locomotives, 

marine engines 

NOx, VOC, CO 

Area Sources agricultural tilling, dry 

cleaners, open burning, 

wildfires 

NOx, VOC, PM10, PM2.5 

This chapter consists of four sections. The first section provides an overview of our 

approach for developing emissions estimates. The second section summarizes our 

emissions projections for the years 2000, 2010, and 2020, and presents our estimates of 

changes in future emissions resulting from the implementation of the 1990 Amendments. 

The third section compares these results with estimates from the First Section 812 

Prospective Analysis. Finally, we conclude this chapter with a summary of the key 

uncertainties associated with estimating emissions. 

 

case, perhaps incorrectly, we interpreted the effective date of 1992 as the determining factor in whether the level of 

emissions stringency in 1990 should include the wood stove NSPS. 

5

 Area sources are also commonly referred to as nonpoint sources. We estimated utility and industrial point source emissions 

at the plant/facility level. We estimated nonroad engine/vehicle, motor vehicle, and area source emissions at the county 

level. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-3 

OVERVIEW OF APPROACH 

For four out of the five major source categories described in this report—all except 

electric generating units—we applied the following general method to estimate 

emissions: 

1. Select a "base" inventory for a specific year. This involves selection of an 

historical year inventory from which projections will be based. 

2. Select activity factors to project growth in the level of pollution-generating 

activity in the target years. The activity factors should provide the best 

possible means for representing future air pollutant emissions levels in the 

absence of controls. 

3. Develop a database of scenario-specific emissions control factors, to 

represent emissions control efficiencies under the two scenarios of interest. 

The control factors are "layered on" to the projected emissions levels absent 

controls to estimate future emissions levels, taking into account those 

controls required for CAAA compliance . 

Air pollutant emissions for the fifth category, EGUs, were estimated by application of the 

Integrated Planning Model (IPM), a model developed by ICF Consulting. IPM estimates 

EGU emissions in the 48 contiguous states and the District of Columbia through an 

optimization procedure that considers costs of electricity generation, costs of pollution 

control, and external projections of electricity demand to forecast the fuel choice, 

pollution control method, and generation for each unit considered in the model. We used 

IPM to estimate EGU emissions in both the with-CAAA and without-CAAA scenarios for 

2000, 2010, and 2020. 

SELECTION OF BASE YEAR INVENTORY 

The without-CAAA scenario emission projections are made from a 1990 base year, while 

the with-CAAA scenario emission projections use a base year of 2000. The logic for these 

base year inventory choices relates to the specific definitions of the scenarios themselves. 

The with-CAAA scenario tracks compliance with CAAA requirements over time; as a 

result, the best basis for projecting the with-CAAA scenario is a current emissions 

inventory that incorporates decisions made since 1990 to comply with the act. The 

without-CAAA scenario, on the other hand, freezes the stringency of regulation at 1990 

levels. The analysis therefore uses 1990 emission rates as a base and adjusts those 

emissions to account for economic activity over time. We determined that this method 

was less problematic than basing projections on a recent emissions inventory and trying 

to simulate the effect of removing CAAA emission controls currently in place. Table 2-2 

summarizes the key databases that were used in this study to estimate emissions for 

historic years 1990 and 2000. Note that, in some cases, we determined that the best 

representation for year 2000 emissions was actually a later year, either 2002 or 2001. 

Those decisions are explained below. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-4 

TABLE 2-2. BASE YEAR EMISSION DATA SOURCES FOR THE WITH- AND WITHOUT-CAAA

SCENARIOS 

SOURCE CATEGORY 

WITHOUT-CAAA SCENARIO – 

1990 WITH-CAAA SCENARIO – 2000 

Electricity Generating 

Units (EGUs) 

1990 EPA Point Source NEI1 Estimated by the EPA Integrated 

Planning Model for 2001 

Non-EGU Industrial Point 

Sources 

1990 EPA Point Source NEI 2002 EPA Point Source NEI 

(Draft) 

Onroad Motor Vehicles MOBILE6.2 Emission Factors and 

1990 NEI VMT Database 

MOBILE6.2 Emission Factors and 

2000 NEI VMT Database2

Nonroad Engines/Vehicles NONROAD 2004 Model 

Simulation for Calendar Year 

1990 

NONROAD 2004 Model Simulation 

for Calendar Year 2000 

Area Sources 1990 EPA Nonpoint Source NEI3 2002 EPA Nonpoint Source NEI 

(Final) 1

 The NEI is EPA’s National Emissions Inventory, conducted every three years. 2

 The California Air Resources Board (ARB) supplied estimates for California. 

3

 Adjustments were made to the 1990 nonpoint source NEI file for priority source categories. 

For EGUs and non-EGU industrial point sources, we estimated 1990 emissions using the 

1990 EPA National Emission Inventory (NEI) point source file. This file is consistent 

with the emission estimates used for the First Section 812 Prospective and is thought to 

be the most comprehensive and complete representation of point source emissions and 

associated activity in that year. Similarly, the 1990 EPA NEI nonpoint source file – with 

a few exceptions – was used to estimate 1990 area source sector emissions.6

 

For base year emissions estimates in the with-CAAA scenario, we drew emissions from a 

variety of sources. Due to resource constraints and the quality of available data, we relied 

on emissions estimates for years other than 2000. In the case of with-CAAA emissions 

from industrial point sources and area sources, we used the point source and nonpoint 

source files from the 2002 EPA NEI.7

 We chose the 2002 NEI to represent the year 2000 

estimates primarily because the 2002 inventory incorporated a number of refinements in 

emissions estimation methods that were not included in the previous inventory, which 

covered 1999 emissions. We judged that the improved quality of the 2002 NEI data 

justified the small expected difference between emissions for these source categories in 

 

6

 The exceptions are where 1990 emissions were re-computed using updated methods developed for the 2002 National 

Emissions Inventory (NEI) for selected source categories with the largest criteria pollutant emissions and most significant 

methods changes. 

7

 We used the draft NEI point source file because the final version of that file was not available at the time the analysis was 

performed. For area sources, we used the final NEI nonpoint source file. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-5 

2000 and in 2002. To estimate with-CAAA EGU emissions, we used data from a 

modified version of IPM that retrospectively modeled emissions for the year 2001.8

The project team estimated 1990 and 2000 emissions for the onroad and nonroad 

vehicle/engine sectors independently using consistent modeling approaches and activity 

estimates. For example, emission factors from EPA’s MOBILE6.2 model were used 

together with data from the 1990 and 2000 NEI vehicle miles traveled (VMT) databases 

to estimate onroad vehicle emissions for 1990 and 2000. Similarly, EPA’s NONROAD 

2004 model was used to estimate 1990 and 2000 emissions for nonroad vehicles/engines. 

SELECTION OF ACTIVITY FACTORS FOR PROJECTIONS 

After specifying base year emissions, we projected emissions to 2000 (for the withoutCAAA scenario), 2010, and 2020. To model emissions in the absence of controls, our 

general approach was to multiply an emission factor – derived from base year emissions 

estimates – by the level of emission-generating activity upon which the emission factor is 

based. These emission-generating activities vary by source category, but they are 

generally related to economic activity, such as transportation, energy consumption, and 

industrial output. Specifically, economic growth projections entered the emissions 

analysis in three places: 

 an electricity demand forecast (included in IPM); 

 a fuel consumption forecast for non-utility sectors; and 

 economic growth projections that serve as activity drivers for several other 

sources of air pollutants. 

For this analysis, we used fully integrated economic growth, energy demand, and fuel 

price projections to model economic growth in both the with-CAAA and the withoutCAAA scenarios. The primary advantage of this approach is that it allowed us to conduct 

an internally consistent analysis of economic growth across all emitting sectors. To 

implement this integrated approach, we chose the Department of Energy’s National 

Energy Modeling System (NEMS), which is used to produce DOE’s Annual Energy 

Outlook (AEO) projections. Our emissions estimates primarily rely on AEO’s 2005 

“reference case” scenarios. We supplemented these projections with additional forecasts 

from other data sources for emissions sources where we determined that AEO’s energy 

and socioeconomic forecasts would not adequately represent growth in emissionsgenerating activities.9

 Table 2-3 presents the values that we used for the AEO 2005 

projections for population, GDP, energy consumption, and oil price values in 2010 and 

2020. For reference, the table also presents the historical values for each variable in 

 

8

 Due to resource constraints and model limitations, we relied primarily on a validation analysis EPA conducted on 2001 

emissions, rather than developing a new analysis for the year 2000. 

9

 These emissions sources include agricultural production-crops, fertilizer application, and nitrogen solutions; agricultural 

tilling; animal husbandry; aircraft; forest wildfires; prescribed burning for forest management; residential wood fireplaces 

and wood stoves; and unpaved roads. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-6 

2002, as reported in AEO 2005. For each variable, the table shows the implied annual 

growth rate that AEO 2005 used to project population, GDP, energy consumption, and oil 

prices from 2002 to 2010 and from 2010 to 2020.10 

TABLE 2-3. SUMMARY OF KEY DRIVER DATA APPLIED IN EMISSIONS PROJECTIONS 

VARIABLE 

HISTORICAL 

DATA 

AEO 2005 

PROJECTIONS 

IMPLIED ANNUAL GROWTH 

RATE 

2002 2010 2020 2002-2010 2010-2020 

Population (millions) 288.6 310.1 337.0 0.90% 0.83% 

GDP (billion 2000 chain-weighted 

dollars) $10,075 $13,084 $17,634 3.32% 3.03% 

Energy Consumption (quadrillion Btu 

per year) 97.99 111.27 125.60 1.60% 1.22% 

World Oil Price (1999$ per barrel) $22.17 $23.00 $26.22 0.46% 1.32% 

One notable exception to the above involves the specification of PM2.5 emissions from 

non-EGU point sources and area sources. After initially attempting to model PM2.5

emissions in the without-CAAA scenario in 2000, 2010, and 2020 using the process 

described above, we determined that the resulting estimates over-attributed emissions 

reductions to the amendments. We applied two separate approaches to correct these 

emissions estimates: For emissions from area sources, we projected emissions from the 

two sectors responsible for the majority of emissions – construction and wood stoves – 

using source-specific data. For emissions from non-EGU point sources, the project team 

determined that emissions reductions from CAAA-mandated controls would be negligible 

in 2000, so we set without-CAAA PM2.5 emissions equal to with-CAAA emissions in that 

year. 

APPLYING CONTROLS TO THE WITH-CAAA SCENARIO 

To estimate the impact of CAAA controls on projected emissions in the with-CAAA

scenario, we modeled the application of controls required by CAAA programs, including 

(among others): 

 Title I VOC and NOx reasonably available control technology (RACT) 

requirements in ozone nonattainment areas (NAAs); 

 Title II on-road vehicle and nonroad engine/vehicle provisions; 

 Title III National Emission Standards for Hazardous Air Pollutants (NESHAPs); 

 Title IV programs focused on emissions from EGUs. 

 

10 The table presents 2002 data in order to be consistent with EPA’s 2002 NEI, which we used to estimate emissions from 

industrial point sources and area sources. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-7 

 Additional EGU regulations, such as the Clean Air Interstate Rule (CAIR), the 

Clean Air Mercury Rule (CAMR), and the Clean Air Visibility Rule (CAVR). 

As a general rule, we incorporated the effects of CAAA rules promulgated through 

September 2005.11 As such, we did not account for the impacts of rules promulgated 

after that date, such as the revised NAAQS for lead. Additionally, we modeled 

reductions from rules that have since been vacated, like the Clean Air Mercury Rule 

(CAMR) and the Clean Air Interstate Rule (CAIR), though CAIR has since been 

remanded. Rather than attempting to estimate the impacts of whatever rules might 

replace CAMR and CAIR, we modeled the rules as promulgated because that was the 

best information available when we made analytic commitments. 

A full list of the CAAA programs modeled for each source category is presented in Table 

2-4, together with the pollutants targeted by each program. For each source category, we 

identified factors to use in modeling the effect of emission controls required by the 

CAAA. For EGUs, onroad motor vehicles, and nonroad engines/vehicles, we used 

control factors included in the three EPA models we used to estimate base year 

emissions: IPM, MOBILE, and NONROAD, respectively. For non-EGU industrial point 

sources and area sources, we relied on control factors developed by the five Regional 

Planning Organizations funded by EPA to address regional air pollution issues, as well as 

factors developed by the California Air Resources Board. 

 

11 One exception is the Coke Ovens Residual Risk rulemaking, promulgated under Title III of the Act in March 2005. We 

omitted this rule because it has a very small impact on criteria pollutant emissions (less than 10 tons per year VOCs) 

relative to the with-CAAA scenario. The primary Maximum Achievable Control Technology (MACT) rule for coke oven 

emissions, however, involves much larger reductions and therefore is included in the with-CAAA scenario. In addition, we 

also modeled emissions reductions from local controls implemented to comply with the 8-hour Ozone NAAQS, the PM2.5

NAAQS, and the Clean Air Visibility Rule, using the proposed or promulgated forms of these rules as of January 2008. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-8 

TABLE 2-4. MAJOR CAAA PROGRAMS MODELED IN THE WITH-CAAA SCENARIO 

SECTOR POLLUTANT CAAA PROGRAMS 

Electricity Generating 

Units (EGUs) 

NOx/SO2 

NOx 

Title IV acid rain emission allowance program; 

Clean Air Interstate Rule (CAIR); Clean Air Mercury 

Rule (CAMR); Cases and Settlements; Additional 

measures to meet PM and ozone NAAQS; 

NOx SIP Call post-2000 

Non-EGU Industrial Point 

Sources 

NOx/VOC/SO2

NOx 

VOC 

Measures required to meet PM and ozone National 

Ambient Air Quality Standards (NAAQS) 

Ozone Transport Commission (OTC) small NOx 

source model rule (where adopted); NOx SIP Call 

2-, 4-, 7-, and 10-year maximum achievable control 

technology (MACT) standards; 

Onroad Motor Vehicles 

NOx/VOC/SO2 

NOx/VOC 

PM/SO2 

Tier 1 tailpipe standards (Title II); Tier 2 tailpipe 

standards; 

National and California low-emission vehicle (LEV) 

program (Title I); Federal and California 

reformulated gasoline for ozone NAAQS NAAs (Title 

I); I/M programs for ozone and CO NAAQS NAAs 

(Title I); NOx and VOC measures included in ozone 

NAAQS SIPs 

Heavy-duty diesel vehicle (HDDV) standards; Diesel 

fuel sulfur content limits (Title II) (1993); Gasoline 

fuel sulfur limits; Additional measures to meet new 

PM NAAQS 

Nonroad Engines/ 

Vehicles 

NOx/VOC/PM 

NOx/PM 

NOx/PM/SO2 

Federal Phase I and II compression ignition (CI) and 

spark-ignition (S-I) engine standards; Federal 

commercial and recreational marine vessel 

standards 

Federal locomotive standards 

Nonroad Diesel Rule 

Area Sources 

NOx/VOC/PM 

NOx/VOC 

VOC 

RACT requirements; NOx and VOC measures 

included in ozone SIPs; Additional measures to 

meet PM and ozone NAAQS 

Ozone Transport Commission (OTC) model rules 

(where adopted) 

2-, 4-, 7-, and 10-year MACT Standards; Federal 

VOC rules for architectural and industrial 

maintenance (AIM) coatings, autobody refinishing, 

and consumer products 

Note: See Hubbell et al. (2010) for additional information regarding rules and regulations attributed to 

the 1990 CAAA. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-9 

EMISSIONS ESTIMATION RESULTS 

Table 2-5 summarizes the national emission estimates by pollutant for each of the 

scenario years evaluated in this study: 2000, 2010, and 2020. As a reference, the table 

also presents total emissions for each pollutant in 1990. Figures 2-1 through 2-4 provide 

a detailed breakdown of the emissions reductions in each target year by source category 

for NOx, VOC, SO2, and primary PM2.5. We show the breakdown of emissions 

reductions by source category for these pollutants because they constitute (or are 

precursors of) the two main air quality impacts that drive the analysis of the benefits of 

the CAAA: ozone and particulate matter pollution. The table and figures also incorporate 

our estimates of emissions reductions from local controls required to meet attainment 

requirements for 8-hour ozone and PM2.5 national ambient air quality standards 

(NAAQS). Reductions needed for compliance, but for which we have not identified a 

specific pollutant reducing measure or sector to achieve the reduction, are incorporated in 

Table 2-5 and are presented as a separate category in Figures 2-1 through 2-4, labeled 

“unidentified measures.” 

For five of the pollutants examined—NOx, SO2, PM10, PM2.5, and NH3—we estimate that 

emissions in the absence of the amendments would increase steadily from 1990 through 

2000, 2010, and 2020, suggesting that emissions controls in place by 1990 would not be 

sufficient to prevent increases in pollutant emissions due to projected growth in economic 

activity. For the remaining two pollutants—VOC and CO—emissions decrease between 

1990 and 2000 as a result of automobile tailpipe controls enacted prior to 1990, but which 

have delayed effects through the 1990s, before increasing from 2000 onward. 

In the with-CAAA scenario, we estimate that emissions of SO2 and NOx will decrease 

steadily from 1990 to 2020, while emissions of VOC, CO, PM10, and PM2.5 will decrease 

from 1990 to 2010 before leveling off between 2010 and 2020. We also estimate that 

emissions of NH3 will increase even in the presence of CAAA regulations, though at a 

slightly slower pace than in the without-CAAA scenario. NH3 is not a specific target of 

CAAA regulations, but some reductions result from efforts to control other pollutants. 

The net result of these trends in the two scenarios is that we estimate that emissions 

reductions, relative to the without-CAAA scenario, will increase for all pollutants 

throughout the 2000 to 2020 period. 

As Figure 2-1 shows, we estimate that reductions in NOx emissions will increase 

substantially from 2000 to 2010 and from 2010 to 2020. All five major source categories 

contribute to these reductions in 2010 and 2020, though the largest reductions come from 

EGUs and on-road motor vehicles. Reductions in NOx emissions from EGUs are driven 

largely by cap-and-trade programs, such as Phase II of the Ozone Transport Commission 

memorandum of understanding and the Clean Air Interstate Rule.12 In the motor vehicle 

sector, the large reductions in NOx emissions in 2010 and 2020 reflect both the delayed 

 

12 Under Phase II of the OTC memorandum of understanding, eleven eastern states committed themselves to achieving 

regional reductions in NOx emissions through a cap-and-trade system similar to the SO2 trading program established under 

Title IV of the amendments. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-10 

impact of Tier 1 NOx tailpipe standards as well as the impact of Tier 2 standards, which 

went into effect in 2004. 

Figure 2-2 shows increasing VOC emissions reductions from 2000 to 2020, with 

contributions from all source categories, with the exception of EGUs. The figure also 

shows a marked increase in on-road and nonroad emissions reductions between 2000 and 

2010, reflecting both the delayed impact of Tier 1 VOC standards and the effect of lowsulfur gasoline regulations. Additionally, about half of the rules affecting nonroad 

sources came into effect between 2000 and 2010, explaining the increase in emissions 

reductions during that time. Area sources also show large emissions reductions across all 

three target years, driven primarily by regulations controlling evaporative emissions from 

solvents, though residential fireplace and woodstove emissions are also projected to 

decline as obsolete woodstoves are replaced with low-emitting models required by the 

CAAA.13 

In Figure 2-3, SO2 emissions reductions increase by more than 60 percent between 2000 

and 2010, with a smaller increase between 2010 and 2020. Most reductions in SO2

emissions in all three target years come from EGUs, with smaller contributions from nonEGU point sources and area sources as well. As with reductions in NOx emissions, the 

CAIR and the Title IV cap and trade program are partly responsible for SO2 reductions 

from EGUs, along with the revised PM2.5 NAAQS. 

Figure 2-4 presents reductions in PM2.5 emissions for the three target years, with a steady 

increase in reductions from 2000 through 2020, as PM2.5 NAAQS requirements ramp up. 

Reductions in primary fine particulate emissions are expected to come from area sources, 

nonroad and onroad vehicles, and EGUs. Reductions from area sources are driven 

largely by the replacement of obsolete residential fireplaces and wood stoves, as well as 

local controls on construction sites for PM NAAQS compliance. As noted above, we set 

PM2.5 emissions at non-EGU industrial point sources in the without-CAAA scenario to be 

equal to emissions in the with-CAAA scenario, so we do not estimate that there will be 

any significant direct PM2.5 emissions reductions from that source category. 

 

13 As noted earlier in this chapter, the woodstove NSPS was interpreted as part of the differential between the with- and 

without-CAAA scenarios. NSPS compliance is required only for new units, which in practice are replaced very slowly. We 

estimate that, almost 20 years after NSPS implementation, in 2010, about 70 percent of the wood stoves in use are preNSPS uncertified models; by 2020, we estimate that turnover will reduce non-certified unit usage to just under 65 percent. 
The Benefits and Costs of the Clean 

TABLE 2-5. EMISSION TOTALS AND REDUCTIONS BY POLLUTANT – ALL SECTORS (THOUSAND TONS PER YEAR) 

POLLUTANT 1990 

2000 2010 20

WITHOUTCAAA WITH-CAAA REDUCTION WITHOUT- CAAA WITH-CAAA REDUCTION WITHOUT- CAAA WITH-

VOC 25,790 24,477 17,798 6,679 26,742 14,117 12,626 31,288 1NOx 25,917 26,688 20,837 5,851 28,517 13,640 14,877 31,740 1CO 154,513 127,093 107,691 19,403 134,151 86,705 47,447 155,970 8SO2 23,143 25,129 15,319 9,810 26,831 10,347 16,484 27,912 PM10 25,454 26,418 21,143 5,275 26,405 20,413 5,992 28,280 2PM2.5'1 5,527 5,822 5,489 333 5,924 5,241 682 6,368 NH3 3,656 4,136 3,983 153 4,405 4,224 181 4,787 1 PM2.5 without-CAAA emissions were adjusted from previously reported values by reducing emissions from non-EGU industrial point s

sources. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-12 

FIGURE 2-1. NOX REDUCTIONS ASSOCIATED WITH CAAA COMPLIANCE BY SOURCE CATEGORY 

FIGURE 2-2. VOC REDUCTIONS ASSOCIATED WITH CAAA COMPLIANCE BY SOURCE CATEGORY 

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

2000 2010 2020

NOx Tons per Year

Unidentified Measures

Area

Nonroad

Onroad Vehicle

Non-EGU Industrial Point

EGU

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

20,000,000

2000 2010 2020

VOC Tons per Year

Unidentified Measures

Area

Nonroad

Onroad Vehicle

Non-EGU Industrial Point

EGU
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-13 

FIGURE 2-3. SO2 REDUCTIONS ASSOCIATED WITH CAAA COMPLIANCE BY SOURCE CATEGORY 

FIGURE 2-4. PRIMARY PM2.5 REDUCTIONS ASSOCIATED WITH CAAA COMPLIANCE BY SOURCE 

CATEGORY 

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

2000 2010 2020

SO2 Tons per Year

Area

Nonroad

Onroad Vehicle

Non-EGU Industrial Point

EGU

0

400,000

800,000

1,200,000

2000 2010 2020

PM2.5 Tons per Year

Area

Nonroad

Onroad Vehicle

Non-EGU Industrial Point

EGU
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-14 

COMPARISON OF EMISSIONS ESTIMATES WITH THE FIRST PROSPECTIVE ANALYSIS 

DIFFERENCES IN METHODOLOGY 

In comparison with the First Prospective 812 Analysis, the Second Prospective includes a 

number of refinements and improvements in emissions estimation methods, as well as a 

different set of regulatory assumptions. 

1. Updated Emissions and Economic Activity Data: Because the Second Prospective 

analysis was developed ten years after the First Prospective, it incorporates 

additional information that was not available when the First Prospective was 

developed. This information includes with-CAAA emissions estimates for the 

historical year 2000 as well as additional historical trend data used to project 

economic activity from 1990 to 2000. 

2. Additional Regulatory Requirements: The Second Prospective Analysis accounts 

for several major CAA regulations that were not yet promulgated in 1996, when 

decisions were made about which regulations to include in the First Prospective. 

These regulations include, but are not limited to, the Clean Air Interstate Rule 

(CAIR); the Clean Air Visibility Rule (CAVR); Tier II vehicle rules and heavyduty diesel vehicle rules, and the local controls required for the revised 8-hour 

ozone and PM2.5 NAAQS. Because of this difference, the Second Prospective 

Analysis models greater emissions reductions in 2000 and 2010 than were 

predicted in the First Prospective, as we discuss in the following section. 

3. Integrated Economic Modeling Approach: In the First Prospective Analysis, we 

relied on a number of modeling tools to project future emissions, including 

projections of economic activity and population growth from the Bureau of 

Economic Analysis, and vehicle miles traveled from EPA’s MOBILE fuel 

consumption model. By using fully-integrated economic growth, energy 

demand, and fuel price projections from DOE’s AEO 2005, we were able to 

achieve a greater degree of internal consistency in the Second Prospective 

Analysis. 

DIFFERENCES IN EMISSIONS RESULTS 

Figures 2-5 and 2-6 show estimates from the First and Second Prospective Analyses of 

cumulative criteria pollutant emissions and emissions reductions for 2000 and 2010, the 

two years that were modeled in both analyses. The figures present emissions data for the 

four pollutants presented in Figures 2-1 through 2-4: VOC, NOx, SO2, and primary PM2.5. 

As Figure 2-5 shows, the Second Prospective Analysis estimates slightly higher 2000 

emissions in the without-CAAA scenario, and slightly lower emissions in the with-CAAA

scenario. VOC and primary PM2.5 emissions estimates are approximately the same in 

both analyses, but the Second Prospective estimates reductions in combined emissions of 

NOx and SO2 of about three million tons more than in the First Prospective. As noted 

above, most of the difference in SO2 emissions reductions is attributable to SO2 controls 

from CAIR, but there are also substantial additional reductions attributable to reduced 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-15 

fuel sulfur content regulations. The difference in NOx emissions reductions is due 

primarily to differences in the onroad and nonroad engine and EGU rules included in the 

Second Prospective, but also to corrections made in the Second Prospective to more 

accurately characterize the impact of the NOx SIP Call provisions for electric generating 

units. 

In Figure 2-6, the difference between emissions estimates in the First and Second 

Prospective Analyses is much more noticeable. Although the without-CAAA scenario 

emissions estimates for VOC, NOx, and SO2 are virtually identical for the two analyses, 

estimates of with-CAAA emissions of these pollutants are all substantially lower in the 

Second Prospective Analysis than in the First Prospective, yielding a difference in 

cumulative emissions reductions of about 15 million tons. As discussed above, the 

Second Prospective estimates much larger emissions reductions primarily because it 

accounts for a number of major control programs that were not yet in place when the last 

analysis was published. 

FIGURE 2-5. FIRST AND SECOND PROSPECTIVE 2000 EMISSIONS AND EMISSIONS REDUCTIONS 

(EXCLUDING CO AND PM1 0 ) 

0

10

20

30

40

50

60

70

80

90

100

1st

Prospective

2nd

Prospective

1st

Prospective

2nd

Prospective

1st

Prospective

2nd

Prospective

without-CAAA Emissions with-CAAA Emissions Emissions Reductions

Emissions (million short tons

Primary PM2.5

SO2

NOx

VOC
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-16 

FIGURE 2-6 . FIRST AND SECOND PROSPECTIVE 2010 EMISSIONS AND EMISSIONS REDUCTIONS 

(EXCLUDING CO AND PM1 0 ) 

UNCERTAINTY IN EMISSIONS ESTIMATES 

Table 2-6 lists several sources of uncertainty associated with generating the emissions 

estimates discussed in this chapter, as well as the expected direction of bias introduced by 

each uncertainty (if known), and the relative significance of each uncertainty in the 

overall 812 benefits analysis. These uncertainty sources are organized by the three 

factors that drive our results: identifying base-year emissions, forecasting growth in 

emissions-related activity, and modeling emissions controls in future years. 

UNCERTAINTIES RELATED TO BASE-YEAR EMISSIONS 

We estimated emissions from onroad motor vehicles, nonroad engines, and area sources 

at the county level, since these source categories are generally not tied to a specific 

location. Accordingly, our estimates of the spatial location of these emissions are less 

precise than for EGUs and industrial point sources. This uncertainty affects our ability to 

model changes in air quality associated with emissions reductions attributed to the 

CAAA. However, we expect that this uncertainty has a minor impact on the overall net 

benefit projections of the analysis. 

A potentially major factor contributing to uncertainty in emissions estimates is our 

specification of the without-CAAA scenario. The Project Team tested the influence of an 

alternative scenario specification by first developing a with-CAAA scenario using 

continuous CEM data available on EPA’s Clean Air Markets website.14 Working from 

this scenario as a base emissions estimate for each EGU, we estimated EGU data for the 

 

14 U.S. Environmental Protection Agency. Clean Air Markets – Data and Maps <http://camddataandmaps.epa.gov/gdm/> 

Accessed March 2009. 

0

10

20

30

40

50

60

70

80

90

100

1st

Prospective

2nd

Prospective

1st

Prospective

2nd

Prospective

1st

Prospective

2nd

Prospective

without-CAAA Emissions with- CAAA Emissions Emissions Reductions

Emissions (million short tons)

Primary PM2.5

SO2

NOx

VOC
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-17 

without-CAAA scenario using an alternative counterfactual approach based on work done 

by Dr. A. Denny Ellerman of Massachusetts Institute of Technology.15 The with-CAAA

results using the alternative EGU data appear very similar to the results using the IPM 

EGU data, but air quality difference maps indicate that overall PM2.5 exposures are 

slightly lower using the CEM data for the with-CAAA scenario in 2000, and PM2.5 

exposures are substantially higher using the data derived using the Ellerman 

counterfactual method for the without-CAAA scenario compared to the corresponding 

core scenarios. 

These exposure differences carry over into benefits calculations. The health benefits of 

the CAAA in 2000 arrived at using the alternative EGU emissions are approximately 50 

percent greater than the benefits in the 2000 core scenario. For the alternative EGU 

emissions scenarios, the substantial, 50 percent difference in air quality outcomes and 

benefits results appears to be derived from our construction of a substantially different 

without-CAAA scenario. The original motivation of the analysis was concern that the 

spatial pattern of emissions for the with-CAAA scenario for 2000 predicted by an IPM run 

for a historical year differed from the spatial pattern observed in the emissions monitor 

data for the same year. The analysis illustrated that the difference in benefits results is 

instead due primarily to differences in the without-CAAA scenario among the two 

alternative scenario specifications. Not surprisingly, uncertainty in estimating a 

counterfactual scenario is much larger than uncertainty in estimating the factual case, at 

least for the EGU sector. 

UNCERTAINTIES RELATED TO GROWTH FACTORS 

When projecting future growth in economic activity, even the most thorough projection 

model must tolerate a high amount of uncertainty. The factors we used to model growth 

in this analysis reflect uncertainty both in the economic activity forecasted and in how 

this activity translates into emissions of criteria pollutants. For example, because the 

AEO 2005 economic growth projection predates the recent economic downtown, it is 

possible that we overestimate emissions in both the with-CAAA and without-CAAA

scenarios. However, because we use the same growth factors to project emissions under 

the with-CAAA and without-CAAA scenarios, this source of uncertainty probably has a 

minor effect on our overall net benefits estimates. In addition, we considered projecting 

emissions under high-growth and low-growth AEO projection scenarios, but we did not 

find sufficient variation in our conclusions to justify such an analysis. For these reasons, 

we do not believe this is a significant factor in our results. 

 

15 Dr. A. Denny Ellerman’s approach relies on multiplying a “baseline” pre-Title IV emissions rate by 2001 CEM heat input 

observations for each electric generating unit. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-18 

Similarly, our projected emissions from on-road motor vehicles are based on vehicle fleet 

compositions included in the MOBILE6.2 model. Any change in fuel prices that might 

cause a shift away from low-fuel-efficiency vehicles could cause us to overestimate 

emissions from this sector. However, we expect that the impact of this uncertainty on our 

estimate of net benefits is minor. 

UNCERTAINTIES RELATED TO EMISSIONS CONTROL MODELING 

When modeling the with-CAAA scenario, we incorporated the effects of rules 

promulgated through September 2005. Accordingly, we did not fully account for rules 

promulgated since that time, such as the revised NAAQS for lead, and we modeled 

reductions from rules that have since been vacated, like the Clean Air Mercury Rule 

(CAMR) and the Clean Air Interstate Rule (CAIR), though CAIR has since been 

remanded. We estimated that CAMR would have only a modest impact on the pollutants 

we examined in this analysis, since mercury controls do not have large co-control benefits 

with other pollutants. However, our analysis projects that CAIR would have a large 

impact on NOx and SO2 emissions at EGUs in 2010 and 2020. Ultimately, a new rule 

will be promulgated to replace CAIR, and the emissions reductions, compliance costs, 

and locations of emissions reductions could all be different from what we modeled in this 

analysis. As a result, it is unclear whether our analysis overestimates or underestimates 

the net benefits of CAAA provisions on EGU emissions. 

Estimates of emissions of volatile organic compounds are also a source of uncertainty 

because VOCs can be emitted through fuel combustion—like SO2 and NOx—as well as 

evaporation of volatile materials. Because evaporation rates depend largely on 

temperature, our estimates of future VOC emissions are influenced by the inherent 

difficulty of predicting future temperatures. The analysis uses projections of average 

daily minimum and maximum temperatures in order to predict average VOC emissions, 

but the resulting estimates do not adequately capture the variability of such emissions. 

The likely significance of this uncertainty, in terms of its impact on the overall net 

benefits estimated in this analysis, is probably minor. 

Our future-year control assumptions are also a source of uncertainty. The flexibility 

allowed by the CAAA in achieving air quality standard target emission levels allows for 

emissions control schemes that may differ significantly from the controls modeled in this 

analysis. This is particularly true in the case of reductions needed for NAAQS 

compliance for which we have not identified a specific sector target. This analysis treats 

those reductions as if they come from area sources, but they could come from any of the 

five source categories we consider. We are not able to determine the direction of any 

possible bias caused by this uncertainty, but we do not expect it to have a major effect on 

our net benefits estimate. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-19 

TABLE 2-6. KEY UNCERTAINTIES ASSOCIATED WITH EMISSIONS ESTIMATION 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF POTENTIAL BIAS FOR 

NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Uncertainties Related To Base-Year Emissions 

Uncertainties in modeling a 

counterfactual emissions scenario. 

Estimating EGU emissions using an 

alternate counterfactual 

projection approach yielded 

increases in air quality impacts and 

health benefits of 50% relative to 

the core scenario’s IPM-generated 

estimates. 

Underestimate. The IPM-based 

counterfactual generated 

substantially lower benefits than 

the alternative counterfactual 

scenario specification we tested, 

which was based on published and 

readily replicated methodologies. 

It is possible, however, that other 

counterfactual specifications 

would yield lower benefits. It is 

also possible that the direction of 

effect might be different for other 

pollutant source categories where 

this is no accepted basis to 

generate an alternative 

counterfactual scenario estimate. 

Potentially major. Analysis 

confirmed that IPM performs well 

when estimating with-CAAA 

emissions, but also highlighted a 

high degree of uncertainty in 

estimating counterfactual 

emissions. Similar uncertainties 

exist for emissions from other 

emitting sectors. There is no clear 

way, however, to determine what 

approach to estimating 

counterfactual emissions is 

superior. 

Uncertainties in biogenic emissions 

inputs increase uncertainty in the 

air quality modeling estimates. 

Uncertainties in biogenic emissions 

may be large (± 80%). The 

biogenic inputs affect the 

emissions-based VOC/NOx ratio 

and, therefore, potentially affect 

the response of the modeling 

system to emissions changes. 

Unable to determine based on 

current information. The biogenic 

emissions change overall 

reactivity, leading to either an 

underestimate or overestimate of 

the model’s response to emission 

reductions. 

Probably minor. Impacts for ozone 

and PM2.5 results. Both oxidation 

potential and secondary organic 

aerosol formation could influence 

PM2.5 formation significantly. 

However, biogenic emissions are 

assumed to be unaffected by the 

CAAA, so this uncertainty should 

not significantly affect net 

benefits. Furthermore, ozone 

benefits contribute only minimally 

to net benefit projections in this 

study. 

Emissions estimated at the county 

level (e.g., low-level source and 

motor vehicle NOx and VOC 

emissions) are spatially and 

temporally allocated based on land 

use, population, and other 

surrogate indicators of emissions 

activity. Uncertainty and error are 

introduced to the extent that area 

source emissions are not perfectly 

spatially or temporally correlated 

with these indicators. 

Unable to determine based on 

current information. 

Probably minor. Potentially major 

for estimation of ozone, which 

depends largely on VOC and NOx

emissions; however, ozone 

benefits contribute only minimally 

to net benefit projections in this 

study. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-20 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF POTENTIAL BIAS FOR 

NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Uncertainties Related To Growth Factors 

Economic growth factors used to 

project emissions are an indicator 

of future economic activity. These 

growth factors reflect uncertainty 

in economic forecasting as well as 

uncertainty in the link to 

emissions. IPM projections may be 

reasonable regionally but may 

introduce significant biases locally. 

Also, the Annual Energy Outlook 

2005 growth factors do not reflect 

the recent economic downturn or 

the volatility in fuel prices since 

the fall of 2005. 

Unable to determine based on 

current information. 

Potentially major. The same set of 

growth factors are used to project 

emissions under both the WithoutCAAA and With-CAAA scenarios, 

mitigating to some extent the 

potential for significant errors in 

estimating differences in 

emissions. Some specific locations 

may be more significantly 

influenced. We estimated gross 

benefits using AEO low-growth and 

high-growth scenarios and found 

differences of ±20%. However, due 

to nonlinearities in the benefits 

estimation model, we could not 

reliably determine in what 

direction over- or underestimating 

growth might bias net benefits 

estimates. 

The on-road source emissions 

projections reflect MOBILE6.2 data 

on the composition of the vehicle 

fleet. If recent volatility in fuel 

prices persists or if fuel prices rise 

significantly (like they did in 2007 

and 2008), the motor vehicle fleet 

may include more smaller, loweremitting automobiles and fewer 

small trucks (e.g., SUVs). 

Overestimate Probably minor. Overall, fuel 

prices affect fleet composition at 

the margin, and we expect changes 

in fleet composition to occur 

gradually over long periods, 

suggesting that any effect would 

take several years to fully 

manifest. 

Uncertainties Related To Emissions Control Modeling 

The With-CAAA scenario includes 

implementation of the Clean Air 

Mercury Rule (CAMR), which has 

been vacated, and Clean Air 

Interstate Rule (CAIR), which was 

vacated but has since been 

remanded. 

Unable to determine based on 

current information. 

Potentially major. Significance in 

2020 will depend on the speed and 

effectiveness of implementing 

potential alternatives to CAIR and 

CAMR. In some areas, emissions 

reductions are expected to be 

overestimated, but in other areas, 

NOx inhibition of ozone leads to 

underestimates of ozone benefits 

(e.g., some urban centers). 

VOC emissions are dependent on 

evaporation, and future patterns 

of temperature are difficult to 

predict. 

Underestimate. Higher 

temperatures in the future are 

more likely than lower 

temperatures because of climate 

change, and higher temperature 

would lead to more emissions in 

the without-CAAA case but 

controls would keep the with-CAAA

emissions roughly constant. 

Probably minor. The analysis uses 

meteorological data from 2002 to 

characterize temperatures during 

the 30-year period from 1990 to 

2020. An acceleration of climate 

change (warming) could increase 

emissions but the increase relative 

to 2002 levels would not likely be 

significant. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

2-21 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF POTENTIAL BIAS FOR 

NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Use of average temperatures (i.e., 

daily minimum and maximum) in 

estimating motor-vehicle emissions 

artificially reduces variability in 

VOC emissions. 

Unable to determine based on 

current information. 

Probably minor. Use of averages 

will overestimate emissions on 

some days and underestimate on 

other days. Effect is mitigated in 

With-CAAA scenarios because of 

more stringent evaporative 

controls that are in place by 2000 

and 2010. 

Uncertainties in the stringency, 

scope, timing, and effectiveness of 

With-CAAA controls included in 

projection scenarios. 

Unable to determine based on 

current information. 

Probably minor. Future controls 

could be more or less stringent, 

widely applicable, or effective 

than projected. Timing of 

emissions reductions may also be 

affected. 

The location of the emissions 

reductions achieved from 

unidentified measures is uncertain. 

We currently treat these 

reductions as if they are achieved 

from non-point sources, but this 

may not be correct in all cases. 

Unable to determine based on 

current information. 

Probably minor. Impacts from 

these uncertainties would be 

localized and would not 

significantly change the overall net 

benefit estimate. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-1

CHAPTER 3 - DIRECT COSTS 

The costs of complying with the 

requirements of the Clean Air Act 

Amendments (CAAA) of 1990 will 

affect all levels of the U.S. economy. 

The impact, initially experienced 

through the direct costs imposed by 

regulations promulgated under the 

amendments, will also be seen in 

patterns of industrial production, 

research and development, capital 

investment, productivity, 

employment, and consumption. The 

purpose of the analysis summarized 

in this chapter is to estimate the 

incremental change in direct annual 

compliance costs from 1990 to 2020 

that are attributable to the 1990 

Clean Air Act Amendments. 

As a measure of the direct 

expenditures associated with CAAA 

compliance, the estimates presented 

here represent a key stand-alone output of the Second Prospective Analysis. In addition, 

we use the direct cost estimates presented in this chapter to generated estimates of 

CAAA-related private costs that will serve as inputs in the computable general 

equilibrium (CGE) model used to estimate the net social costs of the CAAA on the 

economy as a whole.16 Use of a CGE model allows us to estimate how compliance 

costs—along with expected benefits of the CAAA, such as increased labor supply— 

 

16 Private costs differ from the direct cost estimates presented in this chapter in two important ways: (1) they reflect private 

interest rates rather than the 5 percent social discount rate used throughout this report and (2) they reflect transfers (e.g.,

excise taxes on fuel) not included in our direct cost estimates. 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-2 

have a net impact on social welfare through interactions with labor markets and other 

areas of the economy. Further discussion of the CGE modeling conducted to estimate the 

impacts of the CAAA on net social welfare is presented in Chapter 8. 

This chapter consists of four sections. The first section summarizes our approach to 

estimating direct compliance costs. In the second section we present the results of the cost 

analysis. In the third section, we discuss how cost estimates in the Second Prospective 

Analysis differ from those generated for the First Prospective Analysis. We conclude the 

chapter with a discussion of the major analytic uncertainties, including a summary of the 

results of quantitative sensitivity tests of key data and assumptions. 

OVERVIEW OF APPROACH 

The scope of this analysis is to estimate the incremental direct costs for all criteria and 

hazardous air pollutant regulations issued under CAAA programs. Our approach to 

estimating the direct costs of CAAA compliance is closely integrated with our estimates 

of emissions reductions attributable to the amendments. In general, our analysis of 

compliance costs is driven by the results of our analysis of CAAA-related emissions 

reductions, and in some cases, costs and emissions reductions are measured concurrently. 

As with the emissions analysis presented in the previous chapter, we modeled CAAA 

compliance costs in 2000, 2010, and 2020 by comparing the costs of air pollution 

abatement in two scenarios: 

 An historical "with-CAAA" scenario control case that reflects expected or likely 

future measures implemented since 1990 to comply with rules promulgated 

through September 2005; and 

 A counterfactual “without-CAAA” scenario baseline case that freezes the scope 

and stringency of emissions controls at their 1990 levels, while allowing for 

changes in emissions attributable to economic and population growth.17

In addition, we also estimated costs separately for five major source categories: utilities, 

or electricity generating units (EGUs); non-EGU industrial point sources; onroad motor 

vehicles; nonroad engines/vehicles; and area sources. Table 2-1 gives examples of 

emissions sources for each of the six categories examined in this analysis. Additionally, 

the cost analysis considers the costs of local controls required to achieve further progress 

with the 8-hour Ozone NAAQS and the PM2.5 NAAQS as a separate category. Another 

difference between the emissions analysis and the direct cost analysis discussed in this 

chapter is that, whereas the emissions analysis considered emissions of six major criteria 

pollutants (VOCs, NOx, SO2, CO, PM10, and PM2.5) and one other pollutant which is not 

currently regulated under the CAAA in any form (NH3), the cost analysis addresses 

CAAA provisions issued to control emissions of both criteria pollutants and hazardous air 

pollutants (HAPs).18 

 

17 A full list of the regulations incorporated in the with-CAAA scenario is presented in Table 2-3. 

18 Except to the extent they are co-controlled by VOC limits or other measures focused on criteria pollutants, reductions in 

emissions of hazardous air pollutants were omitted because our benefits analysis focuses on the effect of criteria 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-3 

We estimated direct compliance costs in each source category using one of two 

approaches: 

1. Cost Estimates Based on Unit Costs – Costs were estimated by collecting 

information on the costs associated with specific control measures required by 

CAAA regulations, or costs were calculated using estimates of the average cost 

per ton of pollutant emission reduced. 

2. Cost Estimates Based on Optimization – Costs were estimated concurrently with 

emissions estimation through a cost minimizing algorithm that modeled 

attainment with specified emissions reduction targets. This approach was used 

for electric generating units, for example, where costs and emissions outcomes 

are outputs of the Integrated Planning Model. 

COST ESTIMATES BASED ON UNIT COSTS 

To estimate the cost of compliance CAAA regulations for most source categories, we 

obtained unit costs of control devices and other measures from various sources. For costs 

related to the 1-hour Ozone and PM10 National Ambient Air Quality Standards 

(NAAQS), we used cost data from EPA’s AirControlNET database. AirControlNET 

links detailed data on control technologies and pollution prevention measures with EPA’s 

National Emissions Inventory (NEI) to compute the costs associated with source- and 

pollutant-specific emission reductions. To calculate the cost of emissions controls on 

nonroad engines and vehicles, we multiplied unit cost estimates by estimates of 

vehicle/equipment sales and fuel consumption from the 2004 edition of EPA’s 

NONROAD model. The NONROAD model was also used to estimate CAAA-related 

emissions reductions in this sector, and direct cost estimates were developed consistent 

with those results. For these nonroad engine and fuel rules, as well as for controls 

required under other parts of the CAAA, we obtained unit cost estimates from EPA’s 

regulatory impact analyses (RIAs) as well as analyses commissioned by other 

organizations, such as the Ozone Transport Commission and the California Air Resources 

Board. Additional details on the specific data sources used to estimate unit costs for each 

source category are provided in the Second Prospective Cost Report.19

 

pollutants. Benefits of HAP emissions reductions are discussed in the context of a limited case study, however, in Chapter 5 

of this document. In addition, no CAAA emissions control measures are currently targeted to control NH3 emissions, so no 

costs for NH3 control are included in our overall CAAA cost estimates. 

19 See the report, Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis. Available at 

www.epa.gov/oar/sect812. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-4 

COST ESTIMATES BASED ON OPTIMIZATION 

We estimated control costs for EGUs using EPA’s Integrated Planning Model (IPM), 

which determines the utility sector’s least-cost strategy for meeting energy and peak 

demand requirements over a specified period of time, accounting for CAAA-mandated 

emissions caps. In the process of estimating the SO2 and NOx emissions that we 

discussed in the previous chapter, IPM also produced cost estimates for NOx, SO2, and 

mercury controls at EGUs. 

We also used a least-cost optimization process to estimate the costs of local controls 

required to achieve further progress toward and, ultimately, approximate attainment of 

the 8-hour Ozone NAAQS. For each designated nonattainment area, we first modeled the 

application of reasonably available control technology (RACT) and inspection and 

maintenance (I/M) programs. Then, in areas where further emission reductions were 

necessary, a least-cost algorithm was used to identify and apply the control measures to 

meet progress and attainment requirements.20 

Table 3-1 summarizes the cost estimation methods that we used for each source category, 

organized by major rules within each category. 

ADDITIONAL COST ESTIMATION CONSIDERATIONS 

In addition to the general cost estimation methods described above, we also considered 

additional factors when estimating CAAA compliance costs, such as how to account for 

cost savings from “learning by doing,” how to represent the annual costs of control 

measures requiring initial capital investment, and how to estimate the costs of required 

emissions reductions for which control measures have not yet been identified. 

Learning – A significant body of literature suggests that the per unit cost of producing or 

using a given technology declines as experience with that technology increases over 

time.21 The mechanism through which these reductions occur is not well understood, as 

decreases in costs may reflect several different effects, including returns to research and 

development, productivity spillovers from outside an industry, economies of scale, or 

efficiency improvements associated with increased experience with a given technology 

(i.e., learning-curve impacts). Given the multitude of factors that may lead to cost 

reductions over time, it is unclear whether such reductions should be modeled as 

learning-curve effects or as some other form of technological change. Nordhaus (2008) 

suggests that it is difficult to distinguish learning-curve effects from exogenous 

 

20 For PM NAAQS compliance, an optimization approach was not possible, because target emissions reductions were not 

available for each non-attainment area. Instead, we developed a model SIP for all PM nonattainment areas, and estimated 

costs for those measures in the model SIP for each nonattainment area. 

21 These studies include John M. Dutton and Annie Thomas, "Treating Progress Functions as a Managerial Opportunity," 

Academy of Management Review, 1984, Vol. 9, No. 2, 235-247; Dennis Epple, Linda Argote, and Rukmini Devadas, 

"Organizational Learning Curves: A Method for Investigating Intra-plant Transfer of Knowledge Acquired Through Learning by 

Doing," Organizational Science, Vol. 2, No. 1, February 1991; International Energy Agency, Experience Curves for Energy 

Technology Policy, 2000; and Paul L. Joskow and Nancy L. Rose, "The Effects of Technological Change, Experience, and 

Environmental Regulation on the Construction Cost of Coal-Burning Generating Units," RAND Journal of Economics, Vol. 16, 

Issue 1, 1-27, 1985. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-5 

technological change and that learning effects, as estimated separately from technological 

change, will typically be overestimated. Nevertheless, the most detailed peer-reviewed 

empirical studies examining these cost reductions quantify a "learning rate" for different 

technologies and industries that represents the percentage reduction in costs associated 

with each doubling in the cumulative production of a technology. Based on the strength 

of the evidence in this literature, we incorporated the concept of the learning effect into 

our assessment of CAAA costs. 

TABLE 3-1. COST ESTIMATION METHODS BY SOURCE CATEGORY AND RULE (WHERE 

APPLICABLE) 

SOURCE CATEGORY COST ESTIMATION METHOD

EGUs IPM Least-cost optimization 

Non-EGU Industrial Point Sources 

Ozone Transport Commission State Model Rules 

(NOx/VOC): 

NOx SIP Call: 

MACT Rules: 

Refinery Cases & Settlements: 

1-hour Ozone NAAQS: 

Federal Rules (RACT, Control Technique Guidelines, 

National VOC Rules): 

Additional Measures: 

PM10 SIP Measures: 

Ozone Transport Commission -sponsored 2001 analysis .

AirControlNET 

EPA cost estimates (from 1987-1998) 

AirControlNET 

 

Cost/ton from 1st Prospective 

AirControlNET Least Cost Module 

SIP control cost estimates; 

AirControlNET 

Onroad Engines and Fuels 

Title I NAAQS Tailpipe & Evaporative Control 

Standards: 

California and National LEV: 

Fuels: 

I/M Programs: 

EPA RIA unit costs 

California Air Resources Board (CARB) unit cost 

estimates 

Unit costs from First Prospective Analysis, EPA RIAs, 

CARB (for California standards) 

Costs based on information from current I/M programs 

Nonroad Engines and Fuels EPA RIA Unit Costs applied to sales and fuel 

consumption data provided by the NONROAD model, 

consistent with growth projections used to estimate 

emissions 

Area Sources 

Ozone Transport Commission State Model Rules 

(NOx/VOC): 

1-hour Ozone NAAQS: 

RACT & Control Technique Guidelines: 

Additional Measures: 

Ozone Transport Commission-sponsored 2001 analysis 

Cost/ton from 1st Prospective 

AirControlNET 

Local Controls 

8-hour Ozone NAAQS: 

RACT & I/M: 

Additional (Identified) Measures: 

Unidentified Measures: 

PM2.5 NAAQS: 

AirControlNET 

AirControlNET using a least-cost algorithm 

Assumed $15,000/ton 

Model SIP approach with AirControlNET unit costs 

Note: 

Unit costs taken from earlier EPA analyses are inflated to 2006$ and adjusted to account for cost savings from 

learning curve impacts. 

Some cost estimates for onroad and nonroad engines and fuel also reflect costs and/or savings from changes in 

fuel economy. These costs and savings are estimated using AEO 2005 fuel price projections. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-6 

Where possible, we based our learning curve adjustments on learning rates presented in 

the empirical literature. For some sectors, however, empirical estimates of learning rates 

were not available. We identified learning rate estimates for SO2 and NOx control 

technologies in the EGU sector and in the onroad vehicle sector, where we used learning 

rates for vehicle production to estimate the impact of learning on motor vehicle engine 

controls. For other technologies and industries affected by the amendments, we applied a 

default learning rate of 10 percent, consistent with the recommendation of the Council 

that advised EPA on this study.22,23

Cost Accounting – The costs presented in this analysis are expressed as total annualized 

costs (TAC) in 2000, 2010, and 2020. Annualized costs include both operation and 

maintenance (O&M) costs and, for CAAA provisions that require investment in pollution 

control equipment, capital investment costs. In order to make appropriate comparisons of 

costs in 2000, 2010, and 2020, we annualized these investment costs over the expected 

life of the control equipment, rather than assigning total capital investment costs to the 

year in which the investment is expected to be made. We applied a discount rate of five 

percent to annualize capital costs over an estimated equipment life.24 These annualized 

capital costs, combined with the annual O&M costs for a given pollution control measure, 

make up the total annualized cost estimates that we present for the three target years. 

Because some control measures require more capital investment than others, the degree to 

which our discount rate assumption affects our cost estimates varies by source category. 

For CAAA-related rules that affect fuel economy, we also incorporate fuel savings or 

losses into our cost estimates. Where possible, we estimate the value of these benefits or 

costs based on fuel price projections presented in the Energy Information 

Administration's Annual Energy Outlook 2005 (AEO 2005). In addition, for rules that 

affect the fuel economy of an engine over a period of several years, we estimate these 

benefits or costs as the present value of the fuel economy impacts realized over the entire 

life of the engine. 

