The Benefits and Costs of the


Clean Air Act, 1970 to 1990


Prepared for


U.S. Congress


by


U.S. Environmental Protection Agency


October 1997


Contents 

Tables ................................................................................................................................................................. xi


Figures .............................................................................................................................................................. xv


Acronyms and Abbreviations ......................................................................................................................... xvii


Acknowledgments ......................................................................................................................................... xxiii 

Executive Summary .................................................................................................................................... ES-1


Purpose of the Study......................................................................................................................... ES-1


Study Design .................................................................................................................................... ES-1


Study Review ................................................................................................................................... ES-1


Summary of Results ......................................................................................................................... ES-2


Direct Costs................................................................................................................................ ES-2


Emissions ................................................................................................................................... ES-2


Air Quality ................................................................................................................................. ES-3


Physical Effects.......................................................................................................................... ES-5


Economic Valuation .................................................................................................................. ES-7


Monetized Benefits and Costs ................................................................................................... ES-8


Alternative Results ........................................................................................................................... ES-9


Conclusions and Future Directions .................................................................................................. ES-9


Chapter 1: Introduction ................................................................................................................................... 1


Background and Purpose ........................................................................................................................ 1


Clean Air Act Requirements, 1970 to 1990 ........................................................................................... 1


Section 812 of the Clean Air Act Amendments of 1990........................................................................ 2


Analytical Design and Review ............................................................................................................... 2


Target Variable ................................................................................................................................ 2


Key Assumptions ............................................................................................................................. 2


Analytic Sequence ........................................................................................................................... 3


Review Process....................................................................................................................................... 6


Report Organization ............................................................................................................................... 6


Chapter 2: Cost and Macroeconomic Effects ................................................................................................ 7


Direct Compliance Costs ........................................................................................................................ 7


Indirect Effects of the CAA ................................................................................................................... 9


Sectoral Impacts ............................................................................................................................... 9


Aggregate Effects ............................................................................................................................ 9


Uncertainties and Sensitivities in the Cost and Macroeconomic Analysis .......................................... 10


Productivity and Technical Change ............................................................................................... 10


Discount Rates ............................................................................................................................... 11


Exclusion of Health Benefits from the Macroeconomic Model .................................................... 12


i 
The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Chapter 3: Emissions ..................................................................................................................................... 13


Sector-Specific Approach .................................................................................................................... 15


Summary of Results ............................................................................................................................. 15


Uncertainty in the Emissions Estimates ............................................................................................... 17


Chapter 4: Air Quality ................................................................................................................................... 19


General Methodology ........................................................................................................................... 20


Sample Results ..................................................................................................................................... 21


Carbon Monoxide .......................................................................................................................... 21


Sulfur Dioxide................................................................................................................................ 22


Nitrogen Dioxide ........................................................................................................................... 22


Particulate Matter........................................................................................................................... 23


Ozone ............................................................................................................................................. 23


Urban Ozone ................................................................................................................ 23


Rural Ozone ............................................................................................................................ 24


Acid Deposition ............................................................................................................................. 24


Visibility ........................................................................................................................................ 25


Uncertainty in the Air Quality Estimates ............................................................................................. 25


Chapter 5: Physical Effects ........................................................................................................................... 29


Human Health and Welfare Effects Modeling Approach .................................................................... 29


Air Quality ..................................................................................................................................... 29


Population ...................................................................................................................................... 29


Health and Welfare Effects ............................................................................................................ 29


Key Analytical Assumptions ................................................................................................................ 30


Mapping Populations to Monitors ................................................................................................. 32


Choice of Study ............................................................................................................................. 33


Variance Within Studies ................................................................................................................ 33


PM-Related Mortality .................................................................................................................... 34


Short-Term Exposure Studies ................................................................................................. 34


Long-Term Exposure Studies.................................................................................................. 35


Health Effects Modeling Results.......................................................................................................... 37


Avoided Premature Mortality Estimates ....................................................................................... 37


Non-Fatal Health Impacts .............................................................................................................. 37


Other Physical Effects .......................................................................................................................... 38


Ecological Effects .......................................................................................................................... 38


Aquatic and Forest Effects ...................................................................................................... 38


Quantified Agricultural Effects ............................................................................................... 39


Effects of Air Toxics...................................................................................................................... 39


Uncertainty in the Physical Effects Estimates ............................................................................... 41


Chapter 6: Economic Valuation .................................................................................................................... 43


Methods for Valuation of Health and Welfare Effects ........................................................................ 43


Mortality ........................................................................................................................................ 44


Survey-Based Values ..................................................................................................................... 45


Chronic Bronchitis .................................................................................................................. 45


Respiratory-Related Ailments ................................................................................................. 46


Minor Restricted Activity Days .............................................................................................. 46


Visibility .................................................................................................................................. 46


Avoided Cost Estimates ....................................................................................................................... 46


Hypertension and Hospital Admissions......................................................................................... 46


Household Soiling.......................................................................................................................... 47


ii 
Contents 

Other Valuation Estimates ................................................................................................................... 47


Changes in Children’s IQ .............................................................................................................. 47


Work Loss Days and Worker Productivity .................................................................................... 48


Agricultural Benefits ..................................................................................................................... 48


Valuation Uncertainties ........................................................................................................................ 48


Mortity Risk Benefits Transfer ...................................................................................................... 48


Chapter 7: Results and Uncertainty ............................................................................................................. 51


Quantified Uncertainty in the Benefits Analysis ................................................................................. 51


Aggregate Monetized Benefits ............................................................................................................. 52


Comparison of Monetized Benefits and Costs ..................................................................................... 55


Major Sources of Uncertainty .............................................................................................................. 56


Alternative Results ............................................................................................................................... 57


PM Mortality Valuation Based on Life-Years Lost ...................................................................... 57


Alternative Discount Rates ............................................................................................................ 58


Appendix A: Cost and Macroeconomic Modeling ..................................................................................... A-1


Introduction ........................................................................................................................................ A-1


Macroeconomic Modeling ................................................................................................................. A-1


Choice of Macroeconomic Model ...................................................................................................... A-2


Overview of the Jorgenson-Wilcoxen Model .................................................................................... A-2


Structure of the Jorgenson-Wilcoxen Model ............................................................................... A-3


The Business Sector ..................................................................................................................... A-4


The Household Sector .................................................................................................................. A-4


The Government Sector ............................................................................................................... A-5


The Rest-of-the-World Sector ..................................................................................................... A-5


Environmental Regulation, Investment, and Capital Formation ................................................. A-5


The General Equilibrium ............................................................................................................. A-5


Configuration of the No-control Scenario ................................................................................... A-6


Elimination of Compliance Costs in the No-Control Case ................................................... A-7


Capital Costs - Stationary Sources ........................................................................................ A-7


Operating and Maintenance Costs - Stationary Sources ....................................................... A-8


Capital Costs - Mobile Sources ............................................................................................. A-8


Operating and Maintenance - Mobile Sources ...................................................................... A-8


Direct Compliance Expenditures Data ............................................................................................... A-8


Sources of Cost Data....................................................................................................................A-8


Cost of Clean Data ................................................................................................................ A-8


EPA Data ............................................................................................................................... A-8


Commerce Data ..................................................................................................................... A-9


Stationary Source Cost Data ........................................................................................................ A-9


Capital Expenditures Data ..................................................................................................... A-9


Operation and Maintenance Expenditures Data .................................................................. A-10


Recovered Costs .................................................................................................................. A-10


Mobile Source Cost Data ........................................................................................................... A-11


Capital Expenditures Data ................................................................................................... A-11


Operation and Maintenance Expenditures Data ........................................................... A-11


Fuel Price Penalty ......................................................................................................... A-11


Fuel Economy Penalty .................................................................................................. A-12


Inspection and Maintenance Programs ......................................................................... A-13


Maintenance Credits ..................................................................................................... A-13


Fuel Density Credits ..................................................................................................... A-13


Other Direct Cost Data .............................................................................................................. A-13


iii 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Assessment Results .......................................................................................................................... A-14


Compliance Expenditures and Costs ......................................................................................... A-14


Annualization Method ......................................................................................................... A-16


Discounting Costs and Expenditures .................................................................................. A-19


Indirect Economic Effects of the CAA ...................................................................................... A-20


GNP and Personal Consumption ............................................................................................... A-20


Prices..........................................................................................................................................A-23


Sectoral Effects: Changes in Prices and Output by Industry ..................................................... A-23


Changes in Employment Across Industries ............................................................................... A-25


Uncertainties in the Cost Analysis ................................................................................................... A-26


Potential Sources of Error in the Cost Data ............................................................................... A-26


Mobile Source Costs .................................................................................................................. A-28


Stationary Source Cost Estimate Revisions ............................................................................... A-29


Endogenous Productivity Growth in the Macro Model ............................................................. A-29


Amortization Period for Stationary Source Plant and Equipment.............................................A-30


Cost and Macroeconomic Modeling References ............................................................................. A-31


Appendix B: Emissions Modeling ............................................................................................................... B-1


Introduction ........................................................................................................................................ B-1


Comparison of Emissions Projections with Other EPA Data ............................................................ B-1


Control Scenario Projections Versus EPA Trends Projections ................................................... B-1


No-Control Scenario Projections Versus Historical EPA Trends Data ....................................... B-3


Industrial Boilers and Processes ......................................................................................................... B-4


Overview of Approach................................................................................................................. B-4


Industrial Boilers ................................................................................................................... B-4


Industrial Processes and In-Process Fuel Combustion ......................................................... B-4


Establishment of Control Scenario Emissions ...................................................................... B-5


Control Scenario Boiler Emissions ....................................................................................... B-5


Control Scenario Industrial Process Emissions .................................................................... B-7


Development of Economic Driver Data 

for the Control Scenario - Industrial Boilers and Processes ........................................................... B-7


Economic Driver Data for Industrial Boiler Approach ............................................................... B-7


Economic Driver Data for the Industrial Process Approach ....................................................... B-8


No-control Scenario Emissions ................................................................................................... B-8


Industrial Boiler Emissions of SO2, NOx, and TSP ............................................................. B-8


Industrial Boiler Emissions of CO and VOC ........................................................................ B-9


Industrial Process Emissions ................................................................................................. B-9


Lead Emissions ..................................................................................................................... B-9


Off-Highway Vehicles ..................................................................................................................... B-10


Overview of Approach............................................................................................................... B-10


Development of Control Scenario ....................................................................................... B-11


No-control Scenario Emissions Estimates .......................................................................... B-11


National and State-Level Off-Highway Emission Estimates .................................................... B-11


On-Highway ..................................................................................................................................... B-12


Overview of Approach............................................................................................................... B-13


Personal Travel .................................................................................................................... B-13


Iterative Proportional Fitting (IPF) ............................................................................... B-13


Vehicle Ownership Projection (VOP) .......................................................................... B-14


Projection of Vehicle Fleet Composition ..................................................................... B-14


Activity/Energy Computation....................................................................................... B-14


Goods Movement ................................................................................................................ B-15


Other Transportation Activities........................................................................................... B-15


Lead Emissions .......................................................................................................................... B-15


Estimation of No-control Scenario Emissions........................................................................... B-15


Development of Emission Factors ...................................................................................... B-15


Allocation of Highway Activity to States ........................................................................... B-16


iv 
Contents 

Development of Highway Pollutant Estimates ................................................................... B-16


Control Scenario Emissions Calculation ...................................................................... B-16


No-control Scenario Emissions .................................................................................... B-21


Utilities ............................................................................................................................................. B-24


Overview of Approach............................................................................................................... B-24


Establishment of Control Scenario Emissions .................................................................... B-24


Key Assumptions in the Development of the ICF Analysis......................................... B-24


ARGUS Modeling Assumptions .................................................................................. B-26


No-control Scenario Emissions ........................................................................................... B-27


ICF Estimates of SO2, TSP, and NOx Emissions in the No-control Scenario ............ B-27


ARGUS No-control Scenario .............................................................................................. B-29


Estimation of Lead Emissions from Utilities ............................................................................ B-29


CEUM Sensitivity Case ............................................................................................................. B-30


Commercial/Residential ................................................................................................................... B-30


Control Scenario Emissions ....................................................................................................... B-31


Emissions Data .................................................................................................................... B-32


Energy Data ......................................................................................................................... B-33


Economic/Demographic Data ............................................................................................. B-33


No-control Scenario Emissions ................................................................................................. B-34


Emissions Data .................................................................................................................... B-34


Energy Data ......................................................................................................................... B-34


Economic/Demographic Data ............................................................................................. B-35


Emissions Modeling References ...................................................................................................... B-39


Appendix C: Air Quality Modeling ............................................................................................................ C-1


Introduction ........................................................................................................................................ C-1


Carbon Monoxide ............................................................................................................................... C-1


Control scenario carbon monoxide profiles ................................................................................. C-1


No-control scenario carbon monoxide profiles ........................................................................... C-2


Summary differences in carbon monoxide air quality ................................................................ C-4


Key caveats and uncertainties for carbon monoxide ................................................................... C-4


Sulfur Dioxide .................................................................................................................................... C-5


Control scenario sulfur dioxide profiles ...................................................................................... C-5


No-control scenario sulfur dioxide profiles ................................................................................. C-5


Summary differences in sulfur dioxide air quality ...................................................................... C-6


Key caveats and uncertainties for sulfur dioxide......................................................................... C-6


Nitrogen Oxides ................................................................................................................................. C-6


Control scenario nitrogen oxides profiles .................................................................................... C-7


No-control scenario nitrogen oxides profiles .............................................................................. C-8


Summary differences in nitrogen oxides air quality .................................................................... C-8


Key caveats and uncertainties for nitrogen oxides ...................................................................... C-8


Acid Deposition .................................................................................................................................. C-8


Control scenario acid deposition profiles .................................................................................... C-9


No-control scenario acid deposition profiles ............................................................................. C-11


Summary differences in acid deposition ................................................................................... C-12


Key caveats and uncertainties for acid deposition..................................................................... C-12


Particulate Matter ............................................................................................................................. C-13


Control scenario particulate matter profiles .............................................................................. C-14


No-control scenario particulate matter profiles ......................................................................... C-15


Summary differences in particulate matter air quality .............................................................. C-16


Key caveats and uncertainties for particulate matter ................................................................. C-16


Ozone................................................................................................................................................ C-18


Control scenario ozone profiles ................................................................................................. C-21


No-control scenario ozone profiles ............................................................................................ C-21


Summary differences in ozone air quality ................................................................................. C-23


Key caveats and uncertainties for ozone ................................................................................... C-24


v 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Visibility ........................................................................................................................................... C-25


Control scenario visibility.......................................................................................................... C-25


No-control scenario visibility .................................................................................................... C-26


Summary differences in visibility.............................................................................................. C-26


DeciView Haze Index ......................................................................................................... C-26


Modeling Results................................................................................................................. C-28


Key caveats and uncertainties for visibility ............................................................................... C-28


Air Quality Modeling References .................................................................................................... C-30


Appendix D: Human Health and Welfare Effects of Criteria Pollutants ................................................ D-1


Introduction ........................................................................................................................................ D-1


Principles for the Section 812 Benefits Analysis ............................................................................... D-1


General Modeling Approach .............................................................................................................. D-2


Quantifying Changes in Pollutant Exposures..................................................................................... D-2


Air Quality ................................................................................................................................... D-2


Population Distribution ................................................................................................................ D-3


Census Data ........................................................................................................................... D-3


Gridding U.S. Population ...................................................................................................... D-4


Allocating Exposure Estimates to the Population ................................................................. D-4


Method One .................................................................................................................... D-4


Method Two .................................................................................................................... D-4


Estimating Human Health Effects of Exposure ................................................................................. D-5


Types of Health Studies ............................................................................................................... D-5


Epidemiological Studies........................................................................................................D-6


Human Clinical Studies.........................................................................................................D-7


Issues in Selecting Studies To Estimate Health Effects .............................................................. D-9


Peer-Review of Research ...................................................................................................... D-9


Confounding Factors ............................................................................................................. D-9


Uncertainty .......................................................................................................................... D-10


Magnitude of Exposure ....................................................................................................... D-11


Duration of Exposure .......................................................................................................... D-11


Thresholds ........................................................................................................................... D-11


Target Population ................................................................................................................ D-11


Statistical Significance of Exposure-Response Relationships ............................................ D-12


Relative Risks ...................................................................................................................... D-12


Baseline Incidence Data ...................................................................................................... D-12


Estimating Mortality Effects ..................................................................................................... D-13


Using PM as an Indicator .................................................................................................... D-13


Estimating the Relationship Between PM and Premature Mortality .................................. D-13


Prematurity of Mortality: Life-Years Lost as a Unit of Measure ....................................... D-16


Estimating Morbidity Effects .................................................................................................... D-19


Overlapping Health Effects ................................................................................................. D-19


Studies Requiring Adjustments ........................................................................................... D-19


Concentration-Response Functions: Health Effects .................................................................. D-19


Particulate Matter ................................................................................................................ D-19


Ozone...................................................................................................................................D-26


Nitrogen Oxides .................................................................................................................. D-34


Carbon Monoxide ................................................................................................................D-36


Sulfur Dioxide ..................................................................................................................... D-38


Estimating Welfare Effects of Exposure .......................................................................................... D-40


Agricultural Effects ................................................................................................................... D-40


Materials Damage ...................................................................................................................... D-41


Visibility .................................................................................................................................... D-41


Worker Productivity .................................................................................................................. D-41


Ecological Effects ...................................................................................................................... D-41


Modeling Results..............................................................................................................................D-44


Human Health and Welfare Effects References ............................................................................... D-48


vi 
Contents 

Appendix E: Ecological Effects of Criteria Pollutants.............................................................................. E-1


Introduction ........................................................................................................................................ E-1


Benefits From Avoidance of Damages to Aquatic Ecosystems......................................................... E-1


Acid Deposition ........................................................................................................................... E-2


Background ........................................................................................................................... E-2


Current Impacts of Acid Deposition ..................................................................................... E-2


Effects on Water Chemistry ........................................................................................... E-2


Effects on Fish Habitat Quality ...................................................................................... E-4


Economic Damages to Recreational Fishing .................................................................. E-5


Benefits From Acid Deposition Avoidance Under the CAA ................................................ E-5


Recreational Fishing ....................................................................................................... E-5


Eutrophication .............................................................................................................................. E-6


Atmospheric Deposition and Eutrophication ........................................................................ E-7


Valuing Potential Benefits from Eutrophication Avoidance Under the CAA ...................... E-7


Mercury ........................................................................................................................................ E-8


Benefits from Avoided Damages to Wetland Ecosystems ................................................................ E-9


Introduction .................................................................................................................................. E-9


Effects of Acidification................................................................................................................ E-9


Effects of Nutrient Loading ....................................................................................................... E-10


Summary of Wetland Ecosystem Effects .................................................................................. E-11


Benefits from Avoided Damages to Forests .................................................................................... E-11


Introduction ................................................................................................................................ E-11


Current Air Pollutant Effects on Forests ................................................................................... E-12


Acid Deposition Impacts ..................................................................................................... E-12


Ozone Impacts ..................................................................................................................... E-12


Experimental Evidence ................................................................................................. E-12


Observational Evidence ................................................................................................ E-13


Endangered species ............................................................................................................. E-14


Valuation of Benefits From CAA-Avoided Damages to Forests .............................................. E-14


Background ......................................................................................................................... E-14


Commercial Timber Harvesting .......................................................................................... E-15


Non-marketed Forest Services ............................................................................................ E-16


Ecosystem Effects References ......................................................................................................... E-18


Appendix F: Effects of Criteria Pollutants on Agriculture ...................................................................... F-1


Introduction ........................................................................................................................................ F-1


Ozone Concentration Data ................................................................................................................. F-1


Control and No-control Scenario Ozone Concentration Data ..................................................... F-2


Calculation of the W126 Statistic ................................................................................................ F-2


Aggregating Ozone Data to the County Level............................................................................. F-3


Yield Change Estimates ..................................................................................................................... F-3


Exposure-Response Functions ..................................................................................................... F-3


Minimum/Maximum Exposure-Response Functions ........................................................... F-4


Calculation of Ozone Indices ....................................................................................................... F-4


Calculations of County Weights .................................................................................................. F-5


Calculation of Percent Change in Yield ...................................................................................... F-5


Economic Impact Estimates ............................................................................................................... F-5


Agricultural Simulation Model (AGSIM) ................................................................................... F-5


Conclusions ........................................................................................................................................ F-9


Agricultural Effects References ....................................................................................................... F-10


Appendix G: Lead Benefits Analysis ......................................................................................................... G-1


Introduction ........................................................................................................................................ G-1


Methods Used to Measure and Value Health Effects ........................................................................ G-2


Health Benefits to Children ......................................................................................................... G-2


vii 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Changes in IQ ........................................................................................................................ G-2


Quantifying the Relationship Between Blood Lead Levels and IQ ............................... G-2


Valuing Changes in Children’s Intelligence ................................................................... G-3


Children with IQs Less Than 70 ........................................................................................... G-7


Quantifying the Number of Children with IQs Less than 70 ......................................... G-7


Valuing the Reduction in Number of Children with IQs less than 70............................G-8


Changes in Neonatal Mortality ............................................................................................. G-8


Quantifying the relationship between PbB levels and neonatal mortality ..................... G-8


Valuing changes in neonatal mortality ........................................................................... G-8


Health Benefits to Men ................................................................................................................ G-8


Hypertension ......................................................................................................................... G-9


Quantifying the relationship between PbB levels and hypertension .............................. G-9


Valuing reductions in hypertension ................................................................................ G-9


Quantifying the relationship between blood lead and blood pressure .................................. G-9


Changes In Coronary Heart Disease ................................................................................... G-10


Quantifying the relationship between blood pressure and coronary heart disease ....... G-10


Valuing reductions in CHD events ............................................................................... G-11


Changes in Initial Cerebrovascular Accidents 

and Initial Atherothrombotic Brain Infarctions ............................................................... G-12


Quantifying the relationship between blood pressure and first-time stroke ................. G-12


Valuing reductions in strokes ....................................................................................... G-12


Changes in Premature Mortality ......................................................................................... G-13


Quantifying the relationship between blood pressure and premature mortality .......... G-13


Valuing reductions in premature mortality...................................................................G-13


Health Benefits to Women......................................................................................................... G-13


Changes in Coronary Heart Disease....................................................................................G-14


Quantifying the relationship between blood pressure and coronary heart disease ....... G-14


Valuing reductions in CHD events ............................................................................... G-14


Changes in Atherothrombotic Brain Infarctions and Initial Cerebrovascular Accidents ... G-14


Quantifying the relationship between blood pressure and first-time stroke ................. G-14


Valuing reductions in strokes ....................................................................................... G-15


Changes in Premature Mortality ......................................................................................... G-15


Quantifying the relationship between blood pressure and premature mortality .......... G-15


Quantifying Uncertainty ............................................................................................................ G-15


Characterizing Uncertainty Surrounding the Dose-Response Relationships ...................... G-15


Characterizing Uncertainty Surrounding the Valuation Estimates ..................................... G-15


Industrial Processes and Boilers and Electric Utilities .................................................................... G-16


Methods Used to Determine Changes in Lead Emissions


from Industrial Processes from 1970 to 1990 ......................................................................... G-16


TRI Data .............................................................................................................................. G-16


Derivation of Industrial Process Emissions Differentials 1970-1990.................................G-17


Data sources .................................................................................................................. G-17


Estimates of industrial process emissions in the control scenario ............................... G-17


Estimates of industrial process emissions in the no-control scenario .......................... G-18


Matching TRI Data to Industrial Process Emissions Differentials ..................................... G-18


Methods Used to Determine Changes in Lead Emissions


from Industrial Boilers from 1970 to 1990 ............................................................................. G-19


TRI Data .............................................................................................................................. G-19


Derivation of Industrial Combustion Emissions 1970-1990...............................................G-20


Estimates of combustion emissions under the control scenario ................................... G-20


Estimates of combustion emissions under the no-control scenario .............................. G-20


Matching TRI Data to Industrial Combustion Emissions Data .......................................... G-21


Methods Used to Determine Changes in Lead Emissions 

from Electric Utilities from 1975 to 1990 .............................................................................. G-21


Coal-Use Data ..................................................................................................................... G-21


The EPA Interim Emissions Inventory ............................................................................... G-21


Matching the Coal-Use Data to the Interim Emissions Inventory ...................................... G-22


Emissions Factors and Control Efficiencies ....................................................................... G-22


viii 
Use of Air Dispersion Modeling to Estimate Ambient Air Lead Levels .................................. G-23


Determination of Blood Lead Levels from Air Lead Concentrations ....................................... G-23


Relationship Between Air Lead Concentrations and Blood Lead Levels ........................... G-23


Children ........................................................................................................................ G-25


Adults............................................................................................................................G-25


Individuals with initial blood lead levels of 30 μg/dL and greater .............................. G-26


Estimates of Initial Blood Lead Concentrations ................................................................. G-26


Combination of Air Concentration Estimates with Population Data ........................................ G-27


Results........................................................................................................................................G-28


Reduction in Health Effects Attributable to Gasoline Lead Reductions..........................................G-31


Estimating Changes in Amount of Lead in Gasoline from 1970 to 1990 ................................. G-31


Estimating the Change in Blood Lead Levels


from the Change in the Amount of Lead in Gasoline ............................................................. G-31


1970-Forward and 1990-Backward Approaches.................................................................G-32