Local Controls for NAAQS Compliance – When estimating the costs of compliance with 

the 8-Hour Ozone and PM2.5 NAAQS, we first estimated the cost of applying known and 

commercially available control technologies in nonattainment areas. We limited the 

application of these known controls to those with an estimated cost not exceeding 

$15,000 per ton for PM and ozone precursors (i.e., SO2, NOx, and VOCs). The rationale 

for incorporating this threshold into the analysis is that controls more costly than $15,000 

 

22 The Council recommended that we apply a default learning rate of 5 to 10 percent to sectors for which no empirical data 

are available. We chose 10 percent as a default learning rate because this value is more consistent with the learning rates 

presented in the empirical literature than the low end of the Council's recommended range. 

23 The Project Team makes no learning curve adjustments for motor vehicle inspection and maintenance programs. Because 

most states either run centralized inspection centers themselves or regulate the fees charged by decentralized inspection 

centers, it is unclear whether the learning curve impacts for I&M programs would be significant. 

24 Note that the discount rate we use to annualize capital investment costs is distinct from the discount rate used to 

calculate the total net present value of costs and benefits incurred through the full 1990 to 2020 study period. The net 

present value of costs and benefits is examined separately in Chapter 7 where we compare total costs to total benefits. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-7 

per ton may not be cost effective. Thus, local air quality agencies would seek reductions 

from other (unidentified) control measures. This is roughly consistent with the practice 

of the South Coast Air Quality Management District (SCAQMD 2006) in California, 

which attempts to identify viable alternatives for any control requirements with an 

estimated cost exceeding $16,500 per ton. When costs are above this threshold, the 

SCAQMD also conducts more detailed cost-effectiveness and economic impact analyses 

of the controls. 

For areas projected to remain in nonattainment with the 8-Hour Ozone NAAQS with 

identified controls, we estimated the costs associated with reducing emissions using 

additional controls not yet identified. To estimate the cost of these unidentified controls, 

we assumed that the cost of implementing these measures is $15,000 per ton of pollutant 

reduced, consistent with the cost threshold for identified controls. 

DIRECT COMPLIANCE COST RESULTS 

In this section we summarize the compliance cost analysis results by source category. As 

noted above, the control measures included in this analysis are consistent with our 

assumptions in the emissions analysis and reflect any post-1990 regulations promulgated 

(or reasonably anticipated, such as controls to meet RFP requirements) after passage of 

the 1990 CAAA. In general, the emissions analysis and this cost analysis reflect all of the 

regulations that were promulgated before September 2005. Similar to the emissions 

projection analysis, regulations promulgated after September 2005 (e.g., the revised Lead 

NAAQS) are not reflected in this report, in an effort to make the costs and benefits 

analyses as consistent as possible. 

Table 3-2 summarizes the estimated costs of the CAAA by sector for the three analysis 

years: 2000, 2010 and 2020. The table shows that the direct compliance costs in 2000 are 

estimated to be approximately $20 billion and that these costs are dominated by the costs 

of motor vehicle-related provisions of the CAAA as well as MACT standards and electric 

utility controls. The major components of motor vehicle-related control costs in 2000 are 

for emission standards, fuel standards, and vehicle emission inspection programs in 

nonattainment areas. Motor vehicle emissions standard costs in 2000 are primarily for 

low emission vehicle programs, Tier 1 tailpipe standards, and on-board diagnostics. 

Prominent motor vehicle fuel control programs in 2000 include Federal and California 

reformulated gasoline. These two reformulated gasoline programs are focused primarily 

in serious, severe and extreme 1-hour ozone NAAQS nonattainment areas. 

Table 3-2 shows that the estimated costs of complying with 1990 CAAA provisions are 

expected to more than double between 2000 and 2010 as areas develop and implement 8-

hour ozone and PM2.5 NAAQS State Implementation Plans (SIPs). One of the major 

components of CAAA compliance costs in 2010 is the estimated cost to achieve 

sufficient reductions of ozone precursor emissions to demonstrate 8-hour ozone NAAQS 

attainment. As noted above, we estimated 8-hour ozone compliance costs in two phases: 

first, we estimated the cost of applying known and commercially available control 

technologies in nonattainment areas; second, we estimated the costs associated with 

additional emissions reductions required to reach NAAQS attainment using controls not 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-8 

yet identified, at an assumed cost of $15,000 per ton. There is considerable uncertainty in 

this element of the cost analysis because it is unclear how individual areas will approach 

this issue. Because of the significant degree of uncertainty associated with estimating the 

costs of unidentified controls, this component of the cost analysis is reported separately in 

Table 3-2. 

TABLE 3-2. SUMMARY OF 1990 CAAA COMPLIANCE COSTS BY SECTOR 

SOURCE CATEGORY 

ANNUAL COST (MILLION 2006$) 

2000 2010 2020 

Electric Utilities $1,370 $6,640 $10,400 

Non-EGU Industrial Point Sources $3,130 $5,190 $5,140 

NOx SIP Call $0 $134 $133 

MACT $1,500 $3,010 $2,920 

National VOC Rules, RACT, and New CTGs $439 $464 $534 

Refinery Settlements $0 $295 $324 

1-Hour Ozone SIP Measures $1,030 $1,130 $1,090 

PM10 SIP Measures $163 $152 $146 

Onroad Vehicles and Fuels $14,400 $25,700 $28,300 

Motor Vehicle Emission Standards $4,400 $7,650 $7,760 

California and National LEV $562 $2,030 $2,090 

Fuels $4,820 $9,830 $11,200 

Motor Vehicle I/M programs $4,630 $6,250 $7,260 

Nonroad Vehicles and Fuels $298 $359 $1,150 

Nonroad Engines/Vehicle Standards $298 $219 $320 

Fuels $0 $140 $831 

Area Sources $663 $693 $766 

RACT and New CTGs $446 $442 $490 

Ozone Transport Commission Model Rules $134 $181 $212 

1-Hour Ozone NAAQS $82 $70 $64 

Local Controls $0 $5,260 $6,180 

8-Hour Ozone NAAQS $0 $4,270 $4,390 

PM2.5 NAAQS $0 $977 $687 

Clean Air Visibility Rule $0 $0 $1,100 

 

Sub-Total Excluding Unidentified Measures $19,900 $43,900 $52,000 

Additional Estimated Costs for Unidentified Controls for 8-Hour Ozone Compliance 

Non-California areas $8,700 $8,500 

California areas $318 $5,030 

TOTAL $19,900 $53,000 $65,500 

Note: All values are rounded to no more than three significant digits. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-9 

The growth in costs between 2000 and 2020 partially reflects population growth during 

this period and the corresponding increase in emissions-generating activity (e.g., 

increased vehicle miles traveled). Normalized for population growth, annual costs 

increase from approximately $70 per capita in 2000 to $170 per capita in 2010 and $190 

per capita in 2020. These results suggest that annual costs per capita grow by 

approximately 170 percent between 2000 and 2020, whereas annual costs (not normalized 

for population) grow by approximately 230 percent during this period. 

COMPARISON OF COST ESTIMATES WITH THE FIRST PROSPECTIVE ANALYSIS 

In many areas, cost estimation methods in the Second Prospective Analysis were identical 

to those in the First Prospective, even to the point of using the same unit costs (adjusted 

for inflation). In general, the Second Prospective improves on the First Prospective by 

using more current cost estimates (where available) and more advanced least-cost 

optimization tools. In addition, a major methodological innovation included in the 

Second Prospective is the adjustment of compliance costs to account for the learning 

curve effects of increased experience with pollution control measures. 

Figure 3-1 shows the estimated compliance costs in 2000 and 2010 from the First and 

Second Prospective Analyses, organized by source category. Overall, the year 2000 cost 

estimate presented in Table 3-2 is considerably lower than the corresponding cost 

estimate in the First Prospective ($27.6 billion), while the 2010 cost estimate presented in 

Table 3-2 is higher than the corresponding First Prospective estimate ($37.8 billion). 

Costs for electric utilities and area sources are significantly lower than were estimated in 

the First Prospective. The significant difference for utilities likely reflects differences in 

assumptions about the cost of obtaining low-sulfur coal from the Powder River Basin 

(PRB) in Wyoming. Although the Project Team was aware of the downward trend in 

PRB coal costs when the First Prospective was completed, this effect was not fully 

addressed in the data and models available at the time of the First Prospective study. 

It is useful to note that the Second Prospective’s $1.37 billion estimate for EGU 

compliance cost in 2000, which represent the pre-CAIR Title IV program requirements, 

fits well within the range of costs estimated in a series of ex-post econometric studies of 

compliance cost, which yield results of costs in 2000 of $1 to $1.4 billion.25 In addition, 

the National Acid Precipitation Assessment Program‘s (NAPAP) 2005 assessment of the 

Clean Air Act Title IV requirements provides another basis for evaluating the 

reasonableness of the EGU cost estimates presented in this report (NSTC 2005). The 

2005 NAPAP assessment summarizes the findings of several economic studies that 

estimated the cost of fully implementing the Title IV SO2 provisions. According to 

 

25

See, for example, A Denny Ellerman, 2003, "Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap-and-Trade Program," 

MIT Center for Energy and Environmental Policy Research Working Paper number WP-2003-003, available at: 

web.mit.edu/ceepr/www/publications/workingpapers_2000_2004.html#2003. Ellerman cites two papers for these 

estimates: Curtis P. Carlson, Dallas Burtraw, Maureen Cropper, and Karen Palmer, (2000) ”SO2 Control by Electric Utilities: 

What are the Gains from Trade?” Journal of Political Economy, 108 (6):1292-1326; and A. Denny Ellerman, Paul L. Joskow, 

Richard Schmalensee, Juan-Pablo Montero, and Elizabeth Bailey (2000). Markets for Clean Air: The U.S. Acid Rain Program. 

Cambridge University Press. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-10 

NAPAP, these studies estimate annual costs ranging from $1.2 billion to $2.3 billion for 

full implementation in 2010, but these estimates exclude the cost of CAIR, CAMR, and 

some other regulations that are part of the Second Prospective estimate for 2010.26 

Overall, the Second Prospective cost estimates for 2010 are higher than those estimated 

for the First Prospective mainly because many federal motor vehicle control programs not 

included in the First Prospective study with-CAAA scenario have been promulgated since 

the First Prospective was completed. For the same reason, the Second Prospective cost 

estimates are also higher for motor vehicles in 2000, though to a lesser degree. In 

addition, cost estimates in the current analysis are higher than in the First Prospective 

because they include the costs of meeting the 8 hour ozone, PM2.5 NAAQS and Clean Air 

Visibility Rule requirements in 2010. In both 2000 and 2010, estimated costs at area 

sources are higher in the First Prospective than in the Second Prospective, by roughly a 

factor of three, even though estimated emissions reductions are roughly a factor of three 

greater in the Second Prospective. This difference is due primarily to a much lower 

estimated cost per ton to reduce PM2.5 emissions in the Second Prospective – on average, 

cost per ton of PM2.5 reduced is approximately $2,000 in the Second Prospective, and was 

almost $20,000 in the First Prospective. One reason for the reduction is that the controls 

in the Second Prospective are better targeted at fine particulate control - controls in the 

First Prospective were actually focused on sources of PM10, with PM2.5 emissions 

reductions as a co-benefit. In addition, we have learned that pre-2002 NEI emissions 

estimates for PM2.5 were very uncertain, suggesting that perhaps the estimated PM2.5

emissions reductions in the First Prospective were understated. 

 

26 The NAPAP assessment cites a range of $1 billion to $2 billion, in year 2000 dollars. Adjusting for inflation using the GDP 

deflator, this range increases to $1.2 billion to $2.3 billion in year 2006 dollars. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-11 

FIGURE 3-1. FIRST AND SECOND PROSPECTIVE ANNUAL CAAA COMPLIANCE COSTS: 2000 AND 

2010 

First Prospective cost estimates from U.S. EPA, The Benefits and Costs of the Clean air Act 1990 

to 2010, EPA-410-R-99-001, November 1999. 

UNCERTAINTY IN DIRECT COST ESTIMATES 

In a broad analysis of prospective regulatory impacts it is not possible to verify the accuracy of the full range of assumptions regarding changes in consumption patterns, input 

costs, and technological innovation used to estimate costs in future scenarios. Moreover, 

for many of the factors contributing to uncertainty, the degree or even direction of the 

bias is unknown or cannot be determined. Nevertheless, uncertainties and/or sensitivities 

can be identified and in many cases the potential measurement errors can be 

quantitatively characterized. In this section of the chapter, we first discuss several 

quantitative sensitivity analyses undertaken to characterize the impact of key assumptions 

on the ultimate cost analysis. The quantitative analyses presented below were chosen 

either because the parameter in question was a topic of discussion in the Council’s review 

of the direct cost analysis or because we identified the parameter as potentially influential 

and/or uncertain. We then conclude the chapter with a qualitative discussion of the 

impact of both quantified and unquantified sources of uncertainty. 

QUANTITATIVE SENSITIVITY TESTS 

We performed four quantitative sensitivity tests to estimate the impact of alternate 

assumptions on our overall cost estimates. These tests covered our assumptions 

regarding the cost of unidentified controls, the composition of motor vehicle sales and 

fleet fuel efficiency, the failure rate of I/M tests, and the default learning rate applied to 

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

1st

Prospective

2nd

Prospective

1st

Prospective

2nd

Prospective

2000 2010

Compliance Costs (million 2006 $)

Local Controls (Unidentified)

Local Controls (Identified)

Area Sources

Industrial Point Sources

Nonroad Vehicles and Fuels

Onroad Vehicles and Fuels

Electric Utilities
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-12 

sectors for which we could not identify a rate in the empirical literature. The results of 

these sensitivity tests on our 2020 cost estimates are presented in Table 3-3.27 

Local Controls Analysis – Unidentified Controls 

As indicated above, when estimating the cost of local controls required for further 

progress with the 8-hour Ozone and PM2.5 NAAQS, we used a cost cap of $15,000 per 

ton to estimate the costs of identified local controls and also applied a cost of $15,000 per 

ton to unidentified controls. To assess the sensitivity of the local controls analysis to 

changes in these values, we estimated the costs of local controls based on a $10,000-perton cost cap for identified controls and a $10,000-per-ton estimated cost for unidentified 

controls. As indicated in Table 3-3, this alternative approach would yield lower cost 

estimates for both identified local controls and unidentified measures. The estimated costs 

of identified controls decline when the $10,000 cap is applied because controls that cost 

between $10,000 and $15,000 per ton are not implemented. In addition, although the 

application of the $10,000 cost cap increases the emissions reductions to be achieved 

through unidentified controls (relative to when the $15,000 cost cap is used), reducing the 

cost of unidentified controls to $10,000 per ton more than offsets the costs associated 

with these additional emissions reductions. Based on preliminary analyses conducted 

early in the development of the direct cost estimates, we found that in general higher 

thresholds do not change the emissions reductions to be achieved by unidentified 

controls, because few identified controls have a cost per ton higher than the $15,000 

threshold used in the analysis. Accordingly, the major effect of increasing the cost cap 

would be to increase the estimated cost of reductions achieved by unidentified controls, 

whose cost is estimated based on the dollar per ton cap. 

Composition of Motor Vehicle Sales and Fleet Fuel Efficiency 

Our analysis of the costs associated with motor vehicle tailpipe and fuel rules is based on 

sales and fuel efficiency projections from the 2005 version of DOE’s Annual Energy 

Outlook. Since the release of AEO 2005, however, fuel prices have been more volatile 

than in previous years, leading many consumers to shift to more fuel efficient vehicles, 

and the Department of Transportation revised the Federal Corporate Average Fuel 

Economy (CAFE) standards. Given these developments, AEO 2008 projects that 

passenger cars will make up a greater portion of light-duty vehicle sales in 2010 and 2020 

than is projected by AEO 2005. AEO 2008 also assumes that the light-duty vehicle fleet 

will be nearly 15 percent more fuel efficient relative to the projections in AEO 2005. To 

assess the extent to which our cost estimates for the on-road sector would change under 

the alternative AEO 2008 assumptions, we estimated the cost of motor vehicle tailpipe 

and fuel rules for both the 2010 and 2020 target years based on the AEO 2008 data. As 

indicated in Table 3-3, using AEO 2008 projections increases the estimated cost of motor 

vehicle tailpipe standards and reduces the estimated cost of motor vehicle fuel rules in 

2020. Although the alternative estimated cost of fuel rules is about 9 percent less than the 

 

27 We present sensitivity test results for 2020 estimates because the differences between the primary cost estimates and the 

alternative cost estimates discussed in this section are most pronounced in 2020. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-13 

primary estimate presented in Table 3-2, the reduction in estimated costs of both tailpipe 

and fuel CAAA motor vehicle programs in aggregate is more modest, at 3.6 percent.28

Vehicle Inspection Failure Rate 

Our estimates of the repair costs associated with motor vehicle I&M programs employed 

program- and year-specific inspection failure rates derived from 2003 and 2004 data for 

Wisconsin I&M programs. In its June 2007 review of the Draft Direct Cost Report, the 

Council noted that a 2001 National Research Council report referenced a failure rate 

about one-seventh the value derived from the Wisconsin data.29 To assess the sensitivity 

of the I&M cost analysis to the assumed failure rate for annual dynamometer-based 

programs, we developed alternative cost estimates for CAAA-mandated I&M programs 

based on the failure rate reported by the NRC. We found that the estimated cost of these 

programs declined by more than 40 percent when the alternative failure rates were used in 

place of those supporting the Second Prospective Cost Report. In addition, as indicated 

in Table 3-3, using these alternative values reduced total CAAA-related costs for the onroad sector by about 12 percent in 2020. This suggests that the cost estimates for the onroad sector are fairly sensitive to the assumed failure rate for I&M programs, in light of 

the range of failure rates obtained from readily available data sources. 

Default Learning Rate 

As discussed above, we adjusted total program costs to account for “learning curve” 

impacts (i.e., the extent to which the costs of a technology decline as experience with that 

technology increases over time). Wherever possible, we employed technology- or 

industry-specific learning rates obtained from the literature. Where industry-specific 

learning rates were not readily available in the empirical literature, we applied a default 

rate of 10 percent to the following technologies: 

 Selective non-catalytic reduction at electric generating units (EGUs) (O&M costs 

only); 

 Activated carbon injection at EGUs; 

 Motor vehicle fuel rules; 

 Non-road engine and fuel rules; 

 Non-EGU point source controls; 

 Area source controls; and 

 Local controls: EGU, non-EGU point source, and area source. 

 

28 Note that in both our central case estimates and in our sensitivity analysis for fleet composition, the same fleet 

composition is assumed in the with-CAAA and without-CAAA scenarios. It is likely that, as compliance costs increase, the 

CAAA could have a significant effect on fleet composition, but our current analysis does not address that factor. 

29 Committee on Vehicle Emission Inspection and Maintenance Programs, Board on Environmental Studies and Toxicology, 

Transportation Research Board, National Research Council. Evaluating Vehicle Emissions Inspection and Maintenance 

Programs. 2001. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-14 

We tested the sensitivity of the cost analysis to the choice of a default learning rate by reestimating the total costs of the amendments using alternative default learning rates of 5 

and 20 percent for the program areas listed above. The five percent default rate 

represents the low end of the range recommended by the Council, while the 20 percent 

value represents the central tendency presented in the peer-reviewed literature for several 

technologies.30 For the sensitivity test, we did not adjust the cost estimates of program 

areas where the empirical literature supplied specific and applicable learning rates. As 

indicated in Table 3-3, the use of alternative default learning rates had only a small effect 

on the estimated costs of the amendments in 2020. Using a five percent default learning 

rate in 2020 increased the estimated cost of the amendments by 3.2 percent, while a 20 

percent default learning rate reduced costs by six percent. 

TABLE 3-3. RESULTS OF QUANTITATIVE SENSITIVITY TESTS 

PROVISION 

PRIMARY 

ANNUAL COST 

ESTIMATE FOR 

2020 (BILLIONS 

2006 $) 

STRATEGY FOR SENSITIVITY 

ANALYSIS 

ALTERNATIVE 

2020 ESTIMATE 

FROM SENSITIVITY 

TEST (BILLIONS 

2006 $) 

PERCENT CHANGE 

FROM PRIMARY 

COST ESTIMATE 

Local Controls 

(Identified and 

Unidentified) 

$20.39 

$10,000/ton cap on 

identified controls and 

$10,000/ton for unidentified 

controls 

$16.79 -17.6% 

Motor Vehicle Costs $28.28 

Use AEO 2008 projections of 

motor vehicle sales and fleet 

fuel efficiency 

$27.25 -3.6% 

Motor Vehicle Costs $28.28 

Use Inspection Failure Rates 

reported by the National 

Research Council 

$24.82 -12.2% 

Total Costs (All 

Source Categories) $65.48 Use alternate default learning rate of 5 percent $67.60 3.2% 

Total Costs (All 

Source Categories) $65.48 Use alternate default learning rate of 20 percent $61.54 -6.0% 

 

30 For an analysis of the learning rates estimated in the empirical literature, see John M. Dutton and Annie Thomas, "Treating 

Progress Functions as a Managerial Opportunity," Academy of Management Review, Vol 9, No. 2, 1984. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-15 

QUALITATIVE ANALYSIS OF KEY FACTORS CONTRIBUTING TO UNCERTAINTY 

In addition to the uncertainties outlined above, we identified several other areas of 

uncertainty related to the direct compliance costs of the amendments that we did not 

address quantitatively. These include the Project Team’s projections of economic 

activity, the impact of CAAA compliance on productivity, product quality degradation 

resulting from the CAAA, the influence of technological innovation on CAAA 

compliance costs, and the impact of input substitution on the costs of complying with the 

amendments. 

Economic Activity Projections: The cost of the amendments in 2010 and 2020 will 

depend in large part on the future size and composition of the U.S. economy. If the AEO 

2005 economic growth projections used to estimate emissions reductions in 2010 and 

2020 underestimate or overestimate economic activity, we could likewise overestimate or 

underestimate the costs of CAAA compliance. In addition, the particular composition of 

economic output in 2010 and 2020 may deviate from the AEO 2005 projections, which 

would also cause our cost projections to differ from the actual costs of the amendments. 

Industrial Productivity: As stated in the introduction to this chapter, our cost estimates 

represent the direct costs of the CAAA, i.e., the expected expenditures of regulated 

facilities to comply with the amendments. Several peer-reviewed studies have suggested, 

however, that the direct costs of pollution control measures do not adequately represent 

the total costs of environmental protection, due to the effects of pollution abatement on 

industrial productivity.31 Although our cost estimates do not capture these productivity 

effects, the literature is not clear on the magnitude and direction of these effects. While 

some studies have found that pollution control negatively affects productivity, others 

have found that the productivity impact is positive or ambiguous.32 

Effects of the CAAA on Product Quality: In addition to increasing the cost of producing 

goods and services, CAAA requirements may also affect product quality. For example, 

motor vehicle emission control requirements may reduce the performance of automobiles, 

and changes in paint formulations (to reduce VOC emissions) may adversely affect how 

well paint adheres to unfinished surfaces. On the other hand, changes in product quality 

may also have unquantified benefits – while we capture the fuel saving benefits of many 

motor vehicle engine changes, the benefits of low-VOC paint in improving indoor air 

quality and human health are not captured in our estimates. As a result, product quality 

 

31 Barbera, A.J. and McConnell, V.D. (1986) “Effects of Pollution Control on Industry Productivity: A Factor Demand 

Approach.” The Journal of Industrial Economics. Vol. XXXV, 161-172. 

Barbera, A.J. and McConnell, V.D. (1990) “The Impact of Environmental Regulations on Industry Productivity: Direct and 

Indirect Effects.” Journal of Environmental Economics and Management. Vol. 18, 50-65. 

Gray, W.B. and Shadbegian, R.J. (1994) “Pollution Abatement Costs, Regulation, and Plant-Level Productivity.” Center for 

Economic Studies. 

Morgenstern, R.D., Pizer, W.A., and Shih, J-S. (2001) “The Cost of Environmental Protection.” Review of Economics and 

Statistics Vol. 83, No. 4, 732-738. (doi:10.1162/003465301753237812). 

32 Barbera and McConnell (1986) found a negative impact of pollution control on productivity, while Barbera and McConnell 

(1990) and Gray and Shadbegian (1994) found an ambiguous impact, and Morgenstern et al. (1998) found a positive impact. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-16 

effects may reduce the welfare of households that consume products affected by the 

CAAA, or they may improve welfare. Households that substitute to other products due to 

CAAA-related quality changes (e.g., households that substitute from automobiles to lightduty trucks due to CAAA requirements that affect the performance of automobiles more 

than light-duty trucks) may also experience welfare losses or gains, as they would have 

otherwise preferred the product(s) that they would have consumed in the absence of the 

CAAA but may, in the balance, experience previously unrecognized gains. 

Technological Innovation: The CAAA could serve as in impetus for technological 

innovation in the development of new, low-cost technologies or processes to reduce 

emissions. As indicated above, our cost estimates reflect the impact of experience-driven 

improvements in the productivity of existing control technologies—by accounting for 

learning curve impacts—but not the impact of technological innovation. Because we did 

not attempt to model technological innovation that might be spurred by incentives to 

minimize compliance costs, the Second Prospective Analysis may overestimate costs. 

Input Substitution: To minimize the cost of complying with the amendments, regulated 

facilities may alter the mix of inputs used in the production of goods and services. With 

the exception of fuel switching by EGUs (as part of compliance with the Title IV Acid 

Rain Program and CAIR), we did not capture input substitution as a control strategy in 

the Second Prospective Cost Report. Ignoring the possible impact of input substitution 

could also cause our estimates to overstate CAAA compliance costs. 

Table 3-4 lists the key sources of uncertainty noted in the quantitative and qualitative 

discussions above and indicates—where possible—the expected impact of the uncertainty 

on the net benefits estimate of the Second Prospective Analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-17 

TABLE 3-4. KEY UNCERTAINTIES ASSOCIATED WITH COST ESTIMATION 

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS 

FOR NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO KEY 

UNCERTAINTIES ON NET BENEFITS ESTIMATE1

Uncertainty in the 

maximum per ton costs for 

local controls to comply 

with the 8-hour Ozone and 

PM2.5 NAAQS. 

Unable to 

determine based 

on current 

information 

Probably minor. Our analysis of local controls 

assumes a maximum cost of $15,000 per ton 

for local controls implemented to comply 

with 8-hour Ozone and PM2.5 NAAQS 

requirements.5

 Local areas may implement 

more costly controls to comply with the 

NAAQS, but technological innovation may 

lead to the development of less expensive 

controls. 

Uncertainty in the 

projected composition of 

motor vehicle sales and 

the fuel efficiency of the 

motor vehicle fleet. 

Unable to 

determine based 

on current 

information 

Probably minor. We projected the 

composition of motor vehicle sales and the 

fuel efficiency of the motor vehicle fleet 

based on AEO 2005 data. The sensitivity 

analysis of alternative sales and fuel 

efficiency projections presented in this 

report suggests that this uncertainty has a 

small impact on net benefits. 

Uncertainty regarding 

failure rates for motor 

vehicle inspections. 

Unable to 

determine based 

on current 

information 

Probably minor. The repair costs for vehicles 

that fail emission inspections represent a 

small fraction of the estimated net benefits 

of the amendments. The failure rate 

sensitivity analysis presented in this report 

suggests that alternative failure rate 

assumptions could have a large effect on the 

costs for this component of the CAAA, but 

only a minor effect on the estimated net 

benefits of the amendments as a whole. 

Costs for some 

technologies and emissions 

sectors reflect default 

assumptions about the 

rates at which learning 

affects costs because 

empirical information is 

unavailable. 

Underestimate Probably minor. Based on the advice of the 

Council on Clean Air Compliance Analysis, we 

used a conservative learning rate of 10 

percent for those sectors where no empirical 

data were available.2

 In contrast, the 

learning curve literature suggests that the 

average learning rate is approximately 20 

percent, suggesting that learning will reduce 

costs more than is reflected in the present 

analysis.3

Uncertainties in the 

economic growth 

projections that form the 

basis of the cost analysis. 

Unable to 

determine based 

on current 

information 

Probably minor. The project team used AEO 

2005 economic growth projections, which 

suggest that the economy will grow at an 

annual rate of 3.1 percent through 2025.4

 

This growth rate is in line with historical GDP 

growth. 

Incomplete 

characterization of certain 

indirect costs, such as 

productivity impacts for 

regulated industry. 

Unable to 

determine based 

on current 

information 

Probably minor. The literature on the 

productivity impacts of the CAAA is unclear 

with respect to the direction and magnitude 

of these effects. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-18 

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS 

FOR NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO KEY 

UNCERTAINTIES ON NET BENEFITS ESTIMATE1

Product quality 

degradation associated 

with emission control 

technology. 

Unable to 

determine based 

on current 

information 

Unable to determine based on current 

information. Conceptually, the potential for 

CAAA requirements to affect product quality 

could result in an underestimate or 

overestimate of the welfare effects of 

compliance costs, and therefore an 

indeterminate effect on net benefits. 

Unfortunately, few studies exist that address 

the potential product quality effects of CAAA 

regulations. 

Exclusion of the impact of 

technological innovation 

and input substitution on 

compliance costs. 

Underestimate Probably minor. Minimal information is 

available on the potential effects of 

technological innovation on costs. Though 

input substitution is a potential source of cost 

savings, the analysis primarily models mature 

industries and compliance strategies which 

have been established as least-cost 

compliance paths. In addition, many 

regulations, such as RACT, are technologybased and may not allow for much input 

substitution. 

Partial estimation of costs 

for compliance with the 

PM2.5 NAAQS, due to the 

unavailability of emission 

reduction targets for nonattainment areas. 

Overestimate Probably minor. The 2006 PM2.5 NAAQS RIA 

estimates that the incremental costs of 

residual non-attainment (i.e., costs of 

additional reductions from unidentified 

controls needed to reach attainment) are 

approximately $4.3 billion in 2020, yielding 

total cost estimates that exceed the 

estimates presented here by a factor of five 

or more.6

 However, we estimate that the 

costs of the PM2.5 NAAQS represent less than 5 

percent of the net benefits of the 

amendments.7

Uncertainty in the 

emission reduction 

estimates used to estimate 

the costs for select rules. 

Unable to 

determine based 

on current 

information 

Probably minor. Costs for many rules are not 

dependent on the corresponding emissions 

reductions (e.g., fuel sulfur limits, tailpipe 

standards, etc.) 

Exclusion of the impact of 

economic incentive 

provisions, including 

banking, trading, and 

emissions averaging 

provisions. 

Underestimate Probably minor. Economic incentive 

provisions can substantially reduce costs, but 

the major economic programs for trading of 

sulfur and nitrogen dioxide emissions are 

reflected in the analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

3-19 

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS 

FOR NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO KEY 

UNCERTAINTIES ON NET BENEFITS ESTIMATE1

Potential for 

overestimation biases in 

engineering cost 

estimates. 

Underestimate Probably minor. A study by Harrington, 

Morgenstern, and Nelson (1999) evaluated the 

accuracy of EPA and OSHA estimates of 25 ex 

ante regulatory cost estimates relative to ex 

post studies of actual costs, and concluded 

that initial cost estimates by EPA 

tend to overstate costs. The source of these 

biases include a built-in conservative bias, 

inaccuracies in estimating the size of the 

affected universe, the effect of learning on 

reducing costs, the effect of innovation on 

reducing costs, and cost-reducing features of 

regulatory design. Some of these factors are 

discussed elsewhere in this table. The 

magnitude of these biases varies 

substantially, but in no case would we expect 

the overall impact to exceed five percent of 

overall net benefits. 1 The classification of each potential source of error reflects the best judgment of the section 812 Project 

Team. The Project Team assigns a classification of “potentially major” if a plausible alternative 

assumption or approach could influence the overall monetary benefit estimate by approximately five 

percent or more; if an alternative assumption or approach is likely to change the total benefit estimate 

by less than five percent, the Project Team assigns a classification of “probably minor.” 2 U.S. Environmental Protection Agency Science Advisory Board, EPA-SAB-COUNCIL-ADV-07-002, "Benefits 

and Costs of Clean Air Act – Direct Costs and Uncertainty Analysis", Advisory Letter, June 8, 2007. 

Available at http://www.epa.gov/sab/pdf/council-07-002.pdf. 3 For an analysis of the learning rates estimated in the empirical literature, see John M. Dutton and Annie 

Thomas, "Treating Progress Functions as a Managerial Opportunity," Academy of Management Review, 

Vol 9, No. 2, 1984. 4 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2005, February 

2005. 5 The Project Team uses this maximum unit cost value in two ways. First, the Project Team assumes that 

local areas would not implement identified controls costing more than $15,000 per ton. Second, the 

Project Team assumes a cost of $15,000 per ton for unidentified controls. 6 U.S. Environmental Protection Agency. Regulatory Impact Analysis for the Particulate Matter NAAQS. 

October, 2006. 7 For detailed estimates of the costs of PM2.5 NAAQS compliance, see E.H. Pechan and Associates, Inc. and 

Industrial Economics, Inc., Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective 

Analysis, prepared for U.S. EPA, March 2009. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-1

CHAPTER 4 – AIR QUALITY BENEFITS 

Air quality modeling links changes 

in emissions to changes in the 

atmospheric concentrations of 

pollutants that may affect human 

health and the environment. A 

crucial analytical step, air quality 

modeling is one of the more complex 

and resource-intensive components 

of the prospective analysis. This 

chapter outlines how we estimated 

future-year pollutant concentrations 

under both the with-CAAA and 

without-CAAA scenarios. 

The first section of the chapter 

begins with a discussion of some of 

the challenges faced by air quality 

modelers and a brief description of 

the models we used in this analysis. 

The following section provides more 

details on the specific air quality 

modeling tools we deployed to estimate future-year ambient concentrations. This 

methodology section includes a description of how we use modeling results to adjust 

monitor concentration data and estimate ambient concentrations for years and scenarios 

where no monitoring yet exists – the projected and counterfactual (without-CAAA) target 

years and scenarios. The third section of this chapter summarizes the results of the air 

quality modeling and presents the expected effects of the CAAA on future-year pollutant 

concentrations. A brief discussion of the key uncertainties associated with air quality 

modeling concludes the chapter. 

OVERVIEW OF APPROACH 

As we outlined in the First Prospective, air quality modelers face two key challenges in 

attempting to translate emission inventories into pollutant concentrations. First, they 

must model the dispersion and transport of pollutants through the atmosphere. Second, 

they must model pertinent atmospheric chemistry and other pollutant transformation 

processes. These challenges are particularly acute for those pollutants that are not 

emitted directly, but instead form through secondary processes. Ozone is the best 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-2 

example; it forms in the atmosphere through a series of complex, non-linear chemical 

interactions of precursor pollutants, particularly certain classes of volatile organic 

compounds (VOCs) and nitrogen oxides (NOx). We faced similar challenges when 

estimating PM concentrations. Atmospheric transformation of gaseous sulfur dioxide and 

nitrogen oxides to particulate sulfates and nitrates, respectively, contributes significantly 

to ambient concentrations of fine particulate matter. In addition to recognizing the 

complex atmospheric chemistry relevant for some pollutants, air quality modelers also 

must deal with uncertainties associated with variable meteorology and the spatial and 

temporal distribution of emissions. 

Air quality modelers and researchers have responded to the need for scientifically valid 

and reliable estimates of air quality changes by developing sophisticated atmospheric 

dispersion and transformation models. Some of these models have been employed in 

support of the development of federal clean air programs, national assessment studies, 

State Implementation Plans (SIPs), and individual air toxic source risk assessments. In 

this analysis, we focused our air quality modeling efforts on estimating the impact of 

with- and without-CAAA emissions on ambient concentrations of ozone, PM10, and 

PM2.5, as well as acid deposition and visibility for each of the target years: 2000, 2010, 

and 2020. The focus on these pollutants is consistent with the result in the First 

Prospective that most of the quantified benefits of the CAAA are attributable to PM and 

ozone. The ideal model for this analysis is a single integrated air quality model capable of 

estimating ambient concentrations for all of these key pollutants throughout the U.S. In 

the prior First Prospective study, such a model had not yet been sufficiently developed 

and tested. This analysis is the first Section 812 prospective analysis to use an integrated 

modeling system, the Community Multiscale Air Quality (CMAQ) model, to simulate 

national and regional-scale pollutant concentrations and deposition. The CMAQ model 

(Byun and Ching, 1999) is a state-of-the-science, regional air quality modeling system 

that is designed to simulate the physical and chemical processes that govern the 

formation, transport, and deposition of gaseous and particulate species in the atmosphere. 

The emissions data were processed for input to the CMAQ modeling using the SparseMatrix Operator Kernel Emissions (SMOKE) emissions processing system (CEP, 2004). 

The model-ready emission inventories for each scenario and year were then used to 

obtain base year and target year estimates of the key criteria pollutants, as well as many 

other species. The air quality modeling analysis was designed to make use of tools and 

databases that have recently been developed and evaluated by EPA for other nationaland regional-scale air quality modeling studies. In particular, model-ready meteorological 

input files for 2002 were provided by EPA for use in this study. For fine particulate 

matter (PM2.5) and related species, the CMAQ model was applied for an annual 

simulation period (January through December). A 36-km resolution modeling domain 

that encompasses the contiguous 48 states was used for the annual modeling. For ozone 

and related species, the CMAQ model was applied for a five-month simulation period 

that captures the key ozone-season months of May through September. Two 12-km 

resolution modeling domains (that when combined cover the key, ozone-significant areas 

of the contiguous 48 U.S. states) were used for the ozone-season modeling. Altogether, 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-3 

model-ready emission inventories were prepared and the CMAQ model was applied for a 

total of 21 simulations (comprising seven core scenarios and three modeling domains). 

The outputs from the CMAQ model provide the basis for the calculation of health and 

ecological benefits of the CAA. The airborne criteria pollutants of interest include ozone 

and fine particulate matter (PM2.5), where PM2.5 consists of particles less than 2.5 microns 

in diameter. For health benefits analysis, it has become standard EPA practice to calibrate 

the CMAQ results monitor data, rather than use the CMAQ results directly – the process 

is sometimes called, “monitor and model relative adjustment.” We follow that approach 

in this analysis as well, applying a tool called the Modeled Attainment Test Software 

(MATS) to develop and apply the calibration factors for particulate matter results relative 

to nearby monitors. For ozone, the MATS procedure is not necessary; instead we use an 

inverse distance squared weighting procedure called Enhanced Voronoi Neighbor 

Averaging (eVNA), which calibrates the CMAQ model ozone results by weighing data 

from monitors closer to the grid cell more heavily than monitors that are further away. 

The eVNA interpolation and model to monitor calibration process is accomplished within 

the BenMAP benefits analysis tool, which is described in Chapter 5. Visibility is also an 

air quality parameter of interest and this was calculated using a variety of the CMAQ 

output species. In addition, deposition of nitrogen and sulfur was also extracted from the 

model outputs. An overview of the modeling approach is provided in Figure 4-1, which 

summarizes the emissions processing and air quality components. The CMAQ modeling 

components and application of the MATS tool are explained in further detail in the next 

section. 

AIR QUALITY MODELING TOOLS DEPLOYED 

THE CMAQ MODELING SYSTEM 

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science, regional 

air quality modeling system that can be used to simulate the physical and chemical 

processes that govern the formation, transport, and deposition of gaseous and particulate 

species in the atmosphere (Byun and Ching, 1999). The CMAQ tool was designed to 

improve the understanding of air quality issues (including the physical and chemical 

processes that influence air quality) and to support the development of effective 

emissions control strategies on both the regional and local scale. The CMAQ model was 

designed as a “one-atmosphere” model and this concept refers to the ability of the model 

to dynamically simulate ozone, particulate matter, and other species in a single simulation 

which captures interaction effects among these pollutants. In addition to addressing a 

variety of pollutants, CMAQ can be applied to a variety of regions with varying 

geographical, land-use and emissions characteristics, and for a range of different space 

and time scales. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-4 

FIGURE 4-1. SCHEMATIC DIAGRAM OF SECTION 812 AIR QUALITY MODELING ANALYSIS 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-5 

The CMAQ model numerically simulates the physical processes that determine the 

magnitude, temporal variation and spatial distribution of the concentrations of ozone and 

particulate species in the atmosphere and the amount, timing, and distribution of their 

deposition to the earth’s surface. The simulation processes include advection, dispersion 

(or turbulent mixing), chemical transformation, cloud processes, and wet and dry 

deposition. The CMAQ science algorithms are described in detail in Byun and Ching 

(1999). 

The CMAQ model requires several different types of input files. Gridded, hourly 

emission inventories characterize the release of anthropogenic, biogenic and, in some 

cases, geogenic emissions from sources within the modeling domain. The emissions 

represent both low-level and elevated sources and a variety of source categories 

(including, for example, point, onroad mobile, nonroad mobile, area, and biogenic 

emissions). The amount, spatial distribution, and temporal distribution of each emitted 

pollutant or precursor species are key determinants to the resultant simulated air quality 

values. 

The CMAQ model also requires hourly, gridded input fields of several meteorological 

parameters including wind, temperature, mixing ratio, pressure, solar radiation, fractional 

cloud cover, cloud depth, and precipitation. A full list of the meteorological input 

parameters is given in Byun and Ching (1999). The meteorological input fields are 

typically prepared using a data-assimilating prognostic meteorological model, the output 

of which is processed for input to the CMAQ model using the Meteorology-Chemistry 

Interface Processor (MCIP). The prescribed meteorological conditions influence the 

transport, vertical mixing, and resulting distribution of the simulated pollutant 

concentrations. Particular meteorological parameters, such as mixing ratio, can also 

influence the simulated chemical reaction rates. Rainfall and near-surface meteorological 

characteristics govern the wet and dry deposition, respectively, of the simulated 

atmospheric constituents. 

Initial and boundary conditions (IC/BC) files provide information on pollutant 

concentrations throughout the domain for the first hour of the first day of the simulation, 

and along the lateral and top boundaries of the domain for each hour of the simulation. 

Photolysis rates and other chemistry related input files supply information needed by the 

gas-phase and particulate chemistry algorithms.33

 

33 The latest available version of CMAQ, version 4.6, was used for this study. This version of the model supports several 

different gas-phase chemical mechanism, particle treatment, aerosol deposition, and cloud treatment options. All 

simulations conducted as part of this study used the CB05 chemical mechanism. For particles, the AERO4 particle 

treatment, which includes sea salt, was applied. Finally, the plume-in-grid feature of CMAQ was not used for this study. 

More details are available in Second Prospective Analysis of Air Quality in the U.S.: Air Quality Modeling, available at 

www.epa.gov/oar/sect812 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-6 

CMAQ APPLICATION PROCEDURES FOR THE SECOND PROSPECTIVE ANALYSIS 

This specific application of CMAQ includes modeling domain specification and key 

input files. The three modeling domains that were used for this analysis are shown in 

Figure 4-2. 

FIGURE 4-2. CMAQ MODELING DOMAINS FOR THE 812 MODELING STUDY 

NOTE: CONUS IS THE CONTINENTAL US GRID USED FOR PM MODELING; WUS IS THE 

WESTERN US GRID AND EUS IS THE EASTERN US GRID USED FOR OZONE MODELING. 

The 36-km resolution continental U.S. (CONUS) domain is the large area that is covered 

by the outer grid box in Figure 4-2. The CONUS domain includes 148 x 112 grid cells 

(the total number of cells is 16,576). The tick marks denote the 36-km grid cells. For this 

domain, the model was run for the entire 2002 calendar year, using 2002 meteorology but 

varying the emissions inputs as outlined in each of the Second Prospective scenarios 

listed in Figure 4-1. In running the model, the annual simulation period was divided into 

two parts covering January through June and July through December, respectively. Each 

part of the simulation also includes an additional five start-up simulation days, which are 

intended to reduce the influence of uncertainties in the initial conditions on the simulation 

results. 

The Eastern U.S. (EUS) domain is comprised of 213 x 188 grid cells (total = 40,044 

cells) and the Western U.S. (WUS) domain includes 213 by 192 grid cells (total = 40,896 

cells). Together these two domains cover most of the continental U.S. with 12-km 

horizontal resolution. There is some overlap in the central part of the country. For both 

the EUS and WUS domains, the CMAQ model was run for the months of May through 

September. This five-month period is intended to represent the ozone season – runs using 

this domain provide the ozone inputs for subsequent steps of the analysis. The seasonal 

simulation period was also divided into two parts covering May and June and July 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-7 

through September, respectively. Each part of the simulation also includes an additional 

ten start-up simulation days. 

The 36- and 12-km resolution meteorological input files to support modeling in these 

domains were prepared using the Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Fifth Generation Mesoscale Model (MM5). The 

MM5 outputs were postprocessed by EPA for input to CMAQ using the MeteorologyChemistry Interface Processor (MCIP) program. The meteorological input preparation 

methodology and some information on MM5 model performance are provided by 

Dolwick et al. (2007). Existing initial condition, boundary condition, land-use and 

photolysis rate input files prepared by EPA for use in CMAQ modeling for the selected 

modeling domains and simulation period were used. 

After the initial CMAQ results were generated, the original primary PM emissions 

estimates generated for area and non-EGU point sources were found to be inaccurate due 

to two issues: 

1) As described in Chapter 2, some of the fine particulate emissions estimates 

derived from the 1990 NEI, on which the without-CAAA emissions estimates 

were based, were discovered to be inconsistent with those from the 2002 NEI, on 

which the with-CAAA emissions estimates were based. 

2) The original emissions estimates did not include application of transport factors 

for area source fine particulate emissions. These transport factors are countyspecific adjustment factors that are applied to specific types of emissions 

estimates to account for the fact that only a fraction of total fugitive dust 

emissions remain airborne and are available for transport away from the vicinity 

of the source after localized removal (i.e., some of the particles are captured by 

the local vegetation or other surface obstructions). 

To correct these two errors, we first made the necessary adjustments to the primary 

PM2.5 emissions estimates for the affected non-EGU point and area sources, focusing on 

the PM2.5 species that contribute most significantly to primary PM emissions: elemental 

carbon (EC), organic carbon (OC), and crustal material. We then calculated speciesspecific adjustment factors for the CMAQ data, re-compiled the species-specific 

estimates to generate an adjusted version of the original CMAQ results, and then 

generated new MATS input files. All details of the procedure are described in a 

memorandum prepared by the Project Team, which was reviewed in detail by the 

Council’s Air Quality Modeling Subcommittee.34

 

34

 Memorandum of June 14, 2010 to Jim DeMocker, EPA, from Tyra Walsh, Henry Roman, and Jim Neumann, Industrial 

Economics, Inc. (IEc), “Description of the Adjustment to the Primary Particulate Matter Emissions Estimates and the 

Modeled Attainment Test Software Analysis (MATS) Procedure for the 812 Second Prospective Analysis.” The memo is 

available at www.epa.gov/oar/sect812. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-8 

MATS PROCEDURE 

Rather than using the direct CMAQ results as the basis for the health and ecological 

effects analyses, the Project Team conducted additional analyses using a speciated 

monitor and model calibration technique to generate PM2.5 air quality estimates. The 

PM2.5 estimates used in the Second Prospective health analysis were prepared using 

EPA’s Modeled Attainment Test Software (MATS, Version 2.1.1, Build 807). MATS 

estimates quarterly mean PM2.5 chemical component concentrations at monitor locations 

by conducting a Speciated Modeled Attainment Test (SMAT) analysis. MATS can also 

estimate quarterly mean concentration estimates for each PM2.5 chemical component 

concentrations at all grid cells in a grid model such as CMAQ. 

Five of the six MATS PM2.5 concentration estimates for the Second Prospective scenarios 

were prepared using the MATS’ spatial and temporal relative adjustment method. The 

MATS estimates for the 2000 with-CAAA scenario, which represents a historical year for 

which monitor data are available, used a spatial only relative adjustment method, relying 

on available monitor data and a single year of CMAQ modeling. The MATS procedure 

was not applied for the 1990 base year scenario. 

MATS estimates the PM2.5 concentrations in CMAQ grid cells by interpolating values 

from nearby monitors using the inverse distance squared weighting option in the Voronoi 

Neighbor Averaging (VNA) procedure in MATS. This is an algorithm that identifies a 

set of monitors close to the grid cell (called “neighbors”) and then estimates the PM 

species concentration in that grid cell by calculating an inverse-distance weighted average 

of the monitor values (i.e., the concentration values at monitors closer to the grid cell are 

weighted more heavily than monitors that are further away). As noted above, for 

calibrating ozone model results to nearby monitors, only the VNA component of the 

procedure is used, because there is no need for the speciated interpolation approach 

required for PM. 

The spatial MATS analysis conducted for the PM2.5 estimates used the following input 

information: 

 observed quarterly PM2.5 data from 1,232 Federal Reference Method (FRM) 

monitors with sufficient data in 2002 – sufficient data is defined as at least one 

quarter of PM2.5 data. The year 2002 was used because it corresponds to the 

vintage of the emissions estimates, which, as described in Chapter 2, were 

derived from the 2002 National Emissions Inventory; 

 observed daily chemically speciated fine particle mass data from both the PM2.5 

Speciation Trends Network (STN) and the Interagency Monitoring of Protected 

Visual Environments (IMPROVE) network, providing a total of 273 monitors 

with sufficient data in 200235; 

 

35

Most FRM monitors (about 75 percent) are not co-located with a speciation monitor. Therefore, we also used data 

providing speciated PM mass from the STN and IMPROVE monitors. The MATS analysis used speciated data from 273 STN or 

IMPROVE monitors with at least two valid quarters of speciated data in 2002. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-9 

 speciated CMAQ estimates for 6 PM2.5 species (SO4, NO3, elemental carbon, 

organic carbon, NH4, and crustal material) at the 36 kilometer PM2.5 CMAQ grid 

cell level for each of the Second Prospective scenarios (from CMAQ speciated 

output data files). 

The MATS procedure enables the use of monitor data to effectively calibrate the results 

of air quality modeling for use in subsequent steps of the analysis. To illustrate the 

effects of the MATS procedure, compare Figure 4-3, which is a scatter plot comparing 

the direct CMAQ results for those 1,058 PM2.5 monitors with at least two quarters of data 

for 2002, and Figure 4-4, which is a similar scatter plot, comparing the MATS results to 

the same set of PM2.5 monitors. The agreement between monitor and model values in 

Figure 4-4 is greatly improved by the MATS procedure. 

FIGURE 4-3. SCATTER PLOT OF DIRECT CMAQ ESTIMATES AND 2002 PM2.5 FEDERAL REFERENCE 

METHOD (FRM) MONITORS 

Figure 4-5 provides a further illustration of the effect of the MATS procedure, and the 

importance of individual PM species in achieving an effective calibration of the CMAQ 

results to monitor data. The figure provides detailed species-specific CMAQ and MATS 

results for a CMAQ grid cell in the three largest cities and metropolitan areas in the US – 

New York, Los Angeles, and Chicago – and for Tucson, Arizona, a much smaller city but 

CMAQ vrs 2002 FRM Monitors

0

5

10

15

20

25

30

0 5 10 15 20 25 30

2002 FRM Monitors (1,058 Monitors), PM2.5 annual mean (ug/m3)

CMAQ 2000 w CAAA PM2.5 (annual mean, ug/m3)
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-10 

one for which one component of PM, crustal (shown in brown), plays a critical role in our 

analysis. For each city, the two leftmost bars provide the 2002 FRM and STN annual 

average PM2.5 monitor data for a monitor of that type within the grid cell. FRM monitors 

provide only total PM2.5 mass, while the STN monitors provide data for the seven PM 

species (plus estimated water) indicated at the bottom of each graph.36 The remaining 12 

bars in each panel show the CMAQ and MATS-adjusted results for the grid cell for the 

with-CAAA and without-CAAA scenarios, for target years 2000, 2010, and 2020. 

FIGURE 4-4 SCATTER PLOT OF MATS-ADJUSTED CMAQ ESTIMATES AND 2002 PM2.5 FEDERAL 

REFERENCE METHOD (FRM) MONITORS 

 

36 The STN bar charts include an estimated water component, which the MATS input monitor files include to make STN and 

IMPROVE monitor data consistent with FRM monitor data. The water component is not an STN component, but was 

estimated using the SANDWICH (Sulfates, Adjusted Nitrates, Derived Water, Inferred Carbonaceous mass, and estimated 

aerosol acidity (H+)) process. 

MATS with Adjustments vrs 2002 FRM Monitors

0

5

10

15

20

25

30

0 5 10 15 20 25 30

2002 FRM Monitors (1,058 Monitors) (annual mean PM2.5, ug/m3)

Adj. MATS 2000 w CAAA (PM2.5 annual mean, ug/m3)
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-11

FIGURE 4-5. COMPARISON OF CMAQ, MATS, AND MONITOR DATA FOR FOUR SELECTED CITIES 

The Manhattan panel in the upper left corner shows that both the FRM and STN monitors 

indicate a total PM concentration just greater than 15 μg/m3

. The next bar shows that the 

CMAQ data for the 2000 with-CAAA simulation overestimates the PM concentration, by 

about 4 μg/m3

. Comparing the 2002 STN bar with the 2000 with-CAAA CMAQ bar, we 

see that the CMAQ simulation overestimates most constituents in this location, compared 

to the monitor data, but underestimates organic matter (or OC, shown in green). The 

MATS procedure, applied to the STN and CMAQ data, generates species-specific scaling 

factors that result in a MATS-adjusted concentration for the 2000 with-CAAA scenario, 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-12 

shown in the next bar. As a result, the species-specific constituents in the MATS 

adjusted bar are in very nearly the same proportion as they appear for the STN monitor. 

It would also appear from this figure that MATS “overcorrects” in Manhattan because the 

2000 with-CAAA MATS bar is lower than the 2002 STN monitor bar. However, the 

MATS procedure is estimating the concentration at the center of the grid cell, not at the 

location of the STN monitor. In a 36 km grid cell, the monitor location can be many 

kilometers away from the center of the grid cell. MATS considers not only monitors in 

the same grid cell, but also the data at other nearby FRM and STN monitors, and makes a 

spatial interpolation to estimate concentrations at the grid centroid. The Manhattan STN 

monitor is near the intersection of four grid cells, which contain a total of 25 FRM and 

STN monitors, all of which influence the MATS result. 

The remaining MATS estimates for Manhattan, for the 2000 without-CAAA and the 2010 

and 2020 projections, are based on scaling of the corresponding CMAQ simulation by the 

species-specific factors developed from the 2000 with-CAAA to 2002 STN monitor 

comparison. The effect of MATS in Manhattan is to adjust the CMAQ simulation 

concentrations downward. Interestingly, the opposite is generally true in Los Angeles, 

because in that city CMAQ tends to underestimate the monitor data for 2002. The mix of 

species in both cities is similar in 2002, but strikingly different over time, particularly in 

the without-CAAA scenario, where organic carbon (shown in green) in Los Angeles 

derives from mobile sources, and sulfates (shown in yellow) in Manhattan derives from 

long-range transport from coal-burning electric generating units. 

In Chicago, the effect of MATS is more complex, and the importance of considering PM 

species is highlighted. In the with-CAAA scenarios, MATS yields a downward 

adjustment to the CMAQ simulations, because the 2000 with-CAAA CMAQ simulation is 

higher than the 2002 STN monitor value. In the without-CAAA scenarios, however, there 

are much higher emissions of organic carbon, because certain OC emissions controls are 

not in place in the without-CAAA simulations that are in place in the with-CAAA scenario. 

Because CMAQ underestimates the ambient OC component in the 2000 with-CAAA

(shown in green), the factor for OC that is applied to other scenarios yields an increase in 

concentration in the MATS-adjusted values. That increase is large enough to dominate 

the overall adjustment across all eight species, yielding an overall PM2.5 mass increase for 

the without-CAAA scenarios relative to the CMAQ data. 

The data for Tucson also illustrates the importance of the species-specific scaling factors. 

If it were not for changes to one PM species, crustal (shown in brown), there would be 

only a relatively modest difference between the with-CAAA and without-CAAA scenarios 

in future years. In Tucson the crustal component derives largely from construction 

activity, which in this relatively fast growing area of Arizona, and absent more stringent 

dust control measures, could become a larger issue in the projection years. CAAA 

controls on fugitive dust emissions in the construction sector, however, yield a substantial 

difference in this component of PM concentrations, when comparing the with-CAAA and 

without-CAAA scenario results. Other species differ much less across scenarios. In many 

other places like Tucson, the species-specific MATS procedure likely yields a more 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-13 

accurate projection of the impact of the CAAA than a calibration procedure that did not 

take into account the impact of these species-specific control strategies. 

AIR QUALITY RESULTS 

PARTICULATE MATTER 

As mentioned above, the CMAQ modeling results for the 36-km continental U.S. 

(CONUS) modeling domain provide the basis for particulate matter air quality used in the 

calculation of PM-related health effects and to calculate visibility, as well as sulfur and 

nitrogen deposition. Summary results are presented in the maps in Figure 4-6 below, 

representing annual average concentrations across the CONUS domain for each of the 

seven scenario/target year combinations modeled. The rows of Figure 4-6 show modeled 

PM2.5 concentrations for 2000, 2010, and 2020, contrasting the without-CAAA results on 

the left and the with-CAAA results on the right. 

As the figure indicates, over the thirty-year 1990-2020 simulation period air quality is 

projected to worsen somewhat in the absence of CAAA regulations, particularly in the 

Midwest and California, but with CAAA regulations in place air quality is estimated to 

improve markedly as early as the year 2000 and to show continued improvements 

through 2020. In general, the with-CAAA results reflect a calibration of the 2002 model 

year results to monitor values, but as the accompanying Box 4-1 illustrates, such direct 

comparisons are not possible for the counterfactual without-CAAA results. We conclude 

for the analyses described in the text box that the without-CAAA results, with a few 

exceptions, seem to imply a return of air quality conditions comparable to those that 

prevailed in the 1980-1990 period prior to implementation of the CAAA. Such 

comparisons are limited, however, by the sparse PM2.5 monitoring data for this period and 

the uncertainty in adjusting available monitor data for other species. Although the 

improvements attributed to the CAAA are nationwide, the most substantial gains are 

made in those areas that had the worst PM air quality in 1990, suggesting the CAAA has 

been and will continue to be effective in targeting improvements to the areas that would 

have experienced the worst air quality in the absence of the amendments. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-14 

 

BOX 4-1: EVALUATING THE WITHOUT-CAAA SCENARIO RESULTS 

The two scenarios used in this study, the with-CAAA and without-CAAA scenarios, are designed to simulate and forecast air quality 

conditions in the US as we expect them to unfold with full implementation of the CAAA (the with-CAAA), and alternatively as if regulations 

authorized by the CAAA had not been implemented. In effect, the methods we use tie the with-CAAA scenario to monitored air quality in 

the year 2000, providing some measure of credibility for the air quality conditions reflected in our with-CAAA simulation. It is more 

difficult to evaluate the credibility of the without-CAAA scenario, because that scenario simulates hypothetical air quality conditions that 

cannot be observed. The plausibility of the without-CAAA scenario and its differences from the with-CAAA scenario nevertheless can be 

assessed through comparison to other similar air quality conditions. 

One possible analog for conditions in the without-CAAA scenario is areas outside the US that have not implemented air quality regulations 

that match the stringency of those in the US. The problem with comparing US to non-US areas is the difficulty of standardizing factors 

which define air quality, such as meteorology, terrain, and the distribution of air pollutant emission sources. Another major challenge is that 

monitoring networks for fine particle species are sparse or not available for the annual average measure. 

A preferable, though still imperfect, comparison is between without-CAAA forecasts and historical concentrations in US cities. A key issue 

arising for within-US comparisons is that prior to 1990 particulate matter monitors measured total suspended particulates (TSP), or PM10, 

rather than PM2.5. The new PM standard is based on PM2.5, which is now recognized as better correlated with adverse health effects. PM2.5

is therefore the focus of our air quality simulations. Furthermore, the ratios of TSP and/or PM10 to PM2.5 vary considerably by location and 

over time, so a simple transformation of the available monitor data may not be reliable. Nonetheless, it is possible to find times and 

locations in the historical monitor data where at least two and sometimes all three of these measures were simultaneously collected, 

providing a means to estimate a time and location-specific ratio that can be used to infer PM2.5 values. We use this type of information to 

develop estimates of historical PM2.5 concentration in selected U.S. cities for comparison to our without-CAAA scenario projected values. 

The table suggests that our estimates of without-CAAA PM2.5 concentrations in New York, Pittsburgh, and Los Angeles are reasonably 

consistent with estimated historical concentrations in the 1980 to 1990 pre-CAAA period. In Chicago, however, the without-CAAA case 

yields estimates that are much higher than historical estimates. One reason may be the potentially strong influences of projected 

uncontrolled sulfur dioxide emissions from electric power plants near Chicago in the without-CAAA case. In the absence of Title IV these 

plants are projected in our study to use relatively high sulfur, locally mined coal and would not have been required to install scrubber 

technology. 

(ANNUAL AVG 

MICROGRAMS PER 

CUBIC METER) 

ESTIMATED PM2.5 CONCENTRATIONS FOR THIS STUDY ESTIMATED HISTORICAL PM2.5

2000 2010 2020 

1980 

(EST) 

1990 

(EST) 

MAXIMUM 

CITIES 1980-90 

WCAAA W/OCAAA WCAAA W/OCAAA WCAAA W/OCAAA 

New York - 

Manhattan 

12.9 20.6 10.9 21.0 10.0 22.1 N/A 22.4 N/A 

New York - 

Queens/Brooklyn 

13.2 24.8 11.0 25.2 10.1 26.7 N/A 21.5 N/A 

Pittsburgh 14.0 19.2 11.0 19.7 10.0 20.3 29.3 22.3 29.8 

Chicago 15.5 47.7 13.7 47.6 13.4 48.9 25.7 20.4 25.7 

Los Angeles 18.5 25.5 17.1 29.7 17.5 35.5 38.5 29.4 41.9 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-15 

FIGURE 4-6. CMAQ SIMULATED AND MATS ADJUSTED ANNUAL AVERAGE PM2.5 SPECIES 

CONCENTRATION (MICROGRAMS PER CUBIC METERS) FOR THE CONUS DOMAIN 

OUTPUTS FOR THE 1990 TO 2020 PERIOD 

Figure 4-7 makes the gains in 2020 more clear, by illustrating the differences in PM2.5

concentrations between the with-CAAA and without-CAAA scenarios in 2020. The gains 

in some areas, particularly in the eastern half of the US, in California, and in urban 

centers nationwide, are dramatic, with reductions of more than 20 μg/m3 in some areas. 

These are consistent with the large decreases in PM precursor emissions for those areas, 

described in Chapter 2. In some of these areas, the without-CAAA scenario 

concentrations also reach high levels because of the absence of without-CAAA controls 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-16 

(see accompanying text box for a discussion of the without-CAAA scenario). There are 

also some surprisingly large reductions in a few less populous areas, such as, west central 

Idaho and central Virginia. The reductions in Idaho, as well as in a few other isolated 

areas of the rural West, are associated with CAAA requirements to limit emissions from 

agricultural burning operations. The reductions in central Virginia are attributable to 

local controls on a large coal-burning industrial boiler. 

FIGURE 4-7. DIFFERENCE IN CMAQ SIMULATED MATS ADJUSTED ANNUAL AVERAGE PM2.5 SPECIES 

CONCENTRATION (MICROGRAMS PER CUBIC METER) FOR THE CONUS DOMAIN: 

2020 WITH-CAAA MINUS 2020 WITHOUT CAAA SCENARIOS 

Some areas also experience modest increases in PM concentrations with the CAAA – 

these areas show up in light orange on the map. Some of the smallest estimated 

increases, less than 1 μg/m3

, can be introduced by the MATS adjustment procedure, 

particularly when the locations are far from monitors and/or have very low modeled or 

monitored concentrations of a PM species. We interpret very small increases such as 

these as effectively “no change” so adjusted the map legend to group these cells with 

others where are small benefits.37 There remain five cells with disbenefits greater than 1 

 

37 There is one area in northeastern Utah where the MATS procedure yields results for the without-CAAA scenario that are so 

large as to be not plausible. The result was associated with increases in agricultural burning in the without-CAA scenario, 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-17 

μg/m3. The three cells of these five with the smallest disbenefit estimates did not have 

disbenefits in the CMAQ modeling – we therefore conclude that the disbenefit result was 

introduced by the MATS procedure. 

In the remaining two cells, we conclude that implementation of the CAAA led to negative 

benefits, associated with actual increases in emissions resulting in the with-CAAA case 

relative to the without-CAAA case. The largest disbenefit, of 4.1 μg/m3

, is in the 

northwestern corner of New Mexico, in the cell which includes the Four Corners Power 

Plant, one of the largest coal-burning power plants in the West. The emissions data 

indicate sulfur dioxide emissions for that plant that are 14,000 tons greater in the 2020 

with-CAAA case, probably as a combined result of changes in dispatch and sulfur content 

of coal for this plant, which as of December 2010 does not have a sulfur scrubber. The 

other cell shows a disbenefit of 1.25 μg/m3

, and is located in Sweetwater County in south 

central Wyoming, which includes the Pacificorp-Jim Bridger Power Plant. The air 

quality result here is also attributable to a difference in sulfur dioxide emissions from a 

power plant, in this case 2,000 tons greater in the 2020 with-CAAA scenario. The 

dispatch of this unit appears to be identical in both scenarios, so the result is most likely 

attributable to a marginal reallocation of higher sulfur coal. Note that, as indicated in the 

with-CAAA maps in Figure 4-6, these are areas that nonetheless would continue to 

experience PM2.5 concentrations below the 15 μg/m3 PM2.5 annual standard. These 

relatively modest and geographically limited exceptions notwithstanding, it is clear that 

by 2020 the air quality benefits of the CAAA in reducing ambient concentrations of 

particulate matter are large and widespread. 

OZONE 

Figures 4-8 through 4-11 present similar CMAQ output data for ozone, with two 

important differences: (1) the ozone results are reported for the Eastern (EUS) and 

Western (WUS) 12-km modeling domains; and (2) the results presented are the average 

of daily maximum 8-hour ozone concentration, in ppb, over the course of a modeled 

ozone season (May 1 through September 30). The average daily 8-hour maximum may 

seem like an odd metric for evaluating ozone concentrations, but because this is the 

metric used in epidemiological estimation of mortality risks of ozone this metric is 

closely correlated with the major mortality incidence and economic benefits associated 

with ozone precursor controls. Results for the Eastern US are in Figures 4-8 and 4-9, and 

for the Western US in Figures 4-10 and 4-11. 

For the Eastern US, Figure 4-8 shows a similar pattern for ozone as was illustrated for 

particulate matter in Figure 4-6. That is, while there are relatively modest increases in 

 

coupled with otherwise low organic carbon monitor values in nearby monitors – the application of MATS therefore led to 

unusually high organic carbon and PM2.5 measures for that area. For those three cells, we performed a moving average 

smoothing procedure to re-estimate the without-CAAA concentrations, using PM estimates from adjoining cells. The 

adjustment is used only for the purposes of generating the maps in this chapter; for the purposes of health benefits 

modeling and valuation of benefits, we excluded these three suspect cells. The cells represent very rural, sparsely 

populated areas in the Wasatch Mountains, and so we believe that excluding them from the benefits calculations is both 

prudent and has only a modest underestimation effect on the overall health benefits estimates. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-18 

ozone concentrations in the absence of the CAAA, the with-CAAA maps on the right side 

of the graphic show significant and widespread gains in air quality throughout the region, 

with air quality benefits increasing over time. By 2020, Figure 4-9 shows that the 

difference in ozone concentrations is large in most areas of the east, with gains as large as 

30 ppb for this simulated day. 

Two other patterns in Figure 4-9 are also worth noting. First, although the region-wide 

benefits of the CAAA are large, in many urban areas concentrations in the with-CAAA

case are higher than in the without-CAAA case, in some cases near the Gulf Coast and in 

New York City by as much as 15 to 20 ppb. Second, some of the areas with the largest 

improvements, such as those in the heart of the Midwest, include pockets of much smaller 

gains, particularly in some urban centers. In both cases, these results are not unexpected. 

The complex chemistry of ozone includes a phenomenon known as “NOx-scavenging”, 

whereby nitrogen oxides, while participating as an ozone precursor, can also serve to 

scavenge or reduce ozone, particularly during the peak ozone season and in urban centers 

where ozone levels might otherwise be quite high. The CAAA, in reducing the nitrogen 

oxide precursors, may in some cases reduce ozone on a regional level while leading to 

much smaller reductions or even increases in ozone in the center of certain urban areas. 

This effect explains both these results. Nonetheless, as Figure 4-9 makes clear, the 

overall area (and population exposed) of ozone reductions is far greater than the 

corresponding areas with ozone increases. 

Ozone results in the Western US, in Figures 4-10 and 4-11, indicate a similar pattern to 

those for the Eastern US when examining concentrations in urban areas, although in the 

West the largest ozone air quality gains are restricted to a smaller area, centered in the 

areas in California that have historically struggled with ozone attainment. In addition, in 

the Western US there are some more extensive areas in Figure 4-11 with ozone 

disbenefits attributed to the CAAA, particularly in Los Angeles.38 Another interesting 

result, not shown in Figure 4-10, is that we estimate that ozone concentrations will 

actually increase from 1990 to 2000 in most parts of California, in both the withoutCAAA and with-CAAA scenarios, before reductions in 2010 and 2020 bring ambient 

levels below those seen in 1990, at least in most areas. This result is largely attributable 

to the longer attainment deadlines for the severe non-attainment areas in California – our 

scenario assumes that emissions will increase for some period before aggressive regional 

mobile source tailpipe standards and non-road fuel and engine standards, and local-scale 

ozone attainment plans, have their full effect later in our simulation period. 

 

38 We examined this result further and found that, in cells with the largest disbenefits, the 2020 without-CAAA scenario 

yields concentrations of approximately 45 ppb, while concentrations in outlying areas are as high as 100 ppb or slightly 

higher. One effect of CAAA controls is to suppress NOx-scavenging in the city center, where disbenefits are largest, yielding 

with-CAAA concentrations in the 60 to 65 ppb range. The main effect of the CAAA, however, is large decreases in ozone in 

the outlying areas, to concentrations of 60 to 75 ppb. The net effect on a population weighted basis remains a lowering of 

overall exposures. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-19 

FIGURE 4-8. CMAQ SIMULATED AND VNA ADJUSTED DAILY MAXIMUM 8-HOUR OZONE (PPB) FOR 

THE EUS DOMAIN 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-20 

FIGURE 4-9. DIFFERENCE IN SIMULATED DAILY MAXIMUM 8-HOUR OZONE CONCENTRATION (PPB) 

FOR THE EUS DOMAIN FOR 15 JULY: 2020 WITH-CAAA MINUS 2020 WITHOUTCAAA SCENARIOS 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-21 

FIGURE 4-10. CMAQ SIMULATED AND VNA ADJUSTED DAILY MAXIMUM 8-HOUR OZONE (PPB) FOR 

THE WUS DOMAIN 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-22 

FIGURE 4-11. DIFFERENCE IN SIMULATED DAILY MAXIMUM 8-HOUR OZONE CONCENTRATION (PPB) 

FOR THE WUS DOMAIN FOR 15 AUGUST: 2020 WITH-CAAA MINUS 2020 WITHOUTCAAA SCENARIOS 

UNCERTAINTY IN AIR QUALITY ESTIMATES 

Unlike the air quality modeling conducted over a decade ago for the first Section 812 

prospective analysis, which used two different models for ozone and particulate matter, 

the modeling conducted for the Second Prospective analysis utilized EPA’s Community 

Multiscale Air Quality (CMAQ) model, a “one-atmosphere” model that simulates the 

chemical formation, transport, and deposition of ozone and particulate matter together in 

one comprehensive system.39 The use of this comprehensive air quality modeling system 

provides a consistent platform for evaluating the expected responses to changes in 

precursor emissions, reducing many of the uncertainties which pertained in the First 

Prospective as a result of the limited ability of the models to capture important interaction 

effects among the ozone and PM precursor pollutants. 

 

39 Use of an integrated model such as CMAQ for the current study was one of the recommendations made by the Council in 

their review of the First Prospective analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-23 

Nonetheless, air quality modeling is a complex process and, as such, involves many 

uncertainties. We provide a summary of some of the more important classes of air 

quality modeling uncertainties in Table 4-1 below. These include a known 

meteorological bias in the 12-km eastern MM5 domain, which leads to a general 

tendency to underestimate the monthly observed precipitation; uncertainties in secondary 

organic aerosol (SOA) chemistry which lead to underestimation of SOA formation in the 

CMAQ simulations; issues in the detailed CMAQ modeling of some PM precursors; 

reliance for ozone modeling on a 12-km grid, suggesting NOx inhibition of ambient ozone 

levels may be under-represented in some urban areas; and some emissions estimation 

geographic scale/resolution issues. In all cases but the ozone grid resolution and 

modeling of SOA formation, the effect of these uncertainties on our estimate of net 

benefits is of uncertain direction. In addition, in all but one case, modeling of SOA 

formation, we believe the impact of these uncertainties is probably minor, or of an 

influence less than five percent of the total net benefits, based on current information. 

Use of the CMAQ model platform, which has been evaluated in many contexts and used 

extensively by EPA for broad regulatory analyses such as the Second Prospective, has 

been a major factor enhancing our understanding of the impact of air quality modeling 

exercises such as this. 

Another factor contributing to our understanding of key uncertainties is that the air 

quality modeling analysis conducted for the second Section 812 prospective study used 

national-scale modeling databases originally prepared by EPA for use in other recent 

modeling exercises conducted to support national rulemaking, including the latest 

available meteorological and other input databases (for 2002). Given that the modeling 

databases were originally prepared and utilized by EPA in other analyses, a 

comprehensive performance evaluation was not undertaken as part of this Section 812 

prospective analysis; though the overall projections were assessed using the Atmospheric 

Model Evaluation Tool (AMET), which showed bias and error statistics for our results 

were within the acceptable range for model performance.40 As noted in Table 4-1, biases 

or uncertainties could be manifest in the simulated concentration fields due to the use of 

the 36- and 12-km resolution grids, which might not be sufficiently detailed to resolve 

certain sub-grid scale processes in portions of the modeling domain. All air quality 

modeling exercises are affected by inherent uncertainties in model formulation, 

meteorological inputs, and emission inventory estimates. Nevertheless, the modeling was 

conducted following current EPA guidelines and in a manner consistent with EPA 

approaches/practice for similar national-scale modeling exercises. 

One factor identified in Table 4-1 involves uncertainties associated with corrections to the 

air quality outputs completed coincident with the Council review of the study outputs. 

These corrections, reflecting the need to adjust some categories of direct fine particulate 

emissions for the without-CAAA scenario, and to incorporate adjustments to take account 

of processes that remove fugitive dust from the ambient air at or close to the source of 

emissions, owing to the effect of forests, vegetation, and urban structures on fugitive dust, 

 

40 ICF International, Evaluation of CMAQ Model Performance for the 812 Prospective II Study, November 24, 2009, page 31 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-24 

were necessary because of issues identified through quality control assessments the 

Project Team completed. As noted in the table, we believe these factors have been 

addressed through carefully designed post-hoc adjustment of the CMAQ results, however 

in both cases it would have been preferable to have made the adjustments prior to running 

the CMAQ model. Resource and time limitations unfortunately prevented the Project 

Team from re-estimating the CMAQ results to account for these adjustments. 

Perhaps surprisingly, our assessment is that only one of these factors, uncertainty in 

secondary organic aerosol formation, constitutes a major source of uncertainty. This 

result could reflect our inability to apply alternative quantitative air quality modeling 

tools in this already resource-intensive step in the analytic chain, although it is also clear 

that the CMAQ model best reflects the state-of-the-art for the type of national scale air 

quality modeling necessary to support this benefit-cost analysis. As we discuss in 

Chapter 7, the overall contribution of this step in the analytic chain to uncertainty in net 

benefits, compared to other steps, may be considerably less, because of the ability to 

calibrate model results to monitor values for at least the year 2000 with-CAAA scenario. 

It is worth noting, however, that as a whole the air quality modeling process very likely 

contributes a greater than 10 percent uncertainty, of indeterminate direction, to the overall 

uncertainty in benefits estimates. In addition, it is clear there are uncertainties introduced 

by the ex post adjustment of some primary PM emissions estimates and the procedure 

used to re-calibrate the CMAQ air quality to account for this emissions adjustment. 

Although we argue that the overall effect of this source of uncertainty on the net benefits 

is probably minor, in some locations ambient PM from primary PM emissions can be 

more important than secondarily formed fine particles. Overall, we believe that our 

application of the MATS monitor calibration procedure, which provides a speciated 

calibration to ensure better agreement between air quality modeling results and 

comparable monitor data, provides the best attainable consistency between our air quality 

simulation results and monitored values – the ability to calibrate our results to detailed 

monitor data in this step of the analytic chain provides considerably greater confidence 

that our results are “ground-truthed” as much as possible to real world conditions. 

TABLE 4-1. KEY UNCERTAINTIES ASSOCIATED WITH AIR QUALITY MODELING 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR NET 

BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Unknown meteorological 

biases in the 12-km western 

and 36-km MM5 domains due 

to the lack of model 

performance evaluations. 

Unable to determine 

based on current 

information. 

Probably minor. Other evaluations 

using 2002 and similar meteorology 

and CMAQ have shown reasonable 

model performance, but significant 

effects on nitrate results in western 

areas with wintertime PM2.5

problems. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-25 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR NET 

BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Known metrological biases in 

the 12-km eastern MM5 

domain. MM5 has a cold bias 

during the winter and early 

spring, and has a general 

tendency to underestimate 

the monthly observed 

precipitation. MM5’s under 

prediction was greatest in the 

fall and least in the spring 

months. 

Unable to determine 

based on current 

information. 

Probably minor. These biases would 

likely influence PM2.5 formation 

processes, which was modeled on 

the 36-km domain. 

Secondary organic aerosol 

(SOA) chemistry. CMAQ 

version 4.6 has known biases 

(underprediction) in SOA 

formation. 

Underestimate. Possibly major. The modeling 

system underpredicts SOA, which 

has both biogenic and 

anthropogenic components. 

Reductions in NOx can reduce both 

biogenic and anthropogenic SOA and 

reductions in VOC will reduce 

anthropogenic SOA. Since both of 

these precursors are significantly 

impacted by the CAAA, there may 

be large benefits from SOA related 

reductions that are not currently 

captured by the modeling system. 

The CMAQ modeling relies on 

a modal approach to modeling 

PM2.5 instead of a sectional 

approach. The modal 

approach is effective in 

modeling sulfate aerosol 

formation but less effective in 

modeling nitrate aerosol 

formation than the sectional 

approach. 

Unable to determine 

based on current 

information. 

Probably minor in the eastern U.S. 

where annual PM2.5 is dominated by 

sulfate. Potentially major in some 

western U.S. areas where PM2.5 is 

dominated by secondary nitrate 

formation. 

Limited model performance 

evaluation of CMAQ for 2002. 

Unable to determine 

based on current 

information. 

Probably minor. While a 

comprehensive model evaluation 

was not completed, the overall 

results of the CMAQ runs for the 

Second Prospective were assessed 

using AMET, and bias and error 

statistics were within acceptable 

ranges. Further, our application of 

the MATS procedure provides 

further assurance that air quality 

results used in the subsequent 

health assessments are consistent 

with available monitor data. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-26 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR NET 

BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Ozone modeling relies on a 

12-km grid, suggesting NOx

inhibition of ambient ozone 

levels may be underrepresented in some urban 

areas. Grid resolution may 

affect both model 

performance and response to 

emissions changes. 

Unable to determine 

based on current 

information. 

Probably minor. Though potentially 

major ozone results in those cities 

with known NOx inhibition, ozone 

benefits contribute only minimally 

to net benefit projections in this 

study. Grid size affects chemistry, 

transport, and diffusion processes, 

which in turn determine the 

response to changes in emissions, 

and may also affect the relative 

benefits of low-elevation versus 

high-stack controls. 

Emissions estimated at the 

county level (e.g., low-level 

source and motor vehicle NOx

and VOC emissions) are 

spatially and temporally 

allocated based on land use, 

population, and other 

surrogate indicators of 

emissions activity. Uncertainty 

and error are introduced to 

the extent that area source 

emissions are not perfectly 

spatially or temporally 

correlated with these 

indicators. 

Unable to determine 

based on current 

information. 

Probably minor. Potentially major 

for estimation of ozone, which 

depends largely on VOC and NOx

emissions; however, ozone benefits 

contribute only minimally to net 

benefit projections in this study. 

Use of MATS relative response 

factors to calculate changes in 

PM2.5 

Indeterminate Probably minor. Using MATS, air 

quality modeling results were 

projected in a “relative” sense. In 

this approach, the ratio of future 

year model predictions to base year 

model predictions are used to 

adjust ambient measured data up or 

down depending on the relative 

(percent) change in model 

predictions for each location. The 

use of ambient data as part of the 

calculation helps to reduce 

uncertainties in the future year 

predictions, especially if the 

absolute model concentrations are 

over-predicted or under-predicted. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

4-27 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR NET 

BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO 

KEY UNCERTAINTIES ON NET 

BENEFITS ESTIMATE*

Modeling artifacts created by 

changes in emissions inventory 

estimation methods between 

the 1990 inventories used for 

the without-CAAA scenario 

and the 2002 inventories used 

for the with-CAAA scenarios 

were mitigated through 

application of adjustment 

factors for primary PM from 

non-EGU point sources, and 

for the certain subsectors of 

area sources, in the withoutCAAA case. Application of 

these adjustments may result 

in overestimated or 

underestimated changes in 

primary PM contributions to 

ambient concentrations for 

these particular sources. 

Unable to determine 

based on current 

information. 

Probably minor. While primary PM 

can make a significant contribution 

to ambient PM2.5 in some locations, 

secondarily formed fine particles 

dominate the estimates for ambient 

concentration change in this 

analysis. In addition, the effect of 

the inventory adjustments was to 

significantly reduce the differentials 

between the control and 

counterfactual scenarios, implying 

any residual error is more likely to 

reflect an underestimation bias than 

an overestimation bias, particularly 

since the non-EGU primary PM 

reductions were adjusted to a 

scenario differential of zero. 

Adjustments to take account 

of processes that remove 

fugitive dust from the 

ambient air at or close to the 

source of emissions, owing to 

the effect of forests, 

vegetation, and urban 

structures on fugitive dust. 

Analysis of the chemical 

species collected by ambient 

air samplers suggests that the 

modeling process may 

overestimate PM-2.5 from 

fugitive dust sources by as 

much as an order of 

magnitude, if not adjusted for 

this effect. The Project Team 

incorporated adjustments 

post-CMAQ modeling but prior 

to use of PM air quality 

estimates in subsequent steps 

of the analysis. 

Unable to determine 

based on current 

information. 

Probably minor. If adjustment 

factors had been applied as part of 

the CMAQ modeling, evidence 

suggests the entrainment effect 

would have been adequately 

accounted for. The largely linear 

processes of direct PM emissions to 

air quality suggest that our post-hoc

adjustment should also be adequate 

to account for this factor. Further 

assurance that this factor has been 

accounted for is our application of 

the MATS monitor calibration 

procedure, which provides a 

speciated calibration to ensure 

better agreement between air 

quality modeling results and 

comparable monitor data, and the 

fact that the adjustment applies to 

both scenarios, further mitigating 

the impact of this source of 

uncertainty. 

* The classification of each potential source of error is based on those used in the First 

Prospective Analysis. The classification of “potentially major” is used if a plausible alternative 

assumption or approach could influence the overall monetary benefit estimate by approximately 

5% or more; if an alternative assumption or approach is likely to change the total benefit 

estimate by less than 5%, the classification of “probably minor” is used. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-1

CHAPTER 5 - ESTIMATION OF HUMAN HEALTH EFFECTS AND 

ECONOMIC BENEFITS 

A large portion of the overall 

benefits of the Clean Air Act 

Amendments (CAAA) of 1990 are 

due to human health benefits from 

improved air quality. As part of 

the Second Prospective analysis of 

these amendments, we identified 

and, where possible, estimated the 

magnitude of health benefits 

Americans are likely to realize in 

future years as a result of the 

CAAA. We express these health 

benefits as avoided cases of air 

pollution-related health effects, 

such as premature mortality, heart 

disease and respiratory illness. 

Human health benefits of the 1990 

CAAA can be attributed to reduced 

emissions of criteria pollutants 

(Titles I through IV), and reduced 

emission of ozone depleting 

substances (Title VI), however as highlighted in Chapter 1 the Second Prospective 

focuses primarily on human health effects attributed to the reduction of criteria pollutants, 

and within that category, health benefits associated with reduced exposure to fine 

particulate matter (PM2.5) and ozone, as these are the largest contributors to the overall 

health benefits estimates. 

The goal in a benefit-cost analysis such as the Second Prospective is to develop estimates 

of the monetary value of benefits wherever possible – doing so facilitates comparison and 

aggregation of monetized health benefits across endpoints. Therefore, we assigned a 

dollar value to avoided incidences of each health effect. We obtained valuation estimates 

from the economic literature and report them in “dollars per case avoided.” We report 

each of the monetary values of benefits applied in this analysis in terms of a central 

estimate and a probability distribution around that value. The statistical form of the 

probability distribution varies by endpoint. 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-2

This chapter presents an overview of our approach to modeling changes in adverse health 

effects and applying monetary value to these benefits, summarizes the results for major 

health effect categories and discusses key uncertainties related to the analysis. As noted 

above, the chapter focuses primarily on the human health effects associated with 

exposure to criteria pollutants, however we also present the methodology and results of a 

case study of health benefits from a single air toxic pollutant (benzene) for a particular 

area of the United States (the Houston metropolitan area). 

OVERVIEW OF APPROACH 

We estimate the impact of the CAAA on human health by analyzing the difference in the 

expected incidence of adverse health effects between a “with-" and a “without-CAAA” 

regulatory scenario. As described in Chapter 1, the without-CAAA scenario assumes no 

further controls on criteria pollutant emissions aside from those already in place in 1990, 

while the with-CAAA scenario assumes full implementation of the 1990 CAAA. The 

analysis uses a sequence of linked analytical models to estimate health benefits, also 

described in Chapter 1, which includes forecasts of implementation activities undertaken 

in response to the CAAA, estimates of pollutant emissions associated with each scenario 

(see Chapter 2) and air quality modeling of criteria pollutant emissions under each 

scenario (see Chapter 4). 

Estimating health effects benefits from air quality modeling results involves three key 

steps, described in greater detail below. The first step involves estimating the exposure of 

individuals to air pollutants. Although exposure to air pollutants can occur in both 

outdoor and indoor environments, for our purposes it is appropriate to focus on outdoor 

air pollution concentrations as a measure of human exposure. The main reason is that, in 

the second step of our approach, estimating the human response to exposure, the exposure 

measures used in the epidemiological studies used to derive human response are typically 

based on outdoor concentrations. These “concentration-response functions” were 

developed to relate outdoor concentrations to changes in the incidence of health effects 

and mortality in response to pollutant exposure. The third step, valuation of avoided 

human health risk, is accomplished by application of estimates from the literature to 

characterize unit values per case avoided. 

A critical tool in EPA’s analyses of health benefits is the Environmental Benefits 

Mapping and Analysis Program (BenMAP), developed and continuously maintained by 

EPA’s Office of Air and Radiation.41 BenMAP is capable of accepting a wide range of 

air quality inputs, and then performing exposure analysis that includes calibration of 

model results to monitor data for historical years, assessing the changes in health effects 

incidence resulting from those exposures, and estimating the monetized value of those 

avoided health effects. Health effects in BenMAP are based on differences in two 

scenarios of exposure, and health effects and valuation estimates reflect the implications 

of the difference in exposure across scenarios, rather than absolute estimates of incidence 

 

41 For more information, see the BenMAP User’s Manual and Appendices, September 2008, Prepared for the Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, by Abt Associates Inc. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-3

associated with in any given scenario. BenMAP required three types of inputs for this 

analysis: 1) forecasted changes in air quality from the without-CAAA to the with-CAAA

scenarios in 2000, 2010 and 2020; 2) health impact functions that quantify the 

relationship between the forecasted changes in exposure and expected changes in adverse 

health effects; and 3) health valuation functions that assign a monetary value to changes 

in specific health effects. We describe each of these inputs in greater detail below. The 

outputs of BenMAP for this analysis include central estimates and distributions of health 

effects incidence and valuation, at the national and county level, for each of the three 

target years of analysis. 

The Project Team also estimates two other outputs related to avoided premature mortality 

attributed to the CAAA: life-years lost, and changes in life expectancy. EPA developed a 

separate model, the Population Simulation model, to generate these outputs. As 

described below, the population simulation approach provides some advantages over the 

BenMAP model in terms of simulation of the dynamic effects of mortality across a 

population through time, but also has several significant disadvantages relative to 

BenMAP in terms of the spatial resolution of pollutant exposure estimates. As a result, 

the population simulation approach operates as a supplement to the BenMAP-based 

primary estimates for selected measures of the impact of reducing risks of premature 

mortality. 

EXPOSURE ASSESSMENT 

As described in Chapter 4, the Project Team used the Community Multi-scale Air Quality 

(CMAQ) integrated modeling system to simulate the physical and chemical processes 

that govern the formation, transport, and deposition of gaseous and particulate species in 

the atmosphere. The CMAQ results serve as the basis of the air quality inputs required 

for BenMAP. For particulate matter, the CMAQ model was applied for an annual 

simulation period (January through December) and utilized a 36-km resolution modeling 

domain that encompasses the contiguous 48 states. For ozone and related species, the 

CMAQ model was applied for a five-month simulation period that captures the key 

ozone-season months of May through September, and used two 12-km resolution 

modeling domains (that when combined cover the contiguous 48 U.S. states). 

We also described in Chapter 4 the adjustment of the CMAQ results generated by 

combining those results with observed monitoring data, using a method known as the 

monitor and model relative adjustment procedure. This technique was applied for the PM 

estimates using a program called the Modeled Attainment Test Software (MATS) (see 

Chapter 4 for a detailed description of this process). The resulting 36 km grid cell 

concentrations for PM were then used as inputs for BenMAP. For ozone, a similar 

adjustment process was completed, but the analysis was done directly within BenMAP, 

using the enhanced Voronoi Neighbor Averaging (eVNA) procedure.42 The eVNA and 

 

42 As noted in Chapter 4, eVNA and VNA are procedures for interpolating values from nearby monitors using inverse distance 

squared weighting using Voronoi Neighbor Averaging. This is an algorithm that identifies a set of monitors close to the grid 

cell (called “neighbors”) and then estimates the PM species concentration in that grid cell by calculating an inversedistance weighted average of the monitor values (i.e., the concentration values at monitors closer to the grid cell are 

weighted more heavily than monitors that are further away). See the BenMAP manual for further information on the eVNA 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-4

MATS procedures provide gridded estimates of outdoor air quality at the same grid 

resolution as the CMAQ results. These procedures also provide a means for calibrating 

model results in those grid cells where no monitors exist, combining both model results 

with nearby monitor results to yield a “surface” of air quality that avoids the problems 

with direct extrapolation of results from monitors not located within a grid cell boundary. 

HEALTH IMPACT FUNCTIONS 

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

hospital admissions, for a given change in ambient pollutant concentration. A standard 

health impact function has four components: 1) the size of the potentially affected 

population; 2) a baseline incidence rate for the health effect (obtained from a source of 

public health statistics, such as the Centers for Disease Control, or sometimes from an 

epidemiological study itself); 3) a concentration-response (C-R) function (derived from 

epidemiological studies), which relates the change in the number of individuals in a 

population exhibiting a “response” to a change in pollutant concentration experience to 

the size of the exposed population; and 4) the estimated change in the relevant pollutant 

concentration. The first three of these components are discussed in further detail below. 

The fourth is generated through the air quality modeling and exposure estimation 

procedure discussed above. 

Potentially Affected Populations 

Health benefits resulting from the CAAA are related to the change in air pollutant 

exposure experienced by individuals. Because the expected changes in pollutant 

concentrations vary from location to location, individuals in different parts of the country 

may not experience the same level of health benefits. This analysis apportions benefits 

among individuals by matching the change in air pollutant concentration in a grid cell 

with the size of the population that experiences that change. 

BenMAP incorporates 2000 U.S. Census Bureau block-group population data to 

determine the specific populations potentially affected by ozone and PM2.5. For future 

years (2010 and 2020), BenMAP scales the 2000 Census-based population estimates 

using the ratio of forecasted and 2000 county-level population estimates provided by 

Woods and Poole (2007).43 

 

procedure. Abt Associations (2008). BenMAP: Environmental Benefits Mapping and Analysis Program User’s Manual. 

Prepared for the U.S. Environmental Protection Agency’s Office of Air Quality Planning and Standards, Research Triangle 

Park, NC, September. 

43 Woods & Poole Economics Inc., 2007. Complete Demographic Database. Washington, DC. 

http://woodsandpoole.com/index.php. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-5

Baseline Incidence Rates 

Baseline incidence rates are needed to convert the relative changes of a health effect in 

relation to a specific change in air pollution, which are reported in epidemiological 

studies, into the number of avoided cases. For instance, an epidemiological study might 

report that for a 10 ppb decrease in daily ozone levels, hospital admissions decrease by 

three percent. This estimate must then be multiplied by a baseline incidence rate (i.e., an 

estimate of the number of cases of the health effect per year) and the total population to 

determine how this three percent decrease translates into the number of fewer cases. 

For this analysis, we used nationally-representative age-specific incidence and prevalence 

rates, where available, for each health endpoint. We obtained these data from a variety of 

sources, such as the CDC, the National Center for Health Statistics and the American 

Lung Association. Information from individual epidemiological studies was used if data 

from other sources were not available, as these data are often specific to the study 

population and location and therefore may not be as nationally representative.44 For 

future years, mortality rates are projected based on available Bureau of the Census 

projections – other projected baseline incidence rates are generated to be consistent with 

the projections of population growth incorporated into BenMAP. 

Concentration-Response Functions 

We calculate the benefits attributable to the CAAA as the avoided incidence of adverse 

health effects. Such benefits can be measured using C-R functions specific to each health 

effect. C-R functions are equations that relate the change in the number of individuals in 

a population exhibiting a “response” (in this case an adverse health effect such as 

respiratory disease) to a change in pollutant concentration experienced by that population. 

PM2.5 and ozone have been associated with a number of adverse health effects in the 

epidemiological literature, such as premature mortality, hospital admissions, emergency 

room visits, and respiratory and cardiovascular disease. The published scientific 

literature contains information that supports the estimate of some, but not all, of these 

effects. Thus, it is not possible currently to estimate all of the human health benefits 

attributable to the CAAA. In addition, for some of the health effects we do quantify, the 

current economic literature does not support the estimation of the economic value of 

these effects. Table 5-1 lists the human health effects of these pollutants that have been 

identified, indicating which have been included in our benefits estimates and those that 

we did not quantify. See Chapter 2 of Health and Welfare Benefits Analyses to Support 

the Second Section 812 Benefit-Cost Analysis of the Clean Air Act, for a specific list of 

the C-R functions used for each health endpoint.

 

44 See Health and Welfare Benefits Analyses to Support the Second Section 812 Benefit-Cost Analysis of the Clean Air Act, 

February 2011, for a list of data sources and average baseline incidence rates for each health effect. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-6

TABLE 5-1. HUMAN HEALTH EFFECTS OF OZONE AND PM2.5 

POLLUTANT/EFFECT 

QUANTIFIED AND MONETIZED IN BASE 

ESTIMATESA UNQUANTIFIED EFFECTSG,H—CHANGES IN: 

PM/Healthb Premature mortality based on both cohort 

study estimates and on expert elicitationc,d

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial 

infarction) 

Lower respiratory symptoms 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Upper Respiratory symptoms (asthmatic 

population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than 

chronic bronchitis 

Morphological changes 

Altered host defense mechanisms 

Cancer 

Non-asthma respiratory emergency room 

Visits 

UVb exposure (+/-)e

Ozone/Healthf Premature mortality: short-term exposures 

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted-activity days 

School loss days 

Outdoor worker productivity 

Cardiovascular emergency room visits 

Asthma attacks 

Respiratory symptoms 

Chronic respiratory damage 

Increased responsiveness to stimuli 

Inflammation in the lung 

Premature aging of the lungs 

Acute inflammation and respiratory cell 

damage 

Increased susceptibility to respiratory 

infection 

Non-asthma respiratory emergency room 

Visits 

UVb exposure (+/-)e

a Primary quantified and monetized effects are those included when determining the primary estimate of total 

monetized benefits of the alternative standards. 

b 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. 

c 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). 

d 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. 

e May result in benefits or disbenefits. 

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

associated with ozone health including increased airway responsiveness to stimuli, inflammation in the lung, 

acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection. The public 

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

g The categorization of unquantified health effects is not exhaustive. 

h Health endpoints in the unquantified benefits column include both a) those for which there is not consensus on 

causality and b) those for which causality has been established but empirical data are not available to allow 

calculation of benefits. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-7

We rely on the most recently available, published scientific literature to ascertain the 

relationship between air pollution and adverse human health effects. We use a set of 

criteria outlined in Table 5-2 to evaluate potential studies to use as the basis for the C-R 

function. These criteria include consideration of whether the study was peer-reviewed, 

the study design and location, and characteristics of the study population, among others. 

In addition, we consider the input of the Council advising EPA for this study, as well the 

specific advice of the Health Effects Subcommittee (HES) of the Council, which 

explicitly focused on the health effects estimation component of the study. Overall, the 

selection of C-R functions for benefits analysis is guided by the goal of achieving a 

balance between comprehensiveness and scientific defensibility. 

Epidemiological studies provide the basis for the C-R functions used in the health impact 

functions for assessing benefits of the CAAA. These studies also provide an indication of 

a portion of the uncertainty associated with the C-R function, by reporting a confidence 

interval around the mean value, which we use to derive a low, central and high estimate 

of avoided cases. However, this range only represents the statistical error in the 

estimates, which is related to the study population size and frequency of outcome. 

Several other sources of uncertainty exist in the relationship between ambient pollution 

and the health outcomes, including model uncertainty, potential confounding by factors 

that are both correlated with the health outcome and each other, and potential 

misclassification of the study population exposures. For a full list of uncertainties related 

to application of a C-R function to estimate benefits, see the Uncertainty section of this 

chapter and the Second Prospective Uncertainty Report, Uncertainty Analyses to Support 

the Second Section 812 Benefit-Cost Analysis of the Clean Air Act. 

EPA recently conducted an expert elicitation (EE) study, which is the formal elicitation of 

subjective judgments, in order to more fully characterize the uncertainty surrounding the 

PM2.5/mortality C-R function. This study allowed experts to consider and integrate 

several sources of uncertainty in the form of a probability distribution of the C-R 

function. As discussed further below, the EE study results helped to inform our selection 

of a primary C-R function to estimate avoided premature mortality due to CAAA-related 

PM2.5 exposure reductions. 

Avoided premature mortality is the largest contributor to the monetized health benefits of 

PM2.5 and ozone. Therefore, we describe below in further detail the specific C-R 

functions selected to quantify CAAA-related avoided deaths. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-8

TABLE 5-2. SUMMARY OF CONSIDERATIONS USED IN SELECTING C-R FUNCTIONS 

CONSIDERATION COMMENTS 

Peer-Reviewed Research Peer-reviewed research is preferred to research that has not undergone the peerreview process. 

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), 

prospective cohort studies are preferred over ecological studies because they 

control for important individual-level confounding variables that cannot be 

controlled for in ecological studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more 

data) are preferred, because they have greater statistical power to detect 

effects. More recent studies are also preferred because of possible changes in 

pollution mixes, medical care, and lifestyle over time. However, when there are 

only a few studies available, studies from all years will be included. 

Population Attributes The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity 

across age or other relevant demographic factors. In the absence of effect 

estimates specific to age, sex, preexisting condition status, or other relevant 

factors, it may be appropriate to select effect estimates that cover the broadest 

population to match with the desired outcome of the analysis, which is total 

national-level health impacts. When available, multi-city studies are preferred to 

single city studies because they provide a more generalizable representation of 

the C-R function. 

Study Size Studies examining a relatively large sample are preferred because they generally 

have more power to detect small magnitude effects. A large sample can be 

obtained in several ways, either through a large population or through repeated 

observations on a smaller population (e.g., through a symptom diary recorded for 

a panel of asthmatic children). 

Study Location U.S. studies are more desirable than non-U.S. studies because of potential 

differences in pollution characteristics, exposure patterns, medical care system, 

population behavior, and lifestyle. 

Pollutants Included in 

Model 

When modeling the effects of ozone and PM (or other pollutant combinations) 

jointly, it is important to use properly specified impact functions that include 

both pollutants. Using single-pollutant models in cases where both pollutants are 

expected to affect a health outcome can lead to double-counting when pollutants 

are correlated. 

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because 

of the focus on reducing emissions of PM2.5 precursors, and because air quality 

modeling was conducted for this size fraction of PM. Where PM2.5 functions are 

not available, PM10 functions are used as surrogates, recognizing that there will 

be potential downward (upward) biases if the fine fraction of PM10 is more (less) 

toxic than the coarse fraction. 

Economically Valuable 

Health Effects 

Some health effects, such as forced expiratory volume and other technical 

measurements of lung function, are difficult to value in monetary terms. These 

health effects are not quantified in this analysis. 

Non-overlapping 

Endpoints 

Although the benefits associated with each individual health endpoint may be 

analyzed separately, care must be exercised in selecting health endpoints to 

include in the overall benefits analysis because of the possibility of doublecounting of benefits. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-9

PM Mortality C-R Function 

The estimated relationship between particulate matter exposure and premature mortality 

is one of the most important parameters in the overall quantified and monetized benefit 

estimate for this study. An extensive base of literature exists to support development of 

the C-R function linking fine particulate matter exposure with premature mortality. Our 

knowledge of both the potential biological mechanisms linking PM2.5 exposure with 

mortality and the potential magnitude of this effect has grown since the First Prospective 

was completed as the result of continued research and follow-up of existing study 

populations. Both short-term and long-term epidemiological studies have been conducted 

to examine the PM/mortality relationship. Short-term exposure studies attempt to relate 

short-term (often day-to-day) changes in PM concentrations and changes in daily 

mortality rates up to several days after a period of elevated PM concentrations. Longterm exposure studies examine the potential relationship between longer-term (e.g., 

annual) changes in exposure and annual mortality rates. Although positive, significant 

results have been reported using both of these study types, we rely exclusively on longterm studies to quantify PM mortality effects. This is because cohort studies are able to 

discern changes in mortality rates due to long-term exposure to elevated air pollution 

concentrations. This provides a better match to the benefits of air pollution control 

programs under the CAAA, which are also focused on reducing long-term exposure. 

These effect estimates may also include some of the mortality changes due to short-term 

peak exposures.45 Therefore, the use of C-R functions from long-term studies is likely to 

yield a more complete assessment of the effect of PM on mortality risk. 

Among long-term PM studies, we prefer those using a prospective cohort design to those 

using an ecologic or population-level design. Prospective cohort studies follow 

individuals forward in time for a specified period, periodically evaluating each 

individual’s exposure and health status. Population-level ecological studies assess the 

relationship between population-wide health information (such as counts of daily 

mortality) and ambient levels of air pollution. Prospective cohort studies are preferred 

because they are better at controlling a source of uncertainty known as “confounding.” 

Confounding is the mis-estimation of an association that results if a study does not 

control for factors that are correlated with both the outcome of interest (e.g., mortality) 

and the exposure of interest (e.g., PM exposure). For example, smoking is associated 

with mortality. If populations in high PM areas tend to smoke more than populations in 

low PM areas, and a PM exposure study does not include smoking as a factor in its 

model, then the mortality effects of smoking may be erroneously attributed to PM, 

leading to an overestimate of the risk from PM. Prospective cohort studies are better at 

controlling for confounding than ecologic studies because the former follow a group of 

individuals forward in time and can gather individual-specific information on important 

risk factors such as smoking. 

 

45 See Kunzli et al. (2001) for a discussion of this issue. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-10

Two major prospective cohort studies have been conducted in the U.S.: the American 

Cancer Society (ACS) study and the Six Cities study. These two cohorts are large, 

produce consistent results, provide broad geographic coverage and have been 

independently reexamined and reanalyzed. Strengths of the ACS study over the Six 

Cities study include greater geographic coverage (50 U.S. cities) and larger sample size. 

However, a key limitation of this study is a recruitment method that led to a study 

population with higher income, more education, and greater proportion of whites than the 

general U.S. population. In addition, available monitoring data was often assigned to all 

of the individuals within a large metropolitan area, potentially allowing for exposure 

misclassification.46 Both of these limitations could imply that the ACS results are 

potentially biased low. The Six Cities study included a more representative sample of 

subjects within each community and set up monitors purposefully for the study. It was 

therefore able to assign exposures at a finer geographic scale. However, this study only 

included six cities and therefore may not be representative of the entire U.S. population, 

mix of air pollutants, and other potentially important factors. 

The extensive epidemiological literature is complemented by EPA’s 2006 expert 

elicitation (EE) study that asked 12 leading experts in PM health effects to integrate this 

pool of knowledge with the various sources of uncertainty that hinder our ability to 

precisely identify the true mortality impact of a unit change in annual PM2.5 concentration 

(IEc, 2006). The results of the expert elicitation study showed three important findings: 

first, that advances in the scientific literature led many of the interviewed scientists to 

espouse greater confidence in the linkage between PM2.5 exposure and mortality; second, 

that many of the experts believed that the central estimate of the mortality effect was 

considerably higher than the Pope et al. (2002) result used in the First Prospective; and 

third, that most of the experts’ uncertainty distributions of the mortality effect reflected a 

much wider range of possible values, both high and low, than were used in the First 

Prospective study. The expert elicitation study does not, however, provide an integrated 

distribution across all 12 experts of possible values for the PM-mortality C-R function. 

Based on consultations with the Council’s Health Effects Subcommittee (HES), the 812 

Project Team developed a distribution of C-R function coefficients (i.e., the percent 

change in annual all-cause mortality per one μg/m3

 change in annual average PM2.5) for 

use in the PM-mortality C-R function for the Second Prospective study. This distribution 

is rooted in the epidemiological studies that most inform our understanding of the PMmortality C-R function, but reflects the broader findings of the EE study. We based the 

primary C-R coefficient estimate of the Second Prospective study on a Weibull 

distribution with a mean of 1.06 percent decrease in annual all-cause mortality per one 

μg/m3

. This mean is roughly equidistant between the results of the two most well-studied 

PM cohorts, the ACS cohort (0.58, as derived from Pope et al., 2002) and the Six Cities 

cohort (1.5, as derived from Laden et al., 2006), both of whose results have been robust to 

continued follow-up and extensive re-analysis. Half of the coefficient values in this 

 

46 Studies have shown that greater spatial resolution of exposures can result in increased effect estimates (Jerrett et al., 

2005). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-11

distribution fall between these two studies, one-quarter are higher than the Laden mean 

estimate, and one-quarter are lower than the Pope mean estimate; however all coefficient 

values are greater than zero. This distribution is consistent with the EE results described 

above, showing considerable support for higher values based on results from more recent 

studies (e.g., the Laden et al. (2006) Six Cities follow-up) and concerns cited by the 

Council HES that the ACS cohort results may underestimate the true effect. The use of 

all positive values is consistent with both the increased confidence in a causal link 

between PM2.5 exposure and mortality shown in the EE study and the lack of evidence in 

general to support a threshold for mortality effects of PM2.5 in the U.S. population.47 

The results of two recently published cohort studies provide additional support for the 

selection of the Weibull distribution as the primary estimate for the PM Mortality C-R 

function. The first is a large retrospective cohort study of over 13 million Medicare 

participants (i.e., those aged 65 and above) throughout the US (Eftim et al. 2008; Zeger et 

al. 2008). When the entire Medicare cohort was analyzed, authors found a 6.8 percent 

change in annual all-cause mortality in the eastern US (95% CI: 4.9-8.7) and a 13.2 

percent change in the central US (95% CI: 9.5-16.9) per 10 μg/m3 change in the long-term 

(six-year) average annual PM2.5. There was no association found in the western US 

(Zeger et al., 2008). These results are similar to the interquartile range of the Weibull 

distribution selected for the primary estimate for the Second Prospective. An analysis 

restricted to those living in the locations corresponding to the ACS and Six Cities cohort 

study analyses yielded percent changes in annual all-cause mortality per 10 μg/m3 of 

PM2.5 of 10.9 (95% CI: 9.0-12.8) and 20.8 (95%CI: 14.8-27.1) respectively, which are 

somewhat higher than the estimates reported in the original studies (Eftim et al., 2008).48 

One possible explanation for this difference is the lack of control for lifestyle factors in 

the analyses by Eftim et al., such as smoking, potentially leading to confounded results. 