Relating Blood Lead Levels to Population Health Effects ................................................. G-32


Changes in Leaded Gasoline Emissions


and Resulting Decreased Blood Lead Levels and Health Effects .................................... G-32


Lead Benefits Analysis References .................................................................................................. G-36


Appendix H: Air Toxics .............................................................................................................................. H-1


Introduction ........................................................................................................................................ H-1


Limited Scope of this Assessment ..................................................................................................... H-1


History of Air Toxics Standards under the Clean Air Act of 1970 .................................................... H-2


Quantifiable Stationary Source Air Toxics Benefits .......................................................................... H-3


EPA Analyses of Cancer Risks from Selected Air Toxic Pollutants .......................................... H-3


Cancer Risk Estimates from NESHAP Risk Assessments .......................................................... H-4


Non-utility Stationary Source Cancer Incidence Reductions.............................................................H-4


PES Study .................................................................................................................................... H-5


Methodology ......................................................................................................................... H-5


Findings ................................................................................................................................. H-6


ICF Re-analysis............................................................................................................................H-7


Methodology ......................................................................................................................... H-7


Findings ................................................................................................................................. H-8


Mobile Source HAP Exposure Reductions ........................................................................................ H-9


Methodology ..............................................................................................................................H-10


Results........................................................................................................................................H-10


Non-Cancer Health Effects .............................................................................................................. H-11


Ecological Effects ............................................................................................................................ H-11


Conclusions — Research Needs....................................................................................................... H-12


Health Effects ............................................................................................................................ H-12


Exposure Assessment ................................................................................................................ H-13


Ecosystem Effects ...................................................................................................................... H-13


Economic Valuation .................................................................................................................. H-13


Air Toxics References ...................................................................................................................... H-14


Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants I-1


Methods Used to Value Health and Welfare Effects .......................................................................... I-1


Valuation of Specific Health Endpoints ....................................................................................... I-3


Valuation of Premature Mortality Avoided ........................................................................... I-3


Valuation of Hospital Admissions Avoided .......................................................................... I-3


Valuation of Chronic Bronchitis Avoided ............................................................................. I-4


Valuation of Other Morbidity Endpoints Avoided ................................................................ I-6


Valuation of Welfare Effects ........................................................................................................ I-6


Visibility Valuation ................................................................................................................ I-6


Results of Valuation of Health and Welfare Effects ......................................................................... I-16


ix 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Uncertainties ...................................................................................................................................... I-16


The Effect of Discount Rates ...................................................................................................... I-20


The Relative Importance of Different Components of Uncertainty ........................................... I-20


Economic Benefits Associated with Reducing Premature Mortality ......................................... I-21


Economic Valuation References ....................................................................................................... I-27


Appendix J: Future Directions.....................................................................................................................J-1


Research Implications ......................................................................................................................... J-1


Research Topics to Reduce Uncertainty ....................................................................................... J-1


Research Topics to Improve Comprehensiveness ........................................................................ J-3


Future Section 812 Analyses ............................................................................................................... J-4


x 
Tables 

Table ES-1 Criteria Pollutant Health Benefits - Distributions of 1990 Incidences of Avoided 

Health Effects (In Thousands of Incidences Reduced) for 48 State Population. ................ ES-4


Table ES-2 Major Nonmonetized, Adverse Effects Reduced by the Clean Air Act. ............................ ES-5


Table ES-3 Central Estimates of Economic Value per Unit of Avoided Effect


(In 1990 Dollars). ................................................................................................................ ES-6


Table ES-4 Total Monetized Benefits by Endpoint Category for 48 State Population for 1970


to 1990 Period (In Billions of 1990 Dollars) ...................................................................... ES-7


Table ES-5 Alternative Mortality Benefits Mean Estimates for 1970 to 1990 (In Trillions


of 1990 Dollars) Compared to Total 1970 to 1990 Compliance Costs ............................... ES-9


Table 1 Estimated Annual CAA Compliance Costs ($Billions). ........................................................... 8


Table 2 Compliance Cost, GNP, and Consumption Impacts Discounted to 1990


($1990 Billions)....................................................................................................................... 11


Table 3 Summary of Sector-Specific Emission Modeling Approaches. ............................................. 14


Table 4 Uncertainties Associated with Emissions Modeling. .............................................................. 18


Table 5 Key Uncertainties Associated with Air Quality Modeling. .................................................... 26


Table 6 Human Health Effects of Criteria Pollutants. ......................................................................... 31


Table 7 Selected Welfare Effects of Criteria Pollutants. ..................................................................... 32


Table 8 Percent of Population (of the Continental US) Within 50km of a Monitor (Or in a


County with PM monitors), 1970-1990. ................................................................................. 33


Table 9 Criteria Pollutants Health Benefits — Distributions of 1990 Avoided Premature


Mortalities (Thousands of Cases Reduced) for 48 State Population ...................................... 37


Table 10 Criteria Pollutants Health Benefits — Distributions of 1990 Non-Fatal Avoided


Incidence (Thousands of Cases Reduced) for 48 State Population......................................... 38


Table 11. Health and Welfare Effects of Hazardous Air Pollutants. ...................................................... 40


Table 12 Uncertainties Associated with Physical Effects Modeling. .................................................... 42


Table 13 Health and Welfare Effects Unit Valuation (1990 Dollars). ................................................... 44


Table 14 Summary of Mortality Valuation Estimates (Millions of $1990)........................................... 45


Table 15 Estimating Mortality Risk Based on Wage-Risk Studies: Potential Sources


and Likely Direction of Bias. .................................................................................................. 50


Table 16 Present Value of 1970 to 1990 Monetized Benefits by Endpoint Category for 48


State Population (Billions of $1990, Discounted to 1990 at 5 Percent) ................................. 52


Table 17 Total Monetized Benefits for 48 State Population (Present Value in Billions of


1990 Dollars, Discounted to 1990 at 5 Percent) ..................................................................... 53


Table 18 Quantified Uncertainty Ranges for Monetized Annual Benefits and Benefit/Cost


Ratios, 1970-1990 (In Billions of 1990-Value Dollars).......................................................... 55


Table 19 Alternative Mortality Benefits Mean Estimates for 1970 to 1990 (in Trillions of


1990 Dollars, Discounted at 5 percent) Compared to Total 1970 to 1990


Compliance Costs .................................................................................................................... 57


Table 20 Effect of Alternative Discount Rates on Present Value of Total Monetized


Benefits/Costs for 1970 to 1990 (In Trillions of 1990 Dollars). ............................................. 57


Table A-1 Key Distinguishing Characteristics of the Jorgenson-Wilcoxen Model. .............................. A-3


Table A-2 Definitions of Industries Within the J/W Model. .................................................................. A-4


Table A-3 Estimated Capital and O&M Expenditures for Stationary Source Air Pollution


Control (Millions of Current Dollars). ................................................................................ A-10


Table A-4 Estimated Recovered Costs for Stationary Source Air Pollution Control


(Millions of Current Dollars). ............................................................................................. A-11


Table A-5 Estimated Capital and Operation and Maintenance Expenditures for Mobile


Source Air Pollution Control (Millions of Current Dollars). .............................................. A-12


xi

The Benefits and Costs of the Clean Air Act, 1970 to 1990


Table A-6 O&M Costs and Credits (Millions of Current Dollars). ..................................................... A-12


Table A-7 Other Air Pollution Control Expenditures (Millions of Current Dollars)...........................A-14


Table A-8 Summary of Expenditures and Conversion to 1990 Dollars


(Millions of Dollars) ........................................................................................................... A-15


Table A-9 Annualized Costs, 1973-1990 (Millions of 1990 Dollars; Capital Expenditures


Annualized at 5 Percent) ..................................................................................................... A-16


Table A-10 Amortization of Capital Expenditures for Stationary Sources


(Millions of 1990 Dollars) .................................................................................................. A-17


Table A-11 Amortization of Capital Expenditures for Mobile Sources


(Millions of 1990 Dollars) .................................................................................................. A-18


Table A-12 Compliance Expenditures and Annualized Costs, 1973 to 1990 ($1990 millions) ........... A-19


Table A-13 Costs Discounted to 1990 ($1990 Millions) ....................................................................... A-20


Table A-14 Differences in Gross National Product Between the Control and No-Control


Scenarios ............................................................................................................................. A-20


Table A-15 Difference in Personal Consumption Between the Control and No-Control


Scenarios ............................................................................................................................. A-21


Table A-16 GNP and Consumption Impacts Discounted to 1990 ($1990 Billions) .............................. A-21


Table A-17 Percentage Difference in Energy Prices Between the Control and No-Control


Scenarios. ............................................................................................................................ A-23


Table A-18 Potential Sources of Error and Their Effect on Total Costs of Compliance. ...................... A-26


Table A-19 Stationary Source O&M Expenditures as a Percentage of Capital Stock


(Millions of 1990 Dollars) .................................................................................................. A-27


Table A-20 Comparison of EPA and BEA Stationary Source Expenditure Estimates


(Millions of Current Dollars) .............................................................................................. A-28


Table A-21 BEA Estimates of Mobile Source Costs. ............................................................................ A-29


Table A-22 Annualized Costs Assuming 40-Year Stationary Source Capital Amortization


Period, 1973 to 1990 ($1990 Millions). ............................................................................. A-30


Table A-23 Effect of Amortization Periods on Annualized Costs Discounted to 1990


(Billions of $1990) .............................................................................................................. A-30


Table B-1 Correspondence Between Process Emissions Categories Used by MSCET, 

Trends, and J/W Industrial Sectors and Identifier Codes...................................................... B-6


Table B-2 Fuel Use Changes Between Control and No-control Scenarios............................................ B-9


Table B-3 Difference in Control and No-control Scenario Off-Highway Mobile Source


Emissions. ........................................................................................................................... B-12


Table B-4 Sources of Data for Transportation Sector Control Scenario Activity Projection. ............. B-17


Table B-5 Distribution of Households by Demographic Attributes for Control Scenario. ................. B-18


Table B-6 Economic and Vehicle Usage Data for Vehicle Ownership Projection Control


Scenario. .............................................................................................................................. B-19


Table B-7 Control Scenario Personal Characteristics. ......................................................................... B-20


Table B-8 Distribution of Households by Income Class for No-Control Scenario. ............................ B-21


Table B-9 Economic and Vehicle Usage Data for Vehicle Ownership Projection


No-Control Scenario ........................................................................................................... B-22


Table B-10 Percent Changes in Key Vehicle Characteristics Between the Control and


No-Control Scenarios. ......................................................................................................... B-23


Table B-11 J/W Estimates of Percentage Increases in National Electricity Generation


Under No-Control Scenario. ............................................................................................... B-29


Table B-12 Trends Source Categories and (1975 to 1985) Scaling Factors for TSP and CO. .............. B-33


Table B-13 Percentage Change in Real Energy Demand by Households from Control to


No-Control Scenario. .......................................................................................................... B-34


Table B-14 Percentage Change in Commercial Energy Demand from Control to


No-Control Scenario. .......................................................................................................... B-35


Table B-15 J/W Percent Differential in Economic Variables Used in CRESS. .................................... B-35


Table B-16 TSP Emissions Under the Control and No-Control Scenarios by Target Year


(In Thousands of Short Tons).............................................................................................. B-36


Table B-17 SO2 Emissions Under the Control and No-Control Scenarios by Target Year


(In Thousands of Short Tons).............................................................................................. B-36


xii 
Tables 

Table B-18 NOx Emissions Under the Control and No-Control Scenarios by Target Year


(In Thousands of Short Tons).............................................................................................. B-37


Table B-19 VOC Emissions Under the Control and No-Control Scenarios by Target Year


(In Thousands of Short Tons).............................................................................................. B-37


Table B-20 CO Emissions Under the Control and No-Control Scenarios by Target Year


(In Thousands of Short Tons).............................................................................................. B-38


Table B-21 Lead (Pb) Emissions Under the Control and No-Control Scenarios by Target


Year (In Thousands of Short Tons) ..................................................................................... B-38


Table C-1 Summary of CO Monitoring Data......................................................................................... C-2


Table C-2 Format of Air Quality Profile Databases .............................................................................. C-3


Table C-3 Summary of SO2 Monitoring Data. ...................................................................................... C-5


Table C-4 Summary of NO2 Monitoring Data. ..................................................................................... C-7


Table C-5 Summary of NO Monitoring Data. ....................................................................................... C-7


Table C-6 Summary of TSP Monitoring Data ..................................................................................... C-14


Table C-7 Summary of PM10 Monitoring Data .................................................................................. C-15


Table C-8 Fine Particle (PM2.5) Chemical Composition by U.S. Region. ......................................... C-16


Table C-9 Coarse Particle (PM2.5 to PM10) Chemical Composition by U.S. Region. ...................... C-17


Table C-10 PM Control Scenario Air Quality Profile Filenames. ......................................................... C-17


Table C-11 PM No-Control Scenario Air Quality Profile Filenames. ................................................... C-18


Table C-12 Urban Areas Modeled with OZIPM4. ................................................................................. C-19


Table C-13 Summary of Ozone Monitoring Data. ................................................................................. C-21


Table C-14 Apportionment of Emissions Inventories for SAQM Runs. ............................................... C-22


Table C-15 1990 Control Scenario Visibility Conditions for 30 Southwestern U.S. Cities. ................. C-27


Table C-16 1990 No-control Scenario Visibility Conditions for 30 Southwestern U.S. Cities............. C-27


Table C-17 Summary of Relative Change in Visual Range and DeciView Between 1990


Control and No-Control Scenario Visibility Conditions for 30 Southwestern


U.S. Cities. .......................................................................................................................... C-29


Table D-1 Criteria Air Pollutant Monitors in the U.S., 1970 - 1990. ..................................................... D-3


Table D-2 Population Coverage in the “Within 50 km” Model Runs


(Percent of Continental U.S. Population). ............................................................................. D-4


Table D-3 Population Coverage for “Extrapolated to All U.S.” Model Runs (Percent of


Continental U.S. Population). ............................................................................................... D-5


Table D-4 Human Health Effects of Criteria Pollutants. ....................................................................... D-6


Table D-5 PM2.5/PM10 Ratios Used to Estimate PM2.5 Data Used With Pope et al. (1995)


Mortality Relationship ........................................................................................................ D-16


Table D-6 Summary of Concentration-Response Functions for Particulate Matter ............................ D-20


Table D-7 Summary of Concentration-Response Functions for Ozone...............................................D-27


Table D-8 Summary of Concentration-Response Functions for NO2 ................................................. D-35


Table D-9 Summary of Concentration-Response Functions for Carbon Monoxide ............................ D-37


Table D-10 Summary of Concentration-Response Functions for Sulfur Dioxide ................................. D-39


Table D-11 Selected Welfare Effects of Criteria Pollutants .................................................................. D-40


Table D-12 Summary of Functions Quantifying Welfare Benefits ....................................................... D-42


Table D-13 Criteria Pollutants Health Effects — Extrapolated to 48 State U.S. Population


(Cases Per Year-Mean Estimates) ....................................................................................... D-45


Table D-14 Mortality Distribution by Age: Proportion of PM- and Pb-related Premature


Mortalities and Associated Life Expectancies .................................................................... D-46


Table D-15 Quantified Benefits Which Could Not Be Monetized — Extrapolated to the


Entire 48 State Population. .................................................................................................. D-47


Table E-1 Summary of Biological Changes with Surface Water Acidification. ................................... E-3


Table E-2 Comparison of Population of Acidic National Surface Water Survey (NSWS) by


Chemical Category ................................................................................................................ E-4


Table E-3 Results from Benefits Assessments of Aquatic Ecosystem Use Values from Acid


Deposition Avoidance ........................................................................................................... E-6


xiii 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Table F-1 Agriculture Exposure-Response Functions. .......................................................................... F-4


Table F-2 Relative No-Control to Control Percent Yield Change (harvested acres) for the


Minimum Scenario. ............................................................................................................... F-6


Table F-3 Relative No-Control to Control Percent Yield Change (harvested acres) for the


Maximum Scenario. .............................................................................................................. F-6


Table F-4 Change in Farm Program Payments, Net Crop Income, Consumer Surplus,


and Net Surplus Due to the CAA (Millions 1990 $). ............................................................ F-8


Table G-1 Quantified and Unquantified Health Effects of Lead. .......................................................... G-1


Table G-2 Uncertainty Analysis: Distributions Associated With Dose-Response


Coefficients Used to Estimate Lead Health Effects ............................................................ G-16


Table G-3 Air Modeling Parameters. ................................................................................................... G-24


Table G-4 Estimated Indirect Intake Slopes: Increment of Blood Lead Concentration


(in μg/dL) per Unit of Air Lead Concentration (μg/m3). ................................................... G-26


Table G-5 Estimated Lead Emissions from Electric Utilities, Industrial Processes,


and Industrial Combustion (in Tons). ................................................................................. G-28


Table G-6	 Yearly Differences in Number of Health Effects Between the Control and


No-Control Scenarios: Industrial Processes, Boilers, and Electric Utilities


(Holding Other Lead Sources at Constant 1970 Levels) .................................................... G-29


Table G-7	 Yearly Differences in Number of Health Effects Between the Controlled and


Uncontrolled Scenarios: Industrial Processes, Boilers, and Electric Utilities


(Holding Other Lead Sources at Constant 1990 Levels) .................................................... G-30


Table G-8 Lead Burned in Gasoline (In Tons). ....................................................................................G-33


Table G-9	 Yearly Differences in Number of Health Effects Between the Control and


No-Control Scenarios: Lead in Gasoline only (Holding Other Lead Sources at


Constant 1970 Levels). ........................................................................................................ G-34


Table G-10	 Yearly Differences in Number of Health Effects Between the Control and


No-Control Scenarios: Lead in Gasoline only (Holding Other Lead Sources at


Constant 1990 Levels). ........................................................................................................ G-35


Table H-1 Health and Welfare Effects of Hazardous Air Pollutants. .................................................... H-2


Table H-2 Cancer Incidence Reductions and Monetized Benefits for NESHAPs. ................................ H-5


Table I-1 Summary of Mortality Valuation Estimates (Millions of 1990 Dollars) ............................... I-3


Table I-2 Unit Values Used for Economically Valuing Health and Welfare Endpoints ....................... I-8


Table I-3 Criteria Pollutants Health and Welfare Benefits — Extrapolated to Entire 48 State


Population Present Value (In 1990 Using 5% Discount Rate) of Benefits from


1970-1990 (In Billions of 1990 Dollars). ............................................................................. I-17


Table I-4 Present Value of 1970 to 1990 Monetized Benefits by Endpoint Category for


48 State Population (Billions of 1990 Dollars, Discounted to 1990 at 5 Percent) ............... I-18


Table I-5	 Monte Carlo Simulation Model Results for Target Years, Plus Present Value in


1990 Terms of Total Monetized Benefits for Entire 1970 to 1990 Period


(In Billions of 1990-Value Dollars). .................................................................................... I-18


Table I-6 Comparison of 1990 (Single Year) Monetized Benefits by Endpoint for 48 State


Population and Monitored Areas (In Millions of 1990 Dollars). ......................................... I-19


Table I-7 Effect of Alternative Discount Rates on Present Value of Total Monetized


Benefits for 1970 to 1990 (In Trillions of 1990 Dollars). .................................................... I-20


Table I-8 Alternative Estimates of the Present Value of Mortality Associated With PM


(Based on Pope et al., 1996, in Trillions of 1990 Dollars)................................................... I-25


xiv 
Figures 

Figure ES-1 Total Direct Compliance Costs of the CAA (in billions of inflation-adjusted 

dollars.)................................................................................................................................ ES-1


Figure ES-2 1990 Control and No-control Scenario Emissions (in millions of short tons). ................... ES-2


Figure ES-3 Total Direct Costs and Monetized Direct Benefits of the Clean Air Act, 1970


to 1990 (in trillions of 1990 dollars). .................................................................................. ES-8


Figure 1 Summary of Analytical Sequence and Modeled versus Historical Data Basis. ....................... 4


2

 Emission Estimates. .......................................... 16


Figure 3 Control and No-control Scenario Total NO Emission Estimates. ......................................... 16


Figure 2 Control and No-control Scenario Total SO

x


Figure 4 Control and No-control Scenario Total VOC Emission Estimates. ........................................ 16


Figure 5 Control and No-control Scenario Total CO Emission Estimates. ........................................... 16


Figure 6 Control and No-control Scenario Total TSP Emission Estimates. ......................................... 16


Figure 7 Control and No-control Scenario Total Pb Emission Estimates. ............................................ 16


Figure 8 Frequency Distribution of Estimated Ratios for 1990 Control to No-control


Scenario 95th Percentile 1-Hour Average CO Concentrations, by Monitor........................... 21


Figure 9 Frequency Distribution of Estimated Ratios for 1990 Control to No-control


Scenario 95th Percentile 1-Hour Average SO2

 Concentrations, by Monitor. ......................... 22


Figure 10 Frequency Distribution of Estimated Ratios for 1990 Control to No-control


Scenario 95th Percentile 1-Hour Average NO2

 Concentrations, by Monitor. ........................ 23


Figure 11 Distribution of Estimated Ratios for 1990 Control to No-Control Annual Mean


TSP Concentrations, by Monitored County. ........................................................................... 23


Figure 12 Distribution of Estimated Ratios for 1990 Control to No-control OZIPM4


Simulated 1-Hour Peak Ozone Concentrations, by Urban Area. ............................................ 23


Figure 13 Distribution of Estimated Ratios for 1990 Control to No-control SAQM Simulated


Daytime Average Ozone Concentrations, by SAQM Monitor. .............................................. 24


Figure 14 Distribution of Estimated Ratios for 1990 Control to No-control RADM Simulated


Daytime Average Ozone Concentrations, by RADM Grid Cell. ............................................ 24


Figure 15 RADM-Predicted Percent Increase in Total Sulfur Deposition (Wet + Dry) Under the


No-control Scenario. ............................................................................................................... 24


Figure 16 RADM-Predicted Percent Increase in Total Nitrogen Deposition (Wet + Dry) Under


the No-control Scenario. ......................................................................................................... 25


Figure 17 RADM-Predicted Increase in Visibility Degradation, Expressed in DeciViews, for


Poor Visibility Conditions (90th Percentile) Under the No-control Scenario. ....................... 25


Figure 18 Monte Carlo Simulation Model Results for Target Years (in billions of 1990 dollars) ......... 54


Figure 19 Distribution of 1990 Monetized Benefits of CAA (in billions of 1990 dollars) ..................... 54


Figure 20 Uncertainty Ranges Deriving From Individual Uncertainty Factors ...................................... 55


Figure A-1 Percent Difference in Real Investment Between Control and No-control Scenarios. ........ A-22


Figure A-2 Percent Difference in Price of Output by Sector Between Control and No-control


Scenario for 1990. ............................................................................................................... A-22


Figure A-3 Percent Difference in Quantity of Output by Sector Between Control and


No-control Scenario for 1990..............................................................................................A-24


Figure A-4 Percent Difference in Employment by Sector Between Control and No-control


Scenario for 1990. ............................................................................................................... A-24


Figure B-1 Comparison of Control, No-control, and Trends SO2

 Emission Estimates. ......................... B-2


Figure B-2 Comparison of Control, No-control, and Trends NO Emission Estimates. ......................... B-2
 x 

Figure B-3 Comparison of Control, No-control, and Trends VOC Emission Estimates. ....................... B-2


Figure B-4 Comparison of Control, No-control, and Trends CO Emission Estimates. .......................... B-2


Figure B-5 Comparison of Control, No-Control, and Trends TSP Emission Estimates ......................... B-2


xv 
The Benefits and Costs of the Clean Air Act, 1970 to 1990


Figure C-1 Frequency Distribution of Estimated Ratios for 1990 Control to No-Control


Scenario 95th Percentile 1-Hour Average CO Concentrations, by Monitor......................... C-4


Figure C-2 Frequency Distribution of Estimated Ratios for 1990 Control to No-control


Scenario 95th Percentile 1-Hour Average SO2

 Concentrations, by Monitor. ....................... C-6


Figure C-3 Frequency Distribution of Estimated Ratios for 1990 Control to No-control


Scenario 95th Percentile 1-Hour Average NO2

 Concentrations, by Monitor. ...................... C-8


Figure C-4 Location of the High Resolution RADM 20-km Grid Nested Inside the 80-km


RADM Domain. .................................................................................................................... C-9


Figure C-5 RADM-Predicted 1990 Total Sulfur Deposition (Wet + Dry; in kg/ha) Under the


Control Scenario. ................................................................................................................. C-10


Figure C-6 RADM-Predicted 1990 Total Nitrogen Deposition (Wet + Dry; in kg/ha) Under the


Control Scenario. ................................................................................................................. C-10


Figure C-7 RADM-Predicted 1990 Total Sulfur Deposition (Wet + Dry; in kg/ha) Under the


No-control Scenario. ........................................................................................................... C-11


Figure C-8 RADM-Predicted 1990 Total Nitrogen Deposition (Wet + Dry; in kg/ha) Under the


No-control Scenario. ........................................................................................................... C-11


Figure C-9 RADM-Predicted Percent Increase in Total Sulfur Deposition (Wet + Dry; in kg/ha)


Under the No-control Scenario. .......................................................................................... C-12


Figure C-10 RADM-Predicted Percent Increase in Total Nitrogen Deposition (Wet + Dry;


in kg/ha) Under the No-control Scenario. ........................................................................... C-12