The second study is a prospective cohort of female nurses in the Northeastern and 

Midwestern regions of the US (Puett et al. 2008 and 2009). An increase of 10 μg/m3 of 

PM2.5 in the previous year was associated with a 26 percent increase in annual all-cause 

mortality (a hazard ratio of 1.26 with a 95% CI ranging from 1.02 to 1.54).49 This 

estimate is at the upper end of our primary estimate Weibull distribution (roughly 

equivalent to the 95th percentile). However, this study covered only two regions of the 

country and included only females and therefore may not be generalizable to the general 

population of the US. 

A final topic concerns EPA’s choice to estimate avoided mortality and morbidity 

associated with reductions in fine particles using estimates of changes in exposure to fine 

 

47 See “Health Effects Subcommittee of the Council. Review of EPA’s Draft Health Benefits of the Second Section 812 

Prospective Study of the Clean Air Act.” (EPA-COUNCIL-10-001), available at http://www.epa.gov/advisorycouncilcaa 

48 Note that these results are based on a slightly different air quality dataset than the analysis of the full cohort. The 

nationwide estimate is based on a six-year average (2000-2005) and the ACS and Six Cities location-specific results are 

based on two years of data (2000-2002). 

49 Biennial questionnaires on lifestyle factors were administered to participants, allowing for control of a number of 

individual-level confounders. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-12

particle mass as the exposure input in the damage function. The implication of this 

approach is that we assume that all fine particles, regardless of their chemical 

composition, are equally potent per unit concentration in producing premature mortality 

and other health outcomes. If it could be shown that fine particle species exhibit 

significantly differentiated toxicity, then from a benefits analysis perspective, treatment 

of all fine particle species as equally toxic would lead to biased benefits estimates, 

because the composition of fine particle mass varies over space and time, as do the fine 

particle reductions resulting from different air pollutant control strategies. We believe 

that these biases would likely be minor in an analysis such as the 812 study, which 

evaluates a blended particle reduction strategy targeting multiple particle types across the 

entire spectrum of control programs authorized under the Clean Air Act Amendments. 

Nonetheless, we conducted a careful evaluation of the potential for characterizing 

uncertainty in the differential toxicity of the components of fine particle pollution. 

There exists a limited but growing literature addressing the health effects of various fine 

particle components, including sulfate, nitrate, elemental carbon (EC), organic carbon 

(OC), and metals.50 A number of epidemiological studies, mostly time-series studies, 

have associated one or more of the components of fine particle pollution individually 

with mortality; however, so far no clear picture has emerged to implicate specific 

components as being consistently more toxic than fine particles in general or to classify 

any individual components of fine particle pollution as non-toxic. However, the 

epidemiological evidence base is limited by the high correlations among many fine 

particle components (and between those components and fine particles as a whole). It is 

difficult to corroborate this evidence toxicologically, given the fact that human exposure 

to single particle components is not a realistic scenario. The literature base continues to 

expand, but significant investments in both epidemiological and toxicological research 

are needed to understand the potentially complex systems of particle interactions that may 

be responsible for the observed health effects of fine particle pollution. 

Thus, while treatment of all fine particle components as equally toxic may lead to biases 

in benefits estimates, we also acknowledge that any arbitrary assumption about the 

differential toxicities of specific fine particle types may also lead to biases in benefits 

estimates. Any of these biases may mask important spatial variation in the distribution of 

benefits of Clean Air Act programs across the U.S. due to regional variation in fine 

particle species mixes, which could affect selection of the most health beneficial 

measures to meet Clean Air Act requirements such as the National Ambient Air Quality 

Standards. However, the “equal toxicity” fine particle approach is rooted in both 

biological considerations (i.e., the importance of particle size to toxicity) and in largely 

consistent findings across an extensive set of epidemiological studies conducted across 

countries, states, and cities that show PM2.5 concentrations are associated with increased 

mortality and morbidity rates. This consistency of results across a variety of fine particle 

 

50 For specific examples of research addressing differential toxicity of PM components, see Chapter 5 of Uncertainty Analyses 

to Support the Second Section 812 Benefit-Cost Analysis of the Clean Air Act. 

http://www.epa.gov/oar/sect812/may10/IEc_Uncertainty.pdf
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-13

mixes in different locations implies an equivalence of risk resulting from exposure to fine 

particle masses with different concentrations of component species. We conclude that the 

current evidentiary base from the epidemiological and toxicological literatures supports 

the use of an equal toxicity assumption for the present study, especially since the fine 

particle pollution reductions estimated herein reflect a variety of fine particle mixtures 

across different locations and time frames. Furthermore, we conclude that current 

information does not support specification of alternative concentration-response functions 

that would be both scientifically sound and useful for development of policy-relevant 

insights. 

To provide further confidence that the results presented in this chapter are not likely to be 

substantially affected by the possibility that PM2.5 species exhibit differential toxicity, the 

Project Team developed and evaluated estimates of the overall population-weighted 

exposure to PM species. The results are presented in Table 5-3 below, and graphically in 

the two panels of the accompanying Figure 5-1. The results in Figure 5-1indicate that the 

population-weighted composition of fine particulate matter is affected by the control 

strategies applied in the CAAA, but the changes are relatively modest.51 We therefore 

conclude that, even if species-specific toxicity estimates could be derived from the 

existing literature, applying them in this study would not have a large effect on the 

mortality results presented later in this chapter. 

TABLE 5-3. ESTIMATED POPULATION WEIGHTED EXPOSURE FOR PM2.5 SPECIES (MICROGRAMS 

PER CUBIC METER) 

2000 

NO 

CAAA 

2000 

WITH 

CAAA 

2010 

NO 

CAAA 

2010 

WITH 

CAAA 

2020 

NO 

CAAA 

2020 

WITH 

CAAA 

Crustal 1.18 0.82 1.27 0.86 1.51 0.96 

NO3 1.06 0.89 1.17 0.81 1.32 0.69 

NH4 1.87 1.26 1.96 1.03 2.05 0.92 

EC 0.74 0.62 0.77 0.48 0.9 0.41 

OC 5.18 3.94 5.36 3.86 6.02 3.99 

SO4 4.84 3.11 5.02 2.48 5.17 2.22 

 

51 Note that data presented in Table 5-3 are for the most important PM2.5 components; some less important species, with 

lower concentrations, are omitted. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-14

FIGURE 5-1. DISTRIBUTION OF POPULATION WEIGHTED EXPOSURE TO PM2.5 SPECIES AS 

PERCENTAGE OF TOTAL (TOP PANEL) AND IN MICROGRAMS PER CUBIC METER 

(BOTTOM PANEL) 

Ozone Mortality C-R Function 

Several recent epidemiological studies suggest that ozone exposure likely contributes to 

premature mortality.52 Epidemiological data are also supported by recent human and 

 

52 See, for example, National Research Council, 2008, Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. A key recommendation of this NAS panel was that ozone mortality estimates from 

available epidemiological studies represent a separate and additive effect to those from PM/mortality epidemiological 

studies. 

0%

20%

40%

60%

80%

100%

2000

No

CAAA

2000

With

CAAA

2010

No

CAAA

2010

With

CAAA

2020

No

CAAA

2020

With

CAAA

SO4

OC

EC

NH4

NO3

Crustal

0

2

4

6

8

10

12

14

16

18

2000

No

CAAA

2000

With

CAAA

2010

No

CAAA

2010

With

CAAA

2020

No

CAAA

2020

With

CAAA

SO4

OC

EC

NH4

NO3

Crustal
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-15

animal experimental data, which suggestive evidence for plausible pathways by which the 

risk of respiratory or cardiovascular mortality could be increased by ambient ozone. 

Multiple time-series epidemiological studies explore the relationship between short-term 

ozone exposure and premature mortality. Most notably, a large multi-city study known 

as the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was designed 

to explore the association between several pollutants, including ozone, and daily 

mortality that focused on large cities across the US where levels of pollutants were varied 

(Samet et al., 2000). Three recently published studies based on the NMMAPS database 

that focus on the ozone/premature mortality relationship include Bell et al. (2004) (95 

U.S. cities), Schwartz et al. (2005) (14 U.S. cities), and Huang et al. (2005) (19 U.S. 

cities). 

In addition to these multi-city estimates, C-R functions for short-term ozone mortality can 

be derived from meta-analyses, which combine the results of several studies. Three 

meta-analyses were performed to obtain a summary estimate of ozone-related mortality 

risks and to attempt to describe heterogeneity in risk estimates (Ito et al., 2005; Levy et 

al., 2005; Bell et al., 2005). Each of these studies used different statistical techniques and 

datasets and examined statistical concerns, such as confounding, collinearity and possible 

interaction effects.53 

In general, effect estimates from the meta-analyses are higher than the NMMAPS-based 

results. This could potentially be due to publication bias, as the meta-analyses relied 

solely on published studies, which could be more likely to contain statistically significant 

results. NMMAPS generally produces lower estimates than other epidemiological timeseries studies, however, which could reflect specific methodological choices made by 

these investigators. Since these studies are associated with different strengths and 

limitations and no single study emerges as the most suitable to use as the basis for our 

primary estimate, we opted to use a pooled estimate, equally weighting the C-R functions 

from all six of these studies. 

In addition to time-series epidemiological studies, a limited number of studies examine 

the cumulative effect of long-term exposure to ozone on mortality. One such recent study 

(Jerrett et al., 2009) used study population data from the ACS cohort study along with 

ozone monitoring data and reported a significant association between deaths from 

respiratory causes and long-term ozone exposure. In a recent review of the 812 Second 

Prospective Analysis methodology, the Council HES found the use of the Jerrett et al. 

estimate as the primary estimate premature at this time, due to a lack of corroboration 

from other cohort studies .54

 

53 National Research Council (NRC) (2008). Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone 

Air Pollution. The National Academies Press, Washington, DC. 

54 See “Health Effects Subcommittee of the Council. Review of EPA’s Draft Health Benefits of the Second Section 812 

Prospective Study of the Clean Air Act.” (EPA-COUNCIL-10-001), available at http://www.epa.gov/advisorycouncilcaa 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-16

HEALTH VALUATION FUNCTIONS 

In environmental benefit-cost analyses, the dollar value of an environmental benefit, such 

as improved health or avoidance of a case of illness, is the dollar amount necessary such 

that the person would be indifferent between experiencing the benefit and possessing the 

money. In most cases, the dollar amount required to compensate a person for exposure to 

an adverse effect is roughly the same as the dollar amount a person is willing to pay to 

avoid the effect. Therefore, in economic terms, the “willingness-to-pay” (WTP) is the 

appropriate measure of the value of avoiding an adverse effect. For example, the value of 

an avoided respiratory symptom would be a person’s WTP to avoid that symptom. 

For most goods, WTP can be observed by examining actual market transactions. For 

example, if a gallon of bottled drinking water sells for one dollar, it can be observed that 

at least those persons who choose to purchase that good are willing to pay at least one 

dollar for the water. For goods that are not exchanged in the market, such as most 

environmental goods, valuation is not so straightforward. Nevertheless, a value may be 

inferred from observed behavior, such as through estimation of the WTP for mortality 

risk reductions based on observed sales and prices of products that result in similar effects 

or risk reductions, (e.g., non-toxic cleaners or bike helmets). Alternatively, surveys may 

be used in an attempt to directly elicit WTP for an environmental improvement. 

Wherever possible in this analysis, we use estimates of mean WTP. In cases where WTP 

estimates are not available, we use the cost of treating or mitigating the effect as an 

alternative estimate. 

For example, for the valuation of hospital admissions we use the avoided medical costs as 

an estimate of the value of avoiding the health effects causing the admission. These costs 

of illness (COI) estimates generally understate the true value of avoiding a health effect. 

They tend to reflect the direct expenditures related to treatment and not the utility an 

individual derives from improved health status or avoided health effect. We use a range 

of values for most environmental effects, to support the primary central estimate of net 

benefits. Table 5-4 summarizes the mean unit value estimates that we use in this 

analysis. 

Valuation of Premature Mortality 

Some forms of air pollution increase the probability that individuals will die prematurely. 

We use C-R functions for mortality that express the increase in mortality risk as cases of 

“excess premature mortality” per year. The benefit provided by air pollution reductions, 

however, is the avoidance of small increases in the risk of mortality. By summing 

individuals WTP to avoid small increases in risk over enough individuals, we can infer 

the value of a statistical premature death avoided.55 For expository purposes, we express 

this valuation as “dollars per mortality avoided,” or “value of a statistical life” (VSL), 

 

55 Because people are valuing small decreases in the risk of premature mortality, it is expected deaths that are inferred. For 

example, suppose that a given reduction in pollution confers on each exposed individual a decrease in mortal risk of 

1/100,000. Then among 100,000 such individuals, one fewer individual can be expected to die prematurely. If the average 

individual’s WTP for that risk reduction is $50, then the implied value of a statistical premature death avoided in that 

population is $50 x 100,000 = $5 million. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-17

even though the actual valuation is of small changes in mortality risk experienced by a 

large number of people. The economic benefits associated with avoiding premature 

mortality were the largest category of monetized benefits in the First Prospective 

Analysis and continue to be the largest source of monetized benefits for this Second 

Prospective Analysis. Mortality benefits, however, are also the largest contributor to the 

range of uncertainty in monetized benefits. 

Because avoided premature mortality benefits are such an important part of this study’s 

results and findings, the remainder of this section provides an expanded discussion of 

some of the issues in valuing the avoidance of mortality risks from air pollution. We first 

discuss some characteristics of an “ideal” measure of the value of mortality risk 

reductions from air pollution, and then review several dimensions in which the current 

estimates fall short of the ideal measure for this study. For a more detailed discussion of 

the factors affecting the valuation of premature mortality see the Uncertainty section of 

this chapter and the Uncertainty Analyses to Support the Second Section 812 Benefit-Cost 

Analysis of the Clean Air Act. 

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age 

groups appear to be more susceptible to air pollution than others (e.g., the elderly and 

children). Health status prior to exposure also affects susceptibility. At-risk individuals 

include those who have suffered strokes or are suffering from cardiovascular disease and 

angina (Rowlatt, et al. 1998). An ideal economic benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival plus WTP to improve other individuals’ survival 

rates.56 The ideal measure would also take into account the specific nature of the risk 

reduction that is provided to individuals, as well as the context in which risk is reduced. 

To measure this value, it is important to assess how reductions in air pollution reduce the 

risk of dying from the time that reductions take effect onward, and how individuals value 

these changes. Each individual’s survival curve, or the probability of surviving beyond a 

given age, should shift as a result of an environmental quality improvement. For 

example, changing the current probability of survival for an individual also shifts future 

probabilities of that individual’s survival. This probability shift will differ across 

individuals because survival curves are dependent on such characteristics as age, health 

state, and the current age to which the individual is likely to survive. 

 

56 For a more detailed discussion of altruistic values related to the value of life, see Jones-Lee (1992). 
The Benefits and Costs of the Clean 

TABLE 5-4. UNIT VALUES FOR ECONOMIC VALUATION OF HEALTH ENDPOINTS (2006$) 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Premature Mortality (Value of a Statistical Life) 

$7,400,000 $8,900,000 Mean Value of Statistical Life (VSL) based 26 wage-risk and contingent valuat

distribution, with a mean of $7.4 million (in 2006$), provided the best fit to VSL represents the value of a small change in mortality risk aggregated over 

Chronic Bronchitis (CB) 

$399,000 $490,000 

The WTP to avoid a case of pollution-related CB is calculated as the severity of an average CB case, WTP13 is the WTP for a severe case of CB

distribution of WTP for an air pollution-relevant, average severity-level case 

Monte Carlo methods, drawing from each of three distributions: (1) WTP to assigned a 1/9 probability of being each of the first nine deciles of the distriViscusi et al. (1991); (2) the severity of a pollution-related case of CB (relatithe Viscusi study) is assumed to have a triangular distribution, with the most 

6.5 and endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of Wnormally distributed with mean = 0.18 and standard deviation = 0.0669 (from (1992)). This process and the rationale for choosing it is described in detail i

the Clean Air Act, 1990 to 2010 (EPA, 1999). 

Nonfatal Myocardial Infarction (heart attack)  7% discount rate  Age 0–24  Age 25–44  Age 45–54  Age 55–65  Age 66 and over 

$84,171 $93,802 $98,366 

$166,222 

$84,171 

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

medical costs over a 5-year period following a nonfatal MI. Lost earnings est

and Krupnick (1990). Direct medical costs are based on simple average of est(1998) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted value of 5 years of lost ear

age of onset: at 7%a

25–44 $9,631 45–54 $14,195 55–65 $82,051 Direct medical expenses: An average of (2006$): 1. Wittels et al. (1990) ($141,124—no discounting) 2. Russell et al. (1998), 5-year period ($28,787 at 3% discount rate; $27,217 a

x WTP W

 
The Benefits and Costs of the Clean 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Hospital Admissions All respiratory (ages 65+) 

$23,711 $23,711 No distributions available. The COI point estimates (lost earnings plus direct 

ICD-9 code level information (e.g., average hospital care costs and average l

reported in Agency for Healthcare Research and Quality, 2000 (www.ahrq.gov

adjustments are made to cost of illness values for income growth. All respiratory (ages 0–2) $10,002 $10,002 

Chronic Obstructive Pulmonary Disease (COPD) (ages 65+) 

$17,308 $17,308 

Asthma Admissions (ages <65) 

$10,040 $10,040 

Pneumonia Admissions (ages 65+) 

$23,004 $23,004 

COPD, less asthma (ages 20–64) 

$15,903 $15,903 

All Cardiovascular (ages 65+) 

$27,319 $27,319 

All Cardiovascular (ages 20–64) 

$29,364 $29,364 

Ischemic Heart Disease (ages 65+) 

$33,357 $33,357 

Dysrhythmia (ages 65+) 

$19,643 $19,643 

Congestive Heart Failure (ages 65+) 

$19,619 $19,619 

Emergency Room Visits for Asthma 

$369 $369 No distributional information available. Simple average of two unit COI value

(1) $401.62, from Smith et al. (1997) and (2) $336.03, from Stanford et al. (1999). As noted in the text, no adjustments are made to cost of illness values for in
The Benefits and Costs of the Clean 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) 

$28.8 $30.7 Combinations of the three symptoms for which WTP estimates are available 

listed by Pope et al. result in seven different “symptom clusters,” each desc

dollar value was derived for each type of URS, using mid-range estimates of each symptom in the cluster and assuming additivity of WTPs. In the absenc

the frequency with which each of the seven types of URS occurs within the Uassumed a uniform distribution between $10.8 and $50.5 (2006$). 

Lower Respiratory Symptoms (LRS) 

$18 $19 Combinations of the four symptoms for which WTP estimates are available th

by Schwartz et al. result in 11 different “symptom clusters,” each describing 

value was derived for each type of LRS, using mid-range estimates of WTP (IEsymptom in the cluster and assuming additivity of WTPs. The dollar value fo

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

with which each of the 11 types of LRS occurs within the LRS symptom comp

distribution between $8.1 and $28.6 (2006$). 

Asthma Exacerbations 

$50 $54 Asthma exacerbations are valued at $50 per incidence, based on the mean o

the four severity definitions of a “bad asthma day,” described in Rowe and Csurveyed asthmatics to estimate WTP for avoidance of a “bad asthma day,” a

For purposes of valuation, an asthma exacerbation is assumed to be equivaleis moderate or worse as reported in the Rowe and Chestnut (1986) study. Thuniform distribution between $18.3 and $82.9 (2006$). 

Acute Bronchitis $416 $512 Assumes a 6-day episode, with the distribution of the daily value specified as 

high values based on those recommended for related respiratory symptoms iThe low daily estimate of $20.5 (2006$) is the sum of the mid-range values refor two symptoms believed to be associated with acute bronchitis: coughing 

high daily estimate was taken to be twice the value of a minor respiratory re(2006$). The low and high daily values are multiplied by six to get the 6-day 

Work Loss Days (WLDs) 

Variable (U.S. median = $149) 

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

(assuming 2 weeks of vacation) and then by 5—to get median daily wage. U.compiled by Geolytics, Inc. 

Minor Restricted Activity Days (MRADs) 

$59 $64 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribut

triangular with a minimum of $24 and a maximum of $94, with a most likely is based on assumption that value should exceed WTP for a single mild sympt
The Benefits and Costs of the Clean 

HEALTH ENDPOINT 

CENTRAL ESTIMATE OF VALUE 

PER STATISTICAL INCIDENCE 

DERIVATION OF DISTRIBUTIONS OF ESTIMATES 

1990 INCOME 

LEVEL 

2020 INCOME 

LEVEL 

a single symptom—for eye irritation—is $24) and be less than that for a WLD. acknowledges that the actual value is likely to be closer to the point estimat

School Loss Days $89 $89 No distribution available. Point estimate is based on (1) the probability that

from school, a parent will have to stay home from work to care for the childparent’s lost productivity. Calculated using U.S. Bureau of Census data. Schof illness estimates for emergency room visits and hospital admissions, are nlongitudinal income. 

a These values are presented using a seven percent discount rate for this draft report, however these results will be presented using a f

the final report. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-22

A survival curve approach provides a theoretically preferred method for valuing the 

economic benefits of reduced risk of premature mortality associated with reducing air 

pollution, but the approach does not align well with current estimates of individual 

willingness to pay to avoid mortal risks. We have adopted the survival curve approach in 

the population simulation model that we use to generate estimates of life years lost and 

reduced life expectancy associated with air pollution, but implementing that approach 

requires that we use a national measure of the change in air pollution exposure, and also 

does not include a valuation component. As a result, the population simulation model 

results are not used for the primary results. 

The Project Team also considered whether other evidence might support an adjustment to 

the VSL used in this study, particularly to account for the age of individuals affected. In 

general, studies of WTP to reduce mortality risk do not provide information on how VSL 

varies with life expectancy, but there are a few studies that attempt to assess the impact of 

age on VSL.57 Some economic models in the theoretical literature suggest that VSL 

follows an inverted U, rising through middle age and falling at older ages, though this 

model is only partially supported by the relevant empirical evidence (Johansson 2002, 

Hammitt 2007). For example, revealed preference studies of the wage-risk literature 

support the inverted-U hypothesis (Aldy and Viscusi, 2007). These studies are limited, 

however, in that they necessarily include only employed workers and thereby exclude the 

elderly and those in poor health. Stated-preference studies, which can include a broader 

population, yield mixed results. Some suggest little or no effect of age on VSL and others 

suggest a modest decrease at older ages (Krupnick, 2007). Some studies, such as those 

by DeShazo (with Cameron, 2004), Chestnut (et al., 2004), and Alberini (et al., 2004) 

have found the effect of age on VSL to be statistically weak, suggesting a flatter 

relationship of VSL and age with a decline in VSL at much older ages. Consistent with 

Hammitt (2007), we conclude that there is insufficient evidence in the empirical VSL 

literature at this time to support an adjustment to the base VSL for the age of the affected 

population. 

In sum, the economic valuation literature does not yet include good estimates of the value 

of this particular risk reduction commodity. As a result, in this study we value avoided 

premature mortality risk using the value of statistical life approach. As in the First 

Prospective Analysis, we use a mortality risk valuation estimate which is based on an 

analysis of 26 policy-relevant value-of-life studies (see Table 5-5). Five of the 26 studies 

are contingent valuation (CV) studies, which directly solicit WTP information from 

subjects; the remaining studies are wage-risk studies, which base WTP estimates on 

estimates of the additional compensation demanded in the labor market for riskier jobs. 

 

57

For a review of these studies, and this issue in particular see, for example, Hammitt (2007), Aldy and Viscusi (2007), and 

Krupnick (2007). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-22

We used the best estimate from each of the 26 studies to construct a distribution of 

mortality risk valuation estimates for the section 812 study. A Weibull distribution, with 

a mean of $7.4 million (in 2006$), provided the best fit to the 26 estimates. 

An additional uncertainty that is pertinent for this study’s results is the potential bias in 

using estimates of VSL that correspond to small changes in risk for the relatively larger 

changes in mortality risk estimated in this study. As the results section below indicates, 

the large changes in PM2.5 that represent the difference between the with-CAAA and 

without-CAAA scenarios by 2020 lead to a change in annual mortality risk of 

approximately 1 in one thousand for adults aged 25 and older, or 7 in ten thousand for all 

ages, which corresponds to a roughly ten percent change from the national baseline 

mortality risk of approximately 1 in one hundred.58 This risk change is large compared to 

the mean mortality risk faced by subjects in the wage-risk studies that underlie our 

estimate of VSL – the mean risk for individual studies in our group of 26 varies from 4 in 

10,000 to 5 in 100,000, although clearly some individuals in those samples face higher 

individual risks.59 Economic theory suggests that individuals’ incremental willingness to 

pay to reduce mortality risk declines with an increasing size of the risk increment, but the 

rate at which it declines is uncertain.60 Estimates of differences in VSL across individuals 

in wage-risk study samples are also not informative, because they reflect variability in 

individuals’ risk tolerance rather than differences in WTP across a population for varying 

increments of risk reduction. Further, it is not clear whether, in this context, the external 

risk imposed by air polluters on the exposed population implies that willingness-toaccept-compensation (WTAC) to forgo air quality improvement may be the more 

relevant measure. There is some theoretical work which suggests that, while valuation of 

a large risk increment may lead WTP estimates to be overestimated, it may lead WTAC 

estimates to be underestimated.61 Although the Project Team remains concerned that 

there may be a potentially important disparity between the large increment of risk valued 

in this study and relatively smaller increments of risk valued in the underlying VSL 

literature, we conclude that the current literature does not provide a sufficient basis to 

make a quantitative adjustment to our base VSL values to account for this factor. 

When valuing premature mortality for PM, we assume a lag between reduced PM 

exposure and the resulting reductions in incidences of premature mortality.62 This lag 

 

58 Note that we are here reporting the total risk change that results from changes in 2020 exposures. As outlined below, this 

risk is not immediate - instead we model this risk as occurring with latency over the course of the ensuing 20 years. 

59 See W. Kip Viscusi, 1992, Fatal Tradeoffs, (Oxford University Press: New York), Table 4-1. 

60 This issue is discussed to some extent in Thomas J. Kniesner, W. Kip Viscusi, and James P. Ziliak (2010), “Policy relevant 

heterogeneity in the value of statistical life: New evidence from panel data quantile regressions,” Journal of Risk and 

Uncertainty 40:15–31. 

61 See discussion papers provided in support of a recent EPA risk valuation workshop at 

http://www.epa.gov/air/toxicair/2009workshop.html (accessed November 24, 2010) in particular the papers and 

presentations by W. Kip Viscusi. 

62 Note that we do not employ a cessation lag for ozone mortality due to our reliance on short-term studies to estimate these 

benefits. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-23

does not affect the number of estimated incidences, but does alter the monetization of 

benefits. Because we value the “event” rather than the present risk, in this analysis we 

assume that the value of avoided future premature mortality should be discounted. The 

primary estimate reflects a 20-year distributed lag structure, which was recommended by 

the Council HES (2004). Under this scenario, 30 percent of the mortality reductions 

occur in the first year, 50 percent occur equally in years two through five, and the 

remaining 20 percent occur equally in years six through 20. Our valuation of avoided 

premature mortality applies a five percent discount rate to the lagged estimates over the 

periods 2000 to 2020, 2010 to 2030 and 2020 to 2040. We discount over the period 

between the initial PM exposure change (2000, 2010, or 2020) and the timing of the 

resulting change in incidence. 

TABLE 5-5. SUMMARY OF MORTALITY VALUATION ESTIMATES PER STATISTICAL INCIDENCE OF 

PREMATURE MORTALITY (MILLIONS OF 2006$) 

STUDY TYPE OF ESTIMATE 

VALUATION 

(MILLIONS 2006$) 

Kneisner and Leeth (1991) (US) Labor Market $ 0.9 

Smith and Gilbert (1984) Labor Market $ 1.1 

Dillingham (1985) Labor Market $ 1.4 

Butler (1983) Labor Market $ 1.7 

Miller and Guria (1991) Contingent Valuation $ 1.9 

Moore and Viscusi (1988a) Labor Market $ 3.9 

Viscusi, Magat, and Huber (1991b) Contingent Valuation $ 4.2 

Gegax et al. (1985) Contingent Valuation $ 5.1 

Marin and Psacharopoulos (1982) Labor Market $ 4.3 

Kneisner and Leeth (1991) (Australia) Labor Market $ 5.1 

Gerking, de Haan, and Schulze (1988) Contingent Valuation $ 5.2 

Cousineau, Lacroix, and Girard (1988) Labor Market $ 5.6 

Jones-Lee (1989) Contingent Valuation $ 5.9 

Dillingham (1985) Labor Market $ 6.0 

Viscusi (1978, 1979) Labor Market $ 6.3 

R.S. Smith (1976) Labor Market $ 7.1 

V.K. Smith (1976) Labor Market $ 7.2 

Olson (1981) Labor Market $ 8.0 

Viscusi (1981) Labor Market $ 10.0 

R.S. Smith (1974) Labor Market $ 11.1 

Moore and Viscusi (1988a) Labor Market $ 11.3 

Kneisner and Leeth (1991) (Japan) Labor Market $ 11.7 

Herzog and Schlottman (1987) Labor Market $ 14.0 

Leigh and Folson (1984) Labor Market $ 15.0 

Leigh (1987) Labor Market $ 16.0 

Garen (1988) Labor Market $ 20.8 

Source: Viscusi, 1992 and EPA analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-24

HEALTH EFFECTS MODELING RESULTS 

This section presents a summary of the differences in health effects resulting from 

improvements in air quality between the with-CAAA and the without-CAAA scenarios. 

Table 5-6 summarizes the CAAA-related avoided health effects in 2020 for each health 

endpoint included in the analysis and the associated monetary benefits. The mean 

estimate is presented as the primary central estimate, the 5th percentile observation is 

presented as the primary low estimate and the 95th percentile is presented as the primary 

high estimate.63 In general, because the differences in air quality between the with- and 

without-CAAA scenarios are expected to increase from 1990 to 2020 and because 

population is also expected to increase during that time, the health benefits attributable to 

the CAAA are expected to increase consistently from 1990 to 2020. More detailed 

results can be found in Health and Welfare Benefits Analyses to Support the Second 

Section 812 Benefit-Cost Analysis of the Clean Air Act, February 2011. 

AVOIDED PREMATURE MORTALITY ESTIMATES 

Our analysis indicates that the benefit of avoided premature mortality risk reduction 

dominates the overall net benefit estimate. This is, in part, due to the high monetary 

value assigned to the avoidance of premature mortality relative to the unit value of other 

health endpoints. As described in detail in this chapter, there are also significant 

reductions in other short-term and chronic health effects and a substantial number of 

health benefits that we could not quantify or monetize. Mean results for all three target 

years are provided in Table 5-6, and the mean, primary low, and primary high estimates 

for 2020 are presented in Table 5-7. 

As shown in Table 5-7, our primary central estimate implies that PM and ozone 

reductions due to the CAAA in 2020 will result in 230,000 avoided deaths, with a 

primary low and primary high bound on this estimate of 45,000 and 490,000 avoided 

deaths, respectively. These avoided deaths are valued at $1.8 trillion (2006$), with 

primary low and primary high bounds on this estimate of $170 billion to $5.5 trillion. To 

provide some context for these large values, we estimated the per capita risk change and 

monetized benefits. The estimated 230,000 avoided deaths in 2020 are equivalent to a 

total annual mortality risk reduction of 6.8 x 10-4 for the full estimated US population in 

2020. With approximately 2.4 million estimated deaths in 2002, the avoided deaths in 

2020 would increase total deaths by about 9.5 percent. The 230,000 avoided deaths are 

about 16 percent of the total mortality from the top four causes of death in the US in 

2002: heart disease (over 600,000 deaths); cancer (over 550,000 deaths); stroke (over 

130,000 deaths); and chronic lower respiratory disease (just less than 130,000 deaths). 

The monetized benefit per capita in 2020 is about $6,000, increasing from $2,700 in 2000 

and $4,200 in 2010. Monetized benefits per household would be approximately $16,000 

in 2020, increasing from $7,300 in 2000 and $11,000 in 2010. 

 

 

63 The distribution of incidence results represent the uncertainty associated with the coefficient of the C-R function for each 

health endpoint. The distribution around the monetized benefits estimate reflects both uncertainty in the incidence as 

well as uncertainty associated with the valuation estimate. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-25

TABLE 5-6. MEAN CAAA-RELATED AVOIDED ANNUAL INCIDENCE OF HEALTH EFFECTS AND 

ASSOCIATED MONETARY VALUATION IN 2000, 2010, AND 2020 

INCIDENCE VALUATION (MILLIONS 2006$) 

ENDPOINT POLLUTANT 2000 2010 2020 2000 2010 2020 

Mortality 

Mortality – adults 30 

and older 

PM 

110,000 160,000 230,000 $710,000 $1,200,000 $1,700,000 

Mortality – infant PM 160 230 280 $1,300 $1,900 $2,500 

Mortality – all ages Ozone 1,400 4,300 7,100 $10,000 $33,000 $55,000 

Morbidity 

Chronic Bronchitis PM 34,000 54,000 75,000 $14,000 $24,000 $36,000 

Non-fatal Myocardial 

Infarction 

PM 

79,000 130,000 200,000 $8,100 $14,000 $21,000 

Hospital Admissions, 

Respiratory 

PM, Ozone 

20,000 41,000 66,000 $290 $640 $1,100 

Hospital Admissions, 

Cardiovascular 

PM 

26,000 45,000 69,000 $760 $1,300 $2,000 

Emergency Room 

Visits, Respiratory 

PM, Ozone 

58,000 86,000 120,000 $21 $32 $44 

Acute Bronchitis PM 96,000 130,000 180,000 $42 $61 $94 

Lower Respiratory 

Symptoms 

PM 

1,200,000 1,700,000 2,300,000 $22 $30 $42 

Upper Respiratory 

Symptoms 

PM 

980,000 1,400,000 2,000,000 $30 $42 $60 

Asthma Exacerbation PM 1,200,000 1,700,000 2,400,000 $61 $90 $130 

Minor Restricted 

Activity Days 

PM, Ozone 

49,000,000 84,000,000 110,000,000 $2,900 $4,900 $6,700 

Work Loss Days PM 8,000,000 13,000,000 17,000,000 $1,300 $2,000 $2,700 

School Loss Days Ozone 1,200,000 3,200,000 5,400,000 $110 $290 $480 

Outdoor Worker 

Productivity 

Ozone N/A N/A N/A 

$30 $100 $170 

Note: All incidence and valuation results are rounded to two significant figures. All estimates are annual estimates for 

individual target years of the analysis. Mortality valuation estimates reflect a delay in mortality incidence from the time 

of the exposure change in the target year, reflecting application of a 20-year distributed cessation lag as described in the 

text and a 5 percent discount rate. 

It may also be worth noting that most of the changes in mortality risk we estimate occur 

in locations where both the with-CAAA and without-CAAA concentrations are above the 

lowest measured level (LML) in the underlying epidemiological studies. As noted above, 

standard EPA practice is to estimate PM-related mortality without applying an assumed 

concentration threshold, and the LML is itself not a threshold either. The LML approach 

summarizes the distribution of avoided PM mortality impacts according to the baseline 

PM2.5 levels experienced by the population receiving the PM2.5 mortality benefit. Unlike 

an assumed threshold, the LML is a characterization of the fraction of benefits that are 

more uncertain. In general, our confidence in the estimated PM mortality decreases as we 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-26

consider air quality levels further below the LML in the two underlying PM-mortality 

epidemiological studies, Pope et al. (2002) and Laden et al. (2006). 

TABLE 5-7. CAAA-RELATED AVOIDED ANNUAL INCIDENCE OF HEALTH EFFECTS AND ASSOCIATED 

MONETARY VALUATION IN 2020 

INCIDENCE VALUATION (MILLIONS 2006$) 

ENDPOINT POLLUTANT 5TH %ILE MEAN 95TH %ILE 5TH %ILE MEAN 95TH %ILE 

Mortality 

Mortality1 PM, Ozone 45,000 230,000 490,000 $170,000 $1,800,000 $5,500,000 

Morbidity 

Chronic Bronchitis PM 12,000 75,000 130,000 $3,100 $36,000 $130,000 

Non-fatal 

Myocardial 

Infarction 

PM 

80,000 200,000 300,000 $6,200 $21,000 $48,000 

Hospital 

Admissions, 

Respiratory 

PM, Ozone 

24,000 66,000 110,000 $320 $1,100 $1,800 

Hospital 

Admissions, 

Cardiovascular 

PM 

52,000 69,000 84,000 $1,400 $2,000 $2,600 

Emergency Room 

Visits, Respiratory 

PM, Ozone 

64,000 120,000 180,000 $22 $44 $69 

Acute Bronchitis PM -7,000 180,000 340,000 -$4 $94 $220 

Lower Respiratory 

Symptoms 

PM 

1,200,000 2,300,000 3,300,000 $18 $42 $76 

Upper Respiratory 

Symptoms 

PM 

620,000 2,000,000 3,300,000 $17 $60 $130 

Asthma 

Exacerbation 

PM 

270,000 2,400,000 6,700,000 $15 $130 $390 

Minor Restricted 

Activity Days 

PM, Ozone 

91,000,000 110,000,000 140,000,000 $3,800 $6,700 $10,000 

Work Loss Days PM 15,000,000 17,000,000 19,000,000 $2,300 $2,700 $3,000 

School Loss Days Ozone 2,200,000 5,400,000 8,600,000 $190 $480 $770 

Outdoor Worker 

Productivity 

Ozone N/A N/A N/A 

$170 $170 $170 

Notes: 

1

 Includes adult and infant mortality for PM and all ages for ozone. 

All incidence and valuation results are rounded to two significant figures. Mortality valuation estimates reflect a delay in 

mortality incidence from the time of the exposure change in the target year, reflecting application of a 20-year 

distributed cessation lag as described in the text and a 5 percent discount rate. 

Using the Pope et al. (2002) study, approximately 98 percent of the mortality impacts 

occur among populations with exposure to annual mean PM2.5 levels at or above the LML 

of 7.5 μg/m3

. Using the Laden et al. (2006) study, approximately 91 percent of the 

mortality impacts occur at or above the LML of 10 μg/m3

. These analyses confirm that 

the great majority of the mortality benefits occur at or above the cohort study LMLs. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-27

Avoided premature mortality is one of the more commonly cited results of benefits 

analyses for air pollution control. However, as noted in the valuation section of this 

chapter, a more accurate description of the benefit of clean air is a reduction in the risk of 

mortality for the exposed population over many years, which results in the extension of 

lives (sometimes referred to as “lives saved”). Other useful metrics of the benefit of 

cleaner air are the number of life years that are gained through the reduction of mortal 

risks, and the number of years of life expectancy gained on average throughout the 

population. We estimated these metrics through the application of a population 

simulation tool – effectively, we simulated the process of gradually reducing mortality 

risk from air pollution across all individuals in the US 30 years old and older, starting in 

1990 and continuing through 2020. In addition, we tracked the impact of these effects, 

held constant at the 2020 levels, for an additional 30 years, through 2050. Running the 

simulation beyond 2020 allows us to estimate the full effect of changes that begin in 

2020, which because of the cessation lag are not fully realized until many years after the 

end of the study period. Comparing the estimated population in each age cohort across 

the two scenarios allows us to estimate gains in life-years (i.e., one additional person in a 

cohort for one year yields a life year gained), and summing across cohorts and years 

yields cumulative estimates. In addition, analysis of the changes in mortality risk among 

cohorts older than a specific age yields estimates of life expectancy gains at specific 

ages.64

The results of these calculations are presented in Table 5-8 below, and provide further 

evidence of the substantial benefits of CAAA during and after the 1990-2020 period. The 

first panel of the table provides estimates of life-years gained for 2020 and 2040 – these 

are estimates of the life-years gained only in that year of the simulation, but reflect the 

cumulative effect of mortality risk reductions in prior years. The next panel provides 

estimates of cumulative life years gained overall all years since 1990, first for the 1990-

2020 period, and then for the 1990-2040 period, inclusive. 

As expected, life-years gained are largest in the older cohorts, particularly cohorts 60 

years and older, and they increase over time as the effect of mortality risk reduction in 

successive years increases survival rates among all individuals age 30 and over. By 2020, 

the cumulative effects indicate 22 million life-years are gained from the air pollution 

mortality risk reduction. 

The last panel provides the life expectancy results. As early as 2010, the CAAA 

increased life expectancy at 30 years by 0.65 years, with somewhat smaller gains among 

older cohorts. By 2040, the full effect of the CAAA on life expectancy is realized, with a 

total gain in life expectancy of almost one year at age 30 across the entire US population. 

 

64 For a detailed description of the model, see the related report, Uncertainty Analyses to Support the Second Section 812 

Benefit-Cost Analysis of the Clean Air Act, March 2010, and Industrial Economics, Inc. (2006). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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TABLE 5-8. LIFE YEARS GAINED AND LIFE EXPECTANCY GAIN ESTIMATES FROM THE 

POPULATION SIMULATION MODEL 

AGE COHORT 

LIFE-YEARS GAINED IN 

SPECIFIC YEARS 

(ANNUAL) 

CUMULATIVE LIFE YEARS 

GAINED THROUGH TARGET 

YEAR 

LIFE EXPECTANCY GAINS 

(YEARS) 

START AGE END AGE 2020 2040 2020 2040 2010 2020 2040 

30 39 17,000 18,000 260,000 620,000 0.65 0.87 0.91

40 49 60,000 71,000 910,000 2,300,000 0.63 0.84 0.88

50 59 150,000 180,000 2,000,000 5,400,000 0.59 0.79 0.84

60 69 330,000 380,000 3,500,000 11,000,000 0.53 0.71 0.76

70 79 470,000 840,000 5,000,000 20,000,000 0.44 0.59 0.64

80 89 470,000 1,200,000 6,000,000 23,000,000 0.32 0.43 0.48

90 99 320,000 800,000 3,600,000 14,000,000 0.19 0.25 0.27

100+ 60,000 200,000 490,000 3,100,000 0 0 0

Total 1,900,000 3,800,000 22,000,000 80,000,000

Note: Column entries to not add to totals due to rounding. Life expectancy results are incremental period 

conditional life expectancy gains at the start age of the cohort. 

NON-FATAL HEALTH IMPACTS 

We report non-fatal health effects estimates in a similar manner to estimates of premature 

mortality – as a range of estimates for each quantified health endpoint, with the range 

dependent on the quantified uncertainties in the underlying C-R functions. The range of 

results for 2020 is characterized in Table 5-6 with 5th percentile, mean, and 95th percentile 

estimates which correspond to the primary low, central, and high estimates. All estimates 

are expressed as new cases avoided in 2020, with the following exceptions. Hospital 

admissions reflect admissions for a range of respiratory and cardiovascular diseases and 

these results, along with emergency room visits for respiratory disease, do not necessarily 

represent the avoidance of new cases of disease (i.e., air pollution may simply exacerbate 

an existing condition, resulting in an emergency room visit or hospital admission). 

Further, each admission is only counted once, regardless of the length of stay in the 

hospital. Minor restricted activity days, school loss days, and work loss days are 

expressed in terms of person-days. For instance, one “case” of a school loss day 

represents one person out of school for one day. 

AVOIDED HEALTH EFFECTS OF AIR TOXICS 

The prior discussion focuses on the effects of the 1990 CAAA on particulate matter and 

ozone health effects, but the Amendments also address the control of air toxics or 

hazardous air pollutants (HAPs). HAPs are pollutants regulated under Title III of the 

CAAA that can cause adverse effects to human health and ecological resources. The 

Amendments establish a list of HAPs to be regulated, require EPA to establish air toxic 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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emissions standards based on Maximum Achievable Control Technology (MACT) 

standards, and include a provision that requires EPA to establish more stringent air toxics 

standards if MACT controls do not sufficiently protect the public health against residual 

risks. Control of air toxics is expected to result both from these changes and from 

incidental control due to changes in criteria pollutant programs, such as controls on 

volatile organic compounds (VOCs) necessary to achieve the NAAQS for ambient 

tropospheric ozone. 

Both the Retrospective analysis and the First Prospective analysis omitted a quantitative 

estimation of the benefits of reduced concentrations of air toxics, citing gaps in the 

toxicological database, difficulty in designing population-based epidemiological studies 

with sufficient power to detect health effects, limited ambient and personal exposure 

monitoring data, limited data to estimate exposures in some critical microenvironments, 

and insufficient economic research to support valuation of the types of health impacts 

often associated with exposure to individual air toxics. Based on a recommendation by 

the Council, EPA developed a case study of the benefits of CAAA controls on benzene 

emissions in the Houston area (USEPA, 2001).65 The purpose of the case study was to 

demonstrate a methodology that could be used to generate human health benefits from 

CAAA controls on a single HAP in an urban setting, while highlighting key limitations 

and uncertainties in the process. In addition, EPA hoped to gain insight into the use of 

the case study methodology for characterizing benefits nationwide. The case study was 

not intended, however, to provide a comprehensive assessment of the benefits of benzene 

reductions due to the CAAA. 

The case study involved calculating the reduction in the annual number of cases of 

leukemia due to reductions in benzene levels resulting from the 1990 CAAA through the 

year 2020 in the Houston metropolitan area. Benzene was selected for the case study due 

to the availability of human epidemiological studies linking its exposure with adverse 

health effects. The case study focused on Houston because of the presence of significant 

large benzene emitting sources, such as petroleum refineries, as well as sources more 

typical of other urban areas, such as gasoline refueling stations. 

We conducted the case study using the same five steps used in the main 812 criteria 

pollutant analysis: 

1. Scenario Development: We assessed benefits from the reduction in benzene 

concentrations between a without-CAAA scenario, which essentially freezes 

federal, state, and local air pollution controls at the levels of stringency and 

effectiveness that existed in 1990, and a with-CAAA scenario, which assumes that 

all federal, state, and local rules promulgated pursuant to, or in support of, the 

1990 CAAA were implemented. 

 

 

65 A detailed report of the case study methodology and results was completed by Industrial Economics, Inc (IEc, 2009). This 

report can be downloaded from the following website: www.epa.gov/oar/sect812 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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2. Emissions Estimation: We estimated benzene emissions in the Houston area 

under both the with-CAAA and without-CAAA scenarios by extrapolating data 

based on expected growth in emissions-generating activities over time, adjusted 

for the impact of future year control assumptions under each scenario. 

3. Air Quality and Exposure Modeling: We then applied EPA’s American 

Meteorological Society/Regulatory Model (AERMOD) dispersion modeling 

system (USEPA, 2004) to convert emissions estimates to ambient benzene 

concentrations at the Census block group level. The AERMOD output was then 

run through EPA’s Hazardous Air Pollutant Exposure Model, Version 6 

(HAPEM6; ICF International, 2007) to generate benzene exposure concentrations 

for the study population at the Census tract level, which reflect average benzene 

concentrations likely experienced by the study population as they carry out their 

daily activities. 

4. Health Effects Modeling: We next estimated avoided cases of leukemia using a 

life-table based risk assessment model. The life-table model assessed agespecific risks at the Census tract level, based on county-level background rates of 

leukemia, age-specific benzene exposure data from HAPEM6 and an 

epidemiological dose-response function derived from a study of occupational 

benzene exposures (Crump, 1994).66 The model yielded annual age-specific 

Census tract-level avoided cases of leukemia (fatal and non-fatal) for each target 

year. We also estimated the number of cases expected to occur after the end of 

the study period resulting from CAAA-related benzene changes within the study 

period, due to lagging effects of these changes on leukemia risks. 

5. Valuation: We then applied valuation methods from the current economic 

literature to assign monetary value to the avoided leukemia cases. This included 

valuing fatal cancers using the VSL estimate used in the primary 812 analysis 

(i.e., the Weibull distribution based on 26 studies) with an adjustment for medical 

costs associated with the period of cancer illness leading up to death (i.e., “premortality morbidity”).67 We valued non-fatal cancers using two bounding 

estimates, a WTP value for chronic bronchitis and one from a health risk tradeoff 

study that provided a value for avoiding a case of non-fatal lymphoma.68

Table 5-9 presents our primary estimate for avoided fatal and non-fatal cases of leukemia 

due to CAAA-related changes in ambient benzene levels in the Houston area. It includes 

the number of expected annual cases avoided in each study year as well as the total 

cumulative avoided cases throughout the study period and the total cumulative avoided 

cases expected to occur after 2020, due to changes in benzene occurring within the study 

 

66 This study is also the basis for the Inhalation Unit Risk (IUR) published on EPA’s Integrated Risk Information System (IRIS) 

(USEPA, 1998). 

67 This estimate was based on a value presented in EPA's Cost of Illness Handbook (USEPA, 1999) for a "typical" cancer case. 

68 The chronic bronchitis value is the same as that used in EPA’s Regulatory Impact Analysis (RIA) for the PM National Ambient 

Air Quality Standards (NAAQS) (USEPA, 2006). The non-fatal lymphoma value was derived by using the risk-risk ratio from 

Magat et al. (1996) along with our primary VSL estimate. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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period. It also shows the monetary value (the 1990 net present value (NPV), using a five 

percent discount rate) of these avoided leukemia cases. 

Our results indicate that by the year 2020, the change in benzene-related population risk 

due to the 1990 CAAA programs would be equivalent to a total of four cases of leukemia 

in the Houston area, with three of those occurring in Harris County, the most densely 

populated county included in the analysis. We estimated two of the four cases to be fatal 

and two to be non-fatal. Our primary central estimate of total benefits due to CAAArelated reductions in benzene is $8.9 to 13 million (in 2006$), $8.5 million of which is 

due to fatal cases of leukemia, and $0.4 to 4.1 million of which is due to non-fatal cases. 

In addition to the leukemia analysis, we evaluated the numbers of individuals likely to be 

exposed to benzene at levels exceeding EPA's chronic reference concentration (RfC) for 

benzene, which is based on changes in white blood cell counts, under the with-CAAA and 

without-CAAA scenarios. We found no individuals exposed to benzene at concentrations 

exceeding the RfC in either the with-CAAA or without-CAAA scenario. We also 

conducted illustrative analyses of exposure and risk reductions to highly exposed 

subpopulations in the study area, and found potentially significant individual risk 

reductions due to the CAAA for individuals in these groups. For instance, a back-of-the 

envelope calculation of residents living in homes with attached garages, who are expected 

to have higher benzene exposures, suggests that adding attached garage-related benefits 

to our primary estimate could result in an approximate doubling of our primary estimate. 

The effect of the CAAA on lifetime risks of benzene-induced leukemia for Houston 

residents at the Census tract level is explored in Figure 5-2. The map on the left displays 

the distribution of leukemia risks based on benzene exposures levels expected in 2020 

under the without-CAAA scenario. The highest risk levels (i.e., greater than one-in-one 

hundred thousand) occur in Harris County in the downtown Houston area (within the 

rings of the interstate), in the Texas City area of Galveston County where a number of 

refineries and chemical facilities are located and in southeastern Brazoria County, which 

also features major chemical manufacturing and petroleum refining facilities. The map 

on the right shows the distribution in the magnitude of CAAA-related risk reductions 

throughout the Houston area. The highest risk reductions (i.e., greater than a factor of 

three) coincide with the areas identified as those with the highest risks in the first map. 

For instance, the CAAA is expected to reduce risks significantly in the highly populated 

downtown Houston area, where residents are expected to have risks on the order of onein-one hundred thousand or greater. 
The Benefits and Costs of the Clean 

TABLE 5-9. TOTAL ANNUAL BENEFITS FOR EACH STUDY YEAR FROM CAAA-RELATED CHANGES IN BENZENE EXPO

HOUSTON AREA 

ANNUAL AVOIDED CASES OF LEUKEMIA 

TOTAL MONETARY BENEFITS, 

(1990 NPV, MILLIONS OF 2006$, 5% 

AVOIDED 

FATAL CASES 

AVOIDED NONFATAL CASES TOTAL AVOIDED CASES 

BENEFITS FROM 

FATAL CASES OF 

LEUKEMIA 

BENEFITS FROM 

NON-FATAL 

CASES OF 

LEUKEMIA 

Results by Study Year 

2000 0.03 0.02 0.05 $0.12 $0.01 – 0.06 2010 0.09 0.07 0.2 $0.27 $0.01 – 0.13 2020 0.2 0.1 0.3 $0.31 $0.01 – 0.15 

Cumulative Results Cumulative Cases Occurring Within the Study Period 2 2 4 $6.7 $0.32 – 3.3 Additional Cumulative Cases Occurring After 2020* 1 1 2 $1.8 $0.08 – 0.8 Total Cumulative Cases 3 3 6 $8.5 $0.40 – 4.1 * Note: These avoided cases are due to changes in benzene exposure that took place within the study period. However, the case

2020 due to lagging effects of these changes on leukemia risks, as described in the text. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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In summary, this case study demonstrates that the 1990 CAAA controls on benzene 

emissions are expected to result in reductions in the incidence of leukemia in the greater 

Houston area over the period 1990 to 2020. The case study does have some limitations, 

including possible underestimation of benzene emissions from large point sources (e.g., 

refineries), possible exclusion of unquantifiable adverse health effects of benzene (e.g., 

Hodgkin’s and non-Hodgkin’s Lymphoma), and exclusion of new programs established 

after the case study (e.g., Mobile Source Air Toxics Rule). However, it successfully 

demonstrates a methodology that can serve as a useful tool in EPA's evolving HAP 

benefits assessment strategy. It can provide a comprehensive assessment of the impact of 

benzene controls from multiple CAAA Titles on cancer incidence in an urban population, 

using a combination of national and local data to conduct urban-scale modeling of air 

quality and health impacts. Further, the life-table model allows for more careful 

assessment of risk changes over time at the Census tract level, incorporating local, agespecific baseline incidence data with age-specific exposure data and information on the 

lag between exposure changes and risk reductions. 

FIGURE 5-2. EFFECT OF THE CAAA ON LIFETIME RISKS OF BENZENE-RELATED LEUKEMIA IN THE 

HOUSTON AREA 

Determining where this approach might fit within EPA's HAP benefits assessment 

strategy will require additional analysis and evaluation to determine the added value of 

the detailed, urban-scale approach, as well as the potential pool of HAPs suitable for 

assessment via the damage-function approach for cancer and/or non-cancer effects.
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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COMPARISON OF HEALTH EFFECTS MODELING WITH FIRST PROSPECTIVE ANALYSIS 

DIFFERENCES IN METHODOLOGY 

In comparison with the First Prospective 812 Analysis, the Second Prospective includes a 

number of refinements and improvements in health benefits estimation methods. 

 Targeted Criteria Pollutant Analysis: The Second Prospective excludes benefits 

of CAAA-related reductions in carbon monoxide, nitrogen oxides, and sulfur 

dioxide, which were included in the First Prospective, in an effort to streamline 

the quantitative analysis to focus on the two criteria pollutants that yield the 

greatest benefits – PM2.5 and ozone. 

 New Cessation Lag Structure for PM Mortality: The Second Prospective relies 

on the use of a 20-year distributed lag structure assumption for the cessation lag 

between changes in PM exposure and resulting changes in premature mortality. 

This estimate represents a shift from the First Prospective, which applied a 5-year 

distributed lag based on smoking cessation literature. The 20-year distributed lag 

is based on recommendations from the Council HES, is derived from air 

pollution literature and attempts to more closely reflect the disease processes that 

occur from PM exposure.69 

 New C-R Function for PM Mortality: The First Prospective relied upon a C-R 

function derived from the most recently published ACS cohort study at the time 

(Pope et al., 1995). Since this time, additional follow-up has occurred for both 

the ACS and Six Cities cohort studies. In addition, new evidence has emerged on 

the ACS study results that suggest that this estimate is potentially underestimated. 

Our new primary C-R function mean is based on the follow-up literature, 

specifically the Pope et al. (2002) update of the ACS cohort and the Laden et al. 

(2006) update of the Six Cities cohort. Our new C-R function also reflects the 

results of an expert elicitation study, which allowed experts to incorporate 

multiple sources of uncertainty in the C-R function and to adjust the C-R function 

estimates to account for known biases. 

 Ozone Mortality Benefits Estimates: The Second Prospective includes ozonerelated premature mortality. This additional endpoint, which was not included in 

the First Prospective, was added because of advances that have occurred in the 

epidemiological literature that provide consistent evidence for this health 

endpoint.70

 

69 Science Advisory Board (2004). Advisory on 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: Advisory by the Health Effects Subcommittee of 

the Advisory Council on Clean Air Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-002. 

70 As noted earlier, a key recommendation of NRC (2008) was that ozone mortality estimates from available epidemiological 

studies represent a separate and additive effect to those from PM/mortality epidemiological studies. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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 New Health Benefits Modeling Program: The Second Prospective relies on 

EPA’s BenMAP health benefits modeling program. Key advantages of the 

updated model are ease of use, allowing us to more readily perform multiple 

sensitivity tests; updated population and baseline incidence estimates; new C-R 

function options; and the ability to perform integrated exposure analysis using the 

eVNA method described earlier. 

 Air Toxics Case Study: The Second Prospective includes the results of a case 

study demonstrating a methodology for assessing health benefits from a single 

hazardous air pollutant. 

DIFFERENCES IN HEALTH EFFECTS MODELING RESULTS 

The health effects estimates for the Second Prospective are much larger than the 

estimates EPA developed for the First Prospective. The 2020 estimates are new to the 

Second Prospective, but the comparable mean estimate of health benefits in 2000 and 

2010 for the First Prospective were $71 billion in 2000 and $110 billion in 2010, in 

1990$71 - if updated to 2006$, these estimates would be $110 billion in 2000 and $170 

billion in 2010. The Second Prospective results are larger by roughly a factor of 10. 

There are four key reasons we have identified for the increase in benefits: 

1. Scenario differences: The with-CAAA scenario, especially for the 2010 target year, 

includes new rules with substantial additional pollutant reductions that were not 

included in the comparable First Prospective scenario, such as the Clean Air 

Interstate Rule (CAIR). 

2. Improved air quality models: The First Prospective relied on the Regional Acid 

Deposition Model/Regional Particulate Model (RADM/RPM) for PM and deposition 

estimates in the eastern U.S., the Regulatory Modeling System for Aerosols and Acid 

Deposition (REMSAD) for PM estimates in the western U.S., and the Urban Airshed 

Model (versions V and IV) at various regional and urban scales to generate ozone 

estimates. The Second Prospective relies on the integrated CMAQ modeling tool, 

which reflects substantial improvements in air quality modeling, provides more 

comprehensive spatial coverage, and achieves improved model performance.

3. Better, more comprehensive exposure estimates: The First Prospective relied on 

first generation exposure extrapolation tools to generate monitor-adjusted exposure 

estimates away from monitors. Since then, the monitor network, availability of 

speciated data, and the performance of speciated exposure estimation tools have 

improved substantially.

4. Updated dose-response estimates: Since 1999, some concentration response 

functions have been updated, most notably the PM-premature mortality C/R function, 

whose central estimate of the mortality impact of fine PM has nearly doubled. In 

 

71 See The Benefits and Costs of the Clean Air Act 1990 to 2010, USEPA Office of Air and Radiation and Office of Policy, EPA410-R-99-001, November 1999. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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 addition, health effects research has addressed endpoints that were not covered in the 

First Prospective, including premature mortality associated with ozone exposure. 

Although the Agency has not yet conducted a rigorous quantitative analysis to assess the 

impact of these methodology and data improvements, and the differences in study design 

between the first and Second Prospective made such an analysis difficult to perform, the 

impact of most of these factors is to increase the estimates of benefits, in some cases very 

substantially. 

UNCERTAINTY IN HEALTH BENEFITS ESTIMATES 

A number of important assumptions and uncertainties in the health benefits analysis may 

influence the estimate of monetary benefits presented in this study. In this section of the 

chapter, we first discuss several quantitative sensitivity analyses undertaken to 

characterize the impact of key assumptions on the ultimate health benefits estimates. We 

then conclude with a qualitative discussion of the impact of both quantified and 

unquantified sources of uncertainty. 

QUANTITATIVE SENSITIVITY TESTS 

We performed three quantitative sensitivity tests to estimate the impact of alternate 

assumptions on our overall benefits estimates due to avoided premature mortality, the 

largest contributor to our overall health benefits estimates. The three focal areas for 

sensitivity analysis were: (1) the C-R function estimate; (2) the PM/mortality cessation 

lag structure; and (3) the mortality valuation estimate (including both the VSL and the 

discount rate). These are influential assumptions in our analysis and those for which 

plausible alternative quantitative estimates are available. Table 5-10 below provides the 

results of these sensitivity analyses. 

Concentration-Response Function 

Our monetized estimate of the benefits of reducing premature mortality from CAAArelated pollution reductions is based on a single primary estimate C-R function for each 

of the criteria pollutants included in our analysis, PM2.5 and ozone. This selection is 

associated with uncertainty related to potential across-study variation. That is, different 

published studies of the same pollutant/health effect relationship often do not report 

identical findings; in some instances, the differences are substantial. These differences 

can arise from differences in factors such as study design, random sampling for subject 

populations, or modeling choices, such as inclusion of potential confounders. 

In order to estimate the effect of across-study variation on our CAAA-related mortality 

benefits from reductions in PM2.5 and ozone, we performed a sensitivity analysis on the 

C-R functions selected. For PM2.5, our primary estimate is based on a Weibull 

distribution of C-R coefficients with a mean of 1.06 percent decrease in annual all-cause 

mortality per 1 g/m3

 and an interquartile range bracketed by the Pope et al. (2002) ACS 

estimate (0.55 percent) on the low end and the Six Cities Laden et al. (2006) extended 

follow-up estimate (1.5 percent) at the high end. We conducted a sensitivity analysis by 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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first substituting the primary C-R distribution with alternative C-R functions, one based 

on the Pope et al. (2002) ACS study, one based on the Laden et al. (2006) Six Cities 

cohort study as well as the C-R distributions provided by each of the 12 experts included 

in the PM/mortality expert elicitation study. 

For ozone, our primary estimate consists of a pooled estimate of six studies, three based 

on the NMMAPS database (Schwartz, 2005; Bell et al., 2004; Huang et al., 2005) and 

three meta-analyses (Ito et al., 2005; Levy et al., 2005; Bell et al., 2005). We conducted a 

sensitivity analysis by substitute this primary C-R function with the C-R functions 

reported in each of these six individual studies, and separately for the Jerrett et al. (2009) 

cohort study. 

As shown in Table 5-10, substituting alternate PM C-R functions results in total mortality 

benefits estimates that range from between 81 percent lower up to 78 percent higher than 

the primary estimate. Substituting alternative ozone C-R function does not affect the total 

mortality benefits estimate, since ozone does not contribute significantly to this estimate. 

However, the C-R function selection does affect the ozone mortality estimates, ranging 

from 63 percent lower up to 66 percent higher than the primary estimate for ozone 

mortality incidence. As expected, the Jerrett et al. study yields estimates higher than the 

primary pooled estimate. Cohort studies measure the effects of cumulative exposure and 

so should reasonably yield higher estimates than the comparably parameterized timeseries study - but within the range of underlying six studies, albeit at the high end of that 

range. 

PM/Mortality Cessation Lag 

The timing of the cessation lag between PM exposure and mortality remains uncertain. 

Our primary monetized estimate of PM/mortality benefits assumes a 20-year distributed 

lag (30 percent of the mortality reductions occur in the first year, 50 percent occur equally 

in years two through five, and the remaining 20 percent occur equally in years six through 

20). We tested the sensitivity of this assumption by calculating monetized mortality 

benefits based on alternative cessation lag structures. We selected two alternative lag 

structures – a 5-year distributed lag (which was employed in the First Prospective) and a 

smooth function (which assumes an exponential decay model and is based on an analysis 

by Roosli et al., 2005; see Chapter 6 of Uncertainty Analyses to Support the Second 

Section 812 Benefit-Cost Analysis of the Clean Air Act for further details). We also 

calculated benefits assuming no cessation lag. Application of alternative cessation lag 

structures had a smaller impact on the benefits estimates than the C-R function, resulting 

in benefits estimates that range from 22 percent lower up to 16 percent higher than the 

primary estimate. 

Mortality Valuation 

We apply a VSL value to reductions in premature mortality based on a Weibull 

distribution of 26 study estimates. The literature on VSL is extensive, and studies have 

measured VSL using different methodological approaches (e.g., revealed versus stated 

preference) on a variety of study populations (e.g., workers versus a general population 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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sample) in a variety of different risk contexts (e.g., fatal workplace accidents versus 

mortality risk from disease). In addition, several meta-analyses of the literature have 

been conducted in an attempt to synthesize the literature. As a result, there are many 

options for alternative VSL estimates. We selected several alternative VSL estimates 

derived from the literature for sensitivity testing, including two estimates from a metaanalysis by Viscusi and Aldy (2003), an estimate used in past EPA regulatory analyses in 

the form of a normal distribution, and an estimate from a wage-risk study by Viscusi 

(2004). VSL did not affect the benefits results to the same degree as the C-R function, 

with alternative monetized benefits ranging from 21 percent lower to approximately 

equivalent to our primary estimate. 

TABLE 5-10. RESULTS OF QUANTITATIVE SENSITIVITY TESTS 

FACTOR STRATEGY FOR SENSITIVITY ANALYSIS 

RANGE OF PERCENT CHANGES 

FROM MEAN PRIMARY MORTALITY 

BENEFITS ESTIMATE1

PM C-R Function Alternative C-R functions – two from 

empirical literature (Pope et al., 2002 

and Laden et al., 2006) and 12 

subjective estimates from the expert 

elicitation study 

-81% to 78%, 

Based on most extreme 

estimates from PM expert 

elicitation study. Rest of 

alternatives range from 

-41% to 40%. 

Ozone C-R Function 

Alternative C-R functions – three from 

NMMAPS-based studies, three metaanalyses, and the Jerrett et al. (2009) 

cohort long-term exposure study 

0% for total mortality benefits. 

-63% to 66% 

For ozone-related mortality. 

PM/Mortality 

Cessation Lag 

Alternative lag structures – one step 

function and one smooth function 

(based on an exponential decay 

function) 

-22% to 16% 

VSL Alternative VSL estimates -21% to 0% 

Discount Rate Alternative discount rates -6% to 6% 

1 All values in the table represent the percent change from the mean primary estimate. Percent change 

estimates to not vary by target year. 

Our primary monetized benefits estimate of avoided premature mortality also assumes a 

discount rate of five percent. We tested the sensitivity of our primary results by 

substituting alternative discount rates of three and seven percent.72 This assumption has a 

small effect on the benefits estimates; applying a discount rate of seven percent results in 

 

72 Alternative discount rates of three and seven percent are recommended in U.S. Environmental Protection Agency (2000). 

Guidelines for Preparing Economic Analyses, EPA 240-R-00-003, September. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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benefits that are 6 percent lower than the default and applying a three percent discount 

rate results in a benefits estimate 6 percent higher than the default. 

QUALITATIVE ANALYSIS OF KEY FACTORS CONTRIBUTING TO UNCERTAINTY 

In addition to the uncertainties outlined above, we identified several other areas of 

uncertainty related to our health benefits analysis that we did not address quantitatively. 

This includes sources of uncertainty in our estimation of avoided mortality, not related to 

across-study variation; application of C-R functions for national benefits estimation; 

projection of population and baseline incidence rates; and health valuation. 

Table 5-11 provides a summary of the key uncertainties related to the Second Prospective 

health effects modeling analysis. The first column provides a brief description of each 

key assumption made in the analysis. The second column indicates the direction of the 

potential bias with respect to the overall net benefits estimate. The third indicates the 

magnitude of the impact of the potential bias on the net benefits. The Project Team 

assigns a classification of “potentially major” if a plausible alternative assumption or 

approach could influence the overall monetary benefit estimate by approximately five 

percent or more. If an alternative assumption or approach is likely to change the total 

benefit estimate by less than five percent, the Project Team assigns a classification of 

“probably minor.”73 This assessment is intended to provide readers with a sense for the 

quantitative impact on the net benefits estimate if an alternate assumption to that selected 

by the Project Team were to be implemented. Finally, the fourth column provides our 

level of confidence in the selected assumption, based on our assessment of the available 

body of evidence. That is, based on the given available evidence, how certain we are that 

the selected assumption is the most plausible of the alternatives. The Project Team uses 

the following four qualitative categories to express the degree of confidence in the chosen 

assumption: 

 “High” – the current evidence is plentiful and strongly supports the selected 

assumption; 

 “Medium” – some evidence exists to support the assumption, but data gaps are 

present; and 

 “Low” – there are limited data to support the selected assumption. 

 The Project Team uses “N/A” to indicate that the data was so limited that it was 

excluded from the analysis entirely. 

 

73 If the quantitative magnitude of the assumption’s effect on the net benefits cannot be assessed, the Project Team 

indicates that this is “Unknown.” 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-40

TABLE 5-11. KEY UNCERTAINTIES ASSOCIATED WITH HUMAN HEALTH EFFECTS MODELING 

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

UNCERTAINTIES RELATED TO PREMATURE MORTALITY BENEFITS ESTIMATES 

Analysis assumes a 

causal relationship 

between PM exposure 

and premature 

mortality based on 

strong epidemiological 

evidence of a 

PM/mortality 

association. However, 

epidemiological 

evidence alone cannot 

establish this causal 

link. 

Overestimate Potentially major. 

PM/mortality 

effects are the 

largest contributor 

to the net benefits 

estimate. If the 

PM/mortality 

relationship is not 

causal, it would 

lead to a significant 

overestimation of 

net benefits. 

High. 

The assumption of 

causality is suggested 

by the epidemiologic 

and toxicological 

evidence and is 

consistent with 

current practice in the 

development of a best 

estimate of air 

pollution-related 

health benefits. At 

this time, we can 

identify no basis to 

support a conclusion 

that such an 

assumption results in a 

known or suspected 

overestimation bias. 

Analysis assumes a 

causal relationship 

between ozone 

exposure and 

premature mortality 

based on strong 

epidemiological and 

experimental evidence 

of an ozone/mortality 

association. 

Overestimate Probably minor. 

Ozone mortality 

effects are a large 

contributor to the 

net benefits 

estimate, but total 

monetized ozone 

mortality benefits 

remain less than 

five percent of total 

net benefits. If the 

ozone mortality 

relationship is not 

causal, it would 

lead to an 

overestimation of 

net benefits. 

Medium. 

Several 

epidemiological 

studies provide strong 

evidence for 

associations between 

ozone and mortality. 

This data is supported 

by human and animal 

experimental studies 

that provide 

suggestive evidence 

for plausible 

mechanisms. Overall, 

the evidence is highly 

suggestive, but 

additional research is 

needed to more fully 

establish underlying 

mechanisms. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-41

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

It is possible that the 

PM/mortality 

relationship is modified 

by socioeconomic 

status (SES). 

Unable to determine 

based on current 

information. 

Consideration of both 

the Pope and Laden 

studies avoids the 

possible 

underestimation 

effect from the ACS 

cohort, owing to the 

demographics of that 

study population, and 

the possible 

overestimation bias 

associated with the 

more limited 

geographic scope of 

the Six Cities cohort. 

Potentially major. 

Sensitivity analyses 

reported in this 

chapter indicate 

the high sensitivity 

of benefits results 

to the choice of the 

PM/mortality C/R 

function. 

Medium. 

Studies have found 

effect modification of 

the PM/mortality 

effect by SES, as 

assessed through 

education attainment 

(Krewski et al., 2000). 

However, this effect is 

likely to affect only 

the Pope et al. 

estimate. Our 

inclusion of both the 

Pope et al. and Laden 

et al. (which does 

includes a more 

diverse population) 

helps account for the 

possible significance 

of this uncertainty. 

Exposure 

misclassification due to 

reliance on ambient 

monitoring data to 

estimate PM2.5

exposures rather than 

measuring personal 

exposures. 

Underestimate. 

Concentrations 

measured at central 

site monitors may not 

accurately reflect 

exposure experienced 

by the population due 

to variation in 

ambient 

concentrations over 

space within a 

geographic area, 

incomplete 

penetration of 

ambient pollution into 

homes and 

workplaces, patterns 

of population activity 

and indoor sources 

that can contribute 

significantly to 

individual PM2.5

exposures. Reducing 

exposure error can 

result in stronger 

associations between 

pollutants and health 

effects than generally 

observed in studies 

having less exposure 

detail. 

Potentially major. 

Recent analyses 

reported in Krewski 

et al. (2009) 

demonstrate the 

relatively 

significant effect 

that this source of 

uncertainty can 

have on effect 

estimates. 

High. 

The results from 

Krewski et al. (2009) 

and Jerrett et al. 

(2005) suggest that 

exposure error may 

underestimate effect 

estimates (PM ISA). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-42

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

Exclusion of C-R 

functions from shortterm exposure studies 

in PM mortality 

calculations. 

Underestimate Potentially major. 

PM/mortality is the 

top contributor to 

the net benefits 

estimate. If shortterm functions 

contribute 

substantially to the 

overall PM-related 

mortality estimate, 

then the net 

benefits could be 

underestimated. 

Medium.

Long-term PM 

exposure studies likely 

capture a large part of 

the impact of shortterm peak exposure on 

mortality; however, 

the extent of overlap 

between the two 

study types is unclear. 

Assumption that PMrelated mortality 

occurs over a period of 

20 years following the 

critical PM exposure. 

Analysis assumes that 

30% of mortality 

reductions in the first 

year, 50% over years 2 

to 5, and 20% over the 

years 6 to 20 after the 

reduction in PM2.5

Unable to determine 

based on current 

information 

Potentially major. 

PM/mortality is the 

largest contributor 

to monetary 

benefits. Our 

quantitative 

sensitivity analysis 

indicated that 

alternative 

plausible cessation 

lag structures could 

alter the benefits 

estimate between 

25% lower to 13% 

higher than the 

primary estimate. 

Medium.

Recent 

epidemiological 

studies (e.g., 

Schwartz, 2008) have 

shown that the 

majority of the risk 

occurs within 2 years 

of reduced exposure. 

However, our default 

lag assumes 43% of 

mortality reductions 

would occur within 

the first 2 years. The 

evidence directly 

informing the 

cessation lag structure 

is somewhat limited, 

but the current lag is 

supported by the 

Council HES. 

Assumption of a linear, 

no-threshold model for 

PM and ozone mortality 

Overestimate Probably minor. 

Although 

consideration for 

alternative model 

forms (Krewski et 

al., 2009) does 

suggest that 

different models 

can impact risk 

estimates to a 

certain extent, 

generally this 

appears to be a 

moderate source of 

overall uncertainty. 

High. 

The current scientific 

literature does not 

support a populationbased threshold, 

which consistently 

shows effects down to 

the lowest 

measureable levels. If 

a threshold does exist, 

it is likely below the 

range of 

concentrations of 

regulatory interest. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-43

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

Mortality health impact 

did not include 

pollutants other than 

PM or ozone. 

Unable to determine 

based on current 

information 

Probably minor. 

If other criteria 

pollutants 

correlated with PM 

contribute to 

mortality, that 

effect may be 

captured in the PM 

estimate. This 

uncertainty does 

make it difficult to 

disaggregate 

avoided mortality 

benefits by 

pollutant. 

High. 

PM and ozone are the 

two pollutants most 

strongly linked to 

mortality in the 

epidemiological 

literature. It is likely 

that we’ve captured 

the majority of 

mortality benefits due 

to criteria pollutants 

in our analysis. 

Pooling with equal 

weights of ozone 

mortality incidence 

estimates to present a 

primary estimate. 

Unable to determine 

based on current 

information 

Probably minor. 

Pooling with equal 

weights provides a 

central estimate of 

ozone mortality 

benefits, but it is 

not clear that the 

six ozone mortality 

incidence studies 

should be combined 

in this manner. 

Relying on a 

particular single 

study or another 

combination of 

studies may result 

in significantly 

different estimated 

benefits from ozone 

reductions. 

However, ozonerelated avoided 

mortality benefits 

are a minor 

contributor to total 

monetized benefits. 

Medium. 

All six studies are 

associated with 

different strengths 

and limitation. No 

single study has 

emerged as solely 

suitable to support a 

primary estimate. 

Therefore, a pooled 

estimate provides a 

central estimate of 

the available 

literature. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-44

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

No cessation lag was 

used for ozone 

mortality. 

Overestimate Probably minor. 

If there is a time 

lag between 

changes in ozone 

exposure and the 

total realization of 

changes in health 

effects then 

benefits occurring 

in the future should 

be discounted. The 

use of no lag 

assumes that all 

mortality benefits 

are realized in the 

year of the 

exposure change 

and therefore no 

discounting occurs. 

This may lead to an 

overestimate of 

benefits. 

High. 

Due to the use of 

short-term studies of 

ozone mortality, use 

of a no lag structure is 

appropriate and 

supported by the 

Council HES. 

UNCERTAINTIES RELATED TO APPLICATION OF C-R FUNCTIONS 

Application of C-R 

relationships only to 

those subpopulations 

matching the original 

study population. 

Underestimate Probably minor. 

The C-R functions 

for several health 

endpoints (including 

PM-related 

premature 

mortality) were 

applied only to 

subgroups of the 

U.S. population 

(e.g. adults 30+) 

and thus may 

underestimate the 

whole population 

benefits of 

reductions in 

pollutant 

exposures. 

However, the 

background 

incidence rates for 

these age groups 

are likely low and 

therefore would not 

contribute many 

additional cases. 

High. 

The baseline mortality 

and morbidity rates 

for PM-related health 

effects are 

significantly lower in 

those under the age of 

30 (other than 

neonates). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-45

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

Application of 

regionally derived C-R 

estimates to entire U.S. 

Unable to determine 

based on current 

information 

Probably minor. 

This is likely to 

affect morbidity 

estimates rather 

than mortality, as 

mortality estimates 

are based on 

studies that include 

multiple cities. 

Since morbidity is 

not as large of a 

contributor to 

overall benefits, 

this is not likely to 

have a large impact 

on net benefits. 

Medium.

The differences in the 

expected changes in 

health effects 

calculated using 

different underlying 

studies can be large. 

If differences reflect 

real regional 

variation, applying 

individual C-R 

functions throughout 

the U.S. could result 

in considerable 

uncertainty in health 

effect estimates. 

UNCERTAINTIES RELATED TO HEALTH VALUATION 

Use of a Value-of-aStatistical-Life (VSL) 

estimate based on a 

Weibull distribution of 

26 studies 

Unable to determine 

based on current 

information 

Potentially major. 

Mortality valuation 

generally dominates 

monetized benefits. 

Medium. 

The VSL used in this 

analysis is based on 26 

labor market and 

stated preference 

studies published 

between 1974 and 

1991. Although there 

are many more recent 

studies, including 

meta-analyses, 

sensitivity analyses 

reported above 

suggest that these 

alternative sources 

generate results that 

are close to the 

estimates used in the 

analysis. 

Use of cost of illness 

(COI) estimates to 

value some morbidity 

endpoints 

Underestimate Probably minor. 

Mortality valuation 

generally dominates 

monetized benefits; 

therefore specific 

estimates used to 

generate morbidity 

benefits likely 

would not have a 

large impact on net 

benefits. 

Low. 

Morbidity benefits 

such as hospital 

admissions and heart 

attacks are calculated 

using COI estimates, 

which some studies 

have shown are 

generally half as much 

as WTP to avoid the 

illness. However, WTP 

estimate are currently 

not available for all 

health endpoints. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-46

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

Benefits transfer for 

mortality risk 

valuation, including 

differences in age, 

income degree of risk 

aversion, the nature of 

the risk, and treatment 

of latency between 

mortality risks 

presented by PM/ozone 

and the risks evaluated 

in the available 

economic studies. 

Unable to determine 

based on currently 

available information 

Potentially major. 

The mortality 

valuation step is 

clearly a critical 

element in the net 

benefits estimate, 

so any uncertainties 

can have a large 

effect. 

Medium. 

Information on the 

combined effect of 

these known biases is 

relatively sparse, and 

it is therefore difficult 

to assess the overall 

effect of multiple 

biases that work in 

opposite directions. 

However, our VSL 

estimate is based on a 

distribution of the 

results of 26 individual 

studies, which cover a 

range of 

characteristics. 

Inability to value some 

quantifiable morbidity 

endpoints, such as 

impaired lung function. 

Underestimate Probably minor. 

Reductions in lung 

function are a wellestablished effect, 

based on clinical 

evaluations of the 

impact of air 

pollutants on 

human health, and 

the effect would be 

pervasive, affecting 

virtually every 

exposed individual. 

However, the lack 

of a clear 

symptomatic 

presentation of the 

effect, however, 

could limit 

individual WTP to 

avoid lung function 

decrements. 

Low. 

There currently is no 

evidence to determine 

the monetary value of 

the benefits of 

avoided lung function 

reductions. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-47

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

UNCERTAINTIES IN FORECASTED DATA SUPPORTING HEALTH EFFECTS ESTIMATES 

Uncertainty in 

projecting baseline 

incidence rates 

Both Probably minor. 

The magnitude 

varies with the 

health endpoint. 

Mortality baseline 

incidence is at the 

county level and 

projected for 5-year 

increments. 

Morbidity baseline 

incidence has 

varying spatial 

resolution for year 

2000 only. 

Medium.

The county-level 

baseline incidence and 

population estimates 

were obtained from 

databases where the 

relative degree of 

uncertainty is low. 

The baseline data for 

other endpoints are 

not location specific 

(e.g., those taken 

from studies) and 

therefore may not 

accurately represent 

the actual locationspecific rates. 

Income growth 

adjustments 

Both Potentially major. 

Income growth 

increases 

willingness-to-pay 

valuation 

estimates, including 

mortality, over 

time. 

Medium

It is difficult to 

forecast future income 

growth, owing to 

unpredictability of 

future business and 

employment cycles. 

These can have a 

substantial effect on 

short term growth rate 

projections, although 

over longer periods 

economic growth rates 

have tended to 

converge. The use of 

data from AEO 2005, 

however, omits the 

effect of the most 

recent economic 

downturn. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-48

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

Population projections Both Probably minor. 

The demographics 

of population 

forecasting are 

relatively wellestablished, 

however migration 

estimates are quite 

uncertain, 

particularly for 

specific locations. 

Overall, we believe 

that population 

projections are not 

likely to vary more 

than 5 percent at 

the national level. 

Medium.

Population projections 

cannot adequately 

account for future 

population migration 

due to catastrophic 

events. Projected 

population and 

demographics may not 

well represent futureyear population and 

demographics. 

OTHER UNCERTAINTIES 

Variation in effect 

estimates reflecting 

differences in PM2.5

composition 

Unable to determine 

based on current 

information 

Unable to 

determine based on 

current information 

Medium.

Epidemiology studies 

examining regional 

differences in PM2.5-

related health effects 

have found 

differences in the 

magnitude of those 

effects. While these 

may be the result of 

factors other than 

composition (e.g., 

different degrees of 

exposure 

misclassification), 

composition remains 

one potential 

explanatory factor. 

Very limited 

quantification of health 

effects associated with 

exposure to air toxics. 

Underestimate Probably minor. 

Studies have found 

air toxics cancer 

risks to be orders of 

magnitude lower 

than those of 

criteria pollutants. 

N/A 

Current data and 

methods are 

insufficient to develop 

(and value) national 

quantitative estimates 

of the health effects 

of these pollutants. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

5-49

POTENTIAL SOURCE OF 

ERROR 

DIRECTION OF 

POTENTIAL BIAS FOR 

NET BENEFITS 

ESTIMATE 

MAGNITUDE OF 

IMPACT ON NET 

BENFITS ESTIMATE 

DEGREE OF 

CONFIDENCE 

CAAA fugitive dust 

controls implemented 

in PM non-attainment 

areas would reduce 

lead exposures by 

reducing the reentrainment of lead 

particles emitted prior 

to 1990. This analysis 

does not estimate 

these benefits. 

Underestimate Probably minor. 

The health and 

economic benefits 

of reducing lead 

exposure can be 

substantial (e.g., 

see section 812 

Retrospective Study 

Report to 

Congress). 

However, most 

additional fugitive 

dust controls 

implemented under 

the with-CAAA 

scenario (e.g., 

unpaved road dust 

suppression, 

agricultural tilling 

controls, etc.) tend 

to be applied in 

relatively low 

population areas. 

N/A 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-1 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

CHAPTER 6 - ECOLOGICAL AND OTHER WELFARE BENEFITS 

OVERVIEW OF APPROACH 

Air pollution has important impacts not 

only on human health, but on a wide 

range of ecological and environmental 

resources. Clean Air Act provisions 

are designed to be protective of human 

health and the environment, but as a 

practical matter, because human health 

impacts are more readily quantified, 

many of EPA’s air pollution analyses 

have focused much more on human 

health than on ecological health, 

aesthetic effects, or natural resource 

productivity. In general, as science and 

economics have provided greater 

insights into the effects of 

anthropogenic stressors on ecological 

systems, pursuit of environmental 

programs targeted on reductions of 

damage to the environment have 

become more common. For example, 

as we noted in the First Prospective, the original motivation for Title IV of the CAAA 

was addressing the effects of acid rain on ecological resources – it was only after passage 

that it became clear that these provisions also provide very large human health benefits. 

In this chapter, we provide quantitative results for the effects of air pollution on 

ecological health and natural resources where the science and economic base is strongest, 

including the lake acidification effects that motivated Title IV, as well as a broad 

qualitative characterization of effects that are more difficult to quantify. The first portion 

of this chapter involves taking a broad view of pollutants controlled under the CAAA and 

their documented effects on ecological systems, both as individual pollutants and, to the 

extent possible, as one component in multiple-stressor effects on ecosystems and their 

components. We organize our analysis in terms of major pollutant classes and by the 

level of biological organization at which impacts are measured (e.g., regional ecosystem, 

local ecosystem, community, population, organism, etc.). We used a similar strategy in 

the First Prospective, which has been updated here to reflect new scientific literature 

published since 1999, but we also supplement the literature review with a new mapping 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-2 

of air pollutant stressors relative to ecological systems that are most sensitive to those 

stressors – for example, we relate atmospheric deposition of nitrogen to estuarine systems 

that have been classified as sensitive to marginal nitrogen inputs. 

The second portion of the chapter presents the results of a wide range of analyses that 

quantitatively characterize specific effects of air pollution on ecological systems, as well 

as other effects on natural and human systems that contribute to economic welfare. We 

provide quantitative estimates of the benefits of the 1990 CAAA for the following 

effects: 

 Enhanced forest and agricultural plant growth associated with reduced exposure 

to tropospheric ozone, on a national scale; 

 Enhanced visibility in recreational and residential settings associated with 

reduced particulate matter concentrations, also on a national scale; 

 Reduced damage to certain building and structural materials associated with 

reduced exposure to corrosive air pollutants, such as acid deposition, on a 

national scale; 

 Acidification of freshwater bodies and impairment of timber growth associated 

with atmospheric nitrogen and sulfur deposition, for a case study area in New 

York’s Adirondack region. 

The categories of effects ultimately chosen for quantitative assessment here are 

necessarily limited by available methods and data. The scope is largely consistent with 

the recommendations of the Ecological Effects Subcommittee (EES) of the Council, 

which supported EPA’s plans for qualitative characterization of the ecological effects of 

CAA-related air pollutants, an expanded literature review, national analyses where 

possible, and a quantitative, ecosystem-level case study of ecological service benefits. As 

scientific understanding and impact assessment methods grow more comprehensive, 

however, we expect that the focus of subsequent analyses will continue to broaden, and 

also yield greater insight on which effects that can be avoided by air pollution controls 

have the greatest potential ecological and/or economic value. 

Because the breadth and complexity of air pollutant-ecosystem interactions do not allow 

for comprehensive quantitative analysis of all the ecological benefits of the CAAA, we 

stress the importance of continued consideration of those impacts not valued in this report 

in policy decision-making and in further technical research. Judging from the geographic 

breadth and magnitude of the relatively modest subset of impacts that we find sufficiently 

well-understood to quantify and monetize, it is apparent that the economic benefits of the 

CAAA’s reduction of air pollution impacts on ecosystems are substantial. 

QUALITATIVE CHARACTERIZATION OF EFFECTS 

The First Prospective summarized available information on the ecological effects of 

criteria pollutants and hazardous air pollutants regulated under the 1990 Clean Air Act 

Amendments. In this Second Prospective analysis we expand that effort, updating the 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-3 

literature review to reflect published and peer-reviewed research that has become 

available since the development of the 1999 analysis, through 2008. As data limitations 

prevent the quantitative assessment of all potential ecological benefits, the goal of this 

effort is to provide a broad characterization of the range of effects of major air pollutants 

on ecological endpoints. 

Ecosystem impacts can be organized by the pollutants of concern and by the level of 

biological organization at which impacts are directly measured. We address both 

dimensions of categorization in this overview. Table 6-1 summarizes the major 

pollutants of concern, and the documented acute and long-term ecological impacts 

associated with them. 

The following discussion provides more specific information on ecological effects of 

each pollutant class, including information on sources, sensitive ecosystems, and 

summary tables of effects organized by level of biological organization. 

ACIDIC DEPOSITION 

The predominant chemicals associated with acidic precipitation are sulfuric and nitric 

acid (H2SO4 and HNO3). These strong mineral acids are formed from sulfur dioxide 

(SO2) and nitrogen oxides (NOx) in the atmosphere. Sulfur compounds are emitted from 

anthropogenic sources in the form of SO2 and, to a lesser extent, primary sulfates, 

principally from coal and residual-oil combustion and a few industrial processes. The 

principal anthropogenic source of NOx emissions is fuel combustion. In the atmosphere, 

SO2 and NOx are converted to sulfates and nitrates, transported over long distances, and 

deposited over large areas downwind of urban areas or point sources. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-4 

TABLE 6-1. CLASSES OF POLLUTANTS AND ECOLOGICAL EFFECTS 

POLLUTANT 

CLASS 

MAJOR 

POLLUTANTS AND 

PRECURSORS ACUTE EFFECTS LONG-TERM EFFECTS 

Acidic deposition Sulfuric acid, nitric 

acid 

Precursors: Sulfur 

dioxide,

 nitrogen oxides 

Direct toxic effects 

to plant leaves and 

aquatic organisms. 

Progressive deterioration of soil quality due to 

nutrient leaching. Forest health decline. 

Acidification of surface waters. Reduction in acid 

neutralizing capacity in lakes and streams. 

Enhancement of bioavailability of toxic metals 

(aluminum) to aquatic biota. 

Nitrogen 

Deposition 

Nitrogen 

compounds (e.g., 

nitrogen oxides) 

 Nitrogen saturation of terrestrial ecosystems, 

causing nutrient imbalances and reduced forest 

health. Soil and water acidification. Reduction in 

acid neutralizing capacity in lakes and streams. 

Progressive nitrogen enrichment of coastal 

estuaries causing eutrophication. Changes in the 

global nitrogen cycle. 

Ozone Tropospheric ozone 

Precursors: 

Nitrogen oxides 

and volatile 

organic compounds 

(VOCs) 

Direct toxic effects 

to plants. 

Alterations of ecosystem wide patterns of energy 

flow and nutrient cycling; community changes. 

Hazardous Air 

Pollutants (HAPs) 

Mercury, dioxins Direct toxic effects 

to animals. 

Conservation of mercury and dioxins in 

biogeochemical cycles and accumulation in the 

food chain. Sublethal impacts. 

Acidification of ecosystems has been shown to cause direct toxic effects on sensitive 

organisms as well as long-term changes in ecosystem structure and function. The effects 

of acidification can be seen at all levels of biological organization in both terrestrial and 

aquatic ecosystems. Adverse effects in terrestrial ecosystems include acutely toxic 

impacts of acids on terrestrial plants and, more commonly, chronic acidification of 

terrestrial ecosystems leading to nutrient deficiencies in soils, aluminum mobilization, 

and decreased health and biological productivity of forests. These effects can lead to 

changes in individual plant survival, as well as changes in forest populations and 

communities. 

In aquatic ecosystems, acidification-induced effects are mediated by changes in water 

chemistry including reductions in Acid Neutralizing Capacity74 (ANC) and increased 

availability of aluminum (Al3+), which in turn can cause increased mortality in sensitive 

species, changes in community composition, and changes in nutrient cycling and energy 

flows. Acidic deposition has resulted in increased acidity in surface waters, especially in 

areas where acid buffering capacity of soils is reduced and nitrate and sulfate have 

 

74 Acid Neutralizing Capacity (ANC) is a measure of overall buffering capacity of a solution or surface waterbody. A wellbuffered system will resist rapid changes in pH, while a poorly buffered system responds quickly to changes in pH. 

Reductions in ANC put waterbodies at risk of acidification due to this inability to buffer excess H+

 ions. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-5 

leached from upland areas. While many fish species are acid-sensitive, the main lethal 

agent is the increase in dissolved aluminum that occurs with falling pH levels. 

Acid-sensitive ecosystems include those with high acidic deposition and low acid 

neutralizing capacity. Many of these ecosystems occur downwind of emission sources, 

often in mountainous areas where soils are thin and poorly buffered. High elevation sites 

are also more vulnerable because mountain fog is often more acidic than rain. 

Table 6-2 provides a summary of the potential ecological effects of acidification. 

NITROGEN DEPOSITION 

Along with its role in acidification of ecosystems, nitrogen deposition also affects 

nitrogen biogeochemistry, which in turn affects the health of forest and coastal 

ecosystems. Nitrogen is a naturally occurring element, and is essential to both plant and 

animal life, but combustion processes cause this nitrogen to be “fixed” – that is, 

converted from the unreactive N2 form to a reactive form such as nitrate (NO3) or 

ammonia (NH3). The availability of reactive nitrogen limits plant growth in many 

terrestrial ecosystems and is generally the limiting nutrient in marine and coastal waters 

as well. 

By 1990, human activities had more than doubled the amount of reactive nitrogen 

available annually to living organisms. At present, more than 50 percent of the annual 

global reactive nitrogen emissions are generated directly or indirectly by human 

activities. Ammonia emissions to the atmosphere occur largely via volatilization from 

animal wastes. Anthropogenic nitrogen oxide (NOx) emissions to the atmosphere are 

generally a result of fossil fuel combustion, with electric power generation and 

automobiles as the largest two sources. 

Because most terrestrial and coastal ecosystems are nitrogen limited, increased supply of 

nitrogen in terrestrial systems can stimulate uptake by plants and microorganisms, and 

increase biological productivity. Moderate levels of nitrogen input can have a 

"fertilizing" effect, similar to the application of nitrogen fertilizer frequently used in 

timber production or agriculture. In the long run, however, chronic nitrogen deposition 

adversely affects organisms, communities, and biogeochemical cycles of watersheds and 

coastal waters. Biogeochemical cycles change when the nutrient balance is disrupted by 

excess nitrogen because nitrogen is an important nutrient in biological systems. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-6 

TABLE 6-2. EFFECTS OF ACIDIFICATION ON NATURAL SYSTEMS AT VARIOUS LEVELS OF 

ORGANIZATION 

SPATIAL SCALE TYPE OF INTERACTION 

EXAMPLES OF EFFECTS 

FOREST ECOSYSTEMS STREAMS AND LAKES 

Molecular and 

cellular 

Chemical and biochemical 

processes 

Damages to epidermal layers 

and cells of plants through 

deposition of acids; alteration of 

stomatal activity. 

Decreases in pH and increases in 

aluminum ions cause 

pathological changes in structure 

of gill tissue in fish. 

Organism Direct physiological 

response 

In trees, increased loss of 

nutrients via foliar leaching. 

Hydrogen and aluminum ions in 

the water column impair 

regulation of body ions. 

Indirect effects: 

Acidification can 

indirectly affect response 

to altered environmental 

factors or alterations of 

the organism's ability to 

cope with other kinds of 

stress. 

Cation depletion in the soil 

causes nutrient deficiencies in 

plants. Concentrations of 

aluminum ions in soils can reach 

phytotoxic levels. Increased 

sensitivity to other stress factors 

including pathogens and frost. 

In birds, possible calcium 

limitation and growth reduction.

Aluminum ions in the water 

column can be toxic to many 

aquatic organisms through 

impairment of gill regulation. 

Population Change of population 

characteristics like 

productivity or mortality 

rates. 

Decrease of biological 

productivity of sensitive 

organisms. Selection for less 

sensitive organisms. 

Microevolution of resistance. 

Decrease of biological 

productivity and increased 

mortality of sensitive organisms. 

Selection for less sensitive 

organisms. Microevolution of 

resistance. 

Community Changes of community 

structure and competitive 

patterns. 

Alteration of competitive 

patterns. Selective advantage 

for acid-resistant species. Loss 

of acid sensitive species and 

organisms. Decrease in 

productivity. Decrease of 

species richness and diversity. 

Decline in Sugar Maple and red 

spruce in Eastern U.S. and 

Canadian forests. 

Alteration of competitive 

patterns. Selective advantage 

for acid-resistant species. Loss 

of acid sensitive species and 

organisms. Decrease in 

productivity. Decrease in 

species richness and diversity. 

Local Ecosystem 

(e.g., landscape 

element) 

Changes in nutrient cycle, 

hydrological cycle, and 

energy flow of lakes, 

wetlands, forests, 

grasslands, etc. 

Progressive depletion of nutrient 

cations in the soil. Increase in 

the concentration of mobile 

aluminum ions in the soil. 

Acidification of lakes and 

streams. Decrease in acid 

neutralizing capacity. Persistent 

acidic conditions in lakes and 

streams in some regions, despite 

reduction in sulfate deposition. 

Regional 

Ecosystem (e.g., 

watershed) 

Biogeochemical cycles 

within a watershed. 

Region-wide alterations of 

biodiversity. 

Leaching of sulfate, nitrate, 

aluminum, and calcium to 

streams and lakes. Change in 

sulfur and nitrogen 

biogeochemistry in northeastern 

forests. 

Regional acidification of aquatic 

systems due to high deposition 

rates and nitrogen saturation of 

terrestrial ecosystems and 

increased nitrate leaching to 

surface waters. Persistent acidic 

conditions in lakes and streams 

in some regions, despite 

reduction in sulfate deposition. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-7 

Because fresh waters are generally not nitrogen limited, the addition of nitrogen does not 

lead to excessive eutrophication as it does in coastal waters. Coastal waters are an 

extraordinarily important natural resource, providing spawning grounds/nurseries for fish 

and shellfish, foraging and breeding habitat for birds, and generally contributing greatly 

to the productivity of the marine environment. Critical to the health of coastal waters is 

an appropriate balance of nutrients. If present in mild or moderate quantities, nitrogen 

enrichment of coastal waters can cause moderate increases in productivity, leading to 

neutral or positive changes in the ecosystem. However, because coastal waters are 

generally nitrogen limited, too much nitrogen leads to excess production of algae, 

decreasing water clarity and reducing concentrations of dissolved oxygen, a situation 

referred to as eutrophication. 

Table 6-3 summarizes the potential effects of nitrogen deposition on ecosystem structure 

and function. 

TROPOSPHERIC OZONE 

Ozone is a secondary pollutant formed through the oxidation of volatile organic 

compounds (VOCs) in the presence of oxides of nitrogen. Ozone is one of the most 

powerful oxidants known but its impacts have been little studied in faunal species. The 

limited available research has shown a variety of pulmonary impacts to specific 

mammalian and avian species. In contrast, ozone's impacts on plants are much better 

understood. Documented effects on forest trees include visible foliar damage, decreased 

chlorophyll content, accelerated leaf senescence, decreased photosynthesis, increased 

respiration, altered carbon allocation, water balance changes, and damage to epicuticular 

wax. These can lead to changes in canopy structure, carbon allocation, productivity, and 

fitness of trees. 

Ozone sensitivity of plants varies between species, with evergreen species tending to be 

less sensitive to ozone than deciduous species, and with most individual deciduous trees 

being less sensitive than most annual plants. However, there are exceptions to this broad 

ranking scheme, and there can be variability not only between species but even between 

clones of some trees and within cultivars. Life stage also matters: in general, mature 

deciduous trees tend to be more sensitive than seedlings, while the reverse is more typical 

for evergreen trees. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-8 

TABLE 6-3. EFFECTS OF NITROGEN DEPOSITION ON NATURAL SYSTEMS AT VARIOUS LEVELS OF 

ORGANIZATION

SPATIAL SCALE TYPE OF INTERACTION 

EXAMPLES OF EFFECTS 

FOREST ECOSYSTEMS ESTUARINE ECOSYSTEMS 

Molecular and cellular Chemical and biochemical 

processes. 

Increased uptake of 

nitrogen by plants and 

microorganisms. With 

chronic exposure, reduced 

stomatal activity and 

photosynthesis in some 

species. 

Increased assimilation of 

nitrogen by marine 

plants, macroalgae, and 

microorganisms. 

Organism Direct physiological 

response. 

Increases in leaf size of 

terrestrial plants. 

Increase in foliar nitrogen 

concentration in major 

canopy trees. Change in 

carbon allocation to 

various plant tissues. 

Increase in algal growth. 

Indirect effects: Response 

to altered environmental 

factors or alterations of 

the organism's ability to 

cope with other kinds of 

stress. 

Decreased resistance to 

biotic and abiotic stress 

factors including 

pathogens, insects, and 

frost. Disruption of plantsymbiont relationships 

with mycorrhizal fungi. 

Injuries to marine fauna 

through depletion of 

oxygen in the water 

column. Loss of physical 

habitat due to increased 

macroalgal biomass and 

loss of seagrass beds. 

Injury and habitat loss 

through increased shading 

by macroalgae. 

Population Change of population 

characteristics like 

productivity or mortality 

rates. 

Increase in biological 

productivity and growth 

rates of some species. 

Increase in pathogens. 

Increase in algal and 

macroalgal biomass. 

Community Changes of community 

structure and competitive 

patterns. 

Alteration of competitive 

patterns. Selective 

advantage for fast growing 

species and organisms that 

efficiently use additional 

nitrogen. Loss of species 

adapted to nitrogen-poor 

or acidic environments. 

Increase in weedy species 

or parasites. 

Excessive algal growth. 

Changes in species 

composition with increase 

in algal and macroalgal 

species and decrease or 

loss of seagrass beds. 

Loss of species sensitive 

to low oxygen conditions. 

Local Ecosystem 

(e.g., landscape 

element) 

Changes in nutrient cycle, 

hydrological cycle, and 

energy flow of lakes, 

wetlands, forests, 

grasslands, etc. 

Changes in the nitrogen 

cycle. Progressive 

nitrogen saturation. 

Mobilization of nitrate and 

aluminum in soils. Loss of 

calcium and magnesium 

from soil. Change in 

organic matter 

decomposition rate. 

Changes in the nitrogen 

cycle. Increased algal 

growth leading to 

depletion of oxygen, 

increased shading of 

seagrasses. Reduced 

water clarity and 

dissolved oxygen levels. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-9 

SPATIAL SCALE TYPE OF INTERACTION 

EXAMPLES OF EFFECTS 

FOREST ECOSYSTEMS ESTUARINE ECOSYSTEMS 

Regional Ecosystem 

(e.g., watershed) 

Changes in biogeochemical 

cycles within a watershed. 

Region-wide alterations of 

biodiversity. 

Leaching of nitrate and 

aluminum from terrestrial 

sites to streams and lakes. 

Acidification of soils and 

waterbodies. Increased 

emission of greenhouse 

gases from soils to 

atmosphere. Change in 

nutrient turnover and soil 

formation rates. 

Additional input of 

nitrogen from nitrogensaturated terrestrial sites 

within the watershed. 

Regional decline in water 

quality in waterbodies 

draining large watersheds 

(e.g. Chesapeake Bay). 

Changes in the regionalscale nitrogen cycle. 

Global Ecological System Changes in global 

biogeochemical cycles; 

increased availability of 

reactive nitrogen to 

plants. 

Increased input of reactive 

nitrogen; loss of soil 

nutrients. Nitrogen 

saturation and leaching 

throughout forests in 

northeastern United States 

and Western Europe. 

Acidification of surface 

waters. 

Greatly increased transfer 

of nitrogen to coastal 

ecosystems; change in 

structure and function of 

estuarine and nearshore 

systems. 

Impacts to plant communities may occur as a result of ozone exposure, although such 

effects have not been studied as extensively due to ecosystem complexity and the long 

timeframes involved. Experiments with an early successional plant community found 

that ozone reduced vegetative cover, vertical density, species richness, and evenness 

relative to the control, although differences were less pronounced in a drought year. 

Other observed community level effects include reduced competitive ability of sensitive 

species, changed soil microbial communities, and altered species composition and 

relative abundance. 

Table 6-4 summarizes the potential effects of ozone exposure on ecosystems. 

HAZARDOUS AIR POLLUTANTS 

Hazardous air pollutants (HAPs) are a general category of toxic substances covered under 

Title III of the Clean Air Act, which lists 189 HAPs. Of these 189 substances, the best 

understood in terms of the potential for adverse ecological impacts include mercury, 

polychlorinated biphenyls (PCBs), dioxins, and dichlorodiphenyl-trichloroethane (DDT). 

The use of PCBs and DDT was effectively illegal in the United States prior to 1990 (EPA 

1992), and there are currently no plans for additional CAAA regulations of these 

compounds (Federal Register Unified Agenda 1998). With respect to mercury and 

dioxins, regulatory actions have reduced, but have not eliminated, anthropogenic 

emissions. This section discusses environmental effects associated with these two HAPs. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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TABLE 6-4. EFFECTS OF OZONE ON NATURAL SYSTEMS AT VARIOUS LEVELS OF ORGANIZATION

SPATIAL SCALE TYPE OF INTERACTION EXAMPLES OF EFFECTS 

Molecular and 

cellular 

Chemical and 

biochemical processes. 

Oxidation of enzymes of plants, generation 

of toxic reactive oxygen species (hydroxyl 

radicals). Disruption of the membrane 

potential. 

Reduced photosynthesis and nitrogen 

fixation. Increased apoptosis. 

Organism Direct physiological 

response. 

Visible foliar damage, premature needle 

senescence, altered carbon allocation, and 

reduced growth rates. 

Indirect effects: 

Response to altered 

environmental factors or 

alterations of the 

organism's ability to 

cope with other kinds of 

stress. 

Increased sensitivity to biotic and abiotic 

stress factors such as pathogens and frost. 

Disruption of plant-symbiont relationship 

(mychorrhizae), and symbionts. 

Population Change of population 

characteristics like 

productivity or mortality 

rates. 

Reduced biological productivity and 

reproductive success. Selection for less 

sensitive organisms. Potential for 

microevolution for ozone resistance. 

Community Changes of community 

structure and 

competitive patterns. 

Alteration of competitive patterns. Loss of 

ozone sensitive species and organisms 

leading to reduced species richness and 

evenness. Reduction in productivity. 

Changes in microbial species composition 

in soils. 

Local Ecosystem 

(e.g., landscape 

element) 

Changes in nutrient 

cycle, hydrological 

cycle, and energy flow 

of lakes, wetlands, 

forests, grasslands, etc. 