Figure C-11 Distribution of Estimated Ratios for 1990 Control to No-Control Annual Mean


TSP Concentrations, by Monitored County. ....................................................................... C-18


Figure C-12 RADM and SAQM Modeling Domains, with Rural Ozone Monitor Locations. ............... C-20


Figure C-13 Distribution of Estimated Ratios for 1990 Control to No-control OZIPM4


Simulated 1-Hour Peak Ozone Concentrations, by Urban Area. ........................................ C-23


Figure C-14 Distribution of Estimated Ratios for 1990 Control to No-control RADM-Simulated


Daytime Average Rural Ozone Concentrations, by RADM Grid Cell. .............................. C-23


Figure C-15 Distribution of Estimated Ratios for 1990 Control to No-control SAQM-Simulated


Daytime Average Ozone Concentrations, by SAQM Monitor. .......................................... C-23


Figure C-16	 RADM-Predicted Visibility Degradation, Expressed in Annual Average


DeciView, for Poor Visibility Conditions (90th Percentile) Under the Control


Scenario. .............................................................................................................................. C-26


Figure C-17	 RADM-Predicted Visibility Degradation, Expressed in Annual Average


DeciView, for Poor Visibility Conditions (90th Percentile) Under the No-Control


Scenario. .............................................................................................................................. C-26


Figure C-18	 RADM-Predicted Increase in Visibility Degradation, Expressed in Annual


Average DeciView, for Poor Visibility Conditions (90th Percentile) Under the


No-Control Scenario. .......................................................................................................... C-28


Figure H-1 PES Estimated Reductions in HAP-Related Cancer Cases...................................................H-7


Figure H-2 ICF Estimated Reductions in Total HAP-Related Cancer Cases Using Upper


Bound Asbestos Incidence and Lower Bound Non-Asbestos HAP Incidence. .................... H-8


Figure H-3 ICF Estimated Reduction in Total HAP-Related Cancer Cases Using Upper


Bound Incidence for All HAPs. ............................................................................................ H-8


Figure H-4 National Annual Average Motor Vehicle HAP Exposures (μg/m3). ................................. H-11


Figure I-1 Monte Carlo Simulation Model Results for Target Years


(in billions of 1990 dollars) .................................................................................................. I-19


Figure I-2 Uncertainty Ranges Deriving From Individual Uncertainty Factors ................................... I-21


xvi 
Acronyms and Abbreviations 

μeq/L microequivalents per liter 

μg/m3 micrograms per cubic meter 

μg micrograms 

μm micrometers, also referred to as microns 

ACCACAPERS SAB Advisory Council on Clean Air Compliance Analysis Physical 

Effects Review Subcommittee 

AGSIM AGricultural SImulation Model 

AIRS EPA Aerometric Information Retrieval System 

Al3+ aluminum 

ANC acid neutralizing capacity 

ANL Argonne National Laboratories 

APPI Argonne Power Plant Inventory 

AQCR Air Quality Control Region 

ARGUS Argonne Utility Simulation Model 

ASI Acid Stress Index 

ATERIS Air Toxic Exposure and Risk Information System 

ATLAS Aggregate Timberland Assessment System 

AUSM Advanced Utility Simulation Model 

BEA Bureau of Economic Analysis 

bext total light extinction 

BG/ED Block Group / Enumeration District 

BI atherothrombotic brain infarction 

BID Background Information Document 

BP blood pressure 

BTU British Thermal Unit 

c.i. confidence interval 

CA cerebrovascular accident 

CAA Clean Air Act 

CAAA90 Clean Air Act Amendments of 1990 

CAPMS EPA’s Criteria Air Pollutant Modeling System 

CARB California Air Resources Board 

CASAC SAB Clean Air Scientific Advisory Committee 

CDC Centers for Disease Control (now CDCP, Centers for Disease Control 

and Prevention) 

CERL EPA/ORD Corvallis Environmental Research Laboratory (old name; see 

NERL) 

CEUM ICF Coal and Electric Utility Model 

CHD coronary heart disease 

CIPP changes in production processes 

CO carbon monoxide 

CO2 carbon dioxide 

COH coefficient of haze 

COHb blood level of carboxyhemoglobin 

COPD chronic obstructive pulmonary disease 

Council SAB Advisory Council on Clean Air Compliance Analysis 

CPUE catch per unit effort 

xvii 
The Benefits and Costs of the Clean Air Act, 1970 to 1990 

CR concentration-response


CRESS Commercial and Residential Simulation System model


CSTM Coal Supply and Transportation Model


CTG Control Techniques Guidelines


CV contingent valuation


CVM contingent valuation method


D.C. District of Columbia


DBP diastolic blood pressure


DDE dichlorodiphenyldichloroethylene


DDT dichlorodiphenyltrichloroethane


DFEV1 decrement of forced expiratory volume (in one second)


dL deciliter


DOC Department of Commerce


DOE Department of Energy


DOI Department of Interior


DRI Data Resources Incorporated


dV DeciView Haze Index


DVSAM Disaggregate Vehicle Stock Allocation Model


EC extinction coefficient


EDB ethylene dibromide


EDC ethylene dichloride


EFI Electronic Fuel Injection


EI Electronic Ignition


EIA Energy Information Administration


EKMA Empirical Kinetics Modeling Approach


ELI Environmental Law Institute


EOL end-of-line


EPA Environmental Protection Agency


EPRI Electric Power Research Institute


ESEERCO Empire State Electric Energy Research Corporation


ESP electrostatic precipitator


FERC Federal Energy Regulatory Commission


FEV1 forced expiratory volume (in one second)


FGD flue gas desulfurization


FHWA Federal Highway Administration


FIFRA Federal Insecticide, Fungicide, and Rodenticide Act


FIP Federal Information Processing System


FR Federal Register


FRP Forest Response Program


GDP gross domestic product


GEMS Graphical Exposure Modeling System


GM geometric mean


GNP Gross National Product


GSD geometric standard deviation


H2

SO4 sulfuric acid


ha hectares


HAP Hazardous Air Pollutant


HAPEM-MS Hazardous Air Pollutant Exposure Model - Mobile Source


HNO3 nitric acid


hp horsepower


HTCM Hedonic Travel-Cost Model


ICARUS Investigation of Costs and Reliability in Utility Systems


xviii 
Acronyms and Abbreviations 

ICD-9 International Classification of Diseases, Ninth Version (1975 Revision)


ICE Industrial Combustion Emissions model


IEc Industrial Economics, Incorporated


IEUBK EPA’s Integrated Exposure Uptake Biokinetic model


IMS Integrated Model Set


IPF iterative proportional fitting


IQ intelligence quotient


ISCLT Industrial Source Complex Long Term air quality model


J/W Jorgenson / Wilcoxen


kg kilograms


km kilometers


lbs pounds


LRI lower respiratory illness


m/s meters per second


m meters


m3 cubic meters


Mm megameters


MMBTU million BTU


MOBILE5a EPA’s mobile source emission factor model


mpg miles per gallon


MRAD minor restricted activity day


MSCET Month and State Current Emission Trends


MTD metric tons per day


MVATS EPA’s Motor Vehicle-Related Air Toxics Study


MVMA Motor Vehicle Manufacturers Association


Mwe megawatt equivalent


N nitrogen


NA not available


NAAQS National Ambient Air Quality Standard


NAPAP National Acid Precipitation Assessment Program


NARSTO North American Research Strategy for Tropospheric Ozone


NATICH National Air Toxics Information Clearinghouse


NCLAN National Crop Loss Assessment Network


NEA National Energy Accounts


NERA National Economic Research Associates


NERC North American Electric Reliability Council


NERL EPA/ORD National Exposure Research Laboratory (new name for


CERL) 

NESHAP National Emission Standard for Hazardous Air Pollutants 

NHANES First National Health and Nutrition Examination Survey 

NHANES II Second National Health and Nutrition Examination Survey 

NIPA National Income and Product Accounts 

NMOCs nonmethane organic compounds 

NO nitric oxide 

NO2 nitrogen dioxide 

NO3- nitrate ion 

NO nitrogen oxides x 

NPTS Nationwide Personal Transportation Survey


NSPS New Source Performance Standards


NSWS National Surface Water Survey


O&M operating and maintenance


O3 ozone


xix 
The Benefits and Costs of the Clean Air Act, 1970 to 1990 

OAQPS EPA/OAR Office of Air Quality Planning and Standards


OAR EPA Office of Air and Radiation


OMS EPA/OAR Office of Mobile Sources


OPAR EPA/OAR Office of Policy Analysis and Review


OPPE EPA Office of Policy Planning and Evaluation


ORD EPA Office of Research and Development


OZIPM4 Ozone Isopleth Plotting with Optional Mechanism-IV


PACE Pollution Abatement Costs and Expenditures survey


PAN peroxyacetyl nitrate


PAPE Pollution Abatement Plant and Equipment survey


Pb lead


PbB blood lead level


PCB polychlorinated biphenyl


PES Pacific Environmental Services


pH the logarithm of the reciprocal of hydrogen ion concentration, a measure


of acidity 

PIC product of incomplete combustion 

PM10 particulates less than or equal to 10 microns in aerometric diameter 

PM2.5 particulates less than or equal to 2.5 microns in aerometric diameter 

POP population 

Popmild exposed population of exercising mild asthmatics 

Popmod exposed population of exercising moderate asthmatics 

ppb parts per billion 

PPH people per household 

pphm parts per hundred million 

ppm parts per million 

PPRG Pooling Project Research Group 

PRYL percentage relative yield loss 

PURHAPS PURchased Heat And Power 

PVC polyvinyl chloride 

r2 statistical correlation coefficient, squared 

RAD restricted activity day 

RADM Regional Acid Deposition Model 

RADM/EM RADM Engineering Model 

RAMC Resource Allocation and Mine Costing model 

RfD reference dose 

RIA Regulatory Impact Analysis 

ROM Regional Oxidant Model 

RRAD respiratory restricted activity day 

RUM Random Utility Model 

s.e. standard error 

SAB Science Advisory Board 

SAI Systems Applications International 

SAQM SARMAP Air Quality Model 

SARA Superfund Amendment Reauthorization Act 

SARMAP SJVAQS/AUSPEX Regional Modeling Adaptation Project 

SCC Source Classification Code 

SEDS State Energy Data System 

SIC Standard Industrial Classification 

SIP State Implementation Plan 

SJVAQS San Joaquin Valley Air Quality Study 

SMSA Standard Metropolitan Statistical Area 

xx 
Acronyms and Abbreviations 

SO2 sulfur dioxide


SO4

2- sulfate ion


SOS/T State of Science and Technology (refers to a series of NAPAP reports)


SRaw Specific Airway Resistance


STAR Stability Array weather database


TAMM90 Timber Assessment Market Model (revised version)


TEEMS Transportation Energy and Emissions Modeling System


TIUS Truck Inventory and Use Surveys


TRI Toxic Release Inventory


TSP total suspended particulate


U.S. United States


UAM Urban Airshed Model


URI upper respiratory illness


USDA United States Department of Agriculture


USEPA United States Environmental Protection Agency


VC vinyl chloride


VMT vehicle miles traveled


VOC volatile organic compounds


VOP Vehicle Ownership Projection


VR visual range


W126 index of peak weighted average of cumulative ozone concentrations


WLD Work Loss Day


WTP willingness to pay


xxi 
The Benefits and Costs of the Clean Air Act, 1970 to 1990 

xxii

Acknowledgments 

This project is managed under the direction of Robert D. Brenner, Director of the U.S. EPA Office of Air 

and Radiation/Office of Policy Analysis and Review and Richard D. Morgenstern, Associate Assistant Administrator for Policy Planning and Evaluation, U.S. EPA (currently on leave as Visiting Scholar, Resources for the 

Future). The principal project managers are Jim DeMocker, EPA/OAR/OPAR; Al McGartland, Director, EPA/ 

OPPE/OEE; and Tom Gillis, EPA/OPPE/OEE. 

Many EPA staff contributed or reviewed portions of this draft document, including Joel Schwartz, Michael 

Shapiro, Peter Preuss, Tracey Woodruff, Diane DeWitt, Dan Axelrad, Joel Scheraga, Anne Grambsch, Jenny 

Weinberger, Allyson Siwik, Richard Scheffe, Vasu Kilaru, Amy Vasu, Kathy Kaufmann, Mary Ann Stewart, 

Eric Smith, Dennis J. Kotchmar, Warren Freas, Tom Braverman, Bruce Polkowsky, David Mobley, Sharon 

Nizich, David Meisenheimer, Fred Dimmick, Harvey Richmond, John Haines, John Bachmann, Ron Evans, 

Tom McMullen, Bill Vatavuk, Larry Sorrels, Dave McKee, Susan Stone, Melissa McCullough, Rosalina 

Rodriguez, Vickie Boothe, Tom Walton, Michele McKeever, Vicki Atwell, Kelly Rimer, Bob Fegley, Aparna 

Koppikar, Les Grant, Judy Graham, Robin Dennis, Dennis Leaf, Ann Watkins, Penny Carey, Joe Somers, Pam 

Brodowicz, Byron Bunger, Allen Basala, David Lee, Bill O’Neill, Susan Herrod, and Susan Stendebach. Allyson 

Siwik of EPA/OAR/OAQPS and Bob Fegley of EPA/ORD/OSPRE played particularly important roles in coordinating substantive and review contributions from their respective offices. 

A number of contractors developed key elements of the analysis and supporting documents. These contractors include Bob Unsworth, Jim Neumann, Mike Hester, and Jon Discher of Industrial Economics, Incorporated 

(IEc); Leland Deck, Ellen Post, Lisa Akeson, Brad Firlie, Susan Keane, Kathleen Cunningham, and John Voyzey 

of Abt Associates; Bruce Braine, Patricia Kim, Sandeep Kohli, Anne Button, Barry Galef, Cynde Sears, and 

Tony Bansal of ICF Resources; John Langstaff, Michelle Woolfolk, Shelly Eberly, Chris Emery, Till Stoekenius, 

and Andy Gray of ICF/Systems Applications International (ICF/SAI); Dale Jorgenson, Peter Wilcoxen, and 

Richard Goettle of Jorgenson Associates; Jim Lockhart of the Environmental Law Institute (ELI); Beverly 

Goodrich, Rehan Aziz, Noel Roberts, and Lucille Bender of Computer Sciences Corporation; Margaret Sexsmith 

of Analytical Sciences, Incorporated; Ken Meardon of Pacific Environmental Services (PES); David South, 

Gale Boyd, Melanie Tomkins, and K. Guziel of Argonne National Laboratory (ANL); Don Garner; Rex Brown 

and Jacob Ulvila of Decision Science Consortium; and Jim Wilson and Dianne P. Crocker of Pechan Associates. John Pitcher and H. Glenn Court of STRA managed the technical production of an earlier version of the 

draft document. The SARMAP AQM runs were provided by Carol Bohnenkamp of EPA Region 9 and Saffet 

Tanrikulu of the California Air Resources Board. 

Science Advisory Board review of this report is supervised by Donald G. Barnes, Director of the SAB Staff. 

SAB staff coordinating the reviews have included Jack Fowle, Jack Kooyoomjian, Sam Rondberg, Fred Talcott, 

and Randall Bond. Diana Pozun provided administrative support. 

The SAB Council was chaired by Richard Schmalensee of MIT throughout the development of the present 

study. The Council is now chaired by Maureen Cropper of the World Bank as the Council’s focus shifts to the 

upcoming prospective studies. Members who have participated in the review of this draft report include Morton 

Lippmann of New York University Medical Center, William Nordhaus of Yale University, Paul Portney of 

Resources for the Future, Kip Viscusi of Harvard University, A. Myrick Freeman of Bowdoin College, Maureen 

Cropper, Ronald Cummings of Georgia State University, Daniel Dudek of the Environmental Defense Fund, 

Robert Mendelsohn of Yale University, Wayne Kachel of MELE Associates, William Cooper of Michigan 

State University, Thomas Tietenberg of Colby College, Paul Lioy of the Robert Wood Johnson School of 

Medicine, Roger McClellan of the Chemical Industry Institute of Toxicology, George T. Wolff of General 

Motors, Richard Conway of Union Carbide Corporation, and Wallace Oates of the University of Maryland. 

xxiii 
The Benefits and Costs of the Clean Air Act, 1970 to 1990 

The SAB Council Physical Effects Review Subcommittee was chaired by Morton Lippmann. Members 

who have participated in the review include David V. Bates of the University of British Columbia, A. Myrick 

Freeman of Bowdoin College, Gardner Brown, Jr. of the University of Washington, Timothy Larson of the 

University of Washington, Lester Lave of Carnegie Mellon University, Joseph Meyer of the University of 

Wyoming, Robert Rowe of Hagler Bailly, Incorporated, George Taylor of the University of Nevada, Bernard 

Weiss of the University of Rochester Medical Center, and George Wolff of the General Motors Research Laboratory. 

The SAB Council Air Quality Subcommittee was chaired by George Wolff. Members who have participated in the review include Benjamin Liu of the University of Minnesota, Peter Mueller of the Electric Power 

Research Institute, Warren White of Washington University, Joe Mauderly of the Lovelace Biomedical & Environmental Research Institute, Philip Hopke of Clarkson University, Paulette Middleton of Science Policy 

Associates, James H. Price, Jr. of the Texas Natural Resource Conservation Commission, and Harvey Jeffries of 

the University of North Carolina, Chapel Hill. 

This report could not have been produced without the support of key administrative support staff. The 

project managers are grateful to Catrice Jefferson, Nona Smoke, Carolyn Hicks, Eunice Javis, Gloria Booker, 

Thelma Butler, Wanda Farrar, Ladonya Langston, Michelle Olawuyi, and Eileen Pritchard for their timely and 

tireless support on this project. 

xxiv 
Executive Summary 

Purpose of the Study 

Throughout the history of the Clean Air Act, questions have been raised as to whether the health and 

environmental benefits of air pollution control justify 

the costs incurred by industry, taxpayers, and consumers. For the most part, questions about the costs and 

benefits of individual regulatory standards continue 

to be addressed during the regulatory development 

process through Regulatory Impact Analyses (RIAs) 

and other analyses which evaluate regulatory costs, 

benefits, and such issues as scope, stringency, and timing. There has never been, however, any comprehensive, long-term, scientifically valid and reliable study 

which answered the broader question: 

“How do the overall health, welfare, 

ecological, and economic benefits of Clean 

Air Act programs compare to the costs of 

these programs?” 

To address this void, Congress added to the 1990 

Clean Air Act Amendments a requirement under section 812 that EPA conduct periodic, scientifically reviewed studies to assess the benefits and the costs of 

the Clean Air Act. Congress further required EPA to 

conduct the assessments to reflect central tendency, 

or “best estimate,” assumptions rather than the conservative assumptions sometimes deemed appropriate for setting protective standards. 

This report is the first in this ongoing series of 

Reports to Congress. By examining the benefits and 

costs of the 1970 and 1977 Amendments, this report 

addresses the question of the overall value of 

America’s historical investment in cleaner air. The 

first Prospective Study, now in progress, will evaluate the benefits and costs of the 1990 Amendments. 

Study Design 

Estimates of the benefits and costs of the historical Clean Air Act are derived by examining the differences in economic, human health, and environmental outcomes under two alternative scenarios: a “control scenario” and a “no-control scenario.” The control scenario reflects actual historical implementation 

of clean air programs and is based largely on historical data. The no-control scenario is a hypothetical scenario which reflects the assumption that no air pollution controls were established beyond those in place 

prior to enactment of the 1970 Amendments. Each of 

the two scenarios is evaluated by a sequence of economic, emissions, air quality, physical effect, economic valuation, and uncertainty models to measure 

the differences between the scenarios in economic, 

human health, and environmental outcomes. Details 

of this analytical sequence are presented in Chapter 1 

and are summarized in Figure 1 of that chapter. 

Study Review 

EPA is required, under section 812, to consult both 

a panel of outside experts and the Departments of 

Labor and Commerce in designing and implementing 

the study. 

The expert panel was organized in 1991 as the 

Advisory Council on Clean Air Act Compliance 

Analysis (hereafter “Council”) under the auspices of 

EPA’s Science Advisory Board (SAB). Organizing 

the external panel under the auspices of the SAB ensured that the peer review of the study would be conducted in a rigorous, objective, and publicly open 

manner. Eminent scholars and practitioners with expertise in economics, human health sciences, environmental sciences, and air quality modeling served 

on the Council and its technical subcommittees, and 

these reviewers met many times throughout the design and implementation phases of the study. During 

this ongoing, in-depth review, the Council provided 

valuable advice pertaining to the development and 

selection of data, selection of models and assumptions, 

evaluation and interpretation of the analytical findings, and characterization of those findings in several 

successive drafts of the Report to Congress. The 

present report was vastly improved as a result of the 

Council’s rigorous and constructive review effort. 

ES-1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

With respect to the interagency review process, 

EPA expanded the list of consulted agencies and convened a series of meetings during the design and early 

implementation phases from 1991 through late 1994. 

In late 1994, to ensure that all interested parties and 

the public received consistent information about remaining analytical issues and emerging results, EPA 

decided to use the public SAB review process as the 

primary forum for presenting and discussing issues 

and results. The Interagency Review Group was therefore discontinued as a separate process in late 1994. 

A final, brief interagency review, pursuant to Circular A-19, was organized in August 1997 by the Office of Management and Budget and conducted following the completion of the extensive expert panel 

peer review by the SAB Council. During the course 

of the final interagency discussions, it became clear 

that several agencies held different views pertaining 

to several key assumptions in this study as well as to 

the best techniques to apply in the context of environmental program benefit-cost analyses, including the 

present study. The concerns include: (1) the extent to 

which air quality would have deteriorated from 1970 

to 1990 in the absence of the Clean Air Act, (2) the 

methods used to estimate the number of premature 

deaths and illnesses avoided due to the CAA, (3) the 

methods used to estimate the value that individuals 

place on avoiding those risks, and (4) the methods 

used to value non-health related benefits. However, 

due to the court deadline the resulting concerns were 

not resolved during this final, brief interagency review. Therefore, this report reflects the findings of 

EPA and not necessarily other agencies in the Administration. Interagency discussion of some of these 

issues will continue in the context of the future prospective section 812 studies and potential regulatory 

actions. 

Summary of Results 

Direct Costs 

To comply with the Clean Air Act, businesses, 

consumers, and government entities all incurred higher 

costs for many goods and services. The costs of providing goods and services to the economy were higher 

primarily due to requirements to install, operate, and 

maintain pollution abatement equipment. In addition, 

costs were incurred to design and implement regulations, monitor and report regulatory compliance, and 

invest in research and development. Ultimately, these 

higher costs of production were borne by stockholders, business owners, consumers, and taxpayers. 

Figure ES-1 summarizes the historical data on 

Clean Air Act compliance costs by year, adjusted both 

for inflation and for the value of long-term investments in equipment. Further adjusting the direct costs 

incurred each year to reflect their equivalent worth in 

the year 1990, and then summing these annual results, 

yields an estimate of approximately $523 billion for 

the total value of 1970 to 1990 direct expenditures 

(see Appendix A for calculations). 

Emissions 

Emissions were substantially lower by 1990 under the control scenario than under the no-control scenario, as shown in Figure ES-2. Sulfur dioxide (SO2

) 

emissions were 40 percent lower, primarily due to 

utilities installing scrubbers and/or switching to lower 

sulfur fuels. Nitrogen oxides (NOX) emissions were 

30 percent lower by 1990, mostly because of the installation of catalytic converters on highway vehicles. 

Volatile organic compound (VOC) emissions were 45 

percent lower and carbon monoxide (CO) emissions 

were 50 percent lower, also primarily due to motor 

vehicle controls. 

For particulate matter, it is important to recognize the distinction between reductions in directly 

emitted particulate matter and reductions in ambient 

concentrations of particulate matter in the atmosphere. 

As discussed further in the next section, changes in 

particulate matter air quality depend both on changes 

in emissions of primary particles (i.e., air pollution 

which is already in solid particle form) and on changes 

in emissions of gaseous pollutants, such as sulfur dioxide and nitrogen oxides, which can be converted to 

particulate matter through chemical transformation in 

the atmosphere. Emissions of primary particulates 

Figure ES-1. Total Estimated Direct Compliance Costs of 

the CAA (in billions of inflation-adjusted dollars). 

ES-2

Executive Summary 

Figure ES-2. 1990 Control and No-control Scenario 

Emissions (in millions of short tons). 

were 75 percent lower under the control scenario by 

1990 than under the no-control scenario. This substantial difference is primarily due to vigorous efforts 

in the 1970s to reduce visible emissions from utility 

and industrial smokestacks. 

Lead (Pb) emissions for 1990 are reduced by about 

99 percent from a no-control level of 237,000 tons to 

about 3,000 tons under the control scenario.1 The vast 

majority of the difference in lead emissions under the 

two scenarios is attributable to reductions in the use 

of leaded gasoline. 

These reductions were achieved during a period 

in which population grew by 22.3 percent and the national economy grew by 70 percent. 

Air Quality 

The substantial reductions in air pollutant emissions achieved by the Clean Air Act translate into significantly improved air quality throughout the U.S. 