Alteration of ecosystem-wide patterns of 

energy flow and nutrient cycling (e.g., via 

alterations in litter quantity, litter 

nutrient content, and degradation rates; 

also via changing carbon fluxes to soils and 

carbon sequestration in soils). 

 

Regional Ecosystem 

(e.g., watershed) 

Biogeochemical cycles 

within a watershed. 

Region-wide alterations 

of biodiversity. 

Potential for region-wide 

phytotoxicological impacts and reductions 

in net primary production. 

Mercury 

Mercury (Hg) is a toxic element found ubiquitously throughout the environment. About 

50-80 percent of total emissions originate from anthropogenic sources, including fossil 

fuel combustion, leaks from industrial activities, and the disposal or incineration of 

wastes. 

Mercury is generally released in its elemental and inorganic forms. However, it can 

undergo various transformations in the environment, and its chemical form determines 

not only its environmental fate but also its potency as a toxicant. From a biological 

perspective, the most hazardous form of mercury is methylmercury both because of its 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-11 

bioaccumulation and biomagnification potential, and also because organic forms of 

mercury (including methylmercury) are the most toxic. Adverse effects on wildlife 

include neurotoxicity as well as reproductive, behavioral, and developmental effects. 

These types of effects have been observed in laboratory studies of mammals, birds, fish, 

and aquatic invertebrates. While species sensitivity varies, within a species the early life 

stages are generally the most sensitive. 

Dioxins 

Polychlorinated dibenzo-p-dioxins (PCDDs) are a group of 75 organochlorine 

compounds, often referred to as dioxins. Although dioxins can be produced through 

natural events such as forest fires and volcanic eruptions, most environmental inputs are 

anthropogenic in origin. EPA categorizes dioxin sources into five broad groups: 

combustion; metals smelting, refining, and processing sources; chemical manufacturing; 

biological and photochemical processes; and reservoir sources (for example urban 

runoff). 

Dioxins and related compounds are thought to exert most of their toxic effects through 

interaction with the aryl hydrocarbon receptor (AhR). In laboratory studies, particularly 

of rodents, some dioxins have been shown to cause reproductive toxicity, neurotoxicity, 

immune suppression, increased inflammatory responses, and cancer. Fish are among the 

most sensitive species to the effects of dioxin, and early life stages are the most 

vulnerable. The risk that dioxins pose to other wildlife is difficult to assess because both 

laboratory and field studies are few. 

Dioxins are extremely stable chemicals with a persistence that is measured in decades. 

Dioxins are subject to photochemical degradation, but since the penetration of light into 

soils and many natural water bodies is limited, this degradation is slow. Because of 

dioxins' toxicity and persistence, their presence is likely to be an issue of concern for 

decades. 

DISTRIBUTION OF AIR POLLUTANTS IN SENSITIVE ECOSYSTEMS OF THE UNITED 

STATES 

This section describes the spatial and temporal trends of air pollutants regulated by the 

CAAA, highlighting their distribution against sensitive ecosystems across the United 

States. This information provides useful context regarding the geographic distribution of 

potential ecological benefits of the CAAA, particularly for the ecological endpoints 

described above for which data are not available to quantify impacts. 

The maps presented illustrate changes in forecast pollutant levels under the current, 

baseline scenario (with the CAAA) as compared to the counterfactual scenario (without 

the CAAA). The three pollutant classes considered are: acidic deposition, nitrogen 

deposition, and tropospheric ozone. Data are not available to map the distribution of 

HAPs. The pollutant exposure maps presented in this discussion were created using data 

from the Community Multiscale Air Quality Modeling System (CMAQ) Version 4.6, 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-12 

which estimates tropospheric ozone concentrations as well as deposition in kilograms per 

hectare for acidic deposition and total nitrogen.75 

ACIDIC DEPOSITION 

As described in the previous section, ecosystem sensitivity to acid deposition occurs in 

areas with low ANC. High elevation sites tend to be more vulnerable because of thin, 

poorly buffered soils coinciding with acidic deposition from rain, snow, and fog. Acidsensitive areas in the U.S. include the southern Blue Ridge Mountains of eastern 

Tennessee, western North Carolina and northern Georgia; the mid Appalachian Region of 

eastern West Virginia, western Virginia and central Pennsylvania; New York’s Catskill 

and Adirondack Mountains; the Green Mountains of Vermont; the White Mountains of 

New Hampshire, and areas of the Upper Midwest (Wisconsin and Michigan).76 Montane 

areas in the Adirondacks, Northern New England, and the Appalachian region have 

experienced acidification of surface waters and soils, as well as forest decline. 

Figure 6-1 presents acidic deposition from 1990 through 2020 for both with- and withoutCAAA scenarios. Acid deposition estimates are expressed as equivalents per hectare 

(eq/ha).77 Under both regulatory scenarios, acidic deposition is highest in western 

Pennsylvania, southern Ohio and Indiana, western West Virginia, and northern Kentucky. 

Without the CAAA, acidic deposition in these areas increases over time. Further, acidic 

deposition increases over time in the areas surrounding these hotpots. By 2020, 

significant portions of the Northeast, Midwest, and South are projected to have elevated 

levels of acidic deposition. Hotspots also exist in eastern Texas and southern Louisiana. 

As shown in the right column of Figure 6-1, with the CAAA acidic deposition levels 

lessen in and around the areas with the highest acidic deposition. By 2020, elevated 

acidic deposition levels are primarily limited to much smaller areas in the Midwest, 

Northeast, and Gulf Coast. 

 

75 The CMAQ tool is described in more detail in Chapter 4 of this document. 

76 U.S. Environmental Protection Agency (EPA). October 2003. Response of surface water chemistry to the Clean Air Act 

Amendments of 1990. EPA 620/R-03/001. 

77 Acid deposition is calculated using the hydrogen deposition derived from both sulfur and nitrogen deposition as described 

in: U.S. Department of Agriculture, Forest Service, Rocky Mountain Region. January 2000. Screening Methodology for 

Calculating ANC Change to High Elevation Lakes: User’s Guide. The deposition estimates in Figures 6-2 and 6-3 include 

combined wet and dry deposition for the stated years as estimated by the CMAQ modeling system version 4.6. These 

modeled estimates are not calibrated with monitored deposition data such as the National Atmospheric Deposition Program 

(NADP) data 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-13 

FIGURE 6-1. COMBINED NOX AND SOX DEPOSITION ESTIMATES FOR 1990, 2000, 2010, AND 2020 

WITH AND WITHOUT THE CAAA 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-14 

NITROGEN DEPOSITION 

Atmospheric nitrogen deposition is highest in the northeastern and eastern central regions 

of the U.S. Elevated nitrogen deposition in the western and southern United States is 

limited to areas in the vicinity of large nitrogen sources (e.g., livestock production areas), 

high-elevation areas on which cloud droplet deposition may contribute substantial 

nitrogen inputs, and urban areas with relatively high levels of NOx emissions. 

Figure 6-2 presents total nitrogen deposition from years 1990 through 2020 for both the 

with-CAAA and without-CAAA scenarios. In general, total nitrogen deposition is less than 

24 kg/hectare in the conterminous U.S. for each year and regulatory scenario presented. 

However, “hot spots” exist across the U.S. where meteorological conditions and/or high 

nitrogen emissions contribute to relatively high deposition rates. Two particularly 

significant hot spots for nitrogen deposition are located in southern Louisiana and eastern 

North Carolina. Total nitrogen deposition is estimated to increase in both hot spots over 

time regardless of the regulatory scenario. Outside of the two hot spots, total nitrogen 

deposition is highest without the CAAA in the Ohio River Valley (i.e., western 

Pennsylvania, southern Ohio and Indiana, western West Virginia, and northern 

Kentucky). Over time, the total nitrogen deposition increases around the Ohio River 

Valley without the CAAA and decreases slightly with the CAAA. Outside of the Ohio 

River Valley, nitrogen deposition with the CAAA decreases slightly over time in the 

eastern U.S. In the western U.S., total nitrogen deposition with the CAAA remains 

relatively constant over time. 

Estuarine areas in the Northeast are less susceptible to injury from nitrogen loading than 

estuaries in other parts of the country due to the rapid flushing characteristics of estuaries 

in this region. Estuaries along the Southeastern Coast, Gulf Coast, and Southern 

California Coast experience the greatest reduction in total nitrogen deposition. Total 

nitrogen deposition along the West Coast, with the exception of southern California, is 

relatively low in the absence of the CAAA. 

TROPOSPHERIC OZONE 

Areas within the U.S. with elevated tropospheric ozone levels include the Northeast, midAtlantic, Midwest, and California. Combined ozone concentrations are reported for the 

May through September period as ozone levels tend to increase during the spring and 

summer. Figure 6-3 presents combined cumulative ozone season (W126) values for the 

May through September period for both the with-CAAA and without-CAAA scenarios. 

The W126 metric is a weighted sum of hourly concentrations observed between 8 a.m. 

and 8 p.m. where hourly weights are a function of the hourly ozone concentration 

observed. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-15 

FIGURE 6-2. TOTAL NITROGEN DEPOSITION ESTIMATES FOR 1990, 2000, 2010, AND 2020 WITH 

AND WITHOUT THE CAAA78, 79

 

78 Value bins for nitrogen deposition taken from: Rea, A., J. Lynch, R. White, G. Tennant, J. Phelan and N. Possiel. 2009. 

Critical Loads as a Policy Tool: Highlights of the NOx/SOx Secondary National Ambient Air Quality Standard Review. Slide 6: 

Nationwide Total Reactive Nitrogen Deposition (2002). Available online at: 

http://nadp.sws.uiuc.edu/meetings/fall2009/post/session4.html. 

79 Percentiles are calculated using the combined nitrogen deposition data for all years and scenarios presented in the map. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-16 

FIGURE 6-3. W126 CUMULATIVE TROPOSPHERIC OZONE SEASON MEASURES FOR 2000, 2010, 

AND 2020 WITH AND WITHOUT THE CAAA 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-17 

In general, tropospheric ozone concentrations increase over time without the CAAA and 

decrease over time with the CAAA. Elevated ozone concentrations are present in 

California, mid-Atlantic states, and Corn Belt states in 2000 both with and without the 

CAAA; ozone concentrations are, however, slightly less with the CAAA in 2000. In 

2000, ozone hot spots are present in southern California, central Ohio, portions of 

Virginia, North Carolina, and South Carolina, and western Tennessee. Without the 

CAAA, these hot spots grow in size and magnitude. Under the with-CAAA scenario, the 

hot spots decrease in size and magnitude. By 2020, the combined W126 values for nearly 

the entire conterminous U.S. (outside of California) are less than 15 ppm-hours. 

Tropospheric ozone concentrations within the California hot spot are reduced to 25 to 75 

ppm-hours.80

As noted in the previous section, elevated tropospheric ozone levels may negatively 

affect plants in a number of ways, including reducing plant photosynthesis and increasing 

leaf senescence leading to reduced plant growth and productivity. Given the potential 

effects of elevated tropospheric ozone concentrations on plant growth, forested and 

cropland areas across the U.S. are considered particularly sensitive to the effects of 

elevated tropospheric ozone. It follows that these same areas also stand to benefit the 

most from reduced tropospheric ozone concentrations due to the implementation of the 

CAAA. In particular, forested ecosystems in the San Bernardino and Sierra Nevada 

Mountains of California have suffered ecological damages attributed to elevated ozone 

levels. Forests in the southern portions of the Midwest and Northeast regions and the 

Southeast region (except the southernmost areas where ozone concentrations are 

relatively low without the CAAA) are also expected to benefit from reductions in 

tropospheric ozone due to the implementation of the CAAA. In addition, crops in 

California are expected to benefit the most from the implementation of the CAAA. The 

cropland areas in California are located almost entirely within the tropospheric ozone hot 

spot. Other cropland areas expected to benefit from reduced tropospheric ozone 

concentrations associated with the implementation of the CAAA include the Corn Belt 

region, the southern portion of the Midwest region, the Mississippi Valley, Texas, and 

Oklahoma. 

QUANTIFIED RESULTS: NATIONAL ESTIMATES 

AGRICULTURE AND FOREST PRODUCTIVITY EFFECTS 

A significant body of literature exists addressing the effects of tropospheric ozone on 

plants, including commercial tree species and agricultural crops, as noted in the previous 

section. In general, elevated levels of tropospheric ozone have been shown to reduce 

 

80 Within the California hot spot, the modeled CMAQ ozone concentration estimates were low compared to the ozone 

monitoring data. This may have resulted in the eVNA analysis overestimating future ozone concentrations. This 

overestimate is expected to have occurred in this region for both the with-CAAA and without-CAAA scenarios, however, and 

therefore the effect on the difference in ozone concentrations between the two scenarios is uncertain. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-18 

overall plant health and growth by reducing photosynthesis and altering carbon 

allocation. Methods and data also exist to estimate the magnitude of plant growth 

reductions due to elevated tropospheric ozone levels, based on laboratory studies that 

developed exposure-response functions describing the functional relationship between 

plant yield and ozone exposure for a variety of plant species.81 Applying exposureresponse functions, this analysis estimates yield losses in agricultural crops and 

commercial tree species under the counterfactual, without-CAAA scenario relative to the 

baseline, with-CAAA scenario. Relative yield losses (i.e., reductions in crop and tree 

yield under the counterfactual scenario relative to the baseline scenario) measure the 

amount crop and tree yields would be reduced in the absence of CAAA regulations, and 

therefore, indicate a benefit of the CAAA.82 

Table 6-5 provides a summary of estimated relative yield losses by crop/forest type and 

year. Relative yield losses indicate a benefit of the CAAA; the larger the relative yield 

loss without the CAAA, the greater the crop or tree yield with the CAAA. In addition, 

Figures 6-4 and 6-5 provides maps of the crop-subregion-specific and tree-region-specific 

relative yield losses for two representative species: potatoes and softwood trees. The 

results presented generally follow the temporal and spatial pattern of ozone concentration 

reductions attributable to the CAAA, as outlined in Chapter 4, with reductions in 

tropospheric ozone concentrations being greatest along the East Coast, particularly the 

Southeast, in the Midwest (within the Ohio River Valley), and in California. Several 

other factors also affect yield changes in crops and trees, including sensitivity to ozone, 

geographic distribution, growing period length, and the specific time of year the growing 

period occurs. Potatoes and softwoods, as indicated in Table 6-5, suffer relatively larger 

changes in growth than some other species in our analysis, and yield losses tend to 

increase over time as differences in ozone concentrations increase between the withCAAA and without-CAAA scenarios. Across all crops, the largest relative yield losses for 

both crops and trees occur in the Southeast, frequently in Virginia, North Carolina, South 

Carolina, and Tennessee. 

 

81 See, for example, E.H. Lee and W.E. Hogsett. 1996. Methodology for Calculating Inputs for Ozone Secondary Standard 

Benefits Analysis: Part II. Prepared for the U.S. EPA, Office of Air Quality Planning and Standards, Air Quality Strategies and

Standards Division. The application of laboratory-derived functions is less preferable than functions developed from field 

studies. However, the laboratory-derived functions frequently provide the best available information regarding the 

relationship between ozone exposure and crop or tree growth. The exposure-response functions applied in this report have 

been used in other EPA studies, such as: USEPA. July 2007. Review of the National Ambient Air Quality Standards for 

Ozone: Policy Assessment of Scientific and Technical information. EPA-452/R-07-007. 

82 Relative yield losses are estimated instead of relative yield gains because the baseline (with CAAA) scenario in this analysis

defines current conditions, whereas the counterfactual (no CAAA) scenario defines a change in current conditions. The 

models applied in this analysis forecast changes in yield relative to current conditions (i.e., relative to the baseline 

scenario). 
The Benefits and Costs of the Clean 

TABLE 6-5. MINIMUM, MAXIMUM, AND AVERAGE ANNUAL RELATIVE YIELD LOSSES ACROSS ALL FASOM SUBREGIO

ALL FASOM REGIONS FOR TREES BY YEAR (2000, 2010, 2020) 

CROP/FOREST TYPE 

2000 2010 2020 

MINIMUM MAXIMUM AVERAGE MINIMUM MAXIMUM AVERAGE MINIMUM MAXIMUM 

Barley 0.00% 0.02% 0.01% 0.00% 0.06% 0.02% 0.00% 0.07% Corn 0.00% 1.12% 0.18% 0.00% 3.07% 0.44% 0.00% 3.45% Cotton 0.00% 6.60% 1.15% 0.00% 16.67% 3.00% 0.00% 20.31% Oranges 0.00% 1.95% 0.09% 0.00% 4.68% 0.25% 0.00% 7.87% Potato 0.00% 6.17% 1.76% 0.00% 17.54% 4.99% 0.00% 20.80% Rice -0.08% 0.14% 0.00% 0.00% 1.03% 0.11% 0.00% 1.66% Sorghum 0.00% 0.87% 0.14% 0.00% 2.17% 0.35% 0.00% 2.65% Soybean 0.00% 3.60% 1.24% -0.55% 11.73% 3.07% 0.00% 12.74% Processing Tomatoes 0.00% 1.82% 0.31% 0.00% 5.54% 0.96% 0.00% 8.21% Spring Wheat 0.00% 1.50% 0.06% 0.00% 3.67% 0.15% 0.00% 6.98% Winter Wheat 0.00% 6.53% 1.00% 0.00% 18.23% 2.49% 0.00% 19.23% Hardwood Forests 1.60% 7.16% 5.06% 4.20% 19.12% 13.86% 6.61% 23.04% Softwood Forests 0.06% 3.85% 1.77% 0.25% 10.49% 4.88% 0.42% 12.27% Note: Negative relative yield losses indicate yield reductions with the CAAA. For example, the minimum estimate for s

2010 reflects an estimated relative yield loss of -0.55 percent. The negative relative yield loss is due to reductions in Wmetric values under the counterfactual, no CAAA scenario in Florida in September of 2010 (the growing period for soybeFlorida is roughly mid-July through September). In other words, ozone exposure is greater under the with-CAAA scenarimonth and region and, therefore, a net increase in soybean yield occurs assuming a rollback of the CAAA. Ozone concenlower under the baseline, with CAAA scenario in Florida for all other months in 2010. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FIGURE 6-4. RELATIVE ANNUAL YIELD LOSSES IN POTATOES UNDER THE COUNTERFACTUAL (NO 

CAAA) SCENARIO BY FASOM SUBREGION AND YEAR BASED ON SUBREGIONALSPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FIGURE 6-5. RELATIVE ANNUAL YIELD LOSSES IN SOFTWOOD FOREST TYPES UNDER THE 

COUNTERFACTUAL (NO CAAA) SCENARIO BY FASOM REGION AND YEAR BASED ON 

REGIONAL-SPECIFIC OZONE CONCENTRATIONS AND GROWING PERIODS 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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Commercial timber and agriculture operations generally manage their land to maximize 

profits. As such, changes in crop yields between the baseline and counterfactual 

scenarios may affect the distribution of commercial species planted; for example, 

landowners may shift production towards plants that are less sensitive to elevated ozone 

concentrations under the counterfactual scenario. This may occur at the individual plant 

level, replacing one crop or tree species for another with a higher growth rate; or, it may 

occur at the community level, converting agricultural lands to timberlands, or vice versa, 

to adjust for combined yield losses to agricultural crops and commercial tree species. 

Changes in the distribution and yield of crop and tree species may in turn affect the 

supply of and demand for agricultural crops and commercial tree species, resulting in 

changes in the welfare of consumers and within agricultural and timber sectors of the 

economy. To quantify this economic benefit of cleaner air, we used the Forest and 

Agriculture Sector Optimization Model (FASOM). FASOM development was funded by 

EPA’s Climate Economics Branch (CEB) and other EPA, U.S. government, and nongovernmental funders over several decades as a partial equilibrium tool to evaluate the 

welfare and market impacts of public policies affecting agriculture and forestry. The 

model simulates biophysical and economic processes affecting land management and 

land allocation decisions over time to potentially competing agriculture and forest 

activities. Although the latest version of FASOM was developed to evaluate climate and 

biofuels policies, the model is capable of assessing a broad range of factors that might 

affect plant growth; for this project, we worked with the model’s developers to develop 

input files to characterize the impact of ozone on plant and tree growth at a regional and 

crop-specific level, using the exposure-response results described above.83

Although FASOM has been widely applied to agricultural sector analysis and has been 

peer reviewed in many contexts, it has not to date been subject to a validation exercise 

comparing the model results for an historical period to historical data for that period.84 

As a result, the performance of the model in forecasting future agricultural sector effects, 

such as those estimated for this study, has not yet been assessed. Two other potential 

limitations may pertain in EPA’s application of FASOM for this study. First, FASOM 

adopts a model simulation approach which assumes perfect foresight by economic actors 

in the agricultural sector. A perfect foresight assumption may be of concern for some 

 

83 Note that we performed two runs of the FASOM model, one where the response to ozone for those crop/region 

combinations without specific individual concentration-response functions are assumed to be zero, and a second where 

impacts on crop/region combinations without specific concentration-response functions were set to the values used in 

adjacent regions and/or proxy crops where possible (for example, soft white wheat was used for barley and sugarbeets; 

tomatoes for processing were used for potatoes; soybeans for fresh tomatoes; corn for fresh tomatoes if there is not a value 

for soybeans; etc.). We found that the difference in the overall national results between these two runs was negligible, 

however. As a result, in this chapter we report the results from the run that applies proxy crop/region concentrationresponse functions. Note further that the version of FASOM used for this analysis is the version current as of July 21, 2010.

84 See, for example, a review commissioned by USEPA for its application of FASOM to support regulatory analysis of 

renewable fuels standards, concluded in July of 2010 and available at the following web site (accessed November 26, 2010): 

http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-23 

long-term analyses, but is likely to be less problematic for this study because our time 

horizon extends only to 2020. Furthermore, USDA projections of commodity prices and 

outputs also extend nearly to 2020, and FASOM’s projections for their base case agree 

well with the USDA projections. As a result, the effect of perfect foresight on model 

outcomes in the present study is reduced.85 A second potential limitation of FASOM is 

its approach to estimating the sensitivity of imports to changes in domestic prices. 

Although FASOM is not a full international model, it does incorporate an import 

elasticity estimate for the largest and most important commodity crops. This allows the 

model to capture, for example, increases in agricultural imports to the US under a 

scenario in which domestic crop prices are projected to rise. For a number of minor 

crops, traded in very small quantities, however, FASOM holds imports fixed. The effect 

of this factor on our results is not clear, but we estimate that a more flexible import sector 

for these much less important crops would have only a minor effect on our estimates of 

the net benefits of reducing ozone exposure for US crops. We expect the directional bias 

of holding minor crop imports fixed, while small, would be to slightly reduce our 

estimates of the net welfare benefit of reducing ozone exposure, and thereby improving 

productivity, of domestic agricultural crops. 

The economic welfare results of the FASOM modeling are presented in Table 6-6. 

FASOM generates total welfare estimates for the agricultural and forest sectors for each 

of our scenarios, for each target year, reflecting the sum of total consumer and producer 

surplus derived from agriculture and forest production. In general, higher ozone 

concentrations in the without-CAAA scenario lead to reduced agricultural and forest 

productivity, raising prices for these products, which in turn increases producer surplus 

but reduces consumer surplus by a larger amount. As a result, FASOM estimates the net 

welfare benefits of the CAAA to be approximately $1 billion in 2000, $5.5 billion in 

2010, and $10.7 billion in 2020, increasing over time as the differences in ozone 

concentrations grows.86

 

85 Perfect foresight is a basic assumption of the modeling approach on which FASOM is based. Structuring the model based on 

perfect foresight rather than a myopic (recursive) approach allows an expanded array of policy simulations and potential 

insights, which is the main purpose of this type of model. 

86 Note that the year 2000 in FASOM represents average annual activity over the 5-year period from 2000 to 2004; 2010 

represents 2010 through 2014; and 2020 represents 2020 through 2024. Values provided for ozone impacts in 2000, 2010, and 

2020 were applied to the 2000, 2010, and 2020 model periods in FASOM, respectively. The results presented here do not 

includes losses Canada and the rest of the world; for example, in 2020, higher US prices in the without-CAAA scenario result 

in additional consumer surplus losses to non-US consumers of $1.7 billion in the forest sector and $3.3 billion in the 

agricultural sector.
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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TABLE 6-6. SUMMARY OF FASOM RESULTS: TOTAL CONSUMER AND PRODUCER SURPLUS VALUES 

FOR THE AGRICULTURAL AND FOREST SECTORS 

VARIABLE MODEL RUN 2000 2010 2020 

Annual Welfare, 

US Forest Sector 

With Clean Air Act ($ billion) $637 $877 $1426 

Without Clean Air Act ($ billion) $636 $875 $1426 

Damage Estimate ($ billion) $1.5 $1.7 $0 

Percent change 0.24% 0.20% 0%

Annual Welfare, 

US Agriculture 

Sector 

With Clean Air Act ($ billion) $1706 $1831 $1916 

Without Clean Air Act ($ billion) $1706 $1828 $1905 

Damage Estimate ($ billion) −$0.5 $3.8 $10.6 

Percent change -0.03% 0.21% 0.55%

Annual Welfare, 

Forest and 

Agriculture Sector 

Combined 

With Clean Air Act ($ billion) $2343 $2708 $3341 

Without Clean Air Act ($ billion) $2242 $2703 $3331 

Damage Estimate ($ billion) $1.0 $5.5 $10.7 

Percent change 0.05% 0.20% 0.32%

Notes: 

1. Results are expressed in year 2006 dollars. 

In general, FASOM forecasts a relative shift towards forestry and away from agriculture 

under the without-CAAA scenario, indicating that the net impacts of the ozone effects on 

forests and agriculture would make forestry relatively more profitable than in the baseline 

compared with agriculture, resulting in a shift in land use. The model forecasts a sizable 

increase in cropland in the without-CAAA scenario, however there is an even greater 

decline in pasture as the returns to crop production rise relative to livestock production 

with higher crop prices. 

As noted above, the model suggests that the damages attributed to higher ozone 

concentrations indicate that producers gain in many cases, while consumers are always 

substantially worse off with the ozone impacts reducing productivity. The reason that 

producers often are better off is that most forest and agricultural products have relatively 

inelastic demands, which means that a general decline in productivity will tend to 

increase prices by more than the reduction in quantity, increasing revenue and often 

profits as well. In general, FASOM attributes large price increases in response to the 

reductions in productivity for these inelastic products, and production declines in the 

without-CAAA scenario for most agricultural commodities, with larger declines in general 

for those products experiencing larger ozone impacts, and also sizable reductions in 

exports. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FASOM also is capable of modeling land-use changes in response to the higher ozone 

concentrations in the without-CAAA scenario. The model indicates changes in major land 

use categories at the national level over time under the ozone impacts scenario, which is 

leading to a net increase in forest of about 6.1 million acres by the 2020 model period and 

an increase in cropland of 7.6 million acres by 2020 in response to the productivity 

declines. At the same time, the model indicates that cropland pasture (high-quality land 

that is suitable for cropland but is being used as pasture) and pasture (lower-quality land 

that is not suitable for growing crops without improvement) decline by a total of 12.7 

million acres and Conservation Reserve Program (CRP) land decreases by about 1 

million acres. The crop experiencing the largest reduction in acreage is soybeans, while 

there is an increase in wheat acreage and a number of smaller shifts between alternative 

crops. 

VISIBILITY 

Air pollution impairs visibility in both residential and recreational settings, and an 

individual’s willingness to pay (WTP) to avoid reductions in visibility differs in these two 

settings. Benefits of residential visibility relate to the impact of visibility changes on an 

individual’s daily life (e.g., at home, at work, and while engaged in routine recreational 

activities). Benefits of recreational visibility relate to the impact of visibility changes 

manifested at parks and wilderness areas that are expected to be experienced by 

recreational visitors. For the purposes of this analysis, recreational visibility 

improvements are defined as those that occur specifically in federal Class I areas, and 

residential visibility improvements are those that occur within the boundaries of Census 

Metropolitan Statistical Areas (MSAs). 

We calculate household WTP for improvements in both residential and recreational 

visibility. We base our calculations on simulations of future visibility conditions at the 

36-km grid-cell level, as estimated by EPA’s Community Multiscale Air Quality 

(CMAQ) model. The relationship between a household’s WTP and changes in visibility 

is derived from a number of contingent valuation (CV) studies published in the peerreviewed economics literature. The approach we apply to estimate the benefit of 

improvements in recreational visibility is consistent with methods EPA has used in 

analyses conducted since EPA’s First Prospective analysis was completed. In particular, 

this chapter relies heavily on research completed for the PM NAAQS RIA (U.S. EPA, 

2006) for the recreational visibility analysis. Our estimate of the benefit of residential 

visibility is consistent with methods applied in past analyses as well, but in previous 

reviews the Council had expressed concerns about residential visibility estimates based 

on WTP estimates from the McClelland et al. (1991) study. As a result, our estimates in 

this chapter rely on a new “benefits transfer” estimate of WTP derived from other 

published sources of residential visibility WTP. 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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According to the CMAQ simulations, the CAAA has had and will continue to have a 

substantial effect on visibility in both residential and recreational settings. The visibility 

data used in this analysis is annual mean visibility data, by county, measured in 

deciviews.87 Figure 6-6 depicts the change in visibility (measured in deciviews) over the 

30-year time frame, from 1990 to 2020, along the with-CAAA scenario. This map shows 

that, overall, changes in visibility due to the CAAA are greater in the eastern U.S. than 

the western U.S. Additionally, the largest changes in visibility occur in the Midwestern 

states. The county level data presented here are the basis for the residential visibility 

improvements we present below. 

Figure 6-7 summarizes trends in visibility at the 13 most-visited U.S. National Parks. 

Visibility estimates (measured in deciviews) are provided for each of the seven core 

CAAA scenarios. Note that deciviews are inversely related to visual range, such that a 

decrease in deciviews implies an increase in visual range (i.e., improved visibility). 

Conversely, an increase in deciviews implies a decrease in visual range (i.e., decreased 

visibility). The figure illustrates that the CAAA greatly affects visibility at National 

Parks – over the 1990 to 2020 period, visibility markedly improves with the CAAA, and 

markedly declines without the CAAA. Particularly large differences in visibility between 

the with-CAAA and without-CAAA scenarios are seen at Great Smoky Mountains National 

Park, which is the most visited park in the U.S. Note that six of the 13 parks listed in 

Figure 6-7 are not included in the primary monetized recreational visibility estimates 

presented later in this chapter, because they were not included in the park regions studied 

in the underlying economic valuation study. The six parks not included are in the 

northern part of the country, and include Mount Rainier, Olympic, Glacier, Yellowstone, 

Grand Teton, and Acadia. 

 

87 The data was aggregated from the 36-km grid-cell level to the county level using the BenMAP version 3.0.15 "Air Quality 

Grid Aggregation" algorithm. The fourth quarter data is corrected for a missing day (the CMAQ runs modeled 364 days, 

omitting December 31) by reweighting the mean to account for the missing day. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FIGURE 6-6. ESTIMATED CHANGE IN VISIBILITY FOR WITH-CAAA SCENARIO, 1990 TO 2020 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FIGURE 6-7. VISIBILITY TRENDS FOR THE 13 MOST-VISITED U.S. NATIONAL PARKS 

Only one existing study provides defensible monetary estimates of the value of 

recreational visibility (Chestnut and Rowe, 1990b; 1990c). Although the Chestnut and 

Rowe study is unpublished, it was originally developed as part of the National Acid 

Precipitation Assessment Program (NAPAP) and, therefore, has been subject to peerreview as part of that program. The Chestnut and Rowe study measures the demand for 

visibility in Class I areas managed by the National Park Service (NPS) in three broad 

regions of the country: California, the Southwest, and the Southeast. Respondents in five 

states were asked about their WTP to protect national parks or NPS-managed wilderness 

areas within a particular region. The survey used photographs reflecting different 

visibility levels in the specified recreational areas. The visibility levels in these 

photographs were later converted to deciviews for the current analysis. The three regions 

assessed in the study cover 86 of the 156 Class I areas in the United States. Given that 

national parks and wilderness areas exhibit unique characteristics, it is not clear whether 

the WTP estimate obtained from the Chestnut and Rowe study can be transferred to other 

national parks and wilderness areas, without introducing additional uncertainty. As a 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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result, for the primary estimate, we value only those recreational benefits in the areas that 

were directly analyzed in the original Chestnut and Rowe study. 

In the First Prospective analysis, we omitted the results of the benefits estimate for 

residential visibility from the primary benefits estimate due to technical concerns about 

the methodology of the study upon which our original calculations were based 

(McClelland et al., 1991).88 There exists a wide range of published, peer-reviewed 

literature, however, that supports a non-zero value for residential visibility. As a result, 

we have revised our methodology for valuing residential visibility, and now include these 

benefits in our overall primary visibility benefits estimate. 

For valuing residential visibility improvements, we rely upon a benefits transfer approach 

that draws upon information from the published Brookshire (1979), Loehman (1984) and 

Tolley (1986) studies. Each of the studies used provides estimates of household WTP to 

improve visibility conditions from a status quo visual range to an improved visual range. 

While uncertainty exists regarding the precision of these older, stated-preference 

residential valuation studies, we believe their results support the argument that 

individuals have a non-zero value for residential visibility improvements. The implied 

annual per-household WTP estimates from these study, for a hypothetical 10-percent 

improvement, ranges from $14 to $145, with a mean of $69 and median of $53. It is not 

surprising that such a range of values exists, as the areas of the country covered feature 

different landscapes and vistas, populations and prevailing visibility conditions. 

Fortunately, the three recommended studies provide primary visibility values for a variety 

of cities throughout the United States: Atlanta, Boston, Chicago, Denver, Los Angeles, 

Mobile, San Francisco, and Washington D.C. We assign each of the 359 MSAs in the 

contiguous U.S. a value based on geographic proximity to one of the eight study cities, 

with two exceptions: 1) We apply the Loehman et al. (1984) value only to the six San 

Francisco Bay area MSAs, because the study is unique among the three in the manner in 

which visibility changes were described to respondents (i.e., a distribution of days versus 

average conditions), and 2) Values associated with Denver are not assigned on the basis 

of proximity but are instead assigned only to MSAs which meet an elevation range 

threshold of 1500 meters within the MSA, because one would expect that residents of 

Denver, with a dramatic view of the Rocky Mountains that is rarely obstructed by trees, 

would have a greater interest in protecting visibility than a city without a dramatic skyline 

or nearby mountains.89 

 

88 Council review of early drafts of the First Prospective analysis noted that the McClelland et al. (1991) study may not 

incorporate two potentially important adjustments. First, their study does not account for the “warm glow” effect, in 

which respondents may provide higher willingness to pay estimates simply because they favor “good causes” such as 

environmental improvement. Second, while the study accounts for non-response bias, it may not employ the best available 

methods. As a result of these concerns, a prior Council recommended that residential visibility be omitted from the overall 

primary benefits estimate in the First Prospective. 

89 The geographic proximity assignment is preserved for the Los Angeles and Riverside MSAs although these MSAs meet the 

elevation range threshold of 1500 meters. The assignment is preserved because Los Angeles is one of the study cities and 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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The primary estimate of benefits of recreational and residential visibility improvements is 

provided in Table 6-7. The primary estimate for recreational visibility only includes 

benefits in the original study regions (i.e., California, the Southwest, and the Southeast). 

The primary estimate for residential visibility includes benefits in all MSAs. In general, 

benefits to visibility increase over time as visibility improves due to the CAAA. Benefits 

to residential visibility are approximately three times as large as benefits to recreational 

visibility. 

TABLE 6-7. PRIMARY ESTIMATE OF BENEFITS TO VISIBILITY (BILLION 2006$) 

2000 BENEFITS 2010 BENEFITS 2020 BENEFITS 

Recreational Benefits $3.3 $8.6 $19 

Residential Benefits $11 $25 $48 

Total Benefits $14 $34 $67 

In Figures 6-8a and 6-8b below, we map the primary 2020 estimate of benefits of 

recreational and residential visibility improvement by state. Overall, the spatial pattern of 

benefits is similar for recreational and residential visibility. Recreational visibility 

benefits are driven by population and park location, within the original study regions of 

Chestnut and Rowe (1990a). These regions are California, the Southwest (Arizona, 

Nevada, Utah, Colorado, and New Mexico), and the Southeast (Delaware, Maryland, 

West Virginia, Virginia, Kentucky, Tennessee, North Carolina, South Carolina, Georgia, 

Alabama, Florida, and Mississippi). Households express WTP for visibility 

improvements in Class I areas located in-region as well as out-of-region. For this reason, 

there may be high recreational benefits in a state that has no Class I areas. Although 

household WTP is higher for in-region parks, this effect seems to be dominated by the 

effect of population. For example, less populated states such as New Mexico and Utah 

with Class I areas have low benefits to recreational visibility, while more populated states 

such as New York without Class I areas have high recreational visibility benefits. 

 

also because Los Angeles has a particular set of location-specific characteristics that set it apart from Denver. As a 

conservative measure, Riverside MSA is also assigned to the Los Angeles study area because a significant portion of Riverside 

County itself is located in the South Coast Air Quality Management District, and therefore is considered by at least some 

measures to be part of the same regulated airshed as Los Angeles. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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FIGURE 6-8A. PRIMARY ESTIMATE OF RECREATIONAL VISIBILITY BENEFITS IN 2020 (BILLION 

2006$) 

IGURE 6-8B. PRIMARY ESTIMATE OF RESIDENTIAL VISIBILITY BENEFITS IN 2020 (BILLION 

2006$) 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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Residential visibility benefits are driven by population and visibility improvements. 

Overall, benefits are greater in the East. This is due in part to greater population levels as 

well as greater visibility improvements. Benefits are also very high in California due to 

the state’s large population and visibility improvements, especially in and around Los 

Angeles and San Francisco. Residential visibility is also dependent upon the WTP value 

applied. Much of the West uses the WTP value for Denver, which is highest WTP value 

being widely applied. Yet, the West still has lower overall benefits to residential 

visibility.90 This impact shows that the effect of population and visibility improvement 

dominates the effect of the WTP value applied. 

MATERIALS DAMAGE 

Since the mid-19th century air pollution has been suspected of accelerating the 

degradation of natural and man-made materials that are exposed to the outdoor 

environment. Concern over the effect of pollutants on materials has mainly been directed 

towards the economic consequences of damage to materials used in construction, but 

aesthetic damage to historic buildings and monuments is also a concern. Wet and dry 

acidic deposition, alone or combined with other air pollutants, contribute to the increased 

rate of materials damage. Acidic deposition has been shown to have an effect on 

materials including zinc/galvanized steel and other metal, carbonate stone (as monuments 

and building facings), and surface coatings (paints) (NAPAP, 1991). 

Metal structures are usually coated by alkaline corrosion product layers and thus are 

subject to increased corrosion by acidic deposition. In addition, research has 

demonstrated that iron, copper, and aluminum based products are subject to increased 

corrosion due to pollution, in particular SO2 (NAPAP, 1991), that acidic deposition 

accelerates the rate of erosion of carbonate stone (marble and limestone), and that acidic 

deposition has numerous negative effects on painted wood and, in general, increases the 

weathering rate. This analysis focuses on quantifying the impact of sulfur dioxide 

deposition on exterior building and infrastructural materials including carbonate stone, 

galvanized steel, carbon steel, and painted wood, as outlined Table 6-8 below. 

 

90 The WTP value for San Francisco is higher than Denver, but the San Francisco value is not applied to other MSA’s. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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TABLE 6-8. MATERIALS DAMAGE EFFECTS 

POLLUTANT QUANTIFIED EFFECTS—DAMAGE TO:

UNQUANTIFIED EFFECTSa—

DAMAGE TO: 

Sulfur oxides Infrastructural materials – 

galvanized and painted carbon steel 

Commercial buildings – carbonate 

stone, metal, and painted wood 

surfaces 

Residential buildings – carbonate 

stone, metal, and painted wood 

surfaces 

Monuments – carbonate stone 

and metal 

Structural aesthetics 

Automotive finishes – painted 

metal 

Hydrogen ion and 

nitrogen oxides 

 Infrastructural materials – 

galvanized and painted carbon 

steel 

Zinc-based metal products, such 

as galvanized steel 

Commercial and residential 

buildings – carbonate stone, 

metal, and wood surfaces 

Monuments – carbonate stone 

and metal 

Structural aesthetics 

Automotive finishes – painted 

metal 

Carbon dioxide Zinc-based metal products, such 

as galvanized steel 

Formaldehyde Zinc-based metal products, such 

as galvanized steel 

Particulate matter Household cleanliness (i.e., 

household soiling) 

Ozone Rubber products (e.g., tires) 

a The categorization of unquantified effects is not exhaustive. 

This analysis applies the Air Pollution Emissions Experiments and Policy (APEEP) 

analysis model, described in Muller and Mendelsohn (2007, 2009), to link SO2 emissions 

to ambient SO2 levels. Using emission inputs, the air quality model in APEEP forecasts 

seasonal and annual average county concentrations for SO2, amongst other pollutants.91 

As reported in Muller and Mendelsohn (2007) and detailed in the supporting online 

material for that publication, APEEP’s air quality modeling has been statistically tested 

and calibrated against the predictions generated by the Community Multi-scale Air 

Quality Model (CMAQ), using 1996 emissions data and a CMAQ run for 1996 

 

91

 The Project Team considered using the CMAQ SO2 air quality results directly, but the decision to implement the materials 

damage approach described here came too late to cost-effectively recover the relevant ambient SO2 estimates from the 

original CMAQ runs. The overall magnitude of the monetizable materials damage benefits is such a small part of the overall 

benefits of the CAAA that the impact of using APEEP’s air quality tool rather than CMAQ on the overall benefits estimates is 

likely to be very small. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

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conditions. Muller and Mendelsohn (2007) also report comparisons of APEEP’s results 

with available monitor data for this period. The results for the SO2 air quality component 

used in these materials damage calculations appear to suggest good agreement for APEEP 

for concentrations near the mean, but APEEP appears to overpredict SO2 concentrations 

for high-end concentrations. Overall, however, it is important to note that APEEP is 

designed to be a fast-running alternative to CMAQ for use in an integrated assessment 

model – the air quality component of APEEP is a statistical representation of relations 

that are accomplished in a far more sophisticated manner in CMAQ. 

The remaining general steps in the process of estimating materials damage effects are as 

follows: 

 Develop a national inventory of sensitive materials. A key piece of 

information needed to apply the appropriate materials damage concentrationresponse functions is the existing materials inventories. This analysis estimates 

the inventory of four exterior building and infrastructural materials in each 

county in the lower 48 states, including carbonate stone, galvanized steel, carbon 

steel, and painted wood surfaces. 

 Derive concentration-response functions that relate material mass loss to 

ambient SO2. Dose-response functions for man-made materials damages are 

obtained from two sources; the NAPAP studies (Atteraas, Haagenrud, 1982; 

Haynie, 1986) and from the International Cooperative Programme on Effects on 

Materials (ICP, 1998). 

 Estimate the value of lost materials. Materials damage is valued as the cost of 

future materials maintenance activities. The accelerated rate of materials decay 

due to pollution exposure increases the frequency of regularly scheduled future 

maintenance activities. The change in the present value of the maintenance 

schedules extending into the future constitutes the monetary impact of an 

emission change on materials damage. 

Table 6-9 summarizes the benefits of reduced materials damage attributed to CAAA 

programs in 2000, 2010, and 2020. Benefits are given by EPA region. Although the total 

benefits are relatively small compared to other categories of effect, the benefits of CAAA 

programs to materials damage increase over time as we would expect. The spatial 

distribution of the benefits is primarily owing to the distribution of the materials 

inventory and SO2 exposure. The effect of SO2 exposure is a more important driver of 

results than the inventory. For example, the benefits in Region 5 are approximately twice 

as large as those in any other EPA region. This is due to the significant decrease in SO2

exposure associated with the CAAA in this region. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-35 

TABLE 6-9. BENEFITS OF REDUCED MATERIALS DAMAGE DUE TO CAAA PROGRAMS 

EPA REGION 

VALUATION (THOUSAND 2006$) 

2000 2010 2020 

1: CT, ME, MA, NH, RI, VT $720 $2,100 $2,100 

2: NY, NY $9,000 $10,000 $12,000 

3: DE, DC, MD, PA, VA, WV $9,400 $19,000 $23,000 

4: AL, FL, GA, KY, MS, NC, SC, TN $8,400 $16,000 $21,000 

5: IL, IN, MI, MN, OH, WI $26,000 $38,000 $38,000 

6: AR, LA, NM, OK, TX $2,200 $4,000 $7,300 

7: IA, KS, MO, NE $2,000 $1,600 $1,600 

8: CO, MT, ND, SD, UT, WY $400 $570 $730 

9: AZ, CA, NV -$100 $490 $640 

10: ID, OR, WA $340 $510 $560 

Total $58,000 $93,000 $110,000

Notes: Results are rounded to two significant figures. Totals may not sum due to rounding. 

ADIRONDACK CASE STUDY RESULTS 

The Project Team was encouraged to consider case study analysis of a set of ecological 

effects for which national analyses might not be feasible, owing to lack of available data 

or methods. EPA chose to conduct a case study in the Adirondack region of New York 

State, focusing on two ecological service flows that provide benefits in terms of both 

ecosystem health and economic terms: (1) acidification of surface waters and (2) reduced 

yields of commercial timber. The Adirondack region of New York may exhibit the most 

severe ecological impacts from acidic deposition of any region in North America – acid 

deposition is the main cause of both of the effects we studied.92 Adirondack Park is a 

State Park comprising 5,821,183 acres of State and privately owned land in upstate New 

York and is nearly a 100 by 100 mile box of land, intersecting fourteen counties. The 

Park was created in 1892 through an amendment to the State constitution, with the 

purpose of forest and natural resource conservation. Federal programs addressing air 

pollution have been particularly beneficial to the region as, due to its location downwind 

of the highly industrialized Ohio River Valley, most of the acid deposition in the region 

originates from out of state. In addition to its status as a region of particular sensitivity to 

lake acidification and with some existing research on the effects of air pollutants on forest 

 

92 Driscoll, Charles T. et al. May 2003. Chemical Response of Lakes in the Adirondack Region of New York to Declines in 

Acidic Deposition. Environmental Science and Technology 37(10): 2036-2042. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-36 

growth, the Adirondack Region was selected as a setting for this case study due to the 

existence of a regional economic random utility model describing recreational fishing 

behavior. 

Lake Acidification in the Adirondacks 

Surface waters, such as lakes and streams, may be the most susceptible systems to acidic 

deposition as they collect acidic precipitation not only from direct deposition on their 

surfaces but also in the form of runoff from their entire watershed. Acid accumulates in 

surface waters via three main pathways: 

 precipitation, or wet deposition, in which pollutants are dissolved in rain or snow; 

 dry deposition, or direct deposition of gases and particles on surfaces; and 

 cloud-water deposition, involving material dissolved in cloud droplets and 

deposited on vegetation.93

As acids accumulate, ecosystems gradually lose the ability to buffer them, resulting in 

changes to ecosystem structure and function. Acidification of the surface water affects 

the trophic structure of water contributing to declines in the abundance of zooplankton, 

macroinvertebrates, and fish.94 

The ecological service flow affected by lake acidification that is most amenable to 

economic analysis is recreational fishing. Extensive research exists focused on both the 

effects of lake acidification on fisheries and on individuals’ willingness to pay to avoid 

reductions in the quality or quantity of recreational fishing opportunities. This analysis 

employs the following general steps to quantify the benefits of reduced lake acidification 

on recreational fishing in the Adirondacks. A conceptual model depicting the analytic 

steps in terms of inputs, outputs, and ecological and economic models is provided in 

Figure 6-9. 

 Forecast lake acidification levels consistent with the with-CAAA and withoutCAAA scenarios. EPA generated estimates of acidic deposition at a 36-

kilometer grid cell level across the Adirondack region using the CMAQ model. 

We then implemented an ecological model, the Model of Acidification of 

Groundwater in Catchments (MAGIC), to simulate the transport of the acidic 

deposition through the hydrological and terrestrial ecosystems and forecast 

acidification levels in a subset of Adirondack lakes. 

 Extrapolate results of the ecological model within the Adirondacks region. 

We developed a random effects model to explain the relationship between 

acidification of lakes and their specific site characteristics. 

 

93 The U.S. National Acid Precipitation Assessment Program. 1991. Integrated Assessment Report. The NAPAP Office of the 

Director, Washington, DC. 

94 Driscoll, Charles T. et. al. March 2001. Acidic Deposition in the Northeastern United States: Sources and Inputs, Ecosystem 

Effects, and Management Strategies. BioScience 51(3): 180-198. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-37 

 Apply ANC thresholds to classify lakes as either “fishable” or “impaired”. 

Fishable lakes are those for which water quality is not deteriorated to an extent 

which limits recreational fishing. Impaired lakes’ water quality is deteriorated so 

as to reduce fish populations and preclude recreational fishing. Lakes are defined 

as either fishable or impaired based on identified ANC thresholds. As 

uncertainty exists regarding the ANC threshold at which effects are experienced, 

this analysis considers three separate thresholds below which lakes are 

considered impaired. 

 Apply an economic random utility model (RUM) to quantify economic 

benefits of the CAAA in terms of recreational fishing in the Adirondack 

region. We employ a RUM that was developed to account for fishing site 

choices made by recreational fishers based on attributes of sites specifically in the 

Adirondack region. The difference in economic welfare values between the 

value of fishable (i.e., not impaired) lakes in the with-CAAA scenario and the 

without-CAAA scenario represents the benefits to recreational fishing in the 

Adirondack region associated with the CAAA. 

FIGURE 6-9. CONCEPTUAL APPROACH TO ESTIMATING THE ECONOMIC BENEFITS OF REDUCED 

ACIDIFICATION ON ADIRONDACK LAKES 

Table 6-10 summarizes the results of this analysis. Present value cumulative benefits are 

provided for 2000, 2010, and 2020, assuming a five percent discount rate. Single year 

undiscounted benefits are also given for each year. Undiscounted single year benefits 

SOX and NOX

Emissions

Estimates

CMAQ

Model

Deposition Scenario 1:

Full implementation 

of 1990 CAAA

Deposition Scenario 2:

No implementation 

of 1990 CAAA

MAGIC

Model

Scenario 1:

ANC levels for

subset of 

Adirondack

lakes

Random

Effects

Model

ANC Thresholds:

20, 50, and 100

eg/L

List of Adirondack

Lakes that are 

Impaired according

to Scenarios 1 and 2.

Random

Utility

Model

Scenario 1:

Economic

Impact

of lake

acidification

Scenario 2:

Economic

Impact

of lake

acidification

Scenario 2:

ANC levels for

subset of 

Adirondack

lakes

Scenario 2 – Scenario 1 =

Economic Benefit

of CAAA on

Recreational

Fishing
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-38 

increase over time but the benefits do not follow any particular trend across alternative 

threshold assumptions. It should be noted that benefits in each year and under each 

threshold assumption reflect a different subset of lakes. Therefore, benefits are not 

expected to follow any particular trend across years or threshold assumptions. 

Table 6-10 SUMMARY OF ANNUAL AND CUMULATIVE ESTIMATED BENEFITS TO RECREATIONAL 

FISHING IN THE ADIRONDACK REGION (MILLION 2006$) 

YEAR 

ANC THRESHOLD 

ASSUMPTION FOR DEFINING 

“FISHABLE” LAKES 

ADIRONDACK REGION 

SINGLE YEAR UNDISCOUNTED 

CUMULATIVE FIVE PERCENT 

DISCOUNT RATE 

2000 

20 $7 $62 

50 $7 $57 

100 $5 $44 

2010 

20 $8 $143 

50 $8 $132 

100 $6 $101 

2020 

20 $9 $197 

50 $8 $182 

100 $6 $136 

Note: 

1) Cumulative benefits in year 2000 are the cumulative benefits to recreational fishing of 

implementing the CAAA from 1990 to 2000. Similarly, cumulative benefits in 2010 are 

cumulative from 1990 to 2010 and cumulative benefits in 2020 are cumulative from 1990 to 

2020. The single year undiscounted benefits are the benefits to recreation fishing of 

implementing CAAA in that year (2000, 2010, or 2020). 

2) Benefits in this case study are evaluated from 1990 (the year of the passage of the 

CAAA) to 2050 (the forecast horizon for the lake ANC levels with and without the CAAA). 

The benefits in this table are presented for years 2000, 2010, and 2020, however, to be 

consistent with the benefits as calculated in the broader cost-benefit analysis of the CAAA.

Commercial Timber in the Adirondacks 

Reductions in NOx and SOx emissions due to the implementation of the CAAA are also 

believed to reduce forest soil acidity. Reductions in soil acidity have been shown by 

scientists to increase tree growth and improve overall forest health. Such changes in 

forest growth and health would have a positive effect on the timber industry within 

Adirondack Park, potentially increasing the frequency and/or the volume of timber 

harvests. 

Quantifying the magnitude of these benefits requires a function to translate varying levels 

of soil acidity into corresponding tree growth productivity. Unfortunately, speciesspecific dose-response functions relating soil acidity levels with changes in tree growth in 

Adirondack Park are not available. Our analysis instead characterized the existing timber 

industry in Adirondack Park in terms of the types of tree species present, wood products 

harvested, extent of timber harvest activities, and the overall value of timber harvests 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-39 

within the Park. We then estimated changes in percent base saturation (a measure of soil 

acidity) due to the implementation of the CAAA across the Park from 1990 to 2050, 

focusing on soil acidity differences in areas subject to commercial timber activity. 

Specifically, changes in percent base saturation levels in timber harvest areas were 

mapped in relation to potential changes in the growth and health of tree species present in 

these areas and the likely effects of altered tree growth and health on timber harvest rates 

and volumes. In addition, we provide some perspective on the potential order of 

magnitude of benefits of the CAAA on the timber industry in the Adirondacks, 

summarizing existing, relevant research. 

We used estimates of soil percent base saturation levels for 1990, 2000, 2010, 2020, and 

2050 with and without the CAAA to characterize the effect on Adirondack forests.95 

Percent base saturation is the proportion of cation exchange sites (exchange sites are 

areas on soil particles where ions may be adsorbed) occupied by basic cations (Ca2+, 

Mg2+, K+

, and Na+). These basic cations buffer the soil by inhibiting the adsorption of H+

ions. Thus, percent base saturation is a measure of the soil’s buffering capacity. High 

percent base saturation levels indicate large buffering capacity and low soil acidity levels, 

while low percent base saturation levels indicate the converse. Percent base saturation 

point estimates were generated using the same Model of Acidification of Groundwater in 

Catchments (MAGIC) as used in the lake acidification analysis described above. 

Figure 6-10 presents differences in percent base saturation levels with and without the 

CAAA specifically within the timber harvest areas of the Park by year. There is a clear 

temporal trend in the difference in percent base saturation levels with and without the 

CAAA. Specifically, differences between percent base saturation levels with the CAAA 

as compared to without the CAAA increase in each year in the analysis. However, there 

is little spatial variability in percent base saturation differences within individual years. 

The lack of spatial variability becomes more pronounced as time goes on, so that by 2050 

the difference in percent base saturation is between 2.07 and 6.26 percent in almost all 

forested resource management areas in the Park. The lack of spatial variability makes 

sense given the relatively small geographic scope considered in this analysis. The minor 

spatial variation in percent base saturation differences exhibited in 2000 and 2010 is most 

likely related to microhabitat factors (i.e., different soil types and differing precipitation 

levels). 

 

95 While the timeframe for this Second Prospective analysis of the CAAA is through 2020, this case study reports benefits 

through 2050 as we expect that reductions in emissions that occur in 2020 will continue to provide benefits to recreational 

fishing through this time frame. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-40 

FIGURE 6-10. DIFFERENCES IN PERCENT BASE SATURATION VALUES WITH AND WITHOUT THE 

CAAA IN FORESTED RESOURCE MANAGEMENT AREAS IN ADIRONDACK PARK 96, 97

 

96 The differences between percent base saturation levels with the CAAA and without the CAAA are presented rather than 

absolute percent base saturation levels for each scenario to highlight the changes in percent base saturation attributable to 

the implementation of the CAAA. 

97 The ten ranges of difference in percent base saturation values presented in Exhibit 5-8 are equal to the 10th, 20th, ..., and 

100th percentiles for the combined distribution of difference in percent base saturation values across all years in the 

analysis (2000, 2010, 2020, and 2050). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-41 

Also of importance to this analysis is the magnitude of the increase in percent base 

saturation levels in relation to specific forest types within resource management areas. 

We focused on six forest types (sugar maple/beech/yellow birch, red maple/upland, 

spruce/fir, eastern hemlock, eastern white pine, and paper birch) that are prevalent in the 

Park relative to other forest types and contain tree species of commercial value. Table 6-

11 presents the area-weighted mean increase in percent base saturation levels in these 

forest types per year. Of the forest types of interest, the paper birch forest type 

experiences the greatest increase in percent base saturation due to the CAAA, followed 

by the eastern hemlock and the sugar maple/beech/yellow birch forest types. 

TABLE 6-11. AREA-WEIGHTED MEAN DIFFERENCES IN PERCENT BASE SATURATION VALUES WITH 

AND WITHOUT THE CAAA IN FOREST TYPES OF INTEREST 

FOREST TYPE 

AREA-WEIGHTED DIFFERENCE IN PERCENT BASE 

SATURATION 

2000 2010 2020 2050 

Sugar Maple/Beech/Yellow Birch 0.023 0.414 0.820 1.899 

Red Maple/Upland 0.025 0.377 0.758 1.755 

Spruce/Fir 0.028 0.361 0.736 1.702 

Eastern Hemlock 0.028 0.413 0.827 1.908 

Eastern White Pine 0.018 0.419 0.814 1.882 

Paper Birch 0.018 0.457 0.891 2.069 

Other Forest Types 0.015 0.429 0.829 1.918 

The area-weighted increase in percent base saturation levels in sugar maple/beech/yellow 

birch forests is in line with increases in percent base saturation levels in other forest types 

in Adirondack Park. This is an important point given the prevalence of sugar maple in 

this forest type, and the fact that sugar maple is an economically important tree species in 

the Park. Although dose-response functions, which would allow for estimates of growth 

increases in sugar maples due to increased base saturation levels, do not exist, several 

studies have estimated changes in sugar maple growth due to increases in soil acidity 

stemming from elevated nitrogen and/or sulfur deposition.98 Changes in harvest volumes 

comparable to those seen in those existing studies might lead to annual wood harvest 

 

98

 For example, Duchesne et al. (2002) found that sugar maple basal area growth rates were reduced by 17 percent, on 

average, in forest stands exhibiting decreasing basal area growth rates over time (declining stands) compared to sugar 

maple basal area growth rates in stands exhibiting increasing basal area growth rates over time (healthy stands). In 

addition McLaughlin (1998) found that the health of hardwood stands on shallow, poorly buffered soils similar to those 

found in Adirondack Park declined during the 1990s due to decreasing pH and base saturation levels and increased 

aluminum ion concentrations. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-42 

benefits of roughly $1 million to $1.5 million annually, based on the total stumpage 

values for sugar maple pulpwood/chip wood we estimate for the region.99 Whether sugar 

maple growth rate changes would mirror those reported in either of these studies, 

however, is uncertain due to the lack of an established functional relationship. 

Nonetheless, we expect that all tree species in the Park would benefit, in terms of 

increased stand growth and vigor, from increased percent base saturation levels. In some 

cases, increases in growth may allow for both more frequent and larger timber harvests 

(i.e., more frequent timber harvests removing larger volumes of wood). Improved forest 

health may also provide the added benefit of increasing the resiliency of forest stands and 

limiting damage caused by disturbance events. 

UNCERTAINTY IN ECOLOGICAL AND OTHER WELFARE BENEFITS 

As noted above, limitations in the available methods and data mean that the benefits 

assessment in this report does not represent a comprehensive estimate of the economic 

benefits of the CAAA. Moreover, the potential magnitude of long-term economic 

impacts of ecological damages mitigated by the CAAA suggests that great care must be 

taken to consider those ecosystem impacts that are not quantified here. Significant future 

analytical work and basic ecological and economic research is needed to build a sufficient 

base of knowledge and data to support an adequate assessment of ecological benefits. 

For the current analysis, this incomplete coverage of effects represents the greatest source 

of uncertainty in the ecological assessment. This and other key uncertainties are 

summarized in Table 6-12 below. 

In general, our analysis focuses on more acute and readily observable effects. Chronic 

ecological effects of air pollutants, on the other hand, may be poorly understood, difficult 

to observe, or difficult to discern from other influences on dynamic ecosystems. 

Disruptions that may seem inconsequential in the short-term, however, can have hidden, 

long-term effects through a series of interrelationships that can be difficult or impossible 

to observe, quantify, and model. This factor suggests that many of our qualitative and 

quantitative results may underestimate the overall, long-term effects of pollutants on 

ecological systems and resources. 

 

99

 We estimated stumpage values of commonly harvested species in the Adirondack Region by applying average stumpage 

values to the pulpwood and wood chip and roundwood log harvest volume estimates. The average stumpage value for 

pulpwood and wood chips is estimated to be $3 per ton; while, the average stumpage value for roundwood logs is estimated 

to be $150 per thousand board-feet (MBF). Using these estimates, the annual harvest value of pulpwood and wood chips is 

estimated to be approximately $5.4 million, and the annual harvest value of roundwood logs is estimated to be $15 million. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-43 

TABLE 6-12. KEY UNCERTAINTIES ASSOCIATED WITH ECOLOGICAL EFFECTS ESTIMATION 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF POTENTIAL BIAS 

FOR NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO KEY 

UNCERTAINTIES ON NET BENEFITS 

ESTIMATE*

Incomplete coverage of ecological 

effects identified in existing literature, 

including the inability to adequately 

discern the role of air pollution in 

multiple stressor effects on 

ecosystems. Examples of categories of 

potential ecological effects for which 

benefits are not quantified include: 

reduced eutrophication of estuaries, 

reduced acidification of soils, reduced 

bioaccumulation of mercury and 

dioxins in the food chain. 

Underestimate Potentially major. The extent of 

unquantified and unmonetized 

benefits is largely unknown, but the 

available evidence suggests the 

impact of air pollutants on ecological 

systems may be widespread and 

significant. 

Incomplete geographic scope of 

recreational fishing benefits associated 

with reduced lake acidification analysis 

due to case study approach. 

Underestimate Probably minor. As a case study 

limited to the Adirondack region of 

New York State, the estimated 

benefits to recreational fishing reflect 

only a portion of the overall benefits 

of reduced acidification on this 

service flow, but based on the 

magnitude of effects in the 

Adirondacks the national estimate is 

nonetheless likely be less than five 

percent of total benefits. 

Incomplete assessment of long-term 

bioaccumulative and persistent effects 

of air pollutants. 

Underestimate Potentially major. Little is currently 

known about the longer-term effects 

associated with the accumulation of 

toxins in ecosystems. What is known 

suggests the potential for major 

impacts. Future research into the 

potential for threshold effects is 

necessary to establish the ultimate 

significance of this factor. 

Omission of the effects of nitrogen 

deposition as a nutrient with beneficial 

effects. 

Overestimate Probably minor. Although nitrogen 

does have beneficial effects as a 

nutrient in a wide range of ecological 

systems, nitrogen in excess also has 

significant and in some cases 

persistent detrimental effects that are 

also not adequately reflected in the 

analysis. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

6-44 

POTENTIAL SOURCE OF ERROR 

DIRECTION OF POTENTIAL BIAS 

FOR NET BENEFITS 

LIKELY SIGNIFICANCE RELATIVE TO KEY 

UNCERTAINTIES ON NET BENEFITS 

ESTIMATE*

Use of CMAQ model to estimate air 

pollutant deposition levels. 

Unable to determine. As part 

of a performance evaluation 

of CMAQ, EPA compared 

model predictions for some 

forms of deposition relevant 

to this analysis (wet SO2, NOx 

and ammonium) to observed 

deposition data).** The 

evaluation indicated that 

CMAQ overpredicted some 

forms of deposition and 

underpredicted others. The 

relative accuracy of the 

model's predictions varied 

seasonally and geographically. 

Probably minor. 

The Adirondack lake acidification 

analysis uses deposition estimates as 

inputs, but they are calibrated to 

lake-level monitoring data, and the 

monetized benefits estimates for that 

component are a small part of the 

overall net benefits. We also use the 

CMAQ deposition estimates to 

generate maps that highlight the 

relative distribution of deposition for 

various air pollutants across the U.S. 

With respect to net impacts, the 

extent to which the forms of 

deposition and geographic areas that 

are overpredicted in the model are 

offset by those that are 

underpredicted is unknown. 

* The classification of each potential source of error reflects the best judgment of the section 812 Project Team. 

The Project Team assigns a classification of “potentially major” if a plausible alternative assumption or approach 

could influence the overall monetary benefit estimate by approximately five percent or more; if an alternative 

assumption or approach is likely to change the total benefit estimate by less than five percent, the Project Team 

assigns a classification of “probably minor.” 

** See U.S. EPA, Office of Air Quality Planning and Standards, Emissions Analysis and Monitoring Division, Air 

Quality Modeling Group. CMAQ Model Performance Evaluation Report for 2001: Updated March 2005. CAIR Docket 

OAR-2005-0053-2149. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-1 

CHAPTER 7 - COMPARISON OF BENEFITS AND COSTS 

In this chapter we present our 

summary of the primary 

estimates of monetized benefits 

of the CAAA from 1990 to 

2020, compare the benefits 

estimates with the corresponding 

costs, and explore some of the 

major sources of uncertainty in 

the benefits estimates, including 

a summary of outcomes using 

alternative assumptions from 

those employed in the primary 

analysis. 

The overall conclusion of our 

analysis is that the benefits of the 

CAAA substantially exceed its 

costs. Furthermore, the results 

of the uncertainty analysis imply 

that it is extremely unlikely that 

the monetized benefits of the 

CAAA over the 1990 to 2020 

period could be less than its costs. The central benefits estimate exceeds costs by a factor 

of more than 30 to one, whether we are looking at annual or present value measures. 

By our measures, the programs associated with the 1990 Clean Air Act Amendments 

have been, and will likely continue to be, a very good investment. 

AGGREGATING BENEFIT ESTIMATES 

Our primary estimates of the monetized economic benefits for the 1990 to 2020 period 

derive from two types of analyses: (1) the analysis of changes in human health effects 

associated with reduced exposures to criteria pollutants and the valuation of these 

changes, summarized and described in Chapter 5; and (2) the analysis of monetized 

ecological and other welfare benefits (e.g., visibility), described in Chapter 6.100 We 

measure the benefits and present the results from these analyses in slightly different ways, 

 

100 Note that the direct costs were aggregated in Chapter 3. 

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare

Scenario Development

Sector Modeling

Emissions Direct Cost

Air Quality Modeling

Economic Valuation 

Health

Benefit-Cost Comparison 

Welfare
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-2 

in part because they derive from different tools. The main differences have to do with the 

manner in which we conduct uncertainty analyses, as outlined below. 

Although there are some differences in these two types of benefits analysis, in both cases 

we generate annual estimates of benefits that result from a single set of emissions and air 

quality modeling scenarios for the three target years of the study: 2000, 2010, and 2020. 

The consistent use of scenarios across all the benefit and cost analyses allows us to 

aggregate and directly compare monetized benefits estimates to the estimates of costs 

incurred in the target years. In some cases, we need to apply a discount rate to compare 

benefits to costs; for example, we model the effect of particulate matter on premature 

mortality to occur over a period of twenty years from the time of exposure, even though 

the costs to achieve that benefit are incurred at the time of the initial exposure change. In 

this case, we have accounted for the incidence of premature mortality over the assumed 

lag period, and discounted the valuation of this effect back to the target year. Some 

ecological effects, such as the effects of acid deposition on Adirondack lakes, also occur 

with a lag – again, we use a discounting procedure to standardize the benefits results for 

these estimates. 

The annual estimates for the three target years also provide an indication of the trend in 

benefits we project will accrue over the 30-year study period. To generate a cumulative 

measure of benefits over the full 30-year period, however, we must make an assumption 

about the level of benefits that would be realized in the years between the target years. 

We interpolate these values, assuming a trend in benefits accrual that roughly matches the 

trend in emission reductions for PM precursors. Basing our estimate of the benefits 

trajectory on PM precursor reductions acknowledges that the majority of monetized 

benefits, including health and visibility, are attributable to reductions in ambient 

particulate matter. 

The distribution of estimates we generate for the monetized benefits of human health 

effects incorporates both the quantified uncertainty associated with each of the health 

effect estimates and the quantified uncertainty associated with the corresponding 

economic valuation strategy. Quantitative estimates of uncertainties in earlier steps of the 

analysis (i.e., emissions and air quality changes) could not be developed adequately and 

are therefore not applied in the present study. As a result, the range of estimates for 

monetized benefits presented in this chapter, from the primary low estimate to the 

primary high estimate, is narrower than would be expected with a complete accounting of 

the uncertainties in all analytical components.101

In the health benefits analyses we estimate, for each endpoint-pollutant combination, 

distributions of values for both the key parameter of the concentration-response function 

and the valuation coefficients. We combine these distributions by using a computerized, 

 

 

101 The characterization of the uncertainty surrounding economic valuation is discussed in detail in Industrial Economics, Inc., 

Uncertainty Analyses to Support the Second Section 812 Prospective Benefit-Cost Analysis of the Clean Air Act: Draft 

Report, prepared for Office of Air and Radiation, US Environmental Protection Agency, April 2010. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-3 

statistical aggregation technique to estimate the mean of the monetized benefit estimate 

for each endpoint-pollutant combination and to characterize the uncertainty surrounding 

each estimate.102 

The ecological and welfare results are not currently amenable to the same type of 

uncertainty analysis. The modeling procedures for estimating the effects of sulfur and 

nitrogen deposition in acidifying lakes, the effects of ozone in reducing timber and 

agricultural production, and the effects of particulate matter on visibility are all subject to 

uncertainty, but they require substantial resources simply to develop single point 

estimates. We describe key uncertainties in these estimation procedures qualitatively in 

Chapter 6, with some limited sensitivity analyses also presented to characterize the effect 

of key assumptions. The sources of uncertainty in these estimates, however, cannot as 

easily be disaggregated among physical effects modeling and valuation components, and 

they have not been assessed with the BenMAP model used for health benefits uncertainty 

analysis. As a result, we cannot reliably develop an aggregate estimate of the uncertainty 

in the sum of health and welfare benefits estimates. 

ANNUAL BENEFITS ESTIMATES 

We present the results of our aggregation of primary annual health benefits estimates for 

the CAAA in Figure 7-1 below. The figure provides a characterization of both the 

primary central estimate and the range of values generated by the aggregation procedure 

described above, for each of the three target years of the analysis (2000, 2010, and 2020). 

The Primary High estimate corresponds to the 95th percentile value from the health 

benefits aggregation, and the Primary Low estimate corresponds to the 5th percentile 

value. The total benefits estimates are substantial; for example, the Primary Central 

estimate in 2020 is $2.0 trillion. 

Table 7-1 shows the detailed breakdown of benefits estimates for 2000, 2010, and 2020. 

As shown in the table, $1.7 trillion of the $2.0 trillion total benefit estimate in 2020, or 85 

percent, is attributable to reductions in premature mortality associated with reductions in 

ambient particulate matter. The remaining benefits are roughly equally divided among 

three broad categories of benefits: avoided premature mortality associated with ozone 

exposure; avoided morbidity, the largest component of which is avoided acute myocardial 

infarctions and avoided chronic bronchitis; and avoided ecological and other welfare 

benefits, the largest component of which is improved visibility. Because of the 

aggregation procedure used, and because we round all intermediate results to two 

significant digits for presentation purposes, the columns of Table 7-1 may not sum to the 

total estimate presented in the last row. 

 

102 The statistical aggregation technique applied is commonly referred to as Monte Carlo analysis. The technique involves 

many re-calculations of results, using different combinations of input parameters each time. For each calculation, values 

from each input parameter’s statistical distribution are selected at random to ensure that the calculation does not always 

result in extreme values, or rely solely on low end or solely on high end input parameters. The aggregate distribution more 

accurately reflects a reasonable likelihood of the joint occurrence of multiple input parameters. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-4 

FIGURE 7-1. ANNUAL MONETIZED BENEFITS IN 2000, 2010 AND 2020 

Examination of the emissions and aggregate exposure estimates suggests that most of 

these benefits can be attributed to air quality improvements that result from CAAA 

implementation, relative to conditions as they were in 1990, before the CAAA, rather 

than from avoiding degradation of air quality that might have occurred without the 

CAAA. For example, we estimate that emissions of NOx, SO2, and VOCs, three of the 

most important PM and ozone precursors, would have grown just over 20 percent from 

1990 to 2020 in the without-CAAA scenario, which corresponds to an annual growth rate 

of about 0.65 percent. We also estimate that PM2.5 emissions would have grown 

somewhat slower, at 0.5 percent annually, without the CAAA. Reductions along the 

with-CAAA scenario over the same period, however, were more than 60 percent for SO2

and NOx, a reduction of roughly 3 percent per year, and were roughly 45 percent for 

VOCs, a reduction of about 2 percent per year. As a result, about 75 percent of the 

difference in emissions that we estimate would occur by 2020 between the with-CAAA

and without-CAAA scenario can be attributed to reductions in emissions relative to those 

in 1990. 

 

Monetized Direct Benefits (million 2006$)

$0

$1,000,000

$2,000,000

$3,000,000

$4,000,000

$5,000,000

$6,000,000

2000 2010 2020
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-5 

TABLE 7-1. SUMMARY OF MEAN PRIMARY ANNUAL BENEFITS RESULTS 

BENEFIT CATEGORY 

ANNUAL MONETIZED BENEFITS (MILLION 

2006$) BY TARGET YEAR 

2000 2010 2020 NOTES 

Health Effects 

PM Mortality 

PM Morbidity 

Ozone Mortality 

Ozone Morbidity 

$710,000 

$27,000 

$10,000 

$420 

$1,200,000 

$46,000 

$33,000 

$1,300 

$1,700,000 

$68,000 

$55,000 

$2,100 

- PM mortality estimates 

based on Weibull distribution 

derived from Pope et. al 

(2002) and Laden et al., 2006. 

- Ozone mortality estimates 

based on pooled function 

Subtotal Health 

Effects 

$750,000 $1,300,000 $1,900,000 

Visibility 

Recreational 

Residential 

$3,300 

$11,000 

$8,600 

$25,000 

$19,000 

$48,000 

Recreational visibility only 

includes benefits in the 

regions analyzed in Chestnut 

and Rowe, 1990 (i.e., 

California, the Southwest, and 

the Southeast). 

Subtotal Visibility $14,000 $34,000 $67,000 

Agricultural and 

Forest Productivity $1,000 $5,500 $11,000 

Materials Damage $58 $93 $110 

Ecological $6.9 $7.5 $8.2 Reduced lake acidification 

benefits to recreational 

fishing. 

Total: all categories $770,000 $1,300,000 $2,000,000 

Note: See Chapters 5 and 6 of this report for detailed results summaries. Values presented are 

means from results reported as distributions. Estimates presented with two significant figures. 

PM2.5 exposure estimates also support the conclusion that more of the benefit in 2020 can 

be attributed to air quality improvements from implementing CAAA programs than to 

preventing degradation in air quality that might have resulted in the without-CAAA case. 

Although we did not estimate 1990 air quality using the CMAQ/MATS system described 

in Chapter 4, and the PM2.5 monitor network was very sparse in 1990, there was an 

extensive PM10 monitor network at that time. Using PM10 monitor data and regional 

PM2.5/PM10 ratio estimates from the 1996 Particulate Matter Criteria Document, we 

estimated population weighted average exposure to PM2.5 in 1990 of 19.0 μg/m3

. In 

addition, using the CMAQ/MATS system, we estimate population-weighted average 

exposure to PM2.5 along the without-CAAA scenario is about 17 μg/m3 in 2000, and 

increases to 17.7 μg/m3 and 19.2 μg/m3 in 2010 and 2020. Along the with-CAAA

scenario, population weighted average exposure to PM2.5 is 12.2 μg/m3 in 2000, and 

declines to 10.9 μg/m3 in 2010, and 10.5 μg/m3 in 2020. In the without-CAAA scenario 

some improvements in air quality occurred from 1990 to 2000 as a result of the 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-6 

continuing effect of the pre-1990 Clean Air Act requirements already on the books, but 

after 2000 the without-CAAA scenario shows deterioration of air quality through 2020. 

As shown in Table 7-2, there is considerable uncertainty in the estimates of health 

benefits. As described above, the health benefit uncertainty analysis is based on 

underlying statistical uncertainties in the concentration-response and valuation 

coefficients. The low estimates are approximately an order of magnitude less than the 

central estimate; the high estimate is three times the central estimate. Uncertainty 

analyses for non-health benefits were not developed, but as they constitute only about 

five percent of the central estimate, their contribution to the overall uncertainty in benefits 

estimates is likely to be proportionately small. 

TABLE 7-2. DISTRIBUTION OF PRIMARY ANNUAL BENEFITS RESULTS FOR 2020 

BENEFIT CATEGORY 

PRIMARY ANNUAL MONETIZED BENEFITS 

FOR 2020 

(MILLION 2006$) 

LOW CENTRAL HIGH NOTES 

Health Effects 

PM Mortality 

PM Morbidity 

Ozone Mortality 

Ozone Morbidity 

$170,000 

$17,000 

$3,200 

$780 

$1,700,000 

$68,000 

$55,000 

$2,100 

$5,300,000 

$190,000 

$170,000 

$3,600 

Low and high are 5th and 95th

percentile estimates from 

health benefits uncertainty 

analysis 

Subtotal Health 

Effects 

$190,000 $1,900,000 $5,700,000 

Visibility 

Recreational 

Residential 

$19,000 

$48,000 

Only central estimates were 

developed 

Subtotal Visibility $67,000 

Agricultural and 

Forest Productivity $11,000 

Materials Damage $110 Only central estimates were 

developed 

Ecological $8.2 Reduced lake acidification 

benefits to recreational fishing 

Total: all categories $2,000,000 

Note: See Chapters 5 and 6 of this report for detailed results summaries. Estimates presented 

with two significant figures; as a result, columns may not add to totals or subtotals. 

AGGREGATE MONETIZED BENEFITS

As discussed earlier in this chapter, we interpolate benefit estimates between target years 

and then aggregate the resulting annual estimates across the entire 1990 to 2020 period of 

the study to yield a present discounted value of total aggregate benefits for the period. In 

this section we present the results of the aggregation. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-7 

In Table 7-3 we present the mean estimate from the aggregation procedure, along with 

the Primary Low (i.e., 5th percentile of the distribution) and Primary High (i.e., 95th 

percentile of the distribution) estimates, for all provisions we assessed. Aggregating the 

stream of monetized benefits across years involved discounting the stream of monetized 

benefits estimated for each year to the 1990 present value using a five percent discount 

rate. 

TABLE 7-3. PRESENT VALUE OF MONETIZED BENEFITS OF THE CAAA 

PRESENT VALUE (MILLIONS 2006$) 

PRIMARY LOW PRIMARY CENTRAL PRIMARY HIGH 

All Provisions, 1990 to 2020 $1,400,000 $12,000,000 $35,000,000 

Note: Values presented in this table are in millions of 2006$, discounted to 1990 using a 5 

percent discount rate. 

COMPARISON OF BENEFITS AND COSTS 

Table 7-4 presents summary quantitative results for the prospective assessment, with 

costs disaggregated by emissions source category and benefits disaggregated by type. 

We present annual, Primary Central estimate results for each of the three target years of 

the analysis, with all dollar figures expressed as inflation-adjusted 2006 dollars. The final 

columns provide net present value estimates for costs and benefits from 1990 to 2020, 

discounted to 1990 at five percent. The results indicate that the Primary Central estimate 

of benefits clearly exceeds the costs of the CAAA, for each of the target years and for the 

cumulative estimates of present value over the 1990 to 2020 period. 

As Table 7-4 indicates, a very high percentage of the benefits is attributable to reduced 

premature mortality associated with reductions in ambient particulate matter and ozone. 

The CAAA achieves ambient PM reductions through a wide range of provisions 

controlling emissions of both gaseous precursors of PM that form particles in the 

atmosphere (sulfur dioxide and nitrogen oxides as well as, to a lesser extent, organic 

constituents) and directly emitted PM (i.e., dust particles). Because the effects of these 

constituents on ambient PM are nonlinear, and because some precursor pollutants interact 

with each other in ways which influence the total concentration of particulates in the 

atmosphere, separating the effects of individual pollutants on the change in ambient PM 

would require many iterations of our air quality modeling system. Even with such a tool, 

the interactive effects of pollutants are complex – as a result the marginal impact of any 

particular pollutant is dependent on the levels of other pollutants as well. These factors 

make it difficult to reliably link specific costs to specific aggregate benefits for the 

pollutant source-specific components of the CAAA (e.g., electric utilities or additional 

local controls). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-8 

TABLE 7-4. SUMMARY OF QUANTIFIED PRIMARY CENTRAL ESTIMATE BENEFIT AND COSTS 

(ESTIMATES IN MILLION 2006$) 

COST OR BENEFIT CATEGORY 

ANNUAL ESTIMATES 

2000 2010 2020 PRESENT VALUE 

Costs: 

Electric Utilities $1,400 $6,600 $10,000 $49,000 

Industrial Point Sources $3,100 $5,200 $5,100 $43,000 

Onroad Vehicles and Fuels $14,000 $26,000 $28,000 $220,000 

Nonroad Engines and Fuels $300 $360 $1,200 $4,500 

Area Sources $660 $690 $770 $7,600 

Local Controls to Meet NAAQS $0 $14,000 $20,000 $53,000 

 

Total Costs $20,000 $53,000 $65,000 $380,000 

Monetized Benefits: 

Avoided Mortality $720,000 $1,200,000 $1,800,000 $11,000,000 

Avoided Morbidity $27,000 $47,000 $70,000 $410,000 

Ecological and Welfare Effects $15,000 $39,000 $78,000 $310,000 

 

Total Benefits $770,000 $1,300,000 $2,000,000 $12,000,000 

Table 7-5 provides the results of our more detailed comparison of primary benefits 

estimates to primary cost estimates. In the top half of the table we show both annual and 

present value estimates. The cost estimates presented in the table reflect estimates 

presented in Chapter 3. The monetized benefits indicate both the Primary Central 

estimate (the mean) from our statistical aggregation procedure and the Primary Low and 

Primary High estimates (5th and 95th percentile values, respectively). In the bottom half 

of the table we present three alternative methods for comparing benefits to costs. “Net 

benefits” reflect estimates of monetized benefits less costs. The table also notes the 

benefit/cost ratios implied by the benefit ranges, and our estimates of the costs per 

premature mortality avoided. 

The results in Table 7-5 make it abundantly clear that the benefits of the CAAA exceed 

its costs by a wide margin, making the CAAA a very good investment. Our estimates 

rely on a particular set of data, models and assumptions we believe are most appropriate 

at this time. It is possible that another set of data, models, or assumptions might yield 

different estimates of benefits, costs, and benefit-cost comparisons. Nonetheless, the very 

wide margin between estimated benefits and costs, and the results of the uncertainty 

analysis, suggest that it is extremely unlikely that the monetized benefits of the CAAA 

over the 1990 to 2020 period reasonably could be less than its costs, under any alternative 

set of assumptions we can conceive. The central benefits estimate exceeds costs by a 

factor of more than 30 to one, whether we are looking at annual or present value 

measures, and the high estimate exceeds costs by roughly 90 to one. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-9 

TABLE 7-5. SUMMARY COMPARISON OF BENEFITS AND COSTS 

ANNUAL ESTIMATES 

PRESENT VALUE 

ESTIMATE 

2000 2010 2020 1990-2020 

Monetized Direct Costs (millions 2006$): 

Low a 

Central $20,000 $53,000 $65,000 $380,000 

High a 

Monetized Direct Benefits (millions 2006$): 

Lowb $90,000 $160,000 $250,000 $1,400,000 

Central $770,000 $1,300,000 $2,000,000 $12,000,000 

Highb $2,300,000 $3,800,000 $5,700,000 $35,000,000 

Net Benefits (millions 2006$): 

Low $70,000 $110,000 $190,000 $1,000,000 

Central $750,000 $1,200,000 $1,900,000 $12,000,000 

High $2,300,000 $3,700,000 $5,600,000 $35,000,000 

Benefit/Cost Ratio: 

Lowc 5/1 3/1 4/1 4/1 

Central 39/1 25/1 31/1 32/1 

Highc 115/1 72/1 88/1 92/1 

Costs per Premature Mortality Avoided (2006$): 

Central $180,000 $330,000 $280,000 Not estimated 

a The cost estimates for this analysis are based on assumptions about future changes in factors such as 

consumption patterns, input costs, and technological innovation. We recognize that these assumptions 

introduce significant uncertainty into the cost results; however the degree of uncertainty or bias 

associated with many of the key factors cannot be reliably quantified. Thus, we are unable to present 

specific low and high cost estimates. 

b

 Low and high benefits estimates based on primary results and correspond to 5th and 95th percentile 

results from statistical uncertainty analysis, incorporating uncertainties in physical effects and valuation 

steps of benefits analysis. Other significant sources of uncertainty not reflected include the value of 

unquantified or unmonetized benefits that are not captured in the primary estimates and uncertainties in 

emissions and air quality modeling. 

c The low benefit/cost ratio reflects the ratio of the low benefits estimate to the central costs estimate, 

while the high ratio reflects the ratio of the high benefits estimate to the central costs estimate. 

It is also clear from Table 7-5 that costs for criteria pollutant programs grow more 

quickly than benefits at the beginning of the CAAA compliance period, from 2000 to 

2010, and that benefits grow more quickly at the end of the period, from 2010 to 2020. 

This is consistent with the general statement that investments in clean air tend to involve 

upfront costs and benefits that accrue over time. The present value estimates in Table 7-5 

show, however, that the total aggregated value of benefits far exceeds the costs – by our 

measures, therefore, the programs associated with the 1990 Clean Air Act Amendments 

have been, and will likely continue to be, a very good investment. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-10 

As indicated in the table, the low estimate of net benefits for the year 2020 is positive 

(i.e., benefits exceed costs) and of significant magnitude - $190 billion. Our uncertainty 

modeling therefore indicates that the likelihood that the cost estimates of $65 billion in 

2020 could exceed the benefits estimates is much less than five percent. 

OVERVIEW OF UNCERTAINTY ANALYSES 

Completion of a study of this breadth and complexity has required EPA to directly 

confront the role of uncertainty in the key analytic outcomes of the study. While the 

previous section establishes that the primary estimates of benefits of air pollution control 

greatly exceed the primary estimates of costs of CAAA compliance, it is nonetheless 

important to evaluate the extent to which alternative models, assumptions about 

scenarios, and key parameter choices might affect both benefits and costs. Cognizant of 

advice to the Agency from the National Research Council, 103 the Project Team 

developed a three step approach to uncertainty analysis: 

1. Identify important sources of uncertainty in each analytical element, starting with 

emissions profile development. At the end of each of the preceding chapters, we 

provide a table of key uncertainties and our assessment of the direction and 

potential magnitude of the impact of this uncertainty on the key analytic output of 

the study, the monetized net benefits of the CAAA. 

2. Quantify parameter and model uncertainty quantitatively where possible by using 

alternative assumptions or models to estimate intermediate and/or overall net 

benefit results. In addition, explore options for assessing scenario uncertainty 

that propagate through the complete analytic chain. 

3. Compare the results from these quantitative analyses to the primary results, to 

inform the degree of confidence in the primary analytic results and to help 

identify new research directions to address or reduce uncertain and influential 

components of the analysis. 

In the remainder of this section we review each of these three components of our 

uncertainty analysis.104

IDENTIFYING IMPORTANT SOURCES OF UNCERTAINTY 

Within each of the summary uncertainty tables in the prior chapters the Project Team has 

distinguished sources of uncertainty that could have a potentially major impact on the 

overall net benefits estimate presented in this chapter, based either on quantitative 

analyses or, where quantitative assessments are unavailable or infeasible, the judgment of 

Project Team analysts. Potentially major factors are those for which a plausible 

alternative assumption or approach could influence the overall benefit or cost estimate by 

 

103 See National Research Council (2002), Estimating the Public Health Benefits of Proposed Air Pollution Regulations, The 

National Academies Press, Washington, DC, in particular Chapter 5, titled: “Uncertainty.” 

104 For a more thorough description of the methods and results of these uncertainty analyses see the accompanying report 

Uncertainty Analyses to Support the Second Section 812 Benefit-Cost Analysis of the Clean Air Act, March 2009. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-11 

five percent or more. We identify a total of 13 potentially major sources of uncertainty in 

Chapters 2 through 6; these are listed in Table 7-6 below. 

TABLE 7-6. POTENTIALLY MAJOR SOURCES OF UNCERTAINTY FOR ESTIMATING THE COSTS 

AND BENEFITS OF THE CAAA 

POTENTIAL SOURCE OF ERROR ANALYTIC STEP 

DIRECTION OF POTENTIAL BIAS FOR 

NET BENEFITS 

Estimated emissions rates under the 

counterfactual Without-CAAA scenario 

Emissions Under-estimate 

Estimated economic growth – a key driver of 

total emissions – under both scenarios 

Emissions Unable to determine 

Forecast of the final form and compliance with 

EPA’s revisions of the vacated Clean Air 

Mercury Rule and the remanded Clean Air 

Interstate Rule 

Emissions Unable to determine 

Secondary organic aerosol (SOA) chemistry Air Quality Modeling Under-estimate 

Inability to conclusively state that PM 

mortality outcome is causal based on 

epidemiology 

Health Effects Over-estimate 

Effect of socioeconomic status on mortality 

from PM exposure 

Health Effects Unable to determine 

Attribution of exposure to PM in epidemiology 

studies based on monitor data 

Health Effects Under-estimate 

Omission of short-term effects of PM exposure 

on mortality 

Health Effects Under-estimate 

Timing of reduction in mortality risk after 

exposure is reduced (cessation lag) 

Health Effects Unable to determine 

Source of mortality risk valuation includes 

many older studies 

Valuation Unable to determine 

Scenario of mortal risk in available valuation 

studies is generally different from that 

presented by air pollution 

Valuation Unable to determine 

Valuation of risk avoidance can change over 

time and as income increases 

Valuation Unable to determine 

Incomplete coverage of ecological effects of 

air pollutants, including omission of several 

short-term and virtually all long-term 

bioaccumulative and persistent effects. 

Ecological Under-estimate 

* The classification of each potential source of error reflects the best judgment of the section 812 Project 

Team. The Project Team assigns a classification of “potentially major” if a plausible alternative assumption 

or approach could influence the overall monetary benefit estimate by approximately five percent or more. 

See tables at the end of Chapters 2 through 6 above for more information. 

Perhaps not surprisingly, the key emissions estimation uncertainties involve forecasting 

errors, particularly related to estimating future economic and regulatory activity as well 

as estimating behavior under the counterfactual without-CAAA scenario. A key cost 

estimation uncertainty involves estimating NAAQS compliance, particularly when 

currently known emissions reductions measures are not sufficient to achieve full 

compliance with the standard in the future. However, in order for any uncertainty to be 

considered “major” the impact would need to be of a magnitude of approximately $100 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-12 

billion to affect net benefits estimates by as much as five percent. In our judgment, while 

there are several factors that could affect direct cost estimates by a significant percentage, 

no cost estimation uncertainty has the potential to either more than double our current 

total cost estimate of $65 billion, or to reduce the cost estimate to $0 or less, which is the 

magnitude that would be required to constitute five percent of the net benefit estimate. 

Several uncertainties that affect benefits estimates, however, could have an impact of 

$100 billion or greater on the net benefits estimates. Both health effects and valuation 

uncertainties center on estimation of the impact of air pollutants on mortal risk and the 

valuation of that health endpoint. The key ecological uncertainty involves identifying 

what is missing from our necessarily limited quantified ecological benefits. Only one 

potentially major factor was identified for the air quality modeling step – this may be the 

result of our inability to apply alternative quantitative air quality modeling tools in this 

already resource-intensive step in the analytic chain. It is worth noting, however, that as 

a whole the air quality modeling process very likely contributes a greater than 5 percent 

uncertainty, of indeterminate direction, to the overall uncertainty in benefits estimates. In 

addition, the AQMS highlighted uncertainties introduced by the ex post adjustment of 

some primary PM emissions estimates and the procedure used to re-calibrate the CMAQ 

air quality to account for this emissions adjustment. Although we argue that the overall 

effect of this source of uncertainty on the net benefits is probably minor (see Table 4-10 

in Chapter 4), in some locations ambient PM from primary PM emissions can be more 

important than secondarily formed fine particles. Overall, we believe that our application 

of the MATS monitor calibration procedure, which provides a speciated calibration to 

ensure better agreement between air quality modeling results and comparable monitor 

data, provides the best agreement possible between our air quality simulation results and 

monitored values. In the end, however, there is no way to validate the counterfactual, 

without-CAAA scenario estimates. 

Examination of the last column of Table 7-6 suggests a limited ability to estimate the 

joint effect of these factors on the direction of potential bias for net benefits. Seven of the 

factors listed have an indeterminate direction of effect; five yield a potential 

underestimate of net benefits; and one results in a potential overestimate of net benefits. 

The large number of factors with an indeterminate direction imply that the direction of 

the net effect of all factors taken together remains unclear, but the relative confidence that 

the PM exposure-mortality concentration-response function is causal, based on weight-ofevidence, that being the only uncertainty that yields a potential overestimate, suggests 

that our primary results may be more likely to understate net benefits than overstate them. 

A comparison of the qualitative uncertainty tables from the First and Second Prospective 

studies indicates that significant advancements over the First Prospective include the use 

of improved monitoring data for PM2.5, an improved understanding and treatment of 

atmospheric chemistry and the composition of PM2.5 emissions, and the use of longerterm simulations with integrated modeling of criteria pollutants using CMAQ rather than 

a collection of separate air quality models. 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-13 

QUANTIFYING MODEL, PARAMETER, AND SCENARIO UNCERTAINTY 

The benefits values presented in this report are subject to a number of uncertainties 

related to data limitations, analytical choices related to models and input parameters, 

difficulties predicting future scenarios, and other factors. As noted above, among the 

most significant model uncertainties is the extensive list of benefits categories, mostly in 

the ecological area, for which we currently lack the data and/or tools to quantify and 

monetize benefits. These categories are implicitly treated as having zero value though in 

reality they may include physical benefits that have a positive economic value. Examples 

of potentially important, but unquantified ecological effects include nitrogen deposition, 

non-ozone effects on forest and agriculture vegetation, effects of HAPs on ecological 

structure and function, and synergistic effects associated with exposures to mixtures of 

pollutants and interactions of the effects of conventional pollutants such as ozone with 

climate change. The unquantified and unmonetized benefits thus represent an important 

underestimation bias in the summary benefit results. 

The uncertainties in our quantified and monetized primary benefits estimates that are 

most likely to significantly influence the primary benefit results are those affecting the 

largest benefit category: the estimation and valuation of reductions in premature mortality 

due to decreases in PM2.5. Three key uncertainties affecting economic estimates of 

avoided PM mortality include: (1) the C-R function estimate; (2) the PM/mortality 

cessation lag structure; and (3) the mortality valuation estimate. These are influential 

assumptions in our analysis and those for which plausible alternative quantitative 

estimates are available. The companion Second Prospective Section 812 report, 

Uncertainty Analyses to Support the Second Section 812 Benefit-Cost Analysis of the 

Clean Air Act, presents detailed quantitative analyses of the sensitivity of benefits results 

to these and other factors. 

Table 7-7 presents a tabular summary of the results of the full range of uncertainty 

analyses for both costs and benefits, and Figure 7-2 presents a graphical illustration of the 

impacts of effect of alternative assumptions and models on the central estimate and 

distribution of monetized avoided mortality benefits, the primary contributor to 

monetized benefits. 

COST UNCERTAINTIES 

Table 7-7 shows that the impact of our alternative assumptions about mobile source cost 

parameters, learning curves, and unidentified local control costs each have relatively 

modest impacts on total costs, while the I&M failure rate and learning curve assumptions 

have a slightly larger impact on total costs.105 In addition, the assumptions underlying our 

primary cost estimates tend to be conservative; most of the alternatives decrease total 

compliance costs and none increase costs more than about three percent. 

 

 

105 The estimate of the impact on total costs is derived from the relative contribution of the affected cost sector to the 

overall costs of compliance, assuming all other sectors are unaffected. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-14 

TABLE 7-7. QUANTITATIVE ANALYSES OF UNCERTAINTY IN THE 812 SECOND PROSPECTIVE 

 ANALYSIS 

FACTOR AND LOCATION OF 

UNCERTAINTY ANALYSIS 

DISCUSSION IN THIS REPORT 

(WHERE APPLICABLE) 

TYPE OF 

UNCERTAINTY 

EVALUATED ALTERNATIVE ASSUMPTIONS 

IMPACT OF 

ALTERNATIVE 

ASSUMPTIONS ON 2020 

PRIMARY ESTIMATE 

UNCERTAINTIES RELATED TO COST ESTIMATES 

Unidentified controls 

(Chapter 3) 

Parameter Alternate assumption about the 

threshold for, and cost of, applying 

unidentified local controls to achieve 

NAAQS compliance ($10,000/ton). 

-18% of local control 

costs; -2.1% of total 

costs 

I&M program vehicle failure 

rates(Chapter 3) 

Parameter Alternative assumption about failure 

rates for I&M program testing based 

on NRC (2001). 

-11% to -14% for 

mobile source costs; 

-6% to -7.6% of total 

costs 

Learning curve assumptions 

(Chapter 3) 

Parameter Alternate assumptions about the 

learning rate (5 and 20%) 

-6.0% to 3.2% of total 

costs 

Fleet composition and fuel 

efficiency (Chapter 3) 

Scenario Alternate assumption about future 

fleet composition and fuel efficiency 

using AEO 2008. 

-0.2% to -3.6% for 

mobile source costs; 

-0.1% to -2.0% of total 

costs 

UNCERTAINTIES RELATED TO BENEFITS ESTIMATES 

Alternate C-R function for PM 

(Chapter 5) 

Parameter Alternative C-R functions – two from 

empirical literature (Pope et al., 

2002 and Laden et al., 2006) and 12 

subjective estimates from the expert 

elicitation study 

-84% to 79%, 

Based on most 

extreme estimates 

from PM EE study. 

Rest of alternatives 

range from -43% to 41% 

Emissions from EGU sources 

(Chapter 2) 

Scenario Use continuous emissions monitoring 

(CEM) data in place of Integrated 

Planning Model (IPM) results, 

coupled with alternative 

counterfactual consistent with CEM 

approach. 

+50% in 2000 

Due almost entirely to 

the impact of the 

alternative withoutCAAA scenario. 

PM/Mortality Cessation lag 

(Chapter 5) 

Model and 

parameter 

Alternative lag structures – one step 

function and a series of smooth 

functions (based on an exponential 

decay). Smooth functions in some 

cases also require change in C-R 

coefficient. 

-25% to 13% when using 

primary C-R function. 

-48 to 47% when also 

changing C-R function. 

Value of Statistical Life 

(Chapter 5) 

Parameter Alternative VSL estimates -22% to 0% 

Discount rates Parameter Alternate discount rates (5% and 7%) -6% to 6% 

Alternate C-R function for 

ozone (Chapter 5) 

Parameter Alternative C-R functions – three 

from NMMAPS-based studies, three 

from meta-analyses, and one from 

Jerrett et al. cohort study. 

0% for total mortality 

benefits. 

-63% to 66% 

For ozone-related 

mortality. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-15 

FACTOR AND LOCATION OF 

UNCERTAINTY ANALYSIS 

DISCUSSION IN THIS REPORT 

(WHERE APPLICABLE) 

TYPE OF 

UNCERTAINTY 

EVALUATED ALTERNATIVE ASSUMPTIONS 

IMPACT OF 

ALTERNATIVE 

ASSUMPTIONS ON 2020 

PRIMARY ESTIMATE 

Emissions changes by 

emitting sector 

Scenario Altering each sector-specific 

emissions by 10 percent 

$/ton marginal benefit 

for proportional EGU 

sector reductions is 

about 3 times that for 

nonroad and on-road 

sectors, and 50% 

higher than that for 

area and non-EGU 

point source sectors. 

Differential toxicity of PM 

components 

Parameter Potential alternative estimates of 

toxicity for specific PM components 

N/A. No quantitative 

sensitivity analysis 

performed due to 

significant data gaps. 

Dynamic population modeling 

(Chapter 5) 

Model Incorporation of dynamic population 

estimates to calculate life years 

gained and changes in life 

expectancy 

N/A. Life years gained 

and changes in life 

expectancy are 

supplemental 

estimates of 

PM/mortality effects 

and cannot be directly 

compared to the 

primary estimate. 

A further overarching issue with our direct cost methodology is that, for EGU modeling 

and for some components of the ozone NAAQS compliance cost assessment, the method 

employed assumes specific optimizing behavior by polluters. In particular, the IPM 

model used for EGU compliance cost assessment assumes a forward looking approach 

and may incorporate only limited available information on real-world constraints on 

optimizing behavior such as long-term fuel supply contracts. If polluters do not optimize 

in the manner assumed in these models, the direct costs may under-estimate the true costs 

of compliance. For other emitting sectors, where optimization approaches were not 

feasible, the potential for under-estimation from this factor does not apply. 

A potential issue in considering the uncertainty in cost estimates is our inability to 

adequately consider the effects of the CAAA on the quality of goods overall. Our method 

emphasizes that the CAAA does increase the cost of products, and we attempt to hold the 

quality of products constant in the process. In reality it is likely that the CAAA affects 

both price and quality of products. One of the more straightforward examples is that 

motor-vehicle emission controls may reduce performance, though at the same time those 

controls can increase fuel economy. Other examples include substitution of other devices 

for charcoal lighting fluid, reformulation of VOC emitting paints, and other product 

changes that may have altered the quality of those products to consumers. As discussed 

briefly in Chapter 3, however, the CAAA could also change quality in ways that benefit 

consumers, but which we do not capture in our estimates – for example, low VOC paint 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-16 

contributes not only to lower ambient ozone levels, but also reduces consumer exposure 

to VOCs in enclosed indoor environments. Unfortunately, it is very difficult to quantify 

the effect of this factor on our overall cost and net benefit estimates. 

BENEFIT UNCERTAINTIES 

On the benefits side, Table 7-7 and Figure 7-2 show that the most influential assumptions 

affecting benefits are the choice of the C-R function, the cessation lag model for the 

accrual of benefits, and the VSL distribution. While the two most extreme results from 

EPA’s Expert Elicitation (EE) study imply substantial effects of C-R choice (about 80 

percent in either direction) most of the alternatives from the EE study and the published 

epidemiological studies suggest effects on benefits of about 40 percent or less in either 

direction. By themselves, longer cessation lag alternatives can reduce monetized benefits 

by as much as a 25 percent and if coupled with a change in the C-R function, by close to 

half; however, the Council Health Effects Subcommittee advised that much of the risk 

reduction benefits from PM2.5 controls are more likely to accrue sooner rather than later. 

Accelerating benefits increases benefits by about 13 percent when maintaining the same 

C-R function, but could increase them by as much as half when using a smooth function 

based on the Laden Six Cities follow-up effect estimate. VSL distribution choices in one 

case produce the same central estimate; in others they reduce VSL between 7 and 22 

percent. 

A review of the box plots in Figure 7-2 for the factors that have the greatest potential to 

change the central estimate shows that most of the alternatives do not have a dramatic 

effect on the spread of uncertainty. Some alternatives suggest the high end of the 

distribution could be lower, including all of the alternative VSL distributions, which give 

less weight to higher VSL values than the 26-study Weibull. On the other hand, only a 

few alternatives (from EPA’s particulate matter expert elicitation study) significantly 

extend the upper end and hardly any extend the lower end, suggesting our primary 

estimate is unlikely to understate greatly the uncertainty in avoided mortality benefits. In 

all these cases, however, we are unable to develop a probabilistic representation of 

uncertainty in the emissions and air quality modeling steps; incorporating uncertainty in 

these factors would certainly increase the spread between the Primary Low and Primary 

High estimates. 

LESSONS LEARNED AND NEW RESEARCH DIRECTIONS 

Many of the factors contributing to uncertainty in these estimates are the result of 

scientific unknowns that might be addressed through additional research. Identification 

of research directions to address current unknowns can serve an important function - in 

the First Prospective, for example, we identified eight high priority research directions, 

six of which were addressed in the Second Prospective.106 

 

106 The six were: improved emissions inventories and inventory management tools (see Chapter 2 for a description of the 

improvements in the 2002 NEI, and the AirControlNET tool used to estimate emissions reductions necessary for NAAQS 

compliance); improved tools for assessing the full range of social costs associated with regulation, including the taxinteraction effect (see Chapter 8 of this document for a description of the economic modeling tool EMPAX-CGE); a more 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-17 

FIGURE 7-2. SUMMARY OF QUANTITATIVE ANALYSIS OF UNCERTAINTY IN MONETIZED MORTALITY 

BENEFITS ESTIMATES 

The key lessons learned in this analysis lead directly to new research directions to inform 

future assessments, assessments which include both Regulatory Impact Analyses of 

specific rules and broader, policy-oriented documents such as this Second Prospective. 

The key insight from this analysis is that rules that target precursors of fine particulate 

matter are likely to be very cost-effective. Costs per ton of PM control are similar or less 

than previously estimated, and benefits per ton emitted of these precursors are much 

larger than previously thought or estimated in the First Prospective. There are three key 

reasons for the large increase in benefits per ton of PM precursor emitted, involving 

advances in our understanding of: 1. PM species emissions, 2. the fate of these emissions 

as estimated by integrated national-scale air quality modeling systems, and 3. the 

implications of fine particulate air quality for premature mortality. 

 

geographically comprehensive air quality monitoring network, particularly for fine particulate matter (see discussion of the 

MATS procedure in Chapter 4 of this document); development of integrated air quality modeling tools based on an open, 

consistent model architecture (see Chapter 4 for a description of the CMAQ system); increased basic and targeted research 

on the health effects of air pollution, especially particulate matter (see Chapter 5 for multiple examples of recent work 

that was applied in this analysis); continued efforts to assess the cancer and noncancer effects of air toxics exposure (see 

discussion of the air toxics case study in Chapter 5). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-18 

In addition, the results of the study also provide evidence of the significant benefits of 

avoiding mortality associated with ozone exposure, avoiding degradation of visibility in 

residential and recreational settings, and avoiding significant chronic and acute morbidity, 

including chronic bronchitis and acute myocardial infarction. The last two of these 

monetized benefits categories were shown, by themselves, nearly to equal the full costs of 

all provisions of the CAAA. There also remain large categories of health and ecological 

benefits for which we have no quantified or monetized benefit estimates. For example, 

although there is an established literature linking air pollutant exposure with increased 

risk of cerebrovascular accidents (stroke), as well as a literature on the medical costs of 

this condition, that category of effect is not yet included in our estimates of the health 

benefits of reducing air pollutant exposure. 

Insights gleaned from completing this study suggest the following eight areas to be the 

highest priority research needs: 

 Improving cost analyses for rules that are technology-forcing. The overall cost 

analysis in Chapter 3 is characterized by complete coverage of the costs of many 

rules, but the Project Team acknowledges that in some cases, particularly 

involving compliance with tighter future NAAQS standards, application of the 

suite of known, cost-effective current pollutant control measures are not 

sufficient to achieve compliance in all locations. This shortcoming remains one 

of the important focal points for compliance cost research within the Agency. 

One possible direction that the Agency is considering is analysis of historical data 

on the cost and penetration rates of new emissions control technologies, 

particularly those for NAAQS compliance, which could provide insights on the 

process, cost, timing, and potential limits of induced innovation. 