For sulfur dioxide, nitrogen oxides, and carbon monoxide, the improvements in air quality under the control scenario are assumed to be proportional to the 

estimated reduction in emissions. This is because, for 

these pollutants, changes in ambient concentrations 

in a particular area are strongly related to changes in 

emissions in that area. While the differences in control and no-control scenario air quality for each of these 

pollutants vary from place to place because of local 

variability in emissions reductions, by 1990 the national average improvements in air quality for these 

pollutants were: 40 percent reduction in sulfur dioxide, 30 percent reduction in nitrogen oxides, and 50 

percent reduction in carbon monoxide. 

Ground-level ozone is formed by the chemical reaction of certain airborne pollutants in the presence 

of sunlight. Reductions in ground-level ozone are 

therefore achieved through reductions in emissions 

of its precursor pollutants, particularly volatile organic 

compounds (VOCs) and nitrogen oxides (NOX).2 The 

differences in ambient ozone concentrations estimated 

under the control scenario vary significantly from one 

location to another, primarily because of local differences in the relative proportion of VOCs and NOX, 

weather conditions, and specific precursor emissions 

reductions. On a national average basis, ozone concentrations in 1990 are about 15 percent lower under 

the control scenario. For several reasons, this overall 

reduction in ozone is significantly less than the 30 

percent reduction in precursor NOX and 45 percent 

reduction in precursor VOCs. First, significant natural (i.e., biogenic) sources of VOCs limit the level of 

ozone reduction achieved by reductions in man-made 

(i.e., anthropogenic) VOCs. Second, current knowledge of atmospheric photochemistry suggests that 

ozone reductions will tend to be proportionally smaller 

than reductions in precursor emissions. Finally, the 

plume model system used to estimate changes in urban ozone for this study is incapable of handling longrange transport of ozone from upwind areas and multiday pollution events in a realistic manner. 

There are many pollutants which contribute to 

ambient concentrations of particulate matter. The relative contributions of these individual pollutant species to ambient particulate matter concentrations vary 

from one region of the country to the next, and from 

urban areas to rural areas. The most important particle species, from a human health standpoint, may be 

the fine particles which can be respired deep into the 

lungs. While some fine particles are directly emitted 

by sources, the most important fine particle species 

are formed in the atmosphere through chemical conversion of gaseous pollutants. These species are referred to as secondary particles. The three most important secondary particles are (1) sulfates, which 

derive primarily from sulfur dioxide emissions; (2) 

nitrates, which derive primarily from nitrogen oxides 

emissions; and (3) organic aerosols, which can be directly emitted or can form from volatile organic com1

 Results for lead are not shown in Figure ES-2 because the absolute levels of lead emissions are measured in thousands, not 

millions, of tons and will not be discernible on a graph of this scale. 2

 Ambient NOx concentrations are driven by anthropogenic emissions whereas ambient VOCs result from both anthropogenic 

and biogenic sources (e.g., terpenes emitted by trees). 

ES-3 
An nu al Effects Avoi de d /2 (thousands) 

En dpoin t Pollutant(s) 

%ile 

Me an 

%ile 

Un it 

Premature Mortality PM /3 30 and over 112 184 257 cases 

Premature Mortality Lead all 7 2 54 cases 

Chronic Bronchitis PM all 498 674 886 cases 

Lost IQ Points Lead children 7,440 10,400 13,000 points 

IQ less than 70 Lead children 31 45 60 cases 

Hypertension Lead men 20-74 9,740 12,600 15,600 cases 

Coronary Heart Disease Lead 40-74 0 2 64 cases 

At herothrombotic brain infarction Lead 40-74 0 15 cases 

Initial cerebrovascular accident Lead 40-74 0 19 cases 

Hospital Admissions 

Al l Respiratory PM & Ozone all 75 89 103 cases 

Chronic Obst ructive Pulmonary 

 Disease & Pneumonia 

PM & Ozone over 65 52 62 72 cases 

Ischemic H eart Disease PM over 65 7 9 31 cases 

Congestive Heart Fail ure PM & CO 65 and over 28 39 50 cases 

Ot her Respiratory-Related A ilments 

Shortness of breath, days PM children 14,800 68,000 133,000 days 

Acute Bronchitis PM children 0 ,700 21,600 cases 

Upper & Lower Re spiratory 

Symptoms 

PM children 5,400 9,500 13,400 cases 

Any of 19 Ac ut e Symptoms PM & Ozone 18-65 15,400 130,000 244,000 cases 

Ast hma Att acks PM & Ozone ast hmatics 170 850 1,520 cases 

Increase in Respiratory Illness NO2 all 4,840 9,800 14,000 cases 

Any Symptom SO2 ast hmatics 26 264 706 cases 

Restri cted Act ivity and Work Loss D ays 

Minor Restricted Act ivity Days PM & Ozone 18-65 107,000 125,000 143,000 days 

Work Loss Days PM 18-65 19,400 22,600 25,600 days 

 /1 The following additional human welfare effect s were quantified direct ly in economic terms: household soiling 

damage, visibility impairment, decreased worker product ivity, and agri cult ural yield changes. /2 The 5th and 95th percentile outcomes represent the lower and upper bounds, respectively, of the 90 percent 

credible int erval for each effect as estimat ed by uncertainty modeling. The mean is the arit hmetic 

average of all estimates derived by the uncertainty modeling. See Chapter 7 and Appendix I for detai ls. /3 In this analysis, PM is used as a proxy pollut ant for all non-Lead (P b) criteri a pollut ants which may contribut e 

to premature mortality. See Chapter 5 and A ppendix D for additional discussion. 

2

2

4 

6 

1

8

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table ES-1. Criteria Pollutant Health Benefits — Estimated Distributions of 1990 Incidences 

of Avoided Health Effects (in thousands of incidences reduced) for 48 State Population. /1 

En dpoin t Pollutant(s) 

Affected 

Population 

An nu al Effects Avoi de d /2 (thousands) 

5th 95th Un it

%ile 

Me an 

%ile 

Premature Mortality PM /3 

Premature Mortality Lead 

Chronic Bronchitis PM 

Lost IQ Points Lead 

IQ less than 70 Lead 

Hypertension Lead 

Coronary Heart Disease Lead 

At herothrombotic brain infarction Lead 

Initial cerebrovascular accident Lead 

Hospital Admissions 

Al l Respiratory PM & Ozone 

Chronic Obst ructive Pulmonary 

Disease & Pneumonia 

PM & Ozone 

Ischemic H eart Disease PM 

Congestive Heart Fail ure PM & CO 

Ot her Respiratory-Related A ilments 

Shortness of breath, days PM 

Acute Bronchitis PM 

Upper & Lower Re spiratory 

Symptoms 

PM 

Any of 19 Ac ut e Symptoms PM & Ozone 

Ast hma Att acks PM & Ozone 

Increase in Respiratory Illness NO2 

Any Symptom SO2 

Restri cted Act ivity and Work Loss D ays 

Minor Restricted Act ivity Days PM & Ozone 

Work Loss Days PM 

30 and over 

all 

all 

children 

children 

men 20-74 

40-74 

40-74 

40-74 

all 

over 65 

over 65 

65 and over 

children 

children 

children 

18-65 

ast hmatics 

all 

ast hmatics 

18-65 

18-65 

112 184 257 

7 22 54 

498 674 886 

7,440 10,400 13,000 

31 45 60 

9,740 12,600 15,600 

0 22 64 

0 4 15 

0 6 19 

75 89 103 

52 62 72 

7 19 31 

28 39 50 

14,800 68,000 133,000 

0 8,700 21,600 

5,400 9,500 13,400 

15,400 130,000 244,000 

170 850 1,520 

4,840 9,800 14,000 

26 264 706 

107,000 125,000 143,000 

19,400 22,600 25,600 

cases 

cases 

cases 

points 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

days 

cases 

cases 

cases 

cases 

cases 

cases 

days 

days 

/1 The following additional human welfare effects were quantified directly in economic terms: household soiling 

damage, visibility impairment, decreased worker productivity, and agricultural yield changes. /2 The 5th and 95th percentile outcomes represent the lower and upper bounds, respectively, of the 90 percent 

credible interval for each effect as estimated by uncertainty modeling. The mean is the arithmetic 

average of all estimates derived by the uncertainty modeling. See Chapter 7 and Appendix I for details. /3 In this analysis, PM is used as a proxy pollutant for all non-Lead (Pb) criteria pollutants which may contribute 

to premature mortality. See Chapter 5 and Appendix D for additional discussion. 

pound emissions. This highlights an important and 

unique feature of particulate matter as an ambient pollutant: more than any other pollutant, reductions in 

particulate matter are actually achieved through reductions in a wide variety of air pollutants. In other 

words, controlling particulate matter means controlling “air pollution” in a very broad sense. In the present 

analysis, reductions in sulfur dioxide, nitrogen oxides, 

volatile organic compounds, and directly-emitted primary particles achieved by the Clean Air Act result in 

a national average reduction in total suspended particulate matter of about 45 percent by 1990. For the 

smaller particles which are of greater concern from a 

health effects standpoint (i.e., PM10 and PM2.5), the 

national average reductions were also about 45 percent. 

Reductions in sulfur dioxide and nitrogen oxides 

also translate into reductions in formation, transport, 

and deposition of secondarily formed acidic compounds such as sulfate and nitric acid. These are the 

principal pollutants responsible for acid precipitation, 

or “acid rain.” Under the control scenario, sulfur and 

nitrogen deposition are significantly lower by 1990 

than under the no-control scenario throughout the 31 

eastern states covered by EPA’s Regional Acid Deposition Model (RADM). Percentage decreases in sulfur deposition range up to more than 40 percent in the 

upper Great Lakes and Florida-Southeast Atlantic 

Coast areas, primarily because the no-control scenario 

projects significant increases in the use of high-sulfur 

fuels by utilities in the upper Great Lakes and Gulf 

ES-4

Nonmone ti zed Adve rse Effects 

Large Changes in Pulmonary Function 

Other Chronic Respiratory Dise ases 

Inflammation of t he Lung 

Chronic A sthma and Bronchit is 

Changes in Pulmonary Function 

Increased Air way Responsiveness to St imuli 

Centroacinar Fibrosi s 

Inflammation of t he Lung 

Immunologica l Changes 

Chronic Respiratory D iseases 

Extrapulmonary Effect s (i.e., other organ systems) 

Forest and other Ecological Effects 

Materials Damage 

Decreased Time to Onset of Angi na 

Behavioral Effects 

Other Cardiovascular Effect s 

Developmental Effects 

Respiratory Symptoms in N on-A sthmat ics 

Hospit al Admis sions 

Agricult ural Effect s 

Materials Damage 

Ecological Effect s 

Increased Air way Responsiveness to St imuli 

Decreased Pulmonary Function 

Inflammation of t he Lung 

Immunologica l Changes 

Eye Irritation 

Materials Damage 

Eut rophication (e.g., Chesapeake Bay) 

Acid D eposition 

Cardiovascular Diseases 

Reproduct ive Effects in Women 

Other N eurobehavioral, Physiological Effect s in 

Children 

Developmental Effects from Maternal Exposure, inc 

IQ Loss /1 

Ecological Effect s 

All Human Healt h Effects 

Ecological Effect s 

/1 IQ loss from direct , as opposed to maternal, exposure is quantified and 

monetized. See T abl es E S-1 And E S-3. 

Executive Summary 

Coast states. Nitrogen deposition is also significantly lower under the control scenario, with percentage decreases reaching levels of 25 percent or 

higher along the Eastern Seaboard, primarily due 

to higher projected emissions of motor vehicle nitrogen oxides under the no-control scenario. 

Finally, decreases in ambient concentrations of 

light-scattering pollutants, such as sulfates and nitrates, are estimated to lead to perceptible improvements in visibility throughout the eastern states and 

southwestern urban areas modeled for this study. 

Physical Effects 

The lower ambient concentrations of sulfur dioxide, nitrogen oxides, particulate matter, carbon 

monoxide, ozone and lead under the control scenario yield a substantial variety of human health, 

welfare and ecological benefits. For a number of 

these benefit categories, quantitative functions are 

available from the scientific literature which allow 

estimation of the reduction in incidence of adverse 

effects. Examples of these categories include the 

human mortality and morbidity effects of a number of pollutants, the neurobehavioral effects among 

children caused by exposure to lead, visibility impairment, and effects on yields for some agricultural products. 

A number of benefit categories, however, can 

not be quantified and/or monetized for a variety of 

reasons. In some cases, substantial scientific uncertainties prevail regarding the existence and magnitude of adverse effects (e.g., the contribution of 

ozone to air pollution-related mortality). In other 

cases, strong scientific evidence of an effect exists, 

but data are still too limited to support quantitative 

estimates of incidence reduction (e.g., changes in 

lung function associated with long-term exposure 

to ozone). Finally, there are effects for which there 

is sufficient information to estimate incidence reduction, but for which there are no available economic value measures; thus reductions in adverse 

effects cannot be expressed in monetary terms. Examples of this last category include relatively small 

pulmonary function decrements caused by acute 

exposures to ozone and reduced time to onset of 

angina pain caused by carbon monoxide exposure. 

Table ES-1 provides a summary of the key differences in quantified human health outcomes estimated under the control and no-control scenarios. 

Results are presented as thousands of cases avoided 

in 1990 due to control of the pollutants listed in the 

table and reflect reductions estimated for the entire 

U.S. population living in the 48 continental states. Epidemiological research alone cannot prove whether a 

cause-effect relationship exists between an individual 

Table ES-2. Major Nonmonetized, Adverse Effects 

Reduced by the Clean Air Act. 

Pollutant 

Particulate 

Matter 

Ozone 

Carbon 

Monoxide 

Sulfur 

Dioxide 

Nitrogen 

Oxides 

Lead 

Air Toxics 

Nonmonetized Adverse Effects 

Large Changes in Pulmonary Function

Other Chronic Respiratory Diseases

Inflammation of the Lung

Chronic Asthma and Bronchitis

Changes in Pulmonary Function

Increased Airway Responsiveness to Stimuli

Centroacinar Fibrosis

Inflammation of the Lung

Immunological Changes

Chronic Respiratory Diseases

Extrapulmonary Effects (i.e., other organ systems)

Forest and other Ecological Effects

Materials Damage

Decreased Time to Onset of Angina

Behavioral Effects

Other Cardiovascular Effects

Developmental Effects

Respiratory Symptoms in Non-Asthmatics

Hospital Admissions

Agricultural Effects

Materials Damage

Ecological Effects

Increased Airway Responsiveness to Stimuli

Decreased Pulmonary Function

Inflammation of the Lung

Immunological Changes

Eye Irritation

Materials Damage

Eutrophication (e.g., Chesapeake Bay)

Acid Deposition

Cardiovascular Diseases

Reproductive Effects in Women

Other Neurobehavioral, Physiological Effects in

Children

Developmental Effects from Maternal Exposure, inc

IQ Loss /1 

Ecological Effects 

All Human Health Effects 

Ecological Effects 

/1 IQ loss from direct, as opposed to maternal, exposure is quantified and 

monetized. See Tables ES-1 And ES-3. 

ES-5

E nd p oint P o llut ant V a lu a tion (m ean est )

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

pollutant and an observed health effect. Although not 

universally accepted, this study uses the epidemiological findings about correlations between pollution and 

observed health effects to estimate changes in the number of health effects that would occur if pollution levels change. A range is presented along with the mean 

estimate for each effect, reflecting uncertainties which 

have been quantified in the underlying health effects 

literature. 

Adverse human health effects of the Clean Air 

restrial ecosystems. In addition to any intrinsic value 

to be attributed to these ecological systems, human 

welfare is enhanced through improvements in a variety of ecological services. For example, protection of 

freshwater ecosystems achieved through reductions 

in deposition of acidic air pollutants may improve commercial and recreational fishing. Other potential ecological benefits of reduced acid deposition include improved wildlife viewing, maintenance of biodiversity, 

and nutrient cycling. Increased growth and productivity of U.S. forests may have resulted from reducAct “criteria pollutants” sulfur dioxide, 

nitrogen oxides, ozone, particulate matter, carbon monoxide, and lead dominate 

the quantitative estimates in part because, although there are important residual uncertainties, evidence of physical consequences is greatest for these 

pollutants. The Clean Air Act yielded 

other benefits, however, which are important even though they are uncertain 

and/or difficult to quantify. These other 

benefit categories include (a) all benefits 

accruing from reductions in hazardous 

air pollutants (also referred to as air 

toxics), (b) reductions in damage to cultural resources, buildings, and other materials, (c) reductions in adverse effects 

on wetland, forest, and aquatic ecosystems, and (d) a variety of additional human health and welfare effects of criteria pollutants. A more complete list of 

these nonmonetized effects is presented 

in Table ES-2. 

In addition to controlling the six criteria pollutants, the 1970 and 1977 Clean 

Air Act Amendments led to reductions 

in ambient concentrations of a small 

number of hazardous air pollutants. Although they are not fully quantified in 

this report, control of these pollutants 

resulted both from regulatory standards 

set specifically to control hazardous air 

pollutants and from incidental reductions 

achieved through programs aimed at 

controlling criteria pollutants. 

Existing scientific research suggests 

that reductions in both hazardous air 

pollutants and criteria pollutants yielded 

widespread improvements in the functioning and quality of aquatic and terTable ES-3. Cen tra l Estima tes of Econ o mic Value per Unit of 

Avoide d Effe ct (in 1990 dollars). 

E nd p oint P o llut ant V a lu a tion (m ean est..) 

Mortality P M & Lead $4,800,00 0 per case /1 

Chronic Bronchitis P M $260,00 0 per case 

IQ C ha nge s 

Lost IQ Points Lead $3,00 0 per IQ point 

IQ le ss tha n 70 Lead $42,00 0 per case 

Hype rtension Lead $68 0 per case 

Str oke s /2 Lead $200,00 0 per case-male s3 

$150,00 0 per case-fe male s3 

Coron ar y H ea rt Dise ase Lead $52,000 per case 

Hospital A dmissions 

Ischemic He ar t Dise a se PM $10,30 0 per case 

Congestive H ea rt F ailure P M $8,30 0 per case 

COP D P M & Ozone $8,10 0 per case 

Pneumonia PM & Ozone $7,90 0 per case 

All Respir atory PM & Ozone $6,10 0 per case 

Respir atory Illness and Symptoms 

Acute Bro nch itis PM $4 5 per case 

Acute Asth ma P M & Ozone $3 2 per case 

Acute Re spira tory S ymptoms P M, Ozone, N O2, $1 8 per case 

SO2 

Uppe r Re spira tory Symptoms PM $1 9 per case 

Low er Respira tory Sympto ms PM $1 2 per case 

Shortness of Breath PM $ 5.30 per day 

Work Loss Da ys P M $8 3 per day 

Mild Re stric ted A ctivity Da ys P M & Ozone $3 8 per day 

Welfare Benefits 

Visibility DeciView $14 per unit change 

in DeciView 

Household Soiling PM $ 2.50 per household 

per PM-10 

change 

Decreased Worker Productivity Ozone $ 1 /4 

Agricultur e (Net Surplu s) Ozone Change in Economic Surplus 

/1 Alternative results, based on assigning a valu e of $29 3,000 for each life-y ear lost ar e 

presen ted on pg. ES-9. 

/2 Strok es are comprised of atherothrombotic brain infarctions and cerebrov ascular 

accid ents; both are estimated to hav e the same mon etary value. 

/3 The different v aluations for stroke cases reflect di fferences in lo st earnings between 

males and females. See Ap pen dix G for a more complete discussion o f v aluin g 

reductions in strokes. 

/4 Decreased productivity valu ed as change in daily wages: $1 per wo rker per 10% 

decrease in ozone. 

ES-6

En dpoin t Pollu tant(s) 5th %ile Me an 95th %i le 

Mortal ity P M $2,369 $16,632 $40,597 

Mortal ity Lead $121 $1,339 $3,910 

Chronic Bronchitis P M $409 $3,313 $10,401 

IQ (Lost IQ Pt s. + Children w/ 

IQ<70) 

Lead $271 $399 $551 

Hypertens ion Lead $77 $98 $120 

Hospit al Admissions P M, Ozone, Lead, & CO $27 $57 $120 

Respiratory-Related 

Symptoms, Rest ricted 

 Activity, & Decreased 

Productivity 

P M, Ozone, NO2, & SO2 $123 $182 $261 

Soi ling Damage P M $6 $74 $192 

Vi sibil ity particulat es $38 $54 $71 

Agricult ure (Net Surplus) Ozone $11 $23 $3 5 

Executive Summary 

tions in ground-level ozone. More vigorous forest ecosystems in turn yield a variety of benefits, including 

increased timber production; improved forest aesthetics for people enjoying outdoor activities such as hunting, fishing, and camping; and improvements in ecological services such as nutrient cycling and temporary sequestration of global warming gases. These improvements in ecological structure and function have 

not been quantified in this assessment. 

Economic Valuation 

Estimating the reduced incidence of physical effects provides a valuable measure of health benefits 

for individual endpoints. However, to compare or aggregate benefits across endpoints, the benefits must 

be monetized. Assigning a monetary value to avoided 

incidences of each effect permits a summation, in 

terms of dollars, of monetized benefits realized as a 

result of the Clean Air Act, and allows that summation to be compared to the cost of the Clean Air Act. 

Before proceeding through this step, it is important to recognize the substantial controversies and uncertainties which pervade attempts to characterize adverse human health and ecological effects of pollution in dollar terms. To many, dollar-based estimates 

of the value of avoiding outcomes such as loss of human life, pain and suffering, or ecological degradation do not capture the full and true value to society as 

a whole of avoiding or reducing these effects. Adherents to this view tend to favor assessment procedures 

which (a) adopt the most technically defensible dollar-based valuation estimates for analytical purposes 

but (b) leave the moral dimensions of policy evaluation to those who must decide whether, and how, to 

use cost-benefit results in making public policy decisions. This is the paradigm adopted in the present 

study. Given the Congressional mandate to perform a 

cost-benefit study of the Clean Air Act, the Project 

Team has endeavored to apply widely-recognized, 

customary techniques of Applied Economics to perform this cost-benefit analysis. However, EPA believes there are social and personal values furthered 

by the Clean Air Act which have not been effectively 

captured by the dollar-based measures used in this 

study. Therefore, EPA strongly encourages readers to 

look beyond the dollar-based comparison of costs and 

benefits of the Clean Air Act and consider the broader 

value of the reductions in adverse health and environmental effects which have been achieved as well as 

any additional adverse consequences of regulation 

which may not be reflected in the cost estimates reported herein. 

For this study, unit valuation estimates are derived 

from the economics literature and reported in dollars 

per case (or, in some cases, episode or symptom-day) 

avoided for health effects and dollars per unit of 

Table ES-4. Total Estimated Monetized Benefits by Endpoint Category for 48 State Population 

for 1970 to 1990 Period (in billions of 1990 dollars). 

En dpoin t Pollu tant(s) 

Present Value 

5th %ile Me an 95th %i le 

Mortal ity P M 

Mortal ity Lead 

Chronic Bronchitis P M 

IQ (Lost IQ Pt s. + Children w/ 

IQ<70) 

Lead 

Hypertens ion Lead 

Hospit al Admissions P M, Ozone, Lead, & CO 

Respiratory-Related 

Symptoms, Rest ricted 

Activity, & Decreased 

Productivity 

P M, Ozone, NO2, & SO2 

Soi ling Damage P M 

Vi sibil ity particulat es 

Agricult ure (Net Surplus) Ozone 

$2,369 

$121 

$409 

$271 

$77 

$27 

$123 

$6 

$38 

$11 

$16,632 

$1,339 

$3,313 

$399 

$98 

$57 

$182 

$74 

$54 

$23 

$40,597 

$3,910 

$10,401 

$551 

$120 

$120 

$261 

$192 

$71 

$3 5 

3

 All of these summary results are present values of the 1970 to 1990 streams of benefits and costs, discounted at five percent. 

ES-7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

avoided damage for human welfare effects. Similar to 

estimates of physical effects provided by health studies, each of the monetary values of benefits applied in 

this analysis can be expressed in terms of a mean value 

and a range around the mean estimate. This range reflects the uncertainty in the economic valuation literature associated with a given effect. These value 

ranges, and the approaches used to derive them, are 

described in Chapter 6 and Appendix I for each of the 

effects monetized in this study. The mean values of 

these ranges are shown in Table ES-3. 

Monetized Benefits and Costs 

The total monetized economic benefit attributable 

to the Clean Air Act is derived by applying the unit 

values (or ranges of values) to the stream of 

monetizable physical effects estimated for the 1970 

to 1990 period. In developing these estimates, steps 

are taken to avoid double-counting of benefits. In addition, a computer simulation model is used to estimate ranges of plausible outcomes for the benefits 

estimates reflecting uncertainties in the physical effects and economic valuation literature (see Chapter 

7 and Appendix I for details). 

The economic benefit estimation model then generated a range of economic values for the differences 

in physical outcomes under the control and no-control scenarios for the target years of the benefits analysis: 1975, 1980, 1985, and 1990. Linear interpolation 

between these target years is used to estimate benefits in intervening years. These yearly results are then 

adjusted to their equivalent value in the year 1990 and 

summed to yield a range and mean estimate for the 

total monetized benefits of the Clean Air Act from 

Figure ES-3. Total Estimated Direct Compliance Costs of 

the CAA (in trillions of inflation-adjusted dollars). 

Costs Benefits 

0 

10 

20 

30 

40 

50 95th percentile 

5th percentile 

Mean 

Trillions of 1990 Dollars 

1970 to 1990. These results are summarized in Table 

ES-4. 