 Continuing efforts to incorporate a broader range of market benefits in 

economy-wide modeling of the impacts of regulation. The results of Chapter 8 

indicate that there are significant benefits to economic growth when we consider 

the labor force and health expenditure implications of cleaner air. Our 

demonstration of the importance of incorporating benefits-side effects in macroeconomic modeling efforts, however, does not incorporate all possible market 

effects of cleaner air. For example, increased agricultural and forest productivity 

might feasibly be incorporated in the model we employed. Ultimately, it will 

also be important to develop new methods to characterize the large nonmarket 

benefits of cleaner air in these models, including most importantly the welfare 

enhancements (as opposed to simply the market implications) associated with 

reductions in premature mortality. 

 Understanding synergies and antagonistic effects of climate change in 

realizing benefits, as well as for understanding co-benefits of greenhouse gas 

(GHG) control policies. Consideration of climate change was outside the scope 

of this Second Prospective effort, but designing effective and efficient regulatory 

mechanisms for GHG emissions control has rapidly become an important priority 

for the Agency. The methods, data, and results of this study are important for 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-19 

modeling co-benefits of GHG control policies, as many policies targeted at GHG 

reductions also reduce other, conventional pollutants, and those benefits are 

realized sooner than the generally long-term benefits of GHG policies. In 

addition, climate change likely alters the benefits achieved by conventional 

pollutant policies, as for example increases in mean temperature as well as 

increases the frequency of extreme temperature events creates conditions 

conducive to ozone formation. Both areas are important for further research. 

 Developing probabilistic representation of emissions and air quality to support 

uncertainty analysis. As noted earlier in this chapter, a major shortcoming of 

existing quantitative characterizations of uncertainty in benefits and costs of the 

CAAA is the inability to integrate uncertainties in emissions and air quality 

modeling steps. Two areas of research deserve further attention: 1. Developing 

more nimble tools for assessing the air quality implications of emissions control 

policies, or updating those that exist; 2. Developing probabilistic 

characterizations of key parameters that contribute to overall uncertainty in 

emissions and air quality analyses. Pursuit of the latter initiative will likely 

require application of expert elicitation, either formal or informal, to make 

progress. 

 Understanding the potential for differential toxicity to play a role in benefits of 

control programs and, by extension, policy priorities. The issue of speciesspecific particulate matter toxicity remains very complex, involving the effects of 

mixtures and synergies of species that are not currently well understood. It is 

nonetheless important to understand the extent to which rules targeted at specific 

PM species might yield similar benefits as rules targeting total PM mass. 

 Continuing to pursue evidence of the real-world public health impact of 

specific air quality actions. Sometimes referred to as accountability analyses, 

tracking the real-world instances of rapid air quality changes, either 

improvements or reductions in air quality, can yield important corroborating 

evidence of the effects found in epidemiology studies. As we found in our 

uncertainty analyses supporting the Second Prospective, these natural 

experiments also provide insights for the nature of cessation lags, and might be 

useful in better understanding species-specific toxicity.

 Expanding coverage of ecological benefits. There are potentially large 

ecological benefits of air pollution control that are not currently quantified. 

Some of the most important categories of unquantified effects include nitrogen 

deposition effects on estuarine areas, sulfur deposition effects on vegetation and 

other aspects of terrestrial systems, and long-term effects of air toxics. Perhaps 

equally important, but much more subtle, are the long-term effects of a wide 

range of air pollutants on ecosystem structure and function. Even potentially 

beneficial effects of pollutants, such as deposition of the nutrient nitrogen in 

terrestrial and even actively managed farms and forests, might have longer-term 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

7-20 

detrimental effects on nutrient cycling and species selection that are currently 

poorly understood. 

 Expanding coverage of health benefits. Great effort has been expended to better 

characterize the full range of health implications of air pollution. Despite this 

effort, it is still difficult to quantify the link between air pollution and stroke, and 

it is also difficult to assess the incremental effects of gaseous pollutant exposures, 

in part because there are only a limited set of studies that characterize the 

individual contributions of multiple pollutant exposures on health outcomes. 

While the Agency has developed robust benefits analyses for programs that 

control individual gaseous pollutants, such as carbon monoxide, it remains 

difficult to incorporate these effects in multi-pollutant models that include PM, 

ozone, and other gaseous pollutants typically present in many settings in the U.S.

The results of this Second Prospective study clearly provide strong evidence that the 

nation’s investment in clean air has been a wise and cost-effective policy. Continued 

effort is needed to ensure that air pollution policies are pursued in the most cost-effective 

manner possible. Pursuit of these research goals should continue to enhance our ability to 

provide accurate and timely assessments of the costs and benefits of all provision 

authorized under the Clean Air Act and its Amendments. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-1 

CHAPTER 8 - COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS 

The 1990 Clean Air Act Amendments (CAAA) represent a significant change in Federal 

air pollution policy affecting virtually every sector of the U.S. economy, including 

industry as well as individual households. The cost and benefit estimates presented in the 

previous chapters reflect the direct impacts of the CAAA in terms of industry’s and 

households’ direct compliance expenditures and the value of the direct human health, 

visibility, ecological, and other benefits associated with CAAA-related improvements in 

air quality. The cost-benefit information is central to EPA’s analysis of the Amendments, 

but policymakers and the public are also interested in the impact of CAAA programs on 

overall economic performance. Therefore, to supplement the direct cost and benefit 

estimates presented in the previous chapter, the Project Team applied an economy-wide 

computable general equilibrium (CGE) analysis of the Amendments and estimated the 

effect of the CAAA on U.S. gross domestic product and other macroeconomic measures. 

The Project Team performed this analysis with the Economic Model for Policy Analysis 

(EMPAX-CGE), a CGE model employed by EPA for several previous analyses of CAAA 

regulations, including the National Ambient Air Quality Standards (NAAQS) for PM2.5, 

the 8-hour Ozone NAAQS, and the Clean Air Interstate Rule. 

The Project Team’s CGE analysis for the Second Prospective represents a major step 

forward in EPA’s application of CGE models in the context of air pollution policy. 

Unlike previous CGE analyses that focused exclusively on the macroeconomic impacts of 

compliance expenditures, the Second Prospective incorporates impacts related to both 

CAAA costs and some categories of benefits into EMPAX-CGE, to the extent feasible. 

Because both the costs and benefits of CAAA regulations may affect the size and 

composition of the U.S. economy, the Project Team’s approach provides a more 

comprehensive and balanced view of the macroeconomic impacts of air pollution policy 

than previous assessments. To illustrate the extent to which including labor force and 

medical expenditure impacts in EMPAX-CGE affects model results, we applied the 

model in two ways: one model run that reflects only the costs of the CAAA (the cost-only 

case) and a second model run that reflects both the costs and a subset of the total benefits 

of the Amendments (the labor force-adjusted case). 

This chapter presents the CGE analysis in four sections. In the first section, we provide 

an overview of EMPAX-CGE, describing the model’s overall structure and highlighting 

the sectoral and geographic resolution of the model. The second section describes the 

development of the cost- and labor force and health expenditure benefit-side inputs for 

the analysis and documents how these inputs were incorporated into EMPAX-CGE. The 

third section presents the results of our analysis, both in aggregate and by industry. To 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-2 

conclude the chapter, we discuss the major uncertainties of the analysis and their 

implications for results. 

EMPAX-CGE 107

EMPAX-CGE is a multi-industry, multi-region computable general equilibrium model of 

the U.S. economy. Below we describe the main features of the typical CGE model, 

followed by a more detailed overview of the structure and functionality of EMPAX-CGE. 

OVERVIEW OF CGE MODELING 

CGE models simulate the flow of commodities and factors of production (i.e., labor, 

capital, and natural resources) among producers and households to assess how a change 

in policy or an economic shock affects the size and composition of the economy. As 

shown in Figure 8-1, households in CGE models own factors of production (capital, 

labor, and natural resources) that they supply to firms in exchange for wages and other 

forms of income. Firms use these factors in conjunction with intermediate inputs 

purchased from other industries to produce goods and services, which are sold to other 

industries as well as consumers. Goods and services can also be exported, and imported 

goods can be purchased from other countries. 

In modeling the circular flow of the economy depicted in Figure 8-1, CGE models 

capture behavioral changes among households and firms in response to changes in prices. 

At the producer level, CGE models simulate the substitution of inputs as the price of one 

input, such as steel or labor, rises relative to the price of other inputs. This allows the 

simulation of producer behavior in CGE models using minimization of the cost of 

production as an objective, consistent with the behavior of firms in the real economy. 

Similarly, as the price of one good rises relative to the prices of other products and 

services, CGEs model the process whereby households consume less of the more 

expensive good and more of other goods. Related to households’ substitution between 

different goods, CGE models also simulate household substitution between labor and 

leisure as real wages change. Because the productive capacity of the economy is 

dependent, in part, on labor supply, the labor-leisure tradeoff is critical in determining the 

size of the economy. 

 

107 The description of CGE models, in general, and EMPAX-CGE included in this section is based on RTI (2008). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-3 

FIGURE 8-1. CGE MODEL SCHEMATIC 

Source: RTI International, EMPAX-CGE Model Documentation, prepared for U.S. EPA 

Office of Air Quality Planning and Standards, March 2008. 

The general equilibrium component of CGE modeling requires a comprehensive market 

coverage in which all sectors in the economy are in balance and all economic flows are 

accounted for. Establishing equilibrium conditions requires that every commodity that is 

produced must be purchased by firms or consumers within the United States or exported 

to foreign consumers. The requirement for all markets to be in equilibrium during the 

time period of the model simulation is a simplifying assumption of the model, but is 

nonetheless a condition which, over time, is consistent with production in the actual 

economy. Prices of these goods reflect all costs of production. Households receive 

payments for their productive factors and transfers from the government (not shown in 

Figure 8-1), and this income must equal consumer expenditures and savings. In 

aggregate, all markets must clear, meaning that supplies of commodities and factors must 

equal demand, and the income of each household must equal its factor endowments plus 

any net transfers received. An important implication of this market clearing assumption 

is that CGE models assume that the economy is at full employment (i.e., there is no 

involuntary unemployment). Therefore, CGE models do not typically provide insights 

into the unemployment impacts of policy changes. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-4 

OVERVIEW OF EMPAX-CGE 

Similar to other CGE models, EMPAX-CGE is structured to represent the complex 

interactions between consumers and producers in the real economy. To model these 

interactions, EMPAX-CGE performs thousands of calculations with the objective of 

maximizing household utility (well-being) while simultaneously maximizing firm profits. 

While complex, these calculations are a simplified representation of the real economy. 

The behavior of households and firms is inherently multi-faceted and dependent on a 

range of factors, many of which are not well understood. To model this behavior, 

EMPAX-CGE uses a simplified, hierarchical representation of household and firm 

decision-making that reduces the behavior of households and firms to a limited number of 

structured decisions. For example, as shown in Figure 8-2, the first decision for the 

household sector in EMPAX-CGE is the optimization of consumption and leisure. To 

model this decision, EMPAX-CGE assumes that households are free to allocate their time 

between labor and leisure to maximize their welfare. Time that households do not devote 

to leisure represents household labor supplied to producers. Therefore, in effect, the 

leisure-consumption decision also represents a tradeoff between leisure and labor force 

participation. After the consumption-leisure decision, EMPAX-CGE simulates 

household consumption as a series of hierarchical decisions involving consumption goods 

and transportation. 

EMPAX-CGE also models firm behavior as a series of hierarchical decisions. Similar to 

EMPAX-CGE’s treatment of households, this hierarchical structure represents a 

simplification of how firms decide which inputs to use in the production of goods and 

services. As illustrated in Figure 8-3, the first tier of this decision hierarchy is a choice 

between: (1) an indeterminate mix of capital, labor, and energy and (2) goods and 

services produced by other industries, such as steel or computer equipment. Producers 

then optimize among capital, labor, and energy. 

Consistent with simplifying household and firm decision-making into the structured 

frameworks depicted in Figures 8-2 and 8-3, EMPAX also uses a simplified 

representation of the overall structure of the economy. Firms in the U.S. are scattered 

across thousands of industries and produce countless goods and services. Modeling each 

of these sectors individually within an economy-wide model, however, is not feasible due 

to data and computational processing constraints. To address this issue, EMPAX-CGE 

aggregates the economy into 35 distinct industries, as listed in Table 8-1. The industry 

classifications included in EMPAX-CGE were defined so as to maximize the level of 

sectoral detail among energy-intensive and manufacturing industries. EMPAX-CGE also 

separates the electricity industry into fossil fuel generation and non-fossil generation, 

which is important for assessing the impacts of policies that affect only fossil fuel-fired 

electricity, such as air pollutant regulations. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-5 

FIGURE 8-2. EMPAX-CGE DECISION HIERARCHY FOR HOUSEHOLDS 

FIGURE 8-3. EMPAX-CGE NESTED STRUCTURE FOR PRODUCERS 

Consumption Leisure

Domestic Foreign 

Energy 

Purchased 

Transport 

Personal 

Transport 

Transportation Consumption Goods 

Petroleum 

Services Manufactured 

Goods

Goods and 

Services 

(30 types) (5 types) 

Household utility is a function of 

consumption and leisure. 

Output

Capital/Labor/Energy Goods and services produced by 

other industries

Energy

Energy

(5 types)

Capital & Labor

Capital Labor
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-6 

EMPAX-CGE is also designed to reflect regional differences in the overall structure of 

the economy. Because the availability and cost of different production inputs, such as 

labor and energy, vary across different regions of the U.S., the response of a given 

industry to changes in policy may vary by region. To account for this effect, EMPAXCGE models each industry separately in five different regions, as shown in Figure 8-4. 

The specification of the five economic regions included in the model is based, as closely 

as possible, on the structure of the electricity market regions defined by the North 

American Electric Reliability Council (NERC). 108 

TABLE 8-1. INDUSTRIES IN EMPAX-CGE 

EMPAX Industry 

North American Industry Classification 

System (NAICS) 

Energy 

Coal 2121 

Crude oila

 211111, 4861 

Electricity (fossil and nonfossil) 2211 

Natural gas 211112, 2212, 4862 

Petroleum refining b

 324, 48691 

General 

Agriculture 11 

Mining (w/o coal, crude, gas) 21 

Construction 23 

Manufacturing 

Food products 311 

Textiles and apparel 313, 314, 315, 316 

Lumber 321 

Paper and allied 322 

Printing 323 

Chemicals 325 

Plastic and rubber 326 

Glass 3272 

Cement 3273 

Other minerals 3271, 3274, 3279 

Iron and steel 3311, 3312 

Aluminum 3313 

Other primary metals 3314, 3316 

Fabricated metal products 332 

 

108 Economic data and information on non-electricity energy markets are generally available only at the state level, which 

necessitates an approximation of the NERC regions that follows state boundaries. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-7 

Manufacturing equipment 333 

Computers & communication equipment 334 

Electronic equipment 335 

Transportation equipment 336 

Miscellaneous remaining 312, 337, 339 

Services 

Wholesale & retail trade 42, 44, 45 

Transportation c 481-488 

Information 51 

Finance and real estate 52, 54 

Business/professional 53, 55, 56 

Education (w/public) 61 

Health care (w/public) 62 

Other services 71, 72, 81, 92 

a Although NAICS 211111 covers both crude oil and gas extraction, the gas component of this sector is 

addressed in the natural gas energy sector. 

b EMPAX-CGE reports output for the petroleum refining industry based on the delivered price of petroleum 

products. This reflects the value of pipeline transport. 

c

 Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas 

industry. 

FIGURE 8-4. EMPAX-CGE REGIONS 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-8 

EMPAX-CGE assumes that households have perfect foresight of future changes in policy 

and maximize utility over the full time horizon of the model. To adjust to future policy 

changes, households may alter their decisions about labor force participation and modify 

their consumption patterns in terms of their overall level of consumption and the mix of 

goods and services they choose to consume. This is in contrast to static CGEs, which 

model the economy without regard for time (i.e., they effectively model the economy for 

a single time period). 

EMPAX-CGE contains four representative households in each model region, classified 

by income. These household income groups are: 

 $0 to $14,999, 

 $15,000 to $29,999, 

 $30,000 to $49,999, and 

 $50,000 and above. 

These representative households are assumed to possess certain factors of production 

including labor, capital, natural resources, and land inputs to agricultural production. 

Factor prices are equal to the marginal revenue received by firms from employing an 

additional unit of labor or capital, and households allocate income from sales of these 

productive factors to purchases of consumption goods to maximize welfare. 

The outputs generated by EMPAX-CGE include GDP, consumption, and an economic 

welfare measure known as Hicksian equivalent variation (EV). EV is based on the 

concept of willingness-to-pay, which is the maximum amount a household would pay for 

a particular good or service (including leisure), given its budget constraint. Willingness 

to pay reflects the value or welfare that a household derives from the consumption of a 

good or service. For a given policy scenario, the change in EV represents the additional 

money that a household would require (at original prices and income) to make it as well 

off with the new policy as it was under baseline conditions; this amount is “equivalent” to 

the change in utility the household derives from consumption and leisure time. It is 

important to note, however, that EMPAX-CGE’s estimation of EV captures welfare 

associated with market goods and services but does not capture non-market effects. As a 

result, the measure would not reflect some categories of household welfare that are 

important to our cost-benefit analysis, such as avoided pain and suffering associated with 

health effects incidence, improvements in visibility, and changes in service flows that 

derive from well functioning ecological resources. 

The baseline values for the outputs generated by EMPAX-CGE are adapted from the 

economic forecast in the Department of Energy’s Annual Energy Outlook 2007. These 

baseline values represent the U.S. economy under the with-CAAA scenario for the Second 

Prospective.109

 

109 As noted in Chapter 2, the emissions projections for the Second Prospective are based on the economic forecast from 

Annual Energy Outlook (AEO) 2005, not AEO 2007. The AEO 2007 forecast, however, is similar to that in AEO 2005. For the 

year 2020, the AEO 2007 GDP forecast is approximately 3 percent lower than the projection from AEO 2005. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-9 

DEVELOPMENT OF MODEL INPUTS 

The Project Team estimated the macroeconomic impacts of the CAAA as the difference 

between (1) the EMPAX-CGE reference case projections, which represent the withCAAA scenario, and (2) EMPAX-CGE projections for the without-CAAA scenario. To 

conduct the model runs for the without-CAAA scenario, the Project Team developed 

model inputs related to both the costs and benefits of the Amendments. To assess the 

difference in costs associated with CAAA compliance, we estimated CAAA-related 

compliance expenditures by industry and EMPAX region. Based on these estimates, the 

Project Team reduced the cost of production for affected industries from the baseline 

costs of production to develop industry-wide cost structures for the without-CAAA

scenario. The “cost-only” runs therefore estimate the loss in economic productivity 

associated with CAAA compliance costs. 

As noted above, however, the CAAA also yields benefits that result in potentially 

substantial changes in economic production as well. The benefit-side inputs developed 

by the Project Team include (1) medical expenditures associated with pollution-related 

illness, (2) the change in workers’ time endowment due to pollution-related mortality, and 

(3) the change in workers’ time endowment due to pollution-related morbidity. The 

Project Team incorporated changes in medical expenditures into EMPAX-CGE as 

changes in household expenditure patterns. To incorporate changes in the amount of time 

workers can devote to labor or to leisure in the model, we first estimated how health 

effects and mortality estimated in Chapter 5 would affect the exposed population’s ability 

to supply labor to firms. Estimates of lost work time associated with morbidity have been 

estimated in prior work or are available from BenMAP.110 Next, we assumed that 

pollution-related illness and mortality among the labor force reduce workers’ overall time 

endowment (labor and leisure) in proportion to the effect on labor supply. That is, if air 

pollution would reduce labor supply by x percent in 2020, the Project Team assumed that 

the overall time endowment of workers would also decline by x percent in 2020. 

We did not attempt to incorporate time endowment effects for people outside the formal 

economy (e.g., retirees, students, homemakers) into EMPAX-CGE. While the “nonworking” population is clearly affected by air pollution, and those effects are likely to 

influence the level and composition of economic activity, the structure of EMPAX-CGE 

is not conducive to assessing how these populations affect the economy. The results 

presented in this chapter therefore likely underestimate the macroeconomic impacts 

resulting from CAAA-related improvements in public health. 

Below we describe the Project Team’s approach for generating the EMPAX-CGE inputs 

related to the costs and benefits of the CAAA. As noted above, the Project Team used 

these inputs to conduct two analyses of CAAA-related macroeconomic impacts; the first 

reflects only the costs of the CAAA (the cost-only case), while the second reflects both 

the costs and selected human health benefits of the Amendments (the labor force-adjusted 

case). 

 

110 For example, Cropper and Krupnick (1999) estimate income losses resulting from chronic bronchitis and acute myocardial 

infarction. Based on these estimates, we calculated the lost work time per case associated with each of these endpoints. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-10 

COST INPUTS 

To assess the macroeconomic impacts of CAAA-related costs, the Project Team 

incorporated CAAA compliance expenditures by industry and region into EMPAX-CGE. 

Similar to other CGE models, EMPAX-CGE is an expenditure-based model and therefore 

requires expenditure-based inputs to represent the costs of the Amendments. CAAA 

compliance expenditures, however, are not always the equivalent of the direct costs of the 

Amendments presented in Chapter 3. While the direct costs of the CAAA reflect the 

value of the capital, labor, and other resources necessary for CAAA compliance, 

compliance expenditures simply represent the financial resources exchanged for CAAA 

compliance. For example, the direct costs of the Amendments do not include taxes, 

because such payments represent transfers rather than resources expended to control air 

pollutant emissions. In contrast, CAAA compliance expenditures include transfers 

because they represent an exchange of financial resources from one party (e.g., a firm) to 

another (e.g., the government) that can affect the choices made by firms. 

To estimate the compliance expenditures associated with the Amendments, the Project 

Team made three adjustments to the direct cost estimates presented in Chapter 3: 

1. Inclusion of fuel excise taxes: The Project Team included fuel excise taxes in 

the compliance expenditure estimates developed for the EMPAX-CGE analysis. 

Excise taxes were excluded from the direct cost estimates presented in Chapter 3 

because such taxes are transfers. 

2. Industry-specific discount rates: Unlike the direct cost estimates presented in 

Chapter 3, which reflect a 5 percent social discount rate, the compliance 

expenditures presented in this chapter reflect the private discount rates of affected 

industries. For each industry, we estimated the private discount rate based on the 

industry-specific weighted average cost of capital as reported in Ibbotson 

Associates’ Cost of Capital Yearbook.

111 

3. Exclusion of motorist waiting time from cost estimates for inspection and 

maintenance programs: The direct cost estimates for motor vehicle inspection 

and maintenance (I&M) programs in Chapter 3 reflect the value of motorist 

waiting time. Although waiting time represents a welfare loss to society, this 

cost is not incurred as an expenditure. Because CGEs are expenditure-based 

models, we exclude motorist waiting time from the cost-side inputs incorporated 

into EMPAX-CGE. The exclusion of motorist waiting time is unlikely to 

significantly affect the results of the CGE analysis, as these costs represent only 

18 percent of direct CAAA costs associated with I&M programs and less than 5 

percent of direct costs for the entire on-road sector. 

Based on these adjustments, we developed the compliance expenditure estimates 

presented in Table 8-2. For comparison, the exhibit also includes the direct cost estimates 

summarized in Chapter 3. As indicated in the exhibit, the estimated CAAA compliance 

 

111 Ibbotson Associates, Cost of Capital Yearbook, 1997 through 2006 editions. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-11 

expenditures in 2010 are approximately $2.0 billion greater than the Project Team’s 

direct cost estimates for 2010. In 2020, the difference between the two increases to $3.0 

billion. The estimates in Exhibit 8-6 also show that the distribution of compliance 

expenditures across source categories is similar to the distribution of direct costs.112

TABLE 8-2. SUMMARY OF ANNUAL CAAA COMPLIANCE EXPENDITURES AND DIRECT COSTS 

(MILLIONS OF 2006$) 

SOURCE CATEGORY 

2010 2020 

COMPLIANCE 

EXPENDITURES 

(USED FOR EMPAX 

ANALYSIS) 

DIRECT 

COSTS 

COMPLIANCE 

EXPENDITURES 

(USED FOR EMPAX 

ANALYSIS) 

DIRECT 

COSTS 

Electric Generating Units $8,470 $6,640 $13,000 $10,400 

On-road Sources $24,800 $25,800 $27,200 $28,300 

Non-road Sources $750 $359 $1,620 $1,150 

Industrial Point Sources $5,580 $5,180 $5,600 $5,140 

Area Sources $693 $693 $768 $767 

Local Controls (Identified) $5,590 $5,250 $6,790 $6,180 

Unidentified Local Controls $9,020 $9,020 $13,500 $13,500 

TOTAL $54,900 $52,900 $68,500 $65,500 

 

112 

In most of EPA’s recent EMPAX applications to air pollution rules, only a small portion of total costs have been accounted 

for by expenditures in the household sector. In this application, however, a large portion of total compliance costs, 

particularly for mobile source fuels rules, involve increased expenditures by the household sector. For this reason, the 

Project Team gave special consideration to the treatment of these costs. Estimated household compliance expenditures 

associated with petroleum products are implemented as price adjustments to reflect higher motor vehicle fuel prices. The 

petroleum price adjustment is calculated to match compliance expenditures related to household transportation fuel use. 

For other transportation compliance expenditures, the household utility function is adjusted to require additional 

expenditures to achieve a given utility level. These adjustments reflect the additional automotive inspections, maintenance, 

and technologies purchased by households to comply with the Clean Air Act. Other unidentified household compliance costs 

not related to transportation (e.g. non-road related local controls) are treated as lump-sum reductions to household income. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-12 

BENEFIT INPUTS 

As noted above, the Project Team’s analysis of the macroeconomic impacts of CAAArelated health improvements focuses on three specific effects: (1) the change in the 

household time endowment from pollution-related mortality impacts, (2) the change in 

the household time endowment from pollution-related morbidity, and (3) the change in 

medical expenditures associated with pollution-related morbidity. The Project Team 

incorporated these effects into the without-CAAA EMPAX-CGE model runs to estimate 

the size and composition of the economy in the absence of the Amendments. The 

methods employed to quantify these effects and convert them into useable inputs for 

EMPAX-CGE are described below. 

Mortality-related Labor Force Impacts 

The Project Team incorporated pollution-related mortality impacts into EMPAX-CGE as 

a percentage change in the time available to workers for labor and leisure activities (i.e., 

their time endowment). In estimating this percentage change, the Project Team focused 

on the dynamic population effects of premature mortality from particulate matter (PM) 

exposure. While ozone also leads to premature mortality, the benefits results in Chapter 5 

show that reductions in ambient PM concentrations are responsible for approximately 98 

percent of the avoided cases of premature mortality associated with the Amendments in 

both 2010 and 2020. Because of the dominant effect of PM on mortality (relative to 

ozone) and the lack of tools available to examine the dynamic population effects of PM 

and ozone in an integrated fashion, the Project Team focused the mortality component of 

the EMPAX-CGE analysis on changes in PM-related mortality. 

The mortality-related inputs developed by the Project Team reflect the dynamic effects of 

PM mortality on the population over time. When PM concentrations change, the 

resulting population impact grows over time, as the change in population for any given 

year reflects changes in the incidence of PM-related mortality from prior years. For 

example, if PM concentrations are reduced permanently in 2015, the population (and the 

size of the labor force) in 2017 will reflect avoided cases of premature mortality in 2015, 

2016, and 2017. Over time, this dynamic effect leads to a significant number of life years 

saved as the reduction in pollution-related risk is applied to successively larger 

populations each year (due to previous years’ improvements in air quality). 

To capture these dynamic effects, the Project Team used a spreadsheet-based dynamic 

population simulation model described in Chapter 5.113 The model was designed to track 

the effect of alternative assumptions about the mortality effects of PM2.5 on the U.S. 

population, but may also be used to assess how changes in PM2.5 concentrations lead to 

changes in the population over time. The tool incorporates detailed life table data for 

historical years, by age, gender, and cause of death, obtained from the Census Bureau and 

the Centers for Disease Control. It also incorporates Census mortality and population 

projections for future years, again by age and gender, using the projected death and birth 

 

113 For a detailed description of the model, see the related report, Uncertainty Analyses to Support the Second Section 812 

Benefit-Cost Analysis of the Clean Air Act, March 2010, and Industrial Economics, Inc. (2006). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-13 

rates that underlie the Census Bureau’s published population projections. For a given 

model scenario, the model simulates the U.S. population by single year age group and 

gender for each year through 2050. 

To estimate changes in the labor force with the population simulation model, the Project 

Team employed the following three-step approach: 

1. CAAA-related change in population: First, the Project Team entered changes in 

PM2.5 concentrations into the population simulation model based on the air 

quality modeling analysis described in Chapter 4. Netting the model results from 

baseline (with-CAAA) population projections, the Project Team estimated PMrelated changes in population by gender and single-year age group for both the 

2010 and 2020 target years (and for every other year in the model time horizon). 

These changes represent the estimated difference in population between the withCAAA and without-CAAA scenarios. 

2. CAAA-related change in the labor force: To estimate the change in the labor 

force associated with the CAAA, the Project Team applied age- and genderspecific labor force participation rates from the Bureau of Labor Statistics to the 

changes in population estimated in Step 1. 

3. Percent Change in Labor Force: The Project Team estimated the percent change 

in the labor force associated with pollution-related mortality by dividing the total 

labor force changes estimated in Step 2 by baseline (with-CAAA) projections of 

the total labor force. As indicated above, the Project Team assumes that this 

percent change applies to the full time endowment (labor and leisure time) for the 

labor force. 

Morbidity-related Labor Force Impacts 

Similar to pollution-related mortality, pollution-related morbidity was incorporated into 

EMPAX-CGE as a percent change in the labor and leisure time available to workers. 

Unlike the Project Team’s PM-based approach for mortality, the approach for morbidity 

accounts for both PM- and ozone-related impacts. The literature for the various PM and 

ozone endpoints examined use several different metrics for quantifying labor force 

impacts. To standardize these estimates, we converted the values obtained from the 

literature to the number of work days lost per case, by endpoint. We then applied these 

values to the yearly changes in the number of cases for each endpoint to estimate the total 

work days lost for any given year. These values reflect the labor force participation rate 

among those individuals afflicted by each health effect. Because the time endowment in 

EMPAX-CGE measures time on an annual basis, we converted the estimated number of 

work days lost to lost work years, based on an assumed work year of 235 work days.114 

To express work years lost as a percent change in the labor force, we divided the 

estimated work years lost for each target year by the projected size of the labor force. 

The resulting value represents the percent change in workers’ labor time. 

 

114 This estimate is consistent with that used in Jorgenson et al. (2004). 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-14 

As suggested above, estimating the number of work days lost per case for each endpoint 

is a key step in the Project Team’s methodology. Table 8-3 summarizes these endpoint 

values for both PM and ozone. With the exception of chronic bronchitis and acute 

myocardial infarction (AMI), the estimates presented in Table 8-3 were applied to the 

annual change in incidence for each endpoint (i.e., the change in the number of new cases 

per year), as the duration of disease for most endpoints is no more than several weeks. 

Chronic bronchitis and AMI, however, affect individuals over multi-year time horizons. 

We therefore apply the work loss day estimates for these endpoints to changes in the 

prevalence of each disease (i.e., the change in the number of people with the disease, 

relative to the baseline). 

Medical Expenditures 

To estimate the medical expenditures associated with changes in PM and ozone 

concentrations, the Project Team relied upon cost-of-illness estimates from the published 

literature. Table 8-4 presents the annual medical expenditures per case for those 

endpoints for which medical expenditure data were available. We applied the estimates 

presented in the table to the respective annual changes in incidence for each endpoint, 

except for chronic bronchitis and AMI. For these two endpoints, we applied the values 

from Table 8-4 to estimated changes in prevalence. 

Summary of Benefit-Related Inputs 

Table 8-5 summarizes the estimated changes in the labor force (i.e., the worker time 

endowment) associated with the Amendments for the 2010 and 2020 target years. Using 

the estimates in the table, the Project Team modified the time endowment for each model 

household included in EMPAX-CGE. The estimates in the table suggest that the U.S. 

labor force would be 0.34 percent smaller in 2010 and 0.57 percent smaller in 2020 if the 

Amendments had not been enacted. PM mortality effects would make up more than half 

of this reduction. Among morbidity endpoints, AMI and chronic bronchitis would have 

the most significant effect. The labor force impact of ozone pollution would represent 

less than five percent of the reduction in the labor force for each target year. 

Table 8-6 presents the estimated change in pollution-related medical expenditures 

associated with the Amendments. As indicated in the table, the Project Team estimates 

that medical expenditures related to air pollution would be approximately $12.9 billion 

higher in 2010 and $21 billion higher in 2020 in the absence of the Amendments. Similar 

to the labor force effects summarized in Table 8-5, PM-related morbidity, AMI in 

particular, represents most of the estimated change in pollution-related medical 

expenditures. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-15 

TABLE 8-3. WORK DAYS LOST PER CASE, BY MORBIDITY ENDPOINT1

PM2

Acute Myocardial Infarction3 Age <25: N/A 

Age 25-34: 17.7 days 

Age 35-44: 14.5 days 

Age 45-54: 23.7 days 

Age 55-65: 137.0 days 

Age>65: 0 days 

Chronic Bronchitis3 Age <25: N/A 

Age 25-34: 50.3 days 

Age 35-44: 42.2 days 

Age 45-54: 55.5 days 

Age 55-65: 73.5 days 

Age >65: 0 days 

Hospital Admissions, Cardiovascular4 Age 0-14: N/A 

Age 15-44: 18.3 days 

Age 45-64: 17.9 days 

Age >64: 7.0 days 

Hospital Admissions, Respiratory4 Age 0-14: N/A 

Age 15-44: 30.7 days 

Age 45-64: 30.1 days 

Age >64: 7.5 days 

Emergency Room Visits, Respiratory5 Average across all age groups: 0.2 days 

Work Loss Days Average among working age population: 1 day 

Ozone6

School Loss Days7 Average across all age groups: 0.7 days 

Worker Productivity Not applicable8

Hospital Admissions, Respiratory9,10 Age <2: 0 days 

Age >64: 7.5 days 

Emergency Room Visits, Respiratory5 Average across all age groups: 0.2 days 

Notes: 

 N/A indicates that the underlying C-R function does not provide incidence estimates for that age group. 

1. Except for chronic bronchitis and acute myocardial infarction, the number of work days lost is applied to the 

change in annual incidence. For chronic bronchitis and acute myocardial infarction, the work days lost presented 

in this table are applied to annual changes in the prevalence of each disease. 

2. We did not generate separate work loss day estimates for the following PM health endpoints discussed in Chapter 5: 

acute bronchitis, acute respiratory symptoms, asthma exacerbation, lower respiratory symptoms, and upper 

respiratory symptoms. The lost work days associated with these endpoints are already reflected in the work loss 

day endpoint included in this table. 

3. Derived from Cropper and Krupnick (1999). 

4. Agency for Healthcare Research and Quality (2000), as cited in BenMAP user’s guide, Abt Associates (2008). 

5. We assume that each E.R. visit equals one day of lost work time per worker affected. The estimate of 0.2 days per 

case reflects the percentage of cases realized by the working-age population, the ratio of workdays to total days in 

a year (235/365), and the percent of the working-age population in the labor force. 

6. We did not estimate the number of work days lost per case of acute respiratory symptoms associated with ozone 

exposure. 

7. Derived from Abt Associates (2008). Note that 0.7 is the estimated average work loss days per school loss day, 

incorporating work-force participation rates for caregivers. 

8. The benefits analysis presented in Chapter 5 does not estimate the number of cases for the worker productivity 

endpoint. Instead, worker productivity is estimated as the change in income associated with changes in ozone 

concentrations. We estimated the work days lost per dollar of income lost based on the average daily wages of 

outdoor workers. 

9. Derived from Abt Associates (2008). 

10. The dose-response function for ozone-related respiratory hospital admissions does not cover populations older than 

two years old and younger than 65. For this endpoint we do not address potential caregiver time lost for incidence 

in either cohort.
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-16 

TABLE 8-4. ANNUAL MEDICAL EXPENDITURES PER CASE, BY MORBIDITY ENDPOINT (2006$) 1

2010 2020 

PM2

Acute Myocardial Infarction3 $17,600 $17,300 

Chronic Bronchitis4 $715 $810 

Emergency Room Visits, Respiratory5 $369 

Hospital Admissions, Cardiovascular6 $27,400 

Hospital Admissions, Respiratory6 $21,000 

Ozone7

Emergency Room Visits, Respiratory5 $369 

Hospital Admissions, Respiratory6 $16,400 $17,100 

Notes: 

1. Except for chronic bronchitis and acute myocardial infarction, medical expenditures per 

case are applied to the change in annual incidence. For chronic bronchitis and acute 

myocardial infarction, medical expenditures per case are applied to the annual changes in 

the prevalence of each disease, to generate an annual rather than lifetime estimate of 

costs for these chronic diseases. 

2. Medical expenditure estimates for the following PM morbidity endpoints were not readily 

available: acute bronchitis, acute respiratory symptoms, asthma exacerbation, lower 

respiratory symptoms, upper respiratory symptoms, and work loss days. 

3. Derived from Wittels et al. (1990) and Russell et al. (1998), both as cited in Abt Associates 

(2008). 

4. Cropper and Krupnick (1999). 

5. We assume that each E.R. visit equals one day of lost work time per worker affected. The 

estimate of 0.2 days per case reflects the percentage of cases realized by the working-age 

population, the ratio of workdays to total days in a year (235/365), and the percent of the 

working-age population in the labor force. 

6. Agency for Healthcare Research and Quality (2000), as cited in Abt Associates (2008). 

7. Medical expenditure estimates for the following ozone morbidity endpoints were not 

readily available: minor restricted activity days, school loss days, and outdoor worker 

productivity. 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-17 

TABLE 8-5. ANNUAL CHANGE IN LABOR FORCE DUE TO CAAA-RELATED CHANGES IN AIR 

QUALITY (PERCENT CHANGE IN WORKER TIME ENDOWMENT) 

2010 2020 

Pollution-related Change in Worker Time Endowment 0.34% 0.57% 

PM Mortality Subtotal 0.18% 0.31% 

PM Morbidity Subtotal 0.15% 0.25% 

Acute Myocardial Infarction 0.06% 0.09% 

Chronic Bronchitis 0.05% 0.11% 

Emergency Room Visits, Respiratory <0.01% <0.01% 

Hospital Admissions, Cardiovascular <0.01% <0.01% 

Hospital Admissions, Respiratory <0.01% <0.01% 

Work Loss Days 0.04% 0.05% 

Ozone Morbidity Subtotal 0.01% 0.02% 

Emergency Room Visits, Respiratory <0.01% <0.01% 

Hospital Admissions, Respiratory <0.01% <0.01% 

Acute Respiratory Symptoms <0.01% <0.01% 

School Loss Days 0.01% 0.01% 

Worker Productivity <0.01% 0.01% 

TABLE 8-6. CAAA-RELATED CHANGES IN ANNUAL MEDICAL EXPENDITURES (MILLION 2006$) 

2010 2020 

Pollution-related Change in Medical Expenditures $11,900 $19,600

PM Morbidity Subtotal $11,600 $19,000

Acute Myocardial Infarction $9,500 $15,500 

Chronic Bronchitis $375 $919 

Emergency Room Visits, Respiratory $29 $39 

Hospital Admissions, Cardiovascular $1,228 $1,900 

Hospital Admissions, Respiratory $467 $683 

Ozone Morbidity Subtotal $310 $580

Emergency Room Visits, Respiratory $2 $4 

Hospital Admissions, Respiratory $311 $575 

EMPAX-CGE MODEL RESULTS 

Using the inputs summarized in the previous section, the Project Team estimated the 

macroeconomic impacts of the Amendments under both the cost-only case and the labor 

force-adjusted case. As described above, the former captures the general equilibrium 

effects of CAAA compliance expenditures, whereas the latter accounts for the impacts of 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-18 

these expenditures as well as the labor force and medical expenditure impacts associated 

with the Amendments. We present the results of both analyses below. 

MACROECONOMIC IMPACTS OF CAAA COMPLIANCE EXPENDITURES 

Table 8-7 summarizes the results of the EMPAX-CGE cost-only model run. As the 

results in the table indicate, the Project Team estimates that the compliance expenditures 

associated with the Amendments will reduce GDP and consumption by approximately 0.5 

percent in 2010 and 2020, relative to the without-CAAA scenario. The total estimated 

GDP reduction of $79 billion in 2010 and $110 billion in 2020 are 50 to 70 percent larger 

than the total primary cost estimates of $53 billion in 2010 and $65 billion in 2020. The 

difference is attributable to secondary effects of compliance costs on the overall 

economy, a large portion of which are likely the result of increases in energy prices, 

which has broad effects on overall production. Another factor is that investment in 

pollution control capital can divert capital from the purpose of enhancing long-term 

productivity within the industrial sector. 

The percent reduction in equivalent variation is smaller than the corresponding reductions 

in GDP and consumption, at approximately 0.4 percent for both target years. This 

disconnect between the percent reduction in EV and the reductions in GDP and 

consumption suggests that, under the with-CAAA scenario, households allocate a greater 

share of their time endowment to leisure (and less to labor) than under the without-CAAA

scenario. This increase in leisure partially offsets the welfare loss associated with 

reduced consumption. 

TABLE 8-7. SUMMARY OF ANNUAL MACROECONOMIC IMPACTS: COST-ONLY CASE 1

VARIABLE MODEL RUN 2010 2015 2020 

GDP With Clean Air Act ($ billion) $15,027 $17,338 $20,202 

Without Clean Air Act ($ billion) $15,106 $17,430 $20,312 

Change ($ billion) −$79 -$93 −$110 

% change −0.52% -0.53% −0.54%

Consumption With Clean Air Act ($ billion) $10,969 $12,699 $14,881 

Without Clean Air Act ($ billion) $11,023 $12,761 $14,956 

Change ($ billion) −$54 -$62 −$75 

% change −0.49% -0.49% −0.50%

Hicksian EV 

(annual) 

Change ($ billion) −$54 -$62 −$75 

% change −0.38% -0.38% −0.39%

Notes: 

1. Results are expressed in year 2006 dollars. 

Figure 8-5 presents the percent change in output by industry as estimated by EMPAXCGE for the year 2020. The values in the table are typically highest among those 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-19 

industries with the most significant CAAA compliance expenditures relative to baseline 

industry revenue. For example, the electricity industry accounts for approximately 20 

percent of CAAA compliance expenditures (approximately $14 billion, or 3.3 percent of 

benchmark electricity revenue); as a result, EMPAX-CGE estimates that output from the 

electricity industry declines by just less than 4 percent under the with-CAAA scenario 

relative to a U.S. economy without Clean Air Act programs. Because the power industry 

is the largest consumer of coal in the U.S., the reduction in output from the electricity 

industry also results in the secondary effect of reducing coal output by approximately 1.5 

percent. The electricity industry’s CAAA compliance expenditures also leads to higher 

electricity prices that prompt energy-intensive industries to switch to other energy sources 

(e.g., natural gas and oil) and/or seek energy efficiency improvements in their production 

process. In addition, because of CAAA requirements for cleaner (more expensive) fuels, 

petroleum sector output is projected to decline approximately 1.5 percent. The results in 

Figure 8-5 also suggest that the other minerals sector experiences the largest reduction in 

output, in proportional terms, among all industries (over 5 percent). This reflects the 

industry’s high compliance expenditures relative to its size and the industry’s energyintensive production processes. 

The industry-level results presented in Figure 8-5 also reflect the extent to which 

economic activity associated with CAAA compliance, such as new purchases of 

environmental protection goods and services, may partially offset the output losses 

associated with CAAA compliance expenditures. As a result of the CAAA, the demand 

for environmental protection goods and services will be higher relative to a U.S. economy 

without the Amendments. 

MACROECONOMIC IMPACTS OF CAAA COMPLIANCE EXPENDITURES AND HUMAN 

HEALTH BENEFITS 

Building upon the results presented above, Table 8-8 summarizes the results of the 

EMPAX-CGE analysis for the labor force-adjusted case, which captures the full CAAA 

compliance expenditures as well as the labor force and medical expenditure benefits of 

the Amendments. The results presented in the table suggest that over time, the positive 

macroeconomic impacts of CAAA-related labor force and medical expenditure impacts 

slightly outweigh the negative macroeconomic effects of CAAA compliance costs. 115 

For 2010, the results for the labor force-adjusted case show a reduction in GDP and 

consumption relative to the without-CAAA scenario, but the corresponding changes 

become positive in 2020. This largely reflects the rapid growth in the CAAA labor force 

effect between 2010 and 2020 (67 percent) relative to the growth in CAAA compliance 

expenditures (25 percent). We expect the CAAA-related labor force effect to grow more 

quickly than CAAA compliance expenditures during this period because, unlike 

compliance expenditures, the labor force effect is cumulative for the health endpoints 

with the most significant effect on the size of the labor force (i.e., premature mortality, 

 

115 The EMPAX model results do not isolate the impact of the labor force effect on GDP or the impact of changes in medical 

expenditures, as the two were modeled simultaneously. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-20 

chronic bronchitis, and AMI). In addition, the mortality effect is delayed relative to the 

time costs are incurred to reduce exposures because of the impact of the cessation lag.116

FIGURE 8-5. PERCENT CHANGE IN INDUSTRY OUTPUT IN 2020: COST-ONLY CASE 

 

 

116 Note that results for the labor force-adjusted case for years after 2020 indicate that the beneficial effects on the 

economy grow over time, through 2030, from $5 billion in 2020 to $14 billion in 2025 to $24 billion in 2030. EMPAX results 

for 2030, however, are considered less reliable because of the greater uncertainty in forecasting GDP and industry-level 

productivity 20 years into the future. 

-10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0%

Coal 

Crude Oil

Electricity

Natural Gas

Petroleum

Agriculture

Mining (other)

Construction

Food

Textiles & Apparel

Lumber

Pulp & Paper

Printing

Chemicals

Plastics & Rubber

Glass

Cement

Other Minerals

Iron & Steel

Aluminum

Other Primary Metals

Fabricated Metal Products

Machinery & Equipment

Computer Equipment

Electronic Equipment

Transportation Equipment

Miscellaneous Manufacturing

Wholesale & Retail Trade

Transportation Services

Information Services

Finance & Real Estate

Business Services

Education

Health Services

Other Services

Percentage Change with Clean Air Act
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-21 

TABLE 8-8. SUMMARY OF ANNUAL MACROECONOMIC IMPACTS: LABOR FORCE-ADJUSTED CASE 1

VARIABLE MODEL RUN 2010 2015 2020 

GDP With Clean Air Act ($ billion) $15,027 $17,338 $20,202

Without Clean Air Act ($ billion) $15,059 $17,350 $20,197

Change ($ billion) −$32 -$12 $5

% change −0.21% -0.07% 0.02%

Consumption With Clean Air Act ($ billion) $10,969 $12,699 $14,881

Without Clean Air Act ($ billion) $10,972 $12,696 $14,876

Change ($ billion) −$3 $3 $5

% change −0.03% 0.02% 0.03%

Hicksian EV 

(annual) 

Change ($ billion) $11 $22 $29

% change 0.08% 0.13% 0.15%

Notes: 

1. Results are expressed in year 2006 dollars. 

The results in Table 8-8 also suggest that the Amendments lead to an increase in 

household welfare, measured as the change in EV, under the labor force-adjusted case for 

both the 2010 and 2020 target years. The projected 0.8 percent increase in welfare for 

2010 stands in contrast to the projected 0.21 percent reduction in GDP for that year and 

the 0.03 percent reduction in consumption. The fact that welfare rises while economic 

output declines indicates that, under the with-CAAA scenario, households allocate a 

greater share of their time endowment to leisure (and less to labor) than under the 

without-CAAA scenario. This reallocation of household time also occurs under the costonly case, but it only partially offsets the negative welfare impact of reduced 

consumption. Under the labor force-adjusted case, the increase in leisure more than 

offsets the welfare loss associated with reduced consumption. 

Figure 8-6 presents, by industry, the estimated percent change in output in 2020 for the 

labor force-adjusted case. The results in the figure indicate that, when labor force and 

medical expenditure impacts are accounted for, the CAAA leads to increased output for 

many industries and a decline in output for others. Consistent with the cost-only results, 

output in the computer equipment industry increases. The other sectors projected to 

experience an increase in output include many industries that tend to be labor-intensive 

and would benefit from a larger labor pool, such as most service industries. Output for 

health services declines, however, due to the reduction in health services demand 

associated with CAAA-related health improvements. Most of the other industries 

experiencing a reduction in output are either energy producers (e.g., electricity) or 

industries with energy-intensive production processes (e.g., iron and steel). 

 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-22 

FIGURE 8-6. PERCENT CHANGE IN INDUSTRY OUTPUT IN 2020: LABOR FORCE-ADJUSTED CASE 

The results presented in Table 8-8 and Figure 8-6 show that the conclusions drawn from 

macroeconomic analyses of air pollution policy depend significantly on whether such 

analyses capture both the costs and at least some of the benefits of air policy. While the 

results of the EMPAX-CGE cost-only case suggest that the CAAA reduces the output of 

the U.S. economy by approximately 0.5 percent each year, the labor force-adjusted case 

shows that analyzing benefits in conjunction with costs can either reduce the magnitude 

or change the sign of model results. Therefore, general equilibrium analyses that 

-10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0%

Coal 

Crude Oil

Electricity

Natural Gas

Petroleum

Agriculture

Mining (other)

Construction

Food

Textiles & Apparel

Lumber

Pulp & Paper

Printing

Chemicals

Plastics & Rubber

Glass

Cement

Other Minerals

Iron & Steel

Aluminum

Other Primary Metals

Fabricated Metal Products

Machinery & Equipment

Computer Equipment

Electronic Equipment

Transportation Equipment

Miscellaneous Manufacturing

Wholesale & Retail Trade

Transportation Services

Information Services

Finance & Real Estate

Business Services

Education

Health Services

Other Services

Percentage Change with Clean Air Act
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-23 

examine cost-side macroeconomic impacts but ignore or overlook the impacts of policyrelated labor force and health improvements may yield incomplete results that misinform 

policymakers and the public. The analysis presented in this chapter illustrates the 

feasibility of avoiding this outcome by examining both the costs and (a portion of the) 

benefits of air policy in a general equilibrium framework. It is important to note, 

however, that assessing expenditure-based output impacts should not replace the current 

practice of estimating the welfare (i.e., willingness-to-pay) benefits of avoided health 

effects. Unlike willingness-to-pay estimates, the results of CGE models do not reflect the 

non-market value that people place on avoided adverse health impacts. The outputs of 

such models represent a supplement to willingness-to-pay estimates rather than a 

substitute for such estimates. 

Further work is needed, however, to reflect a much broader set of benefits in CGE 

models. As noted earlier, the results in this chapter are designed to supplement, but not 

replace, the more complete primary estimates of benefits and costs. The CGE model 

represents flows of products, labor, and capital between and among producers and 

consumers, but it excludes improvements in well-being due to enhanced longevity and 

health, except to the extent that these increase time available for labor and leisure among 

the workforce and reduce some medical costs. As a result, the vast majority of monetized 

benefits, many but not all of which represent benefits that are not traded in markets, 

cannot currently be reflected in a CGE model. This is the main reason that the beneficial 

results to the economy estimated in this chapter are substantially smaller than the primary 

estimate of benefits based on willingness to pay estimates. It is nonetheless important to 

realize that even the partial set of benefits-related impacts that are reflected in this chapter 

(i.e., labor force and medical expenditure impacts) more than compensate for the market 

costs we estimate to achieve CAAA compliance. 

ANALYTIC LIMITATIONS 

While the analysis presented in this chapter provides a reasonable approximation of the 

macroeconomic impacts of the CAAA, we note the following limitations: 

 Exclusion of labor force and leisure effects for individuals outside the formal 

economy: Given the uncertainty surrounding the macroeconomic impacts of retirees, 

children, and other populations who do not participate in formal labor markets and 

the fact that CGE models are ill-suited to address these uncertainties, the inputs 

developed by the Project Team for this analysis did not reflect changes in the time 

endowment for these individuals. To the extent that people outside formal labor 

markets contribute to the economy, we may underestimate the positive 

macroeconomic impacts of the Amendments. 

 Exclusion of ozone mortality: As described in the methods section, our analysis 

captures PM-related changes in mortality but does not account for mortality impacts 

from ozone exposure. Therefore, we likely underestimate the positive 

macroeconomic impacts of the Amendments. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020 

8-24 

 Exclusion of nonmarket and some market benefits: Our assessment of the 

macroeconomic impacts of the Amendments also excludes several other CAAArelated benefits that may improve economic performance or consumer welfare, such 

as visibility improvements, productivity enhancements in the agriculture and forestry 

sectors, reduced materials damage, and reduced pain and suffering from pollutionrelated illness. Because we do not capture these effects, we very likely underestimate 

the positive macroeconomic impacts of the Amendments. 

 Assumption of separable benefits categories: Our modeling assumes labor supply 

and environmental quality are separable components of the utility function for 

households. This separability does not always hold, however; for example, cleaner 

air may encourage leisure activities such as birding and fishing, making air quality a 

complement to leisure. Prior work suggests that assuming separability may affect 

benefits by up to 30 percent in some cases.117

 Perfect foresight: EMPAX-CGE assumes that households have perfect foresight of 

future changes in policy and modify their current economic behavior accordingly. In 

reality, households often have imperfect information of future policy changes. 

Whether the assumption of perfect foresight leads to overestimation or 

underestimation of impacts is uncertain. 

 EMPAX-CGE parameter uncertainty: Similar to other CGE models, EMPAX-CGE 

requires the specification of several model parameters (e.g., elasticity values). 

Although the model relies upon credible values from the literature, the range of 

published estimates for many parameters varies widely across studies. It is uncertain 

whether the parameters included in EMPAX lead to overestimation or 

underestimation of impacts. 

 

117

We are grateful to the SAB Council for sharing this observation. For further information, see, for example, J.C. Carbone 

and V.K. Smith. 2008. Evaluating policy interventions with general equilibrium externalities. J. Public Econ. 92:1254-1274. 
The Benefits and Costs of the Clean Air Act fron 1990 to 2020

R-1 

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