Combining these benefits results with the cost estimates presented earlier yields the following analytical outcomes.3 

•	 The total monetized benefits of the Clean 

Air Act realized during the period from 

1970 to 1990 range from 5.6 to 49.4 trillion 

dollars, with a central estimate of 22.2 trillion dollars. 

•	 By comparison, the value of direct compliance expenditures over the same period 

equals approximately 0.5 trillion dollars. 

•	 Subtracting costs from benefits results in 

net, direct, monetized benefits ranging 

from 5.1 to 48.9 trillion dollars, with a central estimate of 21.7 trillion dollars, for the 

1970 to 1990 period. 

•	 The lower bound of this range may go down 

and the upper bound may go up if analytical uncertainties associated with compliance costs, macroeconomic effects, emissions projections, and air quality modeling could be quantified and incorporated 

in the uncertainty analysis. While the range 

already reflects many important uncertainties in the physical effects and economic 

valuation steps, the range might also 

broaden further if additional uncertainties 

in these two steps could be quantified. 

•	 The central estimate of 22.2 trillion dollars 

in benefits may be a significant underestimate due to the exclusion of large numbers 

of benefits from the monetized benefit estimate (e.g., all air toxics effects, ecosystem 

effects, numerous human health effects). 

Figure ES-3 provides a graphical representation 

of the estimated range of total monetized benefits and 

compares this range to estimated direct compliance 

costs. Clearly, even the lower bound estimate of monetized benefits substantially exceeds the costs of the 

historical Clean Air Act. As shown by the yearly data 

presented in Chapter 7, monetized benefits consistently and substantially exceeded costs throughout the 

1970 to 1990 period. 

ES-8

---

Executive Summary 

Table ES-5. Alternative Mortality Benefits Mean 

Estimates for 1970 to 1990 (in trillions of 1990 

dollars) Compared to Total 1970 to 1990 Compliance 

Costs. 

Mortal ity Benefit s 

(trillions of doll ars) 

Benefit Estimation Met hod PM PM+Pb 

St atist ical l ife method ($4.8M/cas e) 16.6 18.0 

Life-years lost method ($293,000/year) 9.1 10.1 

Total compliance cost 0.5 

Mortal ity Benefit s 

(trillions of doll ars) 

Benefit Estimation Met hod PM PM+Pb 

St atist ical l ife method ($4.8M/cas e) 16.6 18.0 

Life-years lost method ($293,000/year) 9.1 10.1 

Total compliance cost 0.5 

Alternative Results 

The primary results of this analysis, including aggregate cost and benefit estimates which reflect many 

elements of the uncertainty associated with them, are 

presented above. However, some additional analysis 

is required to address an important issue raised by the 

EPA Science Advisory Board Council on Clean Air 

Act Compliance Analysis (a.k.a. Council) charged 

with reviewing the present study. Specifically, the 

Council believes it is appropriate to also display alternative premature mortality results based on an approach which estimates, and assigns a value to, the 

loss of life-years (i.e., the reduction in years of remaining life expectancy) resulting from the pollution 

exposure. The Council’s position is based on the conclusion that older individuals are more susceptible to 

air pollution-induced mortality. EPA believes, however, that the simplifying assumptions which must be 

adopted to implement a life-years lost approach render its results less reliable, even for the purposes of 

economic efficiency analysis, than a value of statistical life approach. In addition, EPA is concerned about 

any analytical methodology which may be interpreted 

to justify conferring less environmental protection on 

particular individuals or groups of individuals (e.g., 

the elderly and/or sick). EPA therefore prefers at this 

time to continue with its current practice of assigning 

the same economic value to incidences of premature 

mortality regardless of the age and health status of 

those affected, and the primary results presented above 

reflect this view. Nevertheless, complete alternative 

results based on a value of statistical life-years lost 

(VSLY) approach are presented in Chapter 7 and Appendix I and are summarized below. 

Table ES-5 summarizes and compares the results 

of the mortality benefits estimates based on the value 

of statistical life (VSL) and VSLY approaches. Estimated 1970 to 1990 benefits from PM-related mortality alone and total mortality (i.e., PM plus Lead) 

benefits are reported, along with total compliance costs 

for the same period. Adding the VSLY-based mortality benefits estimates to the non-mortality benefits 

estimates from Table ES-4 yields the following results for the overall analysis. 

•	 Alternate Result: The total monetized benefits of the Clean Air Act realized during 

the period from 1970 to 1990 range from 

4.8 to 28.7 trillion dollars, with a central 

estimate of 14.3 trillion dollars. 

•	 Alternate Result: Subtracting costs from 

benefits results in net, direct, monetized 

benefits ranging from 4.3 to 28.2 trillion 

dollars, with a central estimate of 13.7 trillion dollars, for the 1970 to 1990 period. 

The results indicate that the choice of valuation 

methodology significantly affects the estimated monetized value of historical reductions in air pollutionrelated premature mortality. However, the downward 

adjustment which would result from applying a VSLY 

approach in lieu of a VSL approach does not change 

the basic outcome of this study, viz. the estimated 

monetized benefits of the historical Clean Air Act 

substantially exceed the estimated historical costs of 

compliance. 

Conclusions and Future 

Directions 

First and foremost, these results indicate that the 

benefits of the Clean Air Act and associated control 

programs substantially exceeded costs. Even considering the large number of important uncertainties permeating each step of the analysis, it is extremely unlikely that the converse could be true. 

A second important implication of this study is 

that a large proportion of the monetized benefits of 

the historical Clean Air Act derive from reducing two 

pollutants: lead and particulate matter4

 (see Table ES4). Some may argue that, while programs to control 

these two pollutants may have yielded measurable 

4

 Ambient particulate matter results from emissions of a wide array of precursor pollutants, including sulfur dioxide, nitrogen 

oxides, and organic compounds. 

ES-9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

benefits in excess of measurable costs, estimates of 

measurable benefits of many other historical Clean 

Air Act programs and standards considered in isolation might not have exceeded measurable costs. While 

this may or may not be true, this analysis provides no 

evidence to support or reject such conjectures. On the 

cost side, the historical expenditure data used in this 

analysis are not structured in ways which allow attribution of control costs to specific programs or standards. On the benefit side, most control programs 

yielded a variety of benefits, many of which included 

reductions in other pollutants such as ambient particulate matter. For example, new source performance 

standards for sulfur dioxide emissions from coal-fired 

utility plants yielded benefits beyond those associated 

with reducing exposures to gaseous sulfur dioxide. 

The reductions in sulfur dioxide emissions also led to 

reductions in ambient fine particle sulfates, yielding 

human health, ecological, and visibility benefits. 

This retrospective study highlights important areas of uncertainty associated with many of the monetized benefits included in the quantitative analysis 

and lists benefit categories which could not be quantified or monetized given the current state of the science. Additional research in these areas may reduce 

critical uncertainties and/or improve the comprehensiveness of future assessments. Particularly important 

areas where further research might reduce critical 

uncertainties include particulate matter-related mortality incidence, valuation of premature mortality, and 

valuation of particulate-related chronic bronchitis and 

cardiovascular disease. Additional research on hazardous air pollutants and on air pollution-related 

changes in ecosystem structure and function might 

help improve the comprehensiveness of future benefit studies. (See Appendix J for further discussion.) 

Finally, the results of this retrospective study provide useful lessons with respect to the value and the 

limitations of cost-benefit analysis as a tool for evaluating environmental programs. Cost-benefit analysis 

can provide a valuable framework for organizing and 

evaluating information on the effects of environmental programs. When used properly, cost-benefit analysis can help illuminate important effects of changes 

in policy and can help set priorities for closing information gaps and reducing uncertainty. Such proper 

use, however, requires that sufficient levels of time 

and resources be provided to permit careful, thorough, 

and technically and scientifically sound data-gathering and analysis. When cost-benefit analyses are presented without effective characterization of the uncertainties associated with the results, cost-benefit 

studies can be used in highly misleading and damaging ways. Given the substantial uncertainties which 

permeate cost-benefit assessment of environmental 

programs, as demonstrated by the broad range of estimated benefits presented in this study, cost-benefit 

analysis is best used to inform, but not dictate, decisions related to environmental protection policies, 

programs, and research. 

ES-10

1 

Introduction 

Background and Purpose 

As part of the Clean Air Act Amendments of 1990, 

Congress established a requirement under section 812 

that EPA develop periodic Reports to Congress estimating the benefits and costs of the Clean Air Act 

itself. The first such report was to be a retrospective 

analysis, with a series of prospective analyses to follow every two years thereafter. This report represents 

the retrospective study, covering the period beginning 

with passage of the Clean Air Act Amendments of 

1970, until 1990 when Congress enacted the most recent comprehensive amendments to the Act. 

Since the legislative history associated with section 812 is sparse, there is considerable uncertainty 

regarding Congressional intent behind the requirement 

for periodic cost-benefit evaluations of the Clean Air 

Act (CAA). However, EPA believes the principal goal 

of these amendments was that EPA should develop, 

and periodically exercise, the ability to provide Congress and the public with up-to-date, comprehensive 

information about the economic costs, economic benefits, and health, welfare, and ecological effects of 

CAA programs. The results of such analyses might 

then provide useful information for refinement of CAA 

programs during future reauthorizations of the Act. 

The retrospective analysis presented in this Report to Congress has been designed to provide an unprecedented examination of the overall costs and benefits of the historical Clean Air Act. Many other analyses have attempted to identify the isolated effects of 

individual standards or programs, but no analysis with 

the present degree of validity, breadth and integration 

has ever been successfully developed. Despite data 

limitations, considerable scientific uncertainties, and 

severe resource constraints; the EPA Project Team was 

able to develop a broad assessment of the costs and 

benefits associated with the major CAA programs of 

the 1970 to 1990 period. Beyond the statutory goals 

of section 812, EPA intends to use the results of this 

study to help support decisions on future investments 

in air pollution research. Finally, many of the methodologies and modeling systems developed for the 

retrospective study may be applied in the future to the 

ongoing series of section 812 prospective studies. 

Clean Air Act Requirements, 

1970 to 1990 

The Clean Air Act establishes a framework for 

the attainment and maintenance of clean and healthful air quality levels. The Clean Air Act was enacted 

in 1970 and amended twice — in 1977 and most recently in 1990. The 1970 Clean Air Act contained a 

number of key provisions. First, EPA was directed to 

establish national ambient air quality standards for the 

major criteria air pollutants. The states were required 

to develop implementation plans describing how they 

would control emission limits from individual sources 

to meet and maintain the national standards. Second, 

the 1970 CAA contained deadlines and strengthened 

enforcement of emission limitations and state plans 

with measures involving both the states and the federal government. Third, the 1970 Act forced new 

sources to meet standards based on the best available 

technology. Finally, the Clean Air Act of 1970 addressed hazardous pollutants and automobile exhausts. 

The 1977 Clean Air Act Amendments also set new 

requirements on clean areas already in attainment with 

the national ambient air quality standards. In addition, 

the 1977 Amendments set out provisions to help areas that failed to comply with deadlines for achievement of the national ambient air quality standards. For 

example, permits for new major sources and modifications were required. 

The 1990 Clean Air Act Amendments considerably strengthened the earlier versions of the Act. With 

respect to nonattainment, the Act set forth a detailed 

and graduated program, reflecting the fact that problems in some areas are more difficult and complex 

than others. The 1990 Act also established a list of 

189 regulated hazardous air pollutants and a multistep program for controlling emissions of these toxic 

air pollutants. Significant control programs were also 

established for emissions of acid rain precursors and 

stratospheric ozone-depleting chemicals. The biggest 

regulatory procedural change in the Act is the new 

permit program where all major sources are now required to obtain an operating permit. Finally, the 

amendments considerably expanded the enforcement 

provisions of the Clean Air Act, adding administrative penalties and increasing potential civil penalties. 

1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Section 812 of the Clean Air Act 

Amendments of 1990 

Section 812 of the Clean Air Act Amendments of 

1990 requires the EPA to perform a “retrospective” 

analysis which assesses the costs and benefits to the 

public health, economy and the environment of clean 

air legislation enacted prior to the 1990 amendments. 

Section 812 directs that EPA shall measure the effects 

on “employment, productivity, cost of living, economic 

growth, and the overall economy of the United States” 

of the Clean Air Act. Section 812 also requires that 

EPA consider all of the economic, public health, and 

environmental benefits of efforts to comply with air 

pollution standards. Finally, section 812 requires EPA 

to evaluate the prospective costs and benefits of the 

Clean Air Act every two years. 

Analytical Design and Review 

Target Variable 

The retrospective analysis was designed to answer 

the following question: 

“How do the overall health, welfare, 

ecological, and economic benefits of Clean 

Air Act programs compare 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. Resources were used 

more efficiently by recognizing that these RIAs, and 

other EPA analyses, provide complete information 

about the costs and benefits of specific rules. Furthermore, in addition to the fact that the RIAs already provide rule-specific benefit and cost estimates, the broadscale approach adopted in the present study precludes 

reliable re-estimation of the benefits and costs of individual standards or programs. On the cost side, this 

study relies on aggregated compliance expenditure 

data from existing surveys. Unfortunately, these data 

do not support reliable allocation of total costs incurred 

to specific emissions reductions for the various pollutants emitted from individual facilities. Therefore, 

it is infeasible in the context of this study to assign 

costs to specific changes in emissions. Further complications emerge on the benefit side. To estimate 

benefits, this study calculates the change in incidences 

of adverse effects implied by changes in ambient concentrations of air pollutants. However, reductions 

achieved in emitted pollutants contribute to changes 

in ambient concentrations of those, or secondarily 

formed, pollutants in ways which are highly complex, 

interactive, and often nonlinear. Therefore, even if 

costs could be reliably matched to changes in emissions, benefits cannot be reliably matched to changes 

in emissions because of the complex, nonlinear relationships between emissions and the changes in ambient concentrations which are used to estimate benefits. 

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: 

a “no-control scenario” and a “control scenario.” The 

no-control scenario essentially freezes federal, state, 

and local air pollution controls at the levels of stringency and effectiveness which prevailed in 1970. The 

control scenario assumes that all federal, state, and 

local rules promulgated pursuant to, or in support of, 

the CAA during 1970 to 1990 were implemented. This 

analysis then estimates the differences between the 

economic and environmental outcomes associated 

with these two scenarios. For more information on 

the scenarios and their relationship to historical trends, 

see Appendix B. 

Key Assumptions 

Two key assumptions were made during the scenario design process to avoid miring the analytical 

process in endless speculation. First, the “no-control” 

scenario was defined to reflect the assumption that no 

additional air pollution controls were imposed by any 

level of government or voluntarily initiated by private entities after 1970. Second, it is assumed that the 

geographic distribution of population and economic 

activity remains the same between the two scenarios. 

The first assumption is an obvious oversimplification. In the absence of the CAA, one would expect 

to see some air pollution abatement activity, either 

voluntary or due to state or local regulations. It is conceivable that state and local regulation would have 

required air pollution abatement equal to—or even 

greater than—that required by the CAA; particularly 

since some states, most notably California, have done 

so. If one were to assume that state and local regulations would have been equivalent to CAA standards, 

then a cost-benefit analysis of the CAA would be a 

meaningless exercise since both costs and benefits 

would equal zero. Any attempt to predict how state 

and local regulations would have differed from the 

CAA would be too speculative to support the credibility of the ensuing analysis. Instead, the no-control 

scenario has been structured to reflect the assumption 

that states and localities would not have invested further in air pollution control programs after 1970 in 

the absence of the federal CAA. That is, this analysis 

accounts for the costs and benefits of all air pollution 

2

Chapter 1: Introduction 

control from 1970 to 1990. Speculation about the pre- • direct cost estimation


cise fraction of costs and benefits attributable exclu- • macroeconomic modeling


sively to the federal CAA is left to others. Neverthe- • emissions modeling


less, it is important to note that state and local govern- • air quality modeling


ments and private initiatives are responsible for a sig- • health and environmental effects estimation


nificant portion of these total costs and total benefits. • economic valuation


At the same time, it must also be acknowledged that • results aggregation and uncertainty character-


the federal CAA played an essential role in achieving ization


these results by helping minimize the advent of pollu- By necessity, these components had to be com­
 tion havens1, establishing greater incentives for pol- pleted sequentially. The emissions modeling effort had
 lution control research and development than indi- to be completed entirely before the air quality models
 vidual state or local rules could provide; organizing could be configured and run; the air quality modeling
 and promoting health and environmental research, results had to be completed before the health and en-
 technology transfer and other information management vironmental consequences of air quality changes could
 and dissemination services; addressing critical inter- be derived; and so on. The analytical sequence, and
 state air pollution problems, including the regional fine the modeled versus actual data basis for each analyti­
 particle pollution which is responsible for much of cal component, are summarized in Figure 1 and de-
 the estimated monetary benefit of historical air pollu- scribed in the remainder of this section.
 tion control; providing financial resources to state and


local government programs; and many other services. The first step of the analysis was to estimate the
 In the end, however, the benefits of historical air pol- total direct costs incurred by public and private enti­
 lution controls were achieved through partnerships ties to comply with post-1970 CAA requirements.
 among all levels of government and with the active These data were obtained directly from Census Bu­
 participation and cooperation of private entities and reau and Bureau of Economic Analysis (BEA) data
 individuals. on compliance expenditures reported by sources, and


from EPA analyses. These direct cost data were then
 The second assumption concerns changing demo- adopted as inputs to the macroeconomic model used
 graphic patterns in response to air pollution. In the to project economic conditions–such as production
 hypothetical no-control world, air quality is worse than


that in the historical “control” world particularly in levels, prices, employment patterns, and other eco­


nomic indicators–under the two scenarios. To ensure urban industrial areas. It is possible that in the no- a consistent basis for scenario comparison, the analy­ control case more people, relative to the control case, 

would move away from the most heavily polluted ar- sis applied the same macroeconomic modeling system to estimate control and no-control scenario eco­ eas. Rather than speculate on the scale of population 

movement, the analysis assumes no differences in nomic conditions.2 First, a control scenario was condemographic patterns between the two scenarios. Simi- structed by running the macroeconomic model using 

larly, the analysis assumes no changes in the spatial actual historical data for input factors such as ecopattern of economic activity. For example: if, in the nomic growth rates during the 1970 to 1990 period. 

The model was then re-run for the no-control scenario no-control case, an industry is expected to produce by, in essence, returning all post-1970 CAA compli­ greater output than it did in the control case, that in- ance expenditures to the economy. With these addi­ creased output is produced by actual historical plants, tional resources available for capital formation, per- avoiding the need to speculate about the location or sonal consumption, and other purposes, overall eco­ other characteristics of new plants providing additional nomic conditions under the no-control scenario dif­ productive capacity. fered from those of the control scenario. In addition 

Analytic Sequence to providing estimates of the difference in overall economic growth and other outcomes under the two sceThe analysis was designed and implemented in a narios, these first two analytical steps were used to 

sequential manner following seven basic steps which define specific economic conditions used as inputs to 

are summarized below and described in detail later in the emissions modeling effort, the first step in the esthis report. The seven major steps were: timation of CAA benefits.3 

1 “Pollution havens” is a term used to identify individual states or localities which permit comparatively high levels of pollution in 

order to attract and hold polluting industries and other activities. 

2

 Using modeled economic conditions for both scenarios has both advantages and disadvantages. The principal disadvantage is that 

historical economic conditions “predicted” by a macroeconomic model will not precisely duplicate actual historical events and conditions. However, this disadvantage is outweighed by the avoidance of distortions and biases which would result from comparing a 

modeled no-control scenario with actual historical conditions. By using the same macroeconomic model for both scenarios, model errors 

and biases essentially cancel out, yielding more robust estimates of scenario differences, which are what this analysis seeks to evaluate. 

3

 For example, the macroeconomic model projected different electricity sales levels under the two scenarios, and these sales levels 

were used as key input assumptions by the utility sector emissions model. 

3

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

4

Chapter 1: Introduction 

Using appropriate economic indicators from the 

macroeconomic model results as inputs, a variety of 

emissions models were run to estimate emissions levels under the two scenarios. These emissions models 

provided estimates of emissions of six major pollutants4 from each of six key emitting sectors: utilities, 

industrial processes, industrial combustion, on-highway vehicles, off-highway vehicles, and commercial/ 

residential sources. The resulting emissions profiles 

reflect state-wide total emissions from each pollutant-sector combination for the years 1975, 1980, 1985, 

and 1990.5 

The next step toward estimation of benefits involved translating these emissions inventories into 

estimates of air quality conditions under each scenario. 

Given the complexity, data requirements, and operating costs of state-of-the-art air quality models–and the 

afore-mentioned resource constraints–the EPA Project 

Team adopted simplified, linear scaling approaches 

for a number of pollutants. However, for ozone and 

other pollutants or air quality conditions which involve 

substantial non-linear formation effects and/or longrange atmospheric transport and transformation, the 

EPA Project Team invested the time and resources 

needed to use more sophisticated modeling systems. 

For example, urban area-specific ozone modeling was 

conducted for 147 urban areas throughout the 48 contiguous states. 

Up to this point of the analysis, both the control 

and no-control scenario were based on modeled conditions and outcomes. However, at the air quality 

modeling step, the analysis returned to a foundation 

based on actual historical conditions and data. Specifically, actual historical air quality monitoring data 

from 1970 to 1990 were used to define the control 

scenario. Air quality conditions under the no-control 

scenario were then derived by scaling the historical 

data adopted for the control scenario by the ratio of 

the modeled control and no-control scenario air quality. This approach took advantage of the richness of 

the historical data on air quality, provided a realistic 

grounding for the benefit measures, and yet retained 

the analytical consistency conferred by using the same 

modeling approach for both scenarios. The outputs of 

this step of the analysis were statistical profiles for 

each pollutant characterizing air quality conditions at 

each monitoring site in the lower 48 states.6 

The control and no-control scenario air quality 

profiles were then used as inputs to a modeling system which translates air quality to physical outcomes 

–such as mortality, emergency room visits, or crop 

yield losses– through the use of concentration-response functions. These concentration-response functions were in turn derived from studies found in the 

scientific literature on the health and ecological effects of air pollutants. At this point, estimates were 

derived 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 monitor.

7 

In the next step, economic valuation models or 

coefficients were used to estimate the economic value 

of the reduction in incidence of those adverse effects 

which were amenable to such monetization. For example, a distribution of unit values derived from the 

economic literature was used to estimate the value of 

reductions in mortality risk associated with exposure 

to particulate matter. In addition, benefits which could 

not be expressed in economic terms were compiled 

and are presented herein. In some cases, quantitative 

estimates of scenario differences in the incidence of a 

nonmonetized effect were calculated.8 In many other 

cases, available data and techniques were insufficient 

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

Finally, the costs and monetized benefits were 

combined to provide a range of estimates for the partial, net economic benefit of the CAA with the range 

reflecting quantified uncertainties associated with the 

physical effects and economic valuation steps.9 The 

term “partial” is emphasized because only a subset of 

the total potential benefits of the CAA could be represented in economic terms due to limitations in ancal 

4

 These six pollutants are total suspended particulates (TSP), sulfur dioxide (SO2

x and VOCs. 

), nitrogen oxides (NOx), carbon monoxide (CO), 

volatile organic compounds (VOCs), and lead (Pb). The other CAA criteria pollutant, ozone (O3), is formed in the atmosphere through 

the interaction of sunlight and ozone precursor pollutants such as NO

5

 By definition, 1970 emissions under the two scenarios are identical. 

6

 The one exception is particulate matter (PM). For PM, air quality profiles for both Total Suspended Particulates (TSP) and 

10 level. 

particulates less than or equal to 10 microns in diameter (PM ) were constructed at the county level rather than the individual monitor 

7 Or, for PM, by county. 

8

 For example, changes in forced expiratory volume in one second (FEV1

could not be expressed in terms of economic value. 

) as a result of exposure to ozone were quantified but 

9

 Although considerable uncertainties surround the direct cost, macroeconomic modeling, emissions modeling,, and air quality 

modeling steps, the ranges of aggregate costs and benefits presented in this analysis do not reflect these uncertainties. While the 

uncertainties in these components were assessed qualitatively, and in some cases quantitatively, resource limitations precluded the 

multiple macroeconomic model, emissions model, and air quality model runs which would have been required to propagate these 

uncertainties through the entire analytical sequence. As a result, complete quantitative measures of the aggregate uncertainty in the cost 

and benefit estimates could not be derived. However, the ranges presented do reflect quantitative measures of the uncertainties in the 

two most uncertain analytical steps: physical effects estimation and economic valuation. 

5

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

cal resources, available data and models, and the state 

of the science.10 Of paramount concern to the EPA 

Project Team was the paucity of concentration-response functions needed to translate air quality 

changes into measures of ecological effect. In addition, significant scientific evidence exists linking air 

pollution to a number of adverse human health effects which could not be effectively quantified and/or 

monetized.11 

Review Process 

The CAA requires EPA to consult with an outside panel of experts–referred to statutorily as the 

Advisory Council on Clean Air Act Compliance 

Analysis (the Council)–in developing the section 812 

analyses. In addition, EPA is required to consult with 

the Department of Labor and the Department of Commerce. 

The Council was organized in 1991 under the auspices and procedures of EPA’s Science Advisory 

Board (SAB). Organizing the review committee under the SAB ensured that review of the section 812 

studies would be conducted by highly qualified experts in an objective, rigorous, and publicly open 

manner. The Council has met many times during the 

development of the retrospective study to review 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 have been referred to 

subcommittees comprised of both Council members 

and members of other SAB committees. The Council’s 

Physical Effects Review Subcommittee met several 

times and provided its own review findings to the full 

Council. Similarly, the Council’s Air Quality Subcommittee, comprised of members and consultants of the 

SAB Clean Air Scientific Advisory Committee 

(CASAC), held several teleconference meetings to 

review methodology proposals and modeling results. 

With respect to the interagency review process, 

EPA expanded the list of consulted agencies and convened a series of meetings during the design and early 

implementation phases from 1991 through late 1994. 

In late 1994, to ensure that all interested parties and 

the public received consistent information about remaining analytical issues and emerging results, EPA 

decided to use the public SAB review process as the 

primary forum for presenting and discussing issues 

and results. The Interagency Review Group was therefore discontinued as a separate process in late 1994. 

A final, brief interagency review, pursuant to Circular A-19, was organized in August 1997 by the Office of Management and Budget and conducted following the completion of the extensive expert panel 

peer review by the SAB Council. During the course 

of the final interagency discussions, it became clear 

that several agencies held different views pertaining 

to several key assumptions in this study as well as to 

the best techniques to apply in the context of environmental program benefit-cost analyses, including the 

present study. The concerns include: (1) the extent to 

which air quality would have deteriorated from 1970 

to 1990 in the absence of the Clean Air Act, (2) the 

methods used to estimate the number of premature 

deaths and illnesses avoided due to the CAA, (3) the 

methods used to estimate the value that individuals 

place on avoiding those risks, and (4) the methods 

used to value non-health related benefits. However, 

due to the court deadline the resulting concerns were 

not resolved during this final, brief interagency review. Therefore, this report reflects the findings of 

EPA and not necessarily other agencies in the Administration. Interagency discussion of some of these 

issues will continue in the context of the future prospective section 812 studies and potential regulatory 

actions. 

Report Organization 

The remainder of the main text of this report summarizes the key methodologies and findings of retrospective study. The direct cost estimation and macroeconomic modeling steps are presented in Chapter 2. 

The emissions modeling is summarized in Chapter 3. 

Chapter 4 presents the air quality modeling methodology and sample results. Chapter 5 describes the approaches used and principal results obtained through 

the physical effects estimation process. Economic 

valuation methodologies are described in Chapter 6. 

Chapter 7 presents the aggregated results of the cost 

and benefit estimates and describes and evaluates 

important uncertainties in the results. 

Additional details regarding the methodologies 

and results are presented in the appendices and in the 

referenced supporting documents. Appendix A covers the direct cost and macroeconomic modeling. Appendix B provides additional detail on the sector-specific emissions modeling effort. Details of the air quality models used and results obtained are presented or 

referenced in Appendix C. The effects of the CAA on 

human health and visibility; aquatic, wetland, and forest ecosystems; and agriculture are presented in Appendices D, E, and F, respectively. Appendix G presents details of the lead (Pb) benefits analysis. Air 

toxics reduction benefits are discussed in Appendix 

H. The methods and assumptions used to value quantified effects of the CAA in economic terms are described in Appendix I. Appendix J describes some areas of research which may increase comprehensiveness and reduce uncertainties in effect estimates for 

future assessments, and describes plans for future section 812 analyses. 10 It should be noted that there is some uncertainty associated with the estimates of economic costs as well and that some omitted 

components of adverse economic consequences of pollution control programs may be significant. For example, some economists 

argue that the economic costs of the CAA reported herein may be significantly underestimated to the extent potential adverse effects 

of regulation on technological innovation are not captured. Nevertheless, it is clear that the geographic, population, and categorical 

coverage of monetary cost effects is significantly greater than coverage of monetized benefits in this analysis. 

11 For example, while there is strong evidence of a link between exposure to carbon monoxide and reduced time of onset of 

angina attack, there are no valuation functions available to estimate the economic loss associated with this effect. 

6

2 

Cost and Macroeconomic Effects 

The costs of complying with Clean Air Act (CAA) 

requirements through the 1970 to 1990 period affected 

patterns of industrial production, capital investment, 

productivity, consumption, employment, and overall 

economic growth. The purpose of the analyses summarized in this chapter was to estimate those direct 

costs and the magnitude and significance of resulting 

changes to the overall economy. This was accomplished by comparing economic indicators under two 

alternative scenarios: a control scenario serving as the 

historical benchmark, including the historical CAA 

as implemented; and a no-control scenario which assumes historical CAA programs did not exist. The 

estimated economic consequences of the historical 

CAA were taken as the difference between these two 

scenarios. 

Data used as inputs to the cost analysis can be 

classified into two somewhat overlapping categories 

based on the information source: survey-based information (generally gathered by the Census Bureau) and 

information derived from various EPA analyses. For 

the most part, cost estimates for stationary air pollution sources (e.g., factory smokestacks) are based on 

surveys of private businesses that attempt to elicit information on annual pollution control outlays by those 

businesses. Estimates of pollution control costs for 

mobile sources (e.g., automobiles) are largely based 

on EPA analyses, rather than on direct observation 

and measurement of compliance expenditures. For 

example, to determine one component of the cost of 

reducing lead emissions from mobile sources, the 

Project Team used an oil refinery production cost 

model to calculate the incremental cost required to 

produce unleaded (or less-leaded, as appropriate) 

rather than leaded gasoline, while maintaining the 

octane level produced by leaded gasoline. 

As is the case with many policy analyses, a significant uncertainty arises in the cost analysis as a 

consequence of constructing a hypothetical scenario. 

With this retrospective analysis covering almost 

twenty years, difficulties arise in projecting alternative technological development paths. In some cases, 

the analytical assumptions used to project the alternative scenario are not immediately apparent. For example, the surveys covering stationary source compliance expenditures require respondents to report 

pollution abatement expenditures—implicitly asking 

them to determine by how much the company’s costs 

would decline if there were no CAA compliance requirements. While a response might be relatively 

straightforward in the few years following passage of 

the CAA, a meaningful response becomes more difficult after many years of technical change and investment in less-polluting plant and equipment make it 

difficult to determine the degree to which total costs 

would differ under a “no CAA” scenario. In cases such 

as this, assumptions concerning the alternative hypothetical scenario are made by thousands of individual 

survey respondents. Where cost data are derived from 

EPA analyses, the hypothetical scenario assumptions 

are, at least in theory, more apparent. For example, 

when determining the incremental cost caused by pollution-control requirements, one needs to make assumptions (at least implicitly) about what an auto 

would look like absent pollution control requirements. 

In either case, the need to project hypothetical technology change for two decades introduces uncertainty 

into the assessment results, and this uncertainty may 

be difficult to quantify. 

The remainder of this chapter summarizes the 

basic methods and results of the direct compliance 

cost and macroeconomic analyses. Further details regarding the modeling methods and assumptions employed, as well as additional analytical results, are 

presented in Appendix A. 

Direct Compliance Costs 

Compliance with the CAA imposed direct costs 

on businesses, consumers, and governmental units; and 

triggered other expenditures such as governmental 

regulation and monitoring costs and expenditures for 

7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 1. Estimated Annual CAA 

Compliance Costs ($billions). 

Expendit ures Annualized Costs 

$199 0 at: 

Year $current $199 0 3% 5% 7% 

1973 7.2 19 .6 11 .0 11.0 11 .1 

1974 8.5 21 .4 13 .2 13.4 13 .7 

1975 10.6 24 .4 13 .3 13.6 14 .0 

1976 11.2 24 .1 14 .1 14.6 15 .1 

1977 11.9 24 .1 15 .3 15.9 16 .6 

1978 12.0 22 .6 15 .0 15.8 16 .7 

1979 14.4 24 .8 17 .3 18.3 19 .3 

1980 16.3 25 .7 19 .7 20.8 22 .0 

1981 17.0 24 .4 19 .6 20.9 22 .3 

1982 16.0 21 .6 18 .6 20.1 21 .7 

1983 15.5 20 .1 19 .1 20.7 22 .5 

1984 17.3 21 .6 20 .1 21.9 23 .8 

1985 19.1 22 .9 22 .5 24.4 26 .5 

1986 17.8 20 .8 21 .1 23.2 25 .4 

1987 18.2 20 .6 22 .1 24.2 26 .6 

1988 18.2 19 .8 22 .0 24.3 26 .7 

1989 19.0 19 .8 22 .9 25.3 27 .8 

1990 19.0 19 .0 23 .6 26.1 28 .7 

research and development by both government and 

industry. Although expenditures unadjusted for inflation — that is, expenditures denominated in “current 

dollars”— increased steadily from $7 billion to $19 

billion per year over the 1973 to 1990 period,12 annual CAA compliance expenditures adjusted for inflation were relatively stable, averaging near $25 billion (in 1990 dollars) during the 1970s and close to 

$20 billion during most of the 1980s (see Table 1). 

Aggregate compliance expenditures were somewhat 

less than one half of one percent of total domestic 

output during that period, with the percentage falling 

from two thirds of one percent of total output in 1975 

to one third of one percent in 1990. 

Although useful for many purposes, a summary 

of direct annual expenditures may not the best cost 

measure to use when comparing costs to benefits. 

Capital expenditures are investments, generating a 

stream of benefits and opportunity cost13 over the life 

of the investment. The appropriate accounting technique to use for capital expenditures in a cost/benefit 

analysis is to annualize the expenditure. This technique, analogous to calculating the monthly payment 

associated with a home mortgage, involves spreading 

the cost of the capital equipment over the useful life 

of the equipment using a discount rate to account for 

the time value of money. 

For this cost/benefit analysis, “annualized” costs 

reported for any given year are equal to O&M expenditures — including R&D and other similarly recurring expenditures — plus amortized capital costs (i.e., 

depreciation plus interest costs associated with the 

existing capital stock) for that year. Stationary source 

air pollution control capital costs were amortized over 

20 years; mobile source air pollution control costs were 

amortized over 10 years.14 All capital expenditures 

were annualized using a five percent, inflation-adjusted rate of interest. Additionally, annualized costs 

were calculated using discount rates of three and seven 

percent to determine the sensitivity of the cost results 

to changes in the discount rate. Table 1 summarizes 

costs annualized at three, five, and seven percent, as 

well as annual expenditures. 

Total expenditures over the 1973-1990 period, 

discounted to 1990 using a five percent (net of inflation) discount rate, amount to 628 billion dollars (in 

1990 dollars). Discounting the annualized cost stream 

to 1990 (with both annualization and discounting procedures using a five percent rate) gives total costs of 

523 billion dollars (in 1990 dollars). Aggregate annualized costs are less than expenditures because the 

annualization procedure spreads some of the capital 

cost beyond 1990.15 

12 Due to data limitations, the cost analysis for this CAA retrospective starts in 1973, missing costs incurred in 1970-72. This 

limitation is not likely to be significant, however, because relatively little in the way of compliance with the “new” provisions of the 

1970 CAA was required in the first two years following passage. 

13 In this context, “opportunity cost” is defined as the value of alternative investments or other uses of funds foregone as a result of 

the investment. 

14 Although complete data are available only for the period 1973-1990, EPA’s Cost of Clean report includes capital expenditures 

for 1972 (see Appendix A for more details and complete citation). Those capital expenditure data have been used here. Therefore, 

amortized costs arising from 1972 capital investments are included in the 1973-1990 annualized costs, even though 1972 costs are not 

otherwise included in the analysis. Conversely, some capital expenditures incurred in the 1973-1990 period are not reflected in the 

1973-1990 annualized costs — those costs are spread through the following two decades, thus falling outside of the scope of this study 

(e.g., only one year of depreciation and interest expense is included for 1989 capital expenditures). Similarly, benefits arising from 

emission reductions realized after 1990 as a result of capital investments made during the 1970 to 1990 period of this analysis are not 

included in the estimates of benefits included in this report. 

15 This adjustment is required because many 1970 to 1990 investments in control equipment continue to yield benefits beyond 

1990. Annualization of costs beyond 1990 ensures that the costs and benefits of any particular investment are properly scaled and 

matched over the lifetime of the investment. 

8

Chapter 2: Cost and Macroeconomic Effect 

Indirect Effects of the CAA 

Through changing production costs, CAA implementation induced changes in consumer good prices, 

and thus in the size and composition of economic output. The Project Team used a general equilibrium 

macroeconomic model to assess the extent of such 

second-order effects. This type of model is useful because it can capture the feedback effects of an action. 

In the section 812 macroeconomic modeling exercise, 

the feedback effects arising from expenditure changes 

were captured, but the analogous effects arising from 

improvements in human health were not captured by 

the model. For example, the macroeconomic model 

results do not reflect the indirect economic effects of 

worker productivity improvements and medical expenditure savings caused by the CAA. Consequently, 

the macroeconomic modeling exercise provides limited and incomplete information on the type and potential scale of indirect economic effects. 

The effects estimated by the macroeconomic 

model can be grouped into two broad classes: sectoral 

impacts (i.e., changes in the composition of economic 

output), and aggregate effects (i.e., changes in the 

degree of output or of some measure of human welfare). The predicted sectoral effects were used as inputs to the emissions models as discussed in Chapter 

3. In general, the estimated second-order macroeconomic effects were small relative to the size of the 

U.S. economy. See Appendix A for more detail on 

data sources, analytical methods, and results for the 

macroeconomic modeling performed for this assessment. 

Sectoral Impacts 

The CAA had variable compliance impacts across 

economic sectors. The greatest effects were on the 

largest energy producers and consumers, particularly 

those sectors which relied most heavily on consumption of fossil fuels (or energy generated from fossil 

fuels). In addition, production costs increased more 

for capital-intensive industries than for less capitalintensive industries under the control scenario due to 

a projected increase in interest rates. The interest rate 

increase, which resulted in an increase in the cost of 

capital, occurred under the control scenario because 

CAA-mandated investment in pollution abatement 

reduced the level of resources available for other uses, 

including capital formation. 

Generally, the estimated difference in cost impacts 

under the control and no-control scenarios for a particular economic sector was a function of the relative 

energy-intensity and capital-intensity of that sector. 

Increased production costs in energy- and capital-intensive sectors under the control scenario were reflected in higher consumer prices, which resulted in 

reductions in the quantity of consumer purchases of 

goods and services produced by those sectors. This 

reduction in consumer demand under the control scenario led, ultimately, to reductions in output and employment in those sectors. The sectors most affected 

by the CAA were motor vehicles, petroleum refining, 

and electricity generation. The electricity generation 

sector, for example, incurred a two to four percent 

increase in consumer prices by 1990, resulting in a 

three to five and a half percent reduction in output. 

Many other manufacturing sectors saw an output effect in the one percent range. 

Some other sectors, however, were projected to 

increase output under the control scenario. Apart from 

the pollution control equipment industry, which was 

not separately identified and captured in the macroeconomic modeling performed for this study, two example sectors for which output was higher and prices 

were lower under the control scenario are food and 

furniture. These two sectors showed production cost 

and consumer price reductions of one to two percent 

relative to other industries under the control scenario, 

resulting in output and employment increases of similar magnitudes. 

Aggregate Effects 

As noted above, the control and no-control scenarios yield different estimated mixes of investment. 

In particular, the control scenario was associated with 

more pollution control capital expenditure and less 

consumer commodity capital expenditure. As a result, 

the growth pattern of the economy under the control 

scenario differed from the no-control scenario. Under 

the control scenario, the macroeconomic model projected a rate of long-run GNP growth about one twentieth of one percent per year lower than under the nocontrol scenario. Aggregating these slower growth 

effects of the control scenario over the entire 1970 to 

1990 period of this study results, by 1990, in a level 

of GNP one percent (or approximately $55 billion) 

lower than that projected under the no-control scenario. 

9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Although small relative to the economy as a whole, 

the estimated changes in GNP imply that the potential impact of the CAA on the economy by 1990 was 

greater than that implied by expenditures ($19 billion 

in 1990) or annualized costs ($26 billion in 1990, annualized at five percent). Discounting the stream of 

1973-1990 GNP effects to 1990 gives an aggregate 

impact on production of 1,005 billion dollars (in 1990 

dollars discounted at five percent). Of that total, $569 

billion represent reductions in household consumption, and another $200 billion represent government 

consumption, for an aggregate effect on U.S. consumption of goods and services equal to 769 billion dollars. Both the aggregate GNP effects and aggregate 

consumption effects exceed total 1973-1990 expenditures ($628 billion) and annualized costs ($523 billion, with all dollar quantities in $1990, discounted at 

five percent). 

Changes in GNP (or, even, changes in the national 

product account category “consumption”) do not necessarily provide a good indication of changes in social welfare. Social welfare is not improved, for example, by major oil tanker spills even though measured GNP is increased by the “production” associated with clean-up activities. Nevertheless, the effects 

of the CAA on long-term economic growth would be 

expected to have had some effect on economic welfare. One of the characteristics of the macroeconomic 

model used by the Project Team is its ability to estimate a measure of social welfare change which is superior to GNP changes. This social welfare measure 

estimates the monetary compensation which would be 

required to offset the losses in consumption (broadly 

defined) associated with a given policy change. The 

model reports a range of results, with the range sensitive to assumptions regarding how cost impacts are 

distributed through society. For the CAA, the model 

reports an aggregate welfare effect of 493 billion to 

621 billion dollars (in 1990 dollars), depending on 

the distributional assumptions used. This range does 

not differ greatly from the range of results represented 

by 1973-1990 expenditures, compliance costs, and 

consumption changes. 

Uncertainties and Sensitivities in 

the Cost and Macroeconomic 

Analysis 

The cost and macroeconomic analyses for the 

present assessment relied upon survey responses, EPA 

analyses, and a macroeconomic simulation model. 

Although the Project Team believes that the results of 

the cost and macroeconomic analyses are reasonably 

reliable, it recognizes that every analytical step is subject to uncertainty. As noted at the beginning of this 

chapter, explicit and implicit assumptions regarding 

hypothetical technology development paths are crucial to framing the question of the cost impact of the 

CAA. In addition, there is no way to verify the accuracy of the survey results used;16 alternative, plausible 

cost analyses exist that arrive at results that differ from 

some of the results derived from EPA analyses; and it 

is not clear how the use of a general equilibrium macroeconomic model affects the accuracy of macroeconomic projections in a macroeconomy characterized 

by disequilibrium. For many factors engendering uncertainty, the degree or even the direction of bias is 

unknown. In several areas, nevertheless, uncertainties 

and/or sensitivities can be identified that may bias the 

results of the analysis. 

Productivity and Technical Change 

An important component of the macroeconomic 

model used by the Project Team is its treatment of 

technical change and productivity growth. Three factors associated with productivity and technical change 

have been identified which may bias the results of the 

macroeconomic simulation: (1) the long-run effects 

of reducing the “stock” of technology, (2) the possible “chilling” effect of regulations on innovation and 

technical change, and (3) the role of endogenous productivity growth within the macroeconomic model. 

The macroeconomic model projected a decrease 

in the growth of GNP as a result of CAA compliance. 

Decreased growth was due not only to decreased capital investment, but also to decreased factor productivity. The annual decrement in productivity can be 

thought of as a reduction of the stock of available technology. That reduction in stock could be expected to 

affect macroeconomic activity after 1990, as well as 

16 For an example of the difficulties one encounters in assessing the veracity of survey results, see the discussion in Appendix A 

on the apparently anomalous growth in stationary source O&M expenditures in relation to the size of the stationary source air 

pollution control capital stock. 

10

3% 5% 7% 

Expendit ures $52 628 761 

Annualize d Cost s 417 523 657 

GNP 880 1151 

Household Consum ption 500 569 653 

HH and Gov’t Consum ption 676 769 881 

1005 

Chapter 2: Cost and Macroeconomic Effect 

during the 1973-1990 period studied by the Project 

Team. Thus, to the extent that this effect exists, the 

Project Team has underestimated the macroeconomic 

impact of the CAA by disregarding the effect of 1973-

1990 productivity change decrements on post-1990 

GNP. 

Some economists contend that regulations have a 

“chilling” effect on technological innovation and, 

hence, on productivity growth. Two recent studies by 

Gray and Shadbegian,17 which are sometimes cited in 

support of this contention, suggest that pollution abatement regulations may decrease productivity levels in 

some manufacturing industries. The macroeconomic 

model allowed policy-induced productivity change 

through the mechanism of price changes and resultant factor share changes. To the extent that additional 

policy-induced effects on productivity growth exist, 

the Project Team has underestimated the impact of 

the CAA on productivity growth during the 1973-1990 

period, and, thus, has underestimated macroeconomic 

impacts during the 1973-1990 period and beyond. 

The macroeconomic model allowed productivity 

growth to vary with changes in prices generated by 

the model. This use of “endogenous” productivity 

growth is not universal in the economic growth literature — that is, many similar macroeconomic models 

do not employ analogous forms of productivity growth. 

The Project Team tested the sensitivity of the model 

results to the use of endogenous productivity growth. 

If the model is run without endogenous productivity 

growth, then the predicted macroeconomic impacts 

(GNP, personal consumption, etc.) of the CAA are 

reduced by approximately 20 percent. That is, to the 

extent that use of endogenous productivity growth in 

the macroeconomic model is an inaccurate simulation 

technique, then the Project Team has overestimated 

the macroeconomic impact of the CAA. 

Discount Rates 

There is a broad range of opinion in the economics profession regarding the appropriate discount rate 

to use in analyses such as the current assessment. Some 

economists believe that the appropriate rate is one that 

approximates the social rate of time preference — that 

is, the rate of return at which individuals are willing 

to defer consumption to the future. A three percent 

rate would approximate the social rate of time preference (all rates used here are “real”, i.e., net of price 

inflation impacts). Others believe that a rate that approximates the opportunity cost of capital (e.g., seven 

percent or greater) should be used.18 A third school of 

thought holds that some combination of the social rate 

of time preference and the opportunity cost of capital 

is appropriate, with the combination effected either 

by use of an intermediate rate or by use of a multiplestep procedure employing the social rate of time preference as the “discount rate,” but still accounting for 

the opportunity cost of capital. 

The Project Team elected to use an intermediate 

rate (five percent), but recognizes that analytical results aggregated across the study period are sensitive 

to the discount rate used. Consequently, all cost measures are presented at three and seven percent, as well 

as the base case five percent. Table 2 summarizes 

major cost and macroeconomic impact measures expressed in constant 1990 dollars, and discounted to 

1990 at rates of three, five, and seven percent. 

Table 2. Compliance Cost, GNP, and 

Consumption Impacts Discounted to 1990 

($1990 billions) 

3% 5% 7% 

Expenditures $52 628 761 

Annualized Costs 417 523 657 

GNP 880 1005 1151 

Household Consumption 500 569 653 

HH and Gov’t Consumption 676 769 881 

17 Gray, Wayne B., and Ronald J. Shadbegian, “Environmental Regulation and Manufacturing Productivity at the Plant Level,” 

Center for Economic Studies Discussion Paper, CES 93-6, March 1993. Gray, Wayne B., and Ronald J. Shadbegian, “Pollution 

Abatement Costs, Regulation, and Plant-Level Productivity,” National Bureau of Economic Research, Inc., Working Paper Series, 

Working Paper No. 4994, January 1995. 

18 Some would argue that use of the opportunity cost of capital approach would be inappropriate in the current assessment if the 

results of the macroeconomic modeling (such as GNP) were used as the definition of “cost,” since the macro model already accounts 

for the opportunity cost of capital. The appropriate rate would then be the social rate of time preference. 

11

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Exclusion of Health Benefits from the 

Macroeconomic Model 

The macroeconomic modeling exercise was designed to capture the second-order macroeconomic 

effects arising from CAA compliance expenditures. 

Those predicted second-order effects are among the 

factors used to drive the emissions estimates and, ultimately, the benefits modeled for this assessment. The 

benefits of the CAA, however, would also be expected 

to induce second-order macroeconomic effects. For 

example, increased longevity and decreased incidence 

of non-fatal heart attacks and strokes would be expected to improve macroeconomic performance measures. The structure of the overall analysis, however, 

necessitated that these impacts be excluded from the 

macroeconomic simulation. 

The first-order CAA beneficial effects have been 

included in the benefits analysis for this study, including measures that approximate production changes 

(e.g., income loss due to illness, or lost or restricted 

work days; income loss due to impaired cognitive ability; and income loss due to reduced worker production in certain economic sectors). These measures are 

analogous to compliance expenditures in the cost 

analysis. The second-order benefits impacts, which 

would result from price changes induced by CAArelated benefits, have not been estimated. It is likely 

that the estimated adverse second-order macroeconomic impacts would have been reduced had the impact of CAA benefits been included in the macroeconomic modeling exercise; however, the magnitude of 

this potential upward bias in the estimate of adverse 

macroeconomic impact was not quantitatively assessed. 

12

3 

Emissions 

This chapter presents estimates of emissions reductions due to the Clean Air Act (CAA) for six criteria air pollutants. Reductions are calculated by estimating, on a sector-by-sector basis, the differences in 

emissions between the control and no-control scenarios. While the relevant years in this analysis are 

1970 through 1990, full reporting of emissions was 

only made for the 1975 to 1990 period since 1970 

emission levels are, by assumption, identical for the 

two scenarios. The criteria pollutants for which emissions are reported in this analysis are: total suspended 

particulates (TSP),19 carbon monoxide (CO), volatile 

organic compounds (VOC), sulfur dioxide (SO2

), nitrogen oxides (NOx

), and Lead (Pb). 

The purpose of the present study is to estimate 

the differences in economic and environmental conditions between a scenario reflecting implementation 

of historical CAA controls and a scenario which assumes that no additional CAA-related control programs were introduced after 1970. Because of the focus on differences in –rather than absolute levels of– 

emissions between the scenarios, the various sectorspecific emission models were used to estimate both 

the control and no-control scenario emission inventories. This approach ensures that differences between 

the scenarios are not distorted by differences between 

modeled and actual historical emission estimates.20 

Despite the use of models to estimate control scenario emission inventories, the models used were configured and/or calibrated using historical emissions 

estimates. The control scenario utility emissions estimates, for example, were based on the ICF CEUM 

model which was calibrated using historical emissions 

inventory data.21 In other cases, such as the EPA Emissions Trends Report (Trends) methodology22 used to 

estimate industrial process emissions, historical data 

were used as the basis for control scenario emissions 

with little or no subsequent modification. Nevertheless, differences in model selection, model configuration, and macroeconomic input data23 result in unavoidable, but in this case justifiable, differences between national total historical emission estimates and 

national total control scenario emission estimates for 

each pollutant. Comparisons between no-control, control, and official EPA Trends Report historical emissions inventories are presented in Appendix B.24 

19 In 1987, EPA replaced the earlier TSP standard with a standard for particulate matter of 10 microns or smaller (PM10). 

20 By necessity, emission models must be used to estimate the hypothetical no-CAA scenario. If actual historical emissions data 

were adopted for the control scenario, differences between the monitoring data and/or models used to develop historical emission 

inventories and the models used to develop no-control scenario emission estimates would bias the estimates of the differences between 

the scenarios. 

21 See ICF Resources, Inc., “Results of Retrospective Electric Utility Clean Air Act Analysis - 1980, 1985 and 1990,” September 

30, 1992, Appendix C. 

22 EPA, 1994a: U.S. Environmental Protection Agency, “National Air Pollutant Emission Trends, 1900-1993,” EPA-454/R-94-

027, Office of Air Quality Planning and Standards, Research Triangle Park, NC, October 1994. 

23 The Jorgenson/Wilcoxen macroeconomic model outputs were used to configure both the control and no-control scenario 

emission model runs. While this satisfies the primary objective of avoiding “across model” bias between the scenarios, the macroeconomic conditions associated with the control scenario would not be expected to match actual historical economic events and conditions. To the extent actual historical economic conditions are used to estimate official historical emission inventories, conformity 

between these historical emissions estimates and control scenario emission estimates would be further reduced. 

24 In general, these comparisons show close correspondence between control scenario and Trends estimates with the largest 

differences occurring for VOC and CO emissions. The Trends report VOC estimates are generally higher than the control scenario 

estimates due primarily to the inclusion of Waste Disposal and Recycling as a VOC source in the Trends report. This inconsistency is 

of no consequence since Waste Disposal and Recycling sources were essentially uncontrolled by the historical CAA and therefore do 

not appear as a difference between the control and no-control scenarios. The higher CO emission estimates in the Trends Report are 

primarily associated with higher off-highway vehicle emissions estimates. Again, since off-highway emissions do not change between 

the control and no-control scenario in the present analysis, this inconsistency is of no consequence. 

13

Co mmercial / Residential 

used . 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

To estimate no-control scenario emissions, sec- the no-control scenario, activity levels that affect emistor-specific historical emissions are adjusted based on sions from each sector were identified. These activity 

changes in the following two factors: (1) growth by levels include, for example, fuel use, industrial activsector predicted to occur under the no-control scenario; ity, and vehicle miles traveled (VMT). The Jorgensonand (2) the exclusion of controls attributable to spe- Wilcoxen (J/W) general equilibrium model was used 

cific provisions of the CAA. to estimate changes in general economic conditions, 

as well as sector-specific economic outcomes used as 

To adjust emissions for economic changes under inputs to the individual sector emission models.25 

Table 3. Summary of Sector-Specific Emission Modeling Approaches. 

Sector Modeling Approach 

On-High way Vehicles M odeled using ANL's TEEM S; ad ju sted auto mobile emission estimates by 

changes in perso nal travel and econo mic activity in the with out C AA case. 

Truck VM T w as obtained from the Fed er al High way Ad ministration (FHWA ). 

M OBILE5 a emissio n f actors w ere used to calculate emissions. 

Lead emission ch anges from gasolin e were estimated by Abt Associates based 

on historical gasoline sales and the lead content of leaded gasoline in each 

tar get year. 

Off-Highwa y Vehicles ELI analysis based on Trends method s. Recalcu lated historical emissions 

usin g 1 970 control efficiencies from Trends. No adjustment w as made to 

activity levels in the with out the CAA case. 

Elect ric Utilities ICF's Coal an d Electric Utility M od el (CEUM ) used to assess SO2 , N Ox , and 

TSP emission changes. Electr icity sales levels were adjusted with results of 

the J/W mod el. 

The Argo nne Utility Simulation M o del (ARG US) p rovided CO and VOC 

results. C hanges in activity levels w er e adjusted with results of the J/W mod el. 

Lead emissions were calcu lated based on energy consumptio n data and Trends 

emission factors and contro l efficien cies. 

Industrial Co mbustio n ANL in dustrial b oiler analy sis fo r SO2 , N Ox , and TSP using the Industrial 

Combu stion Emission s (ICE) model. 

VOC and CO emissions fro m indu strial bo ilers were calculated b ased on 

Trends methods; r ecalculated usin g 1970 contro l efficien cies. 

Lead emissions calcu lated for boilers and processes based on Trends fu el 

consump tion data, emission factors, an d 1970 contro l efficiencies. 

Industrial Pro cesses ELI analyzed industrial process emissions b ased on Trends metho ds. Adjusted 

historical emissions with J/W sectoral ch an ges in o utput, and 1 970 control 

efficiencies from Trends. 

Lead emissions calcu lated for industrial processes and processes b ased on 

Trends fu el consumption data, emission factors, and 1970 co ntrol eff iciencies. 

Co mmercial / Residential ANL's Co mmer cial an d R esidential Simulatio n System (CRESS) model was 

used . 

25 For example, the change in distribution of households by income class predicted by the J/W model was used as input to the 

transportation sector model system. Changes in household income resulted in changes in vehicle ownership and usage patterns which, 

in turn, influence VMT and emissions. (See Pechan, 1995, p. 43). 

14

Chapter 3: Emissions 

The specific outputs from the J/W model used in 

this analysis are the percentage changes in gross national product (GNP), personal consumption, and output for various economic sectors under the control and 

no-control scenario for the years 1975, 1980, 1985, 

and 1990.26 The sectors for which the results of the J/ 

W model are used include: industrial processes, electric utilities, highway vehicles, industrial boilers, and 

the commercial/residential sector. For the off-highway 

sector, economic growth was not taken into account 

as there was no direct correspondence between J/W 

sectors and the off-highway vehicle source category 

activity. 

In addition to adjusting for economic activity 

changes, any CAA-related control efficiencies that 

were applied to calculate control scenario emissions 

were removed for the no-control scenario. In most 

instances, emissions were recalculated based on 1970 

control levels. 

Uncertainty associated with several key modeling inputs and processes may contribute to potential 

errors in the emission estimates presented herein. Although the potential errors are likely to contribute in 

only a minor way to overall uncertainty in the estimated monetary benefits of the Clean Air Act, the most 

significant emission modeling uncertainties are described at the end of this chapter. 

Sector-Specific Approach 

The approaches used to calculate emissions for 

each sector vary based on the complexity of estimating emissions in the absence of CAA controls, taking 

economic activity levels and CAA regulations into 

account. For the off-highway vehicle and industrial 

process sectors, a relatively simple methodology was 

developed. The approaches used for the highway vehicles, electric utilities, industrial boilers, and commercial/residential sectors were more complex because the J/W model does not address all of the determinants of economic activity in these sectors that 

might have changed in the absence of regulation. The 

approaches by sector used to estimate emissions for 

the two scenarios are summarized in Table 3, and are 

described in more detail in Appendix B. 

Summary of Results 

Figure 2 compares the total estimated sulfur dioxide emission from all sectors under the control and 

no-control scenarios over the period from 1975 to 

1990. Figures 3, 4, 5, 6, and 7 provide similar com­


parisons for NOx


spectively.


, VOCs, CO, TSP, and Lead (Pb) reAdditional tables presented in Appendix B provide further breakdown of the emissions estimates by 

individual sector. The essential results are characterized below. For most sectors, emission levels under 

the control scenario were substantially lower than levels projected under the no-control scenario. For some 

pollutants, for example NOx

, most of the reductions 

achieved under the control scenario offset the growth 

in emissions which would have occurred under the 

no-control case as a result of increases in population 

and economic activity. For other pollutants, particularly lead, most of the difference in 1990 emissions 

projected under the two scenarios reflects significant 

improvement relative to 1970 emission levels. Appendix B also assesses the consistency of the control 

and no-control scenario estimates for 1970 to 1990 

with pre-1970 historical emissions trends data. 

The CAA controls that affected SO2

 emitting 

sources had the greatest proportional effect on industrial process emissions, which were 60 percent lower 

in 1990 than they would have been under the 

no-control scenario. SO2

 emissions from electric utilities and industrial boilers were each nearly 40 percent 

lower in 1990 as a result of the controls. In terms of 

absolute tons of emission reductions, controls on electric utilities account for over 10 million of the total 16 

million ton difference between the 1990 control and 

no-control scenario SO2

 emission estimates. 

CAA regulation of the highway vehicles sector 

led to the greatest percent reductions in VOC and NOx

. 

Control scenario emissions of these pollutants in 1990 

were 66 percent and 47 percent lower, respectively, 

than the levels estimated under the no-control scenario. 

In absolute terms, highway vehicle VOC controls account for over 15 million of the roughly 17 million 

ton difference in control and no-control scenario emissions. 

Differences between control and no-control scenario CO emissions are also most significant for highway vehicles. In percentage terms, highway vehicle 

CO emissions were 56 percent lower in 1990 under 

the control scenario than under the no-control scenario. 

Industrial process CO emission estimates under the 

control scenario were about half the levels projected 

under the no-control scenario. Of the roughly 89 mil26 For details regarding the data linkages between the J/W model and the various emission sector models, see Pechan (1995). 

15

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Figure 2. Control and No-control Scenario Total SO2 

Emission Estimates. 

Figure 3. Control and No-control Scenario Total NOX 

Emission Estimates. 

Figure 4. Control and No-control Scenario Total VOC 

Emission Estimates. 

Figure 5. Control and No-control Scenario Total CO 

Emission Estimates. 

Figure 6. Control and No-control Scenario Total TSP 

Emission Estimates. 

Figure 7. Control and No-control Scenario Total Pb 

Emission Estimates. 

16

Chapter 3: Emissions 

lion ton difference in CO emissions between the two 

scenarios, 84 million tons are attributable to highway 

vehicle controls and the rest is associated with reductions from industrial process emissions. 

For TSP, the highest level of reductions on a percentage basis was achieved in the electric utilities sector. TSP emissions from electric utilities were 93 percent lower in 1990 under the control scenario than 

projected under the no-control scenario. TSP emissions from industrial processes were also significantly 

lower on a percentage basis under the control scenario, 

with the differential reaching 76 percent by 1990. 

This is not an unexpected result as air pollution 

control regulations in the 1970’s focused on solving 

the visible particulate problems from large fuel combustors. In terms of absolute tons, electric utilities 

account for over 5 million of the 16 million ton difference between the two scenarios and industrial processes account for almost 10 million tons. 

The vast majority of the difference in lead emissions under the two scenarios is attributable to reductions in burning of leaded gasoline. By 1990, reductions in highway vehicle emissions account for 221 

thousand of the total 234 thousand ton difference in 

lead emissions. As shown in more detail in Appendix 

B, airborne lead emissions from all sectors were virtually eliminated by 1990. 

As described in the following chapter and in Appendix C, these emissions inventories were used as 

inputs to a series of air quality models. These air quality models were used to estimate air quality conditions under the control and no-control scenarios. 

Uncertainty in the Emissions 

Estimates 

The emissions inventories developed for the control and no-control scenarios reflect at least two major sources of uncertainty. First, potential errors in the 

macroeconomic scenarios used to configure the sector-specific emissions model contribute to uncertainties in the emissions model outputs. Second, the emissions models themselves rely on emission factors, 

source allocation, source location, and other parameters which may be erroneous. 

An important specific source of potential error 

manifest in the present study relates to hypothetical 

emission rates from various sources under the no-control scenario. Emission rates from motor vehicles, for 

example, would have been expected to change during 

the 1970 to 1990 period due to technological changes 

not directly related to implementation of the Clean 

Air Act (e.g., advent of electronic fuel injection, or 

EFI). However, the lack of emissions data from vehicles with EFI but without catalytic converters compelled the Project Team to use 1970 emission factors 

throughout the 1970 to 1990 period for the no-control 

scenario. Although this creates a potential bias in the 

emissions inventories, the potential errors from this 

and other uncertainties in the emissions inventories 

are considered unlikely to contribute significantly to 

overall uncertainty in the monetary estimates of Clean 

Air Act benefits. This conclusion is based on the demonstrably greater influence on the monetary benefit 

estimates of uncertainties in other analytical components (e.g., concentration-response functions). A list 

of the most significant potential errors in the emissions modeling, and their significance relative to overall uncertainty in the monetary benefit estimate, is 

presented in Table 4. 

17

control an d no-contro l scenarios. 

Ov er estimate. 

CEUM Report, p . 7). 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 4. Uncertainties Associated with Emissions Modeling. 

Potential S ource o f Erro r 

Direction of Potential 

Bias in Estimate of 

Emission Reduction 

Benefits 

Significa nce Relati ve to Key 

Uncert ainties in O verall Moneta ry 

Benefit Estimate 

Use o f 1 970 motor vehicle emission facto rs 

for no­ co ntrol scenario without adjustment 

for advent of Electronic Fuel In jection 

(EFI ) and Electr onic I gnition (EI). 

Ov er estimate. Un known, b ut likely to be minor du e 

to o verwh elmin g significance of 

catalysts in determining emissio n 

rates. 

Use o f ARGUS for utility CO an d VOC 

rather than CEUM . 

Un known. Negligible. 

Use o f h istorical fuel con sumption to 

estimate 1975 SO2 , N Ox , TSP utility 

emissions. 

Un known. Negligible. 

Ad option of assu mp tio n th at utility un it 

inventories remain f ixed between the 

control an d no-contro l scenarios. 

Ov er estimate. Un known, b ut likely to be small 

since th e CAA had virtually no effect 

on costs of new co al-f ired p lants 

built p rio r to 197 5 an d these plants 

comprise a large majo rity of total 

coal-fired capacity op eratin g in the 

1970 to 199 0 p erio d. (See IC F 

CEUM Report, p . 7). 

18

4 

Air Quality 

Air quality modeling is the crucial analytical step 

which links emissions to changes in atmospheric concentrations of pollutants which affect human health 

and the environment. It is also one of the more complex and resource-intensive steps, and contributes significantly to overall uncertainty in the bottom-line 

estimate of net benefits of air pollution control programs. The assumptions required to estimate hypothetical no-control scenario air quality conditions are 

particularly significant sources of uncertainty in the 

estimates of air quality change, especially for those 

pollutants which are not linearly related to changes in 

associated emissions. Specific uncertainties are described in detail at the end of this chapter. 

The key challenges faced by air quality modelers 

attempting to translate emission inventories into air 

quality measures involve modeling of pollutant dispersion and atmospheric transport, and modeling of 

atmospheric chemistry and pollutant transformation. 

These challenges are particularly acute for those pollutants which, rather than being directly emitted, are 

formed through secondary formation processes. Ozone 

is the paramount example since it is formed in the 

atmosphere through complex, nonlinear chemical interactions of precursor pollutants, particularly volatile organic compounds (VOCs) and nitrogen oxides 

(NOx

). In addition, atmospheric transport and 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 managing 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. 

Given its comprehensive nature, the present analysis entails all of the aforementioned challenges, and 

involves additional complications as well. For many 

pollutants which cause a variety of human health and 

environmental effects, the concentration-response 

functions which have been developed to estimate those 

effects require, as inputs, different air quality indicators. For example, adverse human health effects of 

particulate matter are primarily associated with the 

respirable particle fraction;27 whereas household soiling is a function of total suspended particulates, especially coarse particles. It is not enough, therefore, to 

simply provide a single measure of particulate matter 

air quality. Even for pollutants for which particle size 

and other characteristics are not an issue, different air 

quality indicators are needed which reflect different 

periods of cumulative exposure (i.e., “averaging periods”). For example, 3-month growing season averages 

are needed to estimate effects of ozone on yields of 

some agricultural crops, whereas adverse human health 

effect estimates require ozone concentration profiles 

based on a variety of short-term averaging periods.28 

Fortunately, in responding to the need for scientifically valid and reliable estimation of air quality 

changes, air quality modelers and researchers have 

developed a number of highly sophisticated atmospheric dispersion and transformation models. These 

models have been employed for years supporting the 

development of overall federal clean air programs, 

national assessment studies, State Implementation 

Plans (SIPs), and individual air toxic source risk assessments. Some of these models, however, require 

massive amounts of computing power. For example, 

completing the 160 runs of the Regional Acid Deposition Model (RADM) required for the present study 

required approximately 1,080 hours of CPU time on a 

Cray-YMP supercomputer at EPA’s Bay City 

Supercomputing Center. 

Given the resource-intensity of many state-of-theart models, the Project Team was forced to make difficult choices regarding where to invest the limited 

27 Particles with an aerometric diameter of less than or equal to 10 microns.


28 For example, ozone concentration-response data exists for effects associated with 1-hour, 2.5-hour, and 6.6-hour exposures.


19

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

resources available for air quality modeling. With a 

mandate to analyze all of the key pollutants affected 

by historical Clean Air Act programs, to estimate all 

of the significant endpoints associated with those pollutants, and to do so for a 20 year period covering the 

entire continental U.S., it was necessary to use simplified approaches for most of the pollutants to be 

analyzed. In several cases related to primary emissions 

—particularly sulfur dioxide (SO2

), nitrogen oxides 

(NOx

), and carbon monoxide (CO)— simple “roll-up 

model” strategies were adopted based on the expectation that changes in emissions of these pollutants 

would be highly correlated with subsequent changes 

in air quality.

29 Significant pollutants involving secondary atmospheric formation, nonlinear formation 

mechanisms, and/or long-range transport were analyzed using the best air quality model which was affordable given time and resource limitations. These 

models, discussed in detail in Appendix C, included 

the Ozone Isopleth Plotting with Optional MechanismIV (OZIPM4) model for urban ozone; various forms 

of the above-referenced RADM model for background 

ozone, acid deposition, sulfate, nitrate, and visibility 

effects in the eastern U.S.; and the SJVAQS/AUSPEX 

Regional Modeling Adaptation Project (SARMAP) 

Air Quality Model (SAQM) for rural ozone in California agricultural areas. In addition, a linear scaling 

approach was developed and implemented to estimate 

visibility changes in large southwestern U.S. urban 

areas. 

By adopting simplified approaches for some pollutants, the air quality modeling step adds to the overall uncertainties and limitations of the present analysis. The limited expanse and density of the U.S. air 

quality monitoring network and the limited coverage 

by available air quality models of major geographic 

areas30 further constrain the achievable scope of the 

present study. Under these circumstances, it is important to remember the extent and significance of gaps 

in geographic coverage for key pollutants when considering the overall results of this analysis. Key uncertainties are summarized at the end of this chapter 

in Table 5. More extensive discussion of the caveats 

and uncertainties associated with the air quality modeling step is presented in Appendix C. In addition, information regarding the specific air quality models 

used, the characteristics of the historical monitoring 

data used as the basis for the control scenario profiles, pollutant-specific modeling strategies and assumptions, references to key supporting documents, 

and important caveats and uncertainties are also presented in Appendix C. 

General Methodology 

The general methodological approach taken in this 

analysis starts with the assumption that actual historical air quality will be taken to represent the control 

scenario. This may seem somewhat inconsistent with 

the approach taken in earlier steps of the analysis, 

which used modeled macroeconomic conditions as the 

basis for estimating macroeconomic effects and emissions. However, the central focus of the overall analysis is to estimate the difference in cost and benefit 

outcomes between the control and no-control scenarios. It is consistent with this central paradigm to 

use actual historical air quality data as the basis for 

estimating how air quality might have changed in the 

absence of the Clean Air Act. 

The initial step, then, for each of the five nonlead (Pb) criteria pollutants31 was to compile comprehensive air quality profiles covering the entire analytical period from 1970 to 1990. The source for these 

data was EPA’s Aerometric Information Retrieval 

System (AIRS), which is a publicly accessible database of historical air quality data. The vast number of 

air quality observations occurring over this twenty year 

period from the thousands of monitors in the U.S. indicates the need to represent these observations by 

statistical distributions. As documented in detail in 

the supporting documents covering SO2

, NOx, CO, and 

ozone,32 both lognormal and gamma distributional 

forms were tested against actual data to determine the 

29 It is important to emphasize that the correlation expected is between changes in emissions and changes in air quality. Direct 

correlations between the absolute emissions estimates and empirical air quality measurements used in the present analysis may not be 

strong due to expected inconsistencies between the geographically local, monitor-proximate emissions densities affecting air quality 

data. 

30 For example, the regional oxidant models available for the present study do not cover some key Midwestern states, where 

human health, agricultural crop, and other effects from ozone may be significant. 

31 Lead (Pb), the sixth criteria pollutant, is analyzed separately. The ability to correlate emissions directly with blood lead levels 

obviates the need for using air quality modeling as an intermediate step toward estimation of exposure. 

32 See SAI SO2

, NOx, and CO Report (1994) and SAI Ozone Report (1995). 

20

Chapter 4: Air Quality 

form which provided the best fit to the historical data.33 

Based on these tests, one or the other statistical distribution was adopted for the air quality profiles developed for each pollutant. In addition to reducing the 

air quality data to a manageable form, this approach 

facilitated transformations of air quality profiles from 

one averaging period basis to another. 

Once the control scenario profiles based on historical data were developed, no-control scenarios were 

derived based on the results of the various air quality 

modeling efforts. Again, the focus of the overall analysis is to isolate the difference in outcomes between 

the control and no-control scenarios. The no-control 

scenario air quality profiles were therefore derived by 

adjusting the control scenario profiles upward (or 

downward) based on an appropriate measure of the 

difference in modeled air quality outcomes. To illustrate this approach, consider a simplified example 

where the modeled concentration of Pollutant A under the no-control scenario is 0.12 ppm, compared to 

a modeled concentration under the control scenario 

of 0.10 ppm. An appropriate measure of the difference between these outcomes, whether it is the 0.02 

ppm difference in concentration or the 20 percent percentage differential, is then used to ratchet up the control case profile to derive the no-control case profile. 

Generally, the modeled differential is applied across 

the entire control case profile to derive the no-control 

case profile. As described below in the individual sections covering particulate matter and ozone, however, 

more refined approaches are used where necessary to 

take account of differential outcomes for component 

species (i.e., particulate matter), long-range transport, 

and background levels of pollutants. 

Sample Results 

The results of the air quality modeling effort include a vast array of monitor-specific air quality profiles for particulate matter (PM10 and TSP),34 SO2

, 

NO2

, NO, CO, and ozone; RADM grid cell-based estimates of sulfur and nitrogen deposition; and estimates 

of visibility degradation for eastern U.S. RADM grid 

cells and southwestern U.S. urban areas. All of these 

data were transferred to the effects modelers for use in 

configuring the human health, welfare, and ecosystem 

physical effects models. Given the massive quantity 

and intermediate nature of the air quality data, they 

are not exhaustively reported herein.35 To provide the 

reader with some sense of the magnitude of the difference in modeled air quality conditions under the control and no-control scenarios, some illustrative results 

for 1990 are presented in this chapter and in Appendix C. In addition, maps depicting absolute levels of 

control and no-control scenario acid deposition and 

visibility are presented to avoid potential confusion 

which might arise through examination of percent 

change maps alone.36 

Carbon Monoxide 

Figure 8 provides an illustrative comparison of 

1990 control versus no-control scenario CO concentrations, expressed as a frequency distribution of the 

ratios of 1990 control to no-control scenario 95th percentile 1-hour average concentrations at individual CO 

monitors. Consistent with the emission changes underlying these air quality results, CO concentrations 

under the control scenario tend to be about half those 

projected under the no-control scenario, with most 

individual monitor ratios ranging from about 0.40 to 

0.60 percent, and a few with ratios in the 0.60 to 0.80 

range. 

Figure 8. Frequency Distribution of Estimated Ratios for 

1990 Control to No-control Scenario 95th Percentile 1-

Hour Average CO Concentrations, by Monitor. 

33 The statistical tests used to determine goodness of fit are described in the SAI reports. 

34 PM data are reported as county-wide values for counties with PM monitors and a sufficient number of monitor observations. 

35 The actual air quality profiles, however, are available on disk from EPA. See Appendix C for further information. 

36 Large percentage changes can result from even modest absolute changes when they occur in areas with good initial (e.g., 

control scenario) air quality. Considering percentage changes alone might create false impressions regarding absolute changes in air 

quality in some areas. For example, Appendix C discusses in detail two such cases: the Upper Great Lakes and Florida-Southeast 

Atlantic Coast areas, which show high percentage changes in sulfur deposition and visibility. 

21

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

In considering these results, it is important to note 

that CO is essentially a “hot spot” pollutant, meaning 

that higher concentrations tend to be observed in localized areas of relatively high emissions. Examples 

of such areas include major highways, major intersections, and tunnels. Since CO monitors tend to be 

located in order to monitor the high CO concentrations observed in such locations, one might suspect 

that using state-wide emissions changes to scale air 

quality concentration estimates at strategically located 

monitors might create some bias in the estimates. 

However, the vast majority of ambient CO is contributed from on-highway vehicles. In addition, the vast 

majority of the change in CO emissions between the 

control and no-control scenario occurs due to catalyst 

controls on highway vehicles. Since CO hot spots result primarily from highway vehicles emissions, controlling such vehicles would mean CO concentrations 

would be commensurately lowered at CO monitors. 

While variability in monitor location relative to actual hot spots and other factors raise legitimate concerns about assuming ambient concentrations are correlated with emission changes at any given monitor, 

the Project Team believes that the results observed 

provide a reasonable characterization of the aggregate 

change in ambient CO concentrations between the two 

scenarios. 

Sulfur Dioxide 

As for CO, no-control scenario SO2 concentrations were derived by scaling control scenario air quality profiles based on the difference in emissions predicted under the two scenarios. Unlike CO, SO2

 is 

predominantly emitted from industrial and utility 

sources. This means that emissions, and the changes 

in emissions predicted under the two scenarios, will 

tend to be concentrated in the vicinity of major point 

sources. Again, while state-wide emissions changes 

are used to scale SO2 concentrations between the scenarios, these state-wide emission changes reflect the 

controls placed on these individual point sources. 

Therefore, the Project Team again considers the distribution of control to no-control ratios to be a reasonable characterization of the aggregate results despite the uncertainties associated with estimation of 

changes at individual monitors. 

Figure 9 provides a histogram of the predicted 

control to no-control ratios for SO2

 which is similar 

to the one presented for CO. The results indicate that, 

on an overall basis, SO2

 concentrations were reduced 

by about one-third. The histogram also shows a much 

wider distribution of control to no-control ratios for 

individual monitors than was projected for CO. This 

result reflects the greater state to state variability in 

SO2

 emission changes projected in this analysis. This 

greater state to state variability in turn is a function of 

the variable responses of SO2

 point sources to historical C control requirements.37 This source-specific variability was not observed for CO because controls were 

applied relatively uniformly on highway vehicles. 

Figure 9. Frequency Distribution of Estimated Ratios for 

1990 Control to No-control Scenario 95th Percentile 1-

Hour Average SO2

 Concentrations, by Monitor. 

Nitrogen Dioxide 

Results for NO2 are presented in Figure 10. These 

results are similar to the results observed for CO, and 

for a similar reason: the vast majority of change in 

NO2

 emissions between the two scenarios is related 

to control of highway vehicle emissions. While 

baseline emissions of NO2

 from stationary sources may 

be significant, these sources were subject to minimal 

controls during the historical period of this analysis. 

On an aggregated basis, overall NO2

 concentrations 

are estimated to be roughly one-third lower under the 

control scenario than under the no-control scenario. 

37 Figure 9 indicates that six monitors were projected to have higher SO2

 concentrations for 1990 under the control scenario than 

under the no-control scenario. All six of these monitors are located in Georgia, a state for which higher 1990 utility SO2

 emissions are 

projected in the control scenario due to increased use of higher-sulfur coal. The projected increase in overall Georgia utility consumption of higher sulfur coal under the control case is a result of increased competition for the low-sulfur southern Appalachian coal 

projected to occur under the control scenario. 

22

Chapter 4: Air Quality 

Figure 10. Frequency Distribution of Estimated Ratios for 

1990 Control to No-control Scenario 95th Percentile 1-

Hour Average NO2

 Concentrations, by Monitor. 

Particulate Matter 

An indication of the difference in outcomes for 

particulate matter between the two scenarios is provided by Figure 11. This graph shows the distribution 

of control to no-control ratios for annual mean TSP in 

1990 for those counties which both had particulate 

monitors and a sufficient number of observations from 

those monitors.38 While the distribution of results is 

relatively wide, reflecting significant county to county 

variability in ambient concentration, on a national 

aggregate basis particulate matter concentrations unFigure 11. Frequency Distribution of Estimated Ratios for 

1990 Control to No-control Annual Mean TSP Concentrations, by Monitored County. 

der the control scenario were just over half the level 

projected under the no-control scenario. The significant county to county variability observed in this case 

reflects point source-specific controls on particulate 

matter precursors, especially SO2

, and the effects of 

long-range transport and transformation. 

Ozone 

Urban Ozone 

Figure 12 presents a summary of the results of the 

1990 OZIPM4 ozone results for all 147 of the modeled urban areas. In this case, the graph depicts the 

distribution of ratios of peak ozone concentrations 

estimated for the control and no-control scenarios. 

While the vast majority of simulated peak ozone concentration ratios fall below 1.00, eight urban areas 

show lower simulated peak ozone for the no-control 

scenario than for the control scenario. For these eight 

urban areas, emissions of precursors were higher under the no-control scenario; however, the high proportion of ambient NOx

 compared to ambient nonmethane organic compounds (NMOCs) in these areas 

results in a decrease in net ozone production in the 

vicinity of the monitor when NOx

 emissions increase.39 

Figure 12. Distribution of Estimated Ratios for 1990 

Control to No-control OZIPM4 Simulated 1-Hour Peak 

Ozone Concentrations, by Urban Area. 

38 Given the relative importance of particulate matter changes to the bottom line estimate of CAA benefits, and the fact that a 

substantial portion of the population lives in unmonitored counties, a methodology was developed to allow estimation of particulate 

matter benefits for these unmonitored counties. This methodology was based on the use of regional air quality modeling to interpolate 

between monitored counties. It is summarized in Appendix C and described in detail in the SAI PM Interpolation Report (1996). 

39 Over an unbounded geographic area, NOx

 reductions generally decrease net ozone production. 

23

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Rural Ozone 

Figures 13 and 14 present frequency distributions 

for control to no-control ratios of average ozone-season daytime ozone concentrations at rural monitors 

as simulated by SAQM and RADM, respectively. 

Figure 13. Distribution of Estimated Ratios for 1990 

Control to No-control SAQM Simulated Daytime Average 

Ozone Concentrations, by SAQM Monitor. 

Both the RADM and SAQM results indicate relatively little overall change in rural ozone concentrations. This is primarily because reductions in ozone 

precursor emissions were concentrated in populated 

areas. 

Figure 14. Distribution of Estimated Ratios for 1990 

Control to No-control RADM Simulated Daytime Average 

Ozone Concentrations, by RADM Grid Cell. 

Acid Deposition 

Figure 15 is a contour map showing the estimated 

percent increase in sulfur deposition under the no-control scenario relative to the control scenario for 1990. 

Figure 16 provides comparable information for nitrogen deposition. 

Figure 15. RADM-Predicted Percent Increase in Total 

Sulfur Deposition (Wet + Dry) Under the No-control 

Scenario. 

These results show that acid deposition rates increase significantly under the no-control scenario, 

particularly in the Atlantic Coast area and in the vicinity of states for which relatively large increases in 

emissions are projected under the no-control scenario 

(i.e., Kentucky, Florida, Michigan, Mississippi, Connecticut, and Florida). 

In the areas associated with large increases in sulfur dioxide emissions, rates of sulfur deposition increase to greater than or equal to 40 percent. The high 

proportional increase in these areas reflects both the 

significant increase in acid deposition precursor emissions in upwind areas and the relatively low deposition rates observed under the control scenario.40 

Along the Atlantic Coast, 1990 nitrogen deposition rates increase by greater than or equal to 25 percent under the no-control scenario. This is primarily 

due to the significant increase in mobile source nitrogen oxide emissions along the major urban corridors 

of the eastern seaboard. 

40 Even small changes in absolute deposition can yield large percentage changes when initial absolute deposition is low. See 

Appendix C for further discussion of this issue. 

24

Chapter 4: Air Quality 

Figure 16. RADM-Predicted Percent Increase in Total 

Nitrogen Deposition (Wet + Dry) Under the Nocontrol Scenario. 

Visibility 

The difference in modeled 1990 control and 

no-control scenario visibility conditions projected by 

the RADM/EM for the eastern U.S. is depicted by the 

contour map presented in Figure 17. This figure shows 

the increase in modeled annual average visibility degradation, in DeciView41 terms, for 1990 when movFigure 17. RADM-Predicted Percent Increase in 

Visibility Degradation, Expressed in DeciViews, for 

Poor Visibility Conditions (90th Percentile) Under the 

No-control Scenario. 

ing from the control to the no-control scenario. Since 

the DeciView metric is based on perceptible changes 

in visibility, these results indicate noticeable deterioration of visibility in the eastern U.S. underthe nocontrol scenario. 

Visibility changes in 30 southwestern U.S. urban 

areas were also estimated using emissions scaling techniques. This analysis also found significant, perceptible changes in visibility between the two scenarios. 

Details of this analysis, including the specific outcomes for the 30 individual urban areas, are presented 

in Appendix C. 

Uncertainty in the Air Quality 

Estimates 

Uncertainty prevades the projected changes in air 

quality presented in this study. These uncertainties 

arise due to potential inaccuracies in the emissions 

inventories used as air quality modeling inputs and 

due to potential errors in the structure and parameterization of the air quality models themselves. In addition, an important limitation of the present study is 

the lack of available data and/or modeling results for 

some pollutants in some regions of the country (e.g., 

visibility changes in western U.S. Class I areas such 

as the Grand Canyon). The inability to provide comprehensive estimates of changes in air quality due to 

the Clean Air Act creates a downward bias in the 

monetary benefit estimates. 

The most important specific sources of uncertainty 

are presented in Table 5, and are described further in 

Appendix C. While the list of potential errors presented in Table 5 is not exhaustive, it incorporates the 

uncertainties with the greatest potential for contributing to error in the monetary benefit estimates. Overall, the uncertainties in the estimated change in air 

quality are considered small relative to uncertainties 

contributed by other components of the analysis. 

41 The DeciView Haze Index (dV) is a relatively new visibility indicator aimed at measuring visibility changes in terms of human 

perception. It is described in detail in Appendix C. 

25

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 5. Key Uncertainties Associated with Air Quality Modeling. 

Potential S ource o f Erro r 

Direction of 

Potential Bias 

in Estim ate o f 

Air Quality 

Benefits 

Significa nce Relati ve to Key 

Uncert ainties in O verall Moneta ry 

Benefit Estimate 

Use o f OZIPM 4 mod el, which do es not 

cap ture lon g-range and night-time tran sp ort of 

ozone. U se of a regional oxidant mod el, such 

as UAM -V, wou ld mitigate err ors associated 

with ne glectin g transport. 

Un derestimate. Sign ificant, but pro bably not majo r. 

Ov er all av erage ozo ne response of 15% to 

NO x and VO C reductions of 

ap pro ximately 30% and 45% , 

resp ectively . Even if ozon e respon se 

doub led to 30% , estimate of mon etized 

benefits of CA A will n ot change v ery 

much. Sign if icant benefits of ozon e 

reduction, h owever, could not be 

monetized . 

Use o f early biogenic emission estimates in 

RA DM to estimate rur al ozone chan ges in the 

eastern 31 states. 

Un derestimate. Pro bably min or. Errors ar e estimated to 

be w ithin -15% to +25 % of the ozone 

predictio ns. 

Use o f p roxy pollutants to scale up some 

particu late species in some areas. Un certainty 

is cr eated to the extent species of concern are 

not p erfectly correlated with the p roxy 

pollutants. 

Un known. Potentially significant. Giv en the relativ e 

imp ortance o f the estimated chan ges in 

fine p article con centrations to the 

monetized benefit estimate, any 

uncertainty associated with fine p articles 

is p otentially significant. How ev er , the 

potential error is mitigated to some e xtent 

since p roxy p ollutant measures ar e app lied 

to b oth scenarios. 

Use o f state-wide average emission reductions 

to con figure air qu ality models. I n some 

cases, co ntrol p rograms may hav e been 

tar geted to prob lem areas, so usin g state-wide 

averages wo uld miss relatively large 

reductions in pop ulated areas. 

Un derestimate. Pro bably min or. 

Exclusion of visib ility b enefits in C lass I 

areas in the South western U.S. 

Un derestimate. Pro bably min or. No sensitivity analy sis 

has been performed ; however, monetized 

benefits of reduced visibility imp airment 

in the So uthwest would prob ably not 

significantly alter the estimate o f 

monetized benefits. 

26

Chapter 4: Air Quality 

Table 5 (con’t). Key Uncertainties Associated with Air Quality Modeling. 

Potential S ource o f Erro r 

Direction of 

Potential Bias 

in Estim ate o f 

Air Quality 

Benefits 

Significa nce Relati ve to Key 

Uncert ainties in O verall Monetary 

Benefit Estimate 

Lack of mod el coverage in western 17 states 

for acid dep osition. 

Un derestimate. Pro bably min or. No sensitivity analysis 

has been performed ; how ev er, monetized 

benefits of reduced acid depositio n in the 

17 wester n states would prob ably n ot 

significantly alter the estimate of 

monetized benefits. 

Use o f spatially an d geograph ically 

aggregated emissions d ata to con figure 

RA DM . Lack of available day-specific 

meteor ological data results in inability to 

acco unt f or temperature effects on VOCs and 

effect of localized meteor ology aroun d major 

point sou rces. 

Un known. Potentially significant. Any effect w hich 

might inf lu en ce the d ir ection of longrange tran sp ort of fine particu lates su ch as 

sulfates and nitrates cou ld significantly 

influence the estimates of total mon etized 

benefits of the CAA . 

Use o f con stant con centration for or ganic 

aer oso ls b etween the two scenarios. Ho ld in g 

organic aer osol concentratio ns fixed omits the 

effect of changes in this constituent of fin e 

particu late matter. 

Un derestimate. Pro bably min or, because (a) nitrates were 

also held f ixed and nitrates an d organic 

aer oso ls mo ve in opp osite d irections so 

the exclusio n o f b oth mitigates th e effect 

of omittin g either, (b) sulfates are by far 

the do min ant species in the eastern U.S., 

and (c) larger error s wo uld b e introdu ced 

by usin g emissions scaling to estimate 

changes in organic aero sols sin ce a 

significant f raction of or ganic aerosols ar e 

cau sed by biogenic gas-p hase VOC 

emissions which do not change between 

the scen ar io s. 

Un av ailability of ozon e mod els fo r r ural areas 

outside the R ADM and SAQM do mains. 

Un derestimate. Pro bably min or. M isses p otential hu man 

health, welfare, and ecological benefits of 

reducing rural ozone in agr icultural an d 

other rural areas; however, ozo ne chan ges 

are likely to be small given limited 

precursor reductions in rural areas. 

RA DM co ntrol:no-contro l ratios are in 

fact, relatively small. 

Use o f p eak episo de chan ges to estimate 

changes in annual d istribution of ozone 

concentratio n. 

Un known. Pro bably min or, p articularly since relativ e 

changes in ozone concentration b etween 

the scen ar io s were small. 

27

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

28

5 

Physical Effects 

Human Health and Welfare 

Effects Modeling Approach 

This chapter identifies and, where possible, estimates the principal health and welfare benefits enjoyed by Americans due to improved air quality resulting from the CAA. Health benefits have resulted 

from avoidance of air pollution-related health effects, 

such as premature mortality, respiratory illness, and 

heart disease. Welfare benefits accrued where improved air quality averted damage to measurable resources, including agricultural production and visibility. The analysis of physical effects required a combination of three components: air quality, population, 

and health or welfare effects. As structured in this 

study, the 3-step process involved (1) estimating 

changes in air quality between the control and no-control scenarios, (2) estimating the human populations 

and natural resources exposed to these changed air 

quality conditions, and (3) applying a series of concentration-response equations which translated 

changes in air quality to changes in physical health 

and welfare outcomes for the affected populations. 

Air Quality 

The Project Team first estimated changes in concentrations of criteria air pollutants between the control scenario, which at this step was based on historical air quality, and the no-control scenario. Air quality improvements resulting from the Act were evaluated in terms of both their temporal distribution from 

1970 to 1990 and their spatial distribution across the 

48 conterminous United States. Generally, air pollution monitoring data provided baseline ambient air 

quality levels for the control scenario. Air quality 

modeling was used to generate estimated ambient concentrations for the no-control scenario. A variety of 

modeling techniques was applied, depending on the 

pollutant modeled. These modeling approaches and 

results are summarized in Chapter 4 and presented in 

detail in Appendix C. 

Population 

Health and some welfare benefits resulting from 

air quality improvements were distributed to individuals in proportion to the reduction in exposure. Predicting individual exposures, then, was a necessary 

step in estimating health effects. Evaluating exposure 

changes for the present analysis required not only an 

understanding of where air quality improved as a result of the CAA, but also how many individuals were 

affected by varying levels of air quality improvements. 

Thus, a critical component of the benefits analysis 

required that the distribution of the U.S. population 

nationwide be established. 

Three years of U.S. Census data were used to represent the geographical distribution of U.S. residents: 

1970, 1980, and 1990. Population data was supplied 

at the census block group level, with approximately 

290,000 block groups nationwide. Allocating air quality improvements to the population for the other target years of this study – 1975 and 1985 – necessitated 

interpolation of the three years of population data. 

Linear interpolation was accomplished for each block 

group in order to maintain the variability in growth 

rates throughout the country. 

Health and Welfare Effects 

Benefits attributable to the CAA were measured 

in terms of the avoided incidence of physical health 

effects and measured welfare effects. To quantify such 

benefits, it was necessary to identify concentrationresponse relationships for each effect being considered. As detailed in Appendix D, such relationships 

were derived from the published science literature. In 

the case of health effects, concentration-response functions combined the air quality improvement and population distribution data with estimates of the number 

of fewer individuals that suffer an adverse health effect per unit change in air quality. By evaluating each 

concentration-response function for every monitored 

location throughout the country, and aggregating the 

29

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

resulting incidence estimates, it was possible to generate national estimates of incidence under the control and no-control scenarios. 

In performing this step of the analysis, the Project 

Team discovered that it was impossible to estimate 

all of the health and welfare benefits which have resulted from the Clean Air Act. While scientific information was available to support estimation of some 

effects, many other important health and welfare effects could not be estimated. Furthermore, even though 

some physical effects could be quantified, the state of 

the science did not support assessment of the economic 

value of all of these effects. Table 6 shows the health 

effects for which quantitative analysis was prepared, 

as well as some of the health effects which could not 

be quantified in the analysis. Table 7 provides similar 

information for selected welfare effects. 

While the 3-step analytical process described 

above was applied for most pollutants, health effects 

for lead were evaluated using a different methodology. Gasoline as a source of lead exposure was addressed separately from conventional point sources. 

Instead of using ambient concentrations of lead resulting from use of leaded gasoline, the concentration-response functions linked changes in lead releases 

directly to changes in the population’s mean blood 

lead level. The amount of leaded gasoline used each 

year was directly related to mean blood lead levels 

using a relationship described in the 1985 Lead Regulatory Impact Analysis (U.S. EPA, 1985). Health effects resulting from exposure to point sources of atmospheric lead, such as industrial facilities, were considered using the air concentration distributions modeled around these point sources. Concentration-response functions were then used to estimate changes 

in blood lead levels in nearby populations. 

Most welfare effects were analyzed using the same 

basic 3-step process used to analyze health effects, 

with one major difference in the concentration-response functions used. Instead of quantifying the relationship between a given air quality change and the 

number of cases of a physical outcome, welfare effects were measured in terms of the avoided resource 

losses. An example is the reduction in agricultural crop 

losses resulting from lower ambient ozone concentrations under the control scenario. These agricultural 

benefits were measured in terms of net economic surplus. 

Another important welfare effect is the benefit 

accruing from improvements in visibility under the 

control scenario. Again, a slightly different methodological approach was used to evaluate visibility improvements. Visibility changes were a direct output 

of the models used to estimate changes in air quality.

42 The models provided estimates of changes in 

light extinction, which were then translated mathematically into various specific measures of perceived visibility change.43 These visibility change measures were 

then combined with population data to estimate the 

economic value of the visibility changes. Other welfare effects quantified in terms of avoided resource 

losses include household soiling damage by PM10 and 

decreased worker productivity due to ozone exposure. 

The results of the welfare effects analysis are found 

in Chapter 6 and in Appendices D and F. 

Because of a lack of available concentration-response functions (or a lack of information concerning 

affected populations), ecological effects were not 

quantified for this analysis. However, Appendix E 

provides discussion of many of the important ecological benefits which may have accrued due to historical 

implementation of the CAA. 

Key Analytical Assumptions 

Several important analytical assumptions affect 

the confidence which can be placed in the results of 

the physical effects analysis. The most important of 

these assumptions relate to (a) mapping of potentially 

exposed populations to the ambient air quality monitoring network, (b) choosing among competing scientific studies in developing quantitative estimates of 

physical effects, (c) quantifying the contribution to 

analytical uncertainty of within-study variances in 

effects estimates and, perhaps most important in the 

context of the present study, (d) estimating particulate matter-related mortality based on the currently 

available scientific literature. 

Because these resultant uncertainties were caused 

by the inadequacy of currently available scientific information, there is no compelling reason to believe 

42 These models, and the specific visibility changes estimated by these models, are described in summary fashion in the previous 

chapter and are discussed in detail in Appendix C. 

43 These visibility measures are described in Appendix C. 

30

Quantified Health Effects Unquantified Health Effects Other Possible Effects 

Respiratory sym ptoms 

Minor restricted activity days 

Respiratory restricted activity 

days 

Hospital adm is sions 

Em ergency room vis its 

Asthma attacks 

Changes in pulm onary function 

Chronic Sinusitis & Hay Fever 

Increased airway 

responsiv eness to stimul i 

Centroacinar fibrosis 

Inflam mation in the lung 

Immunologic changes 

Chronic respiratory diseases 

Extrapulm onary effect s (e.g., 

changes in struct ure, 

function of other organs) 

Reduced UV-B exposure 

attenuation 

Mortalit y* 

Bronchitis - Chronic and Acut e 

Hospital adm is sions 

Lower respirat ory il lnes s 

Upper respiratory illness 

Chest illness 

Respiratory sym ptoms 

Minor restricted activity days 

All restricted activity days 

Days of work loss 

Moderate or worse ast hm a 

stat us (asth matics) 

Changes in pulm onary function Chronic respiratory diseases 

other than chronic 

bron chit is 

Inflam mation in the lung 

Hospital Admissions -

congest ive heart failure 

Decreased tim e to onset of 

angina 

Behavioral effect s 

Other hospital adm iss ions 

Other cardiovascular effect s 

Developmental effects 

Respiratory illness Increased airway 

respon siven ess 

Decreased pulmonary function 

Inflam mation in the lung 

Immunological changes 

In exercis ing asthm atics: 

Changes in pulm onary function 

Respiratory sym ptoms 

Com bined responses of 

respiratory sym ptoms and 

pulmonary function 

changes 

Respiratory sym ptoms in nonasthmatics 

Hospital adm is sions 

Lead Mortalit y 

Hypertension 

Non-fatal coronary heart 

disease 

Non-fatal strokes 

IQ loss effect on lifetime 

earnings 

IQ loss effects on special 

education needs 

Health effects for individuals in 

age ranges other than those 

st udied 

Neurobehavioral function 

Other cardiovascular diseases 

Reproduct ive effect s 

Fet al effect s from m aternal 

exposure 

Delinquent and anti-social 

behavior in children 

* This analys is e stimat es exces s m ortalit y using PM as an indicator of t he poll ut ant mix to which individuals were exposed. 

Chapter 5: Physical Effects 

Table 6. Human Health Effects of Criteria Pollutants. 

Pollutant Quantified Health Effects Unquantified Health Effects Other Possible Effects 

Ozon e Respiratory sym ptoms 

Minor restricted activity days 

Respiratory restricted activity 

days 

Hospital adm is sions 

Em ergency room vis its 

Asthma attacks 

Changes in pulm onary function 

Chronic Sinusitis & Hay Fever 

Increased airway 

responsiv eness to stimul i 

Centroacinar fibrosis 

Inflam mation in the lung 

Immunologic changes 

Chronic respiratory diseases 

Extrapulm onary effect s (e.g., 

changes in struct ure, 

function of other organs) 

Reduced UV-B exposure 

attenuation 

Particu late Matter/ 

TSP/ Sulfates 

Mortalit y* 

Bronchitis - Chronic and Acut e 

Hospital adm is sions 

Lower respirat ory il lnes s 

Upper respiratory illness 

Chest illness 

Respiratory sym ptoms 

Minor restricted activity days 

All restricted activity days 

Days of work loss 

Moderate or worse ast hm a 

stat us (asth matics) 

Changes in pulm onary function Chronic respiratory diseases 

other than chronic 

bron chit is 

Inflam mation in the lung 

Carbon Monoxide Hospital Admissions -

congest ive heart failure 

Decreased tim e to onset of 

angina 

Behavioral effect s 

Other hospital adm iss ions 

Other cardiovascular effect s 

Developmental effects 

Nitrogen O xides Respiratory illness Increased airway 

respon siven ess 

Decreased pulmonary function 

Inflam mation in the lung 

Immunological changes 

Sulfu r Dioxide In exercis ing asthm atics: 

Changes in pulm onary function 

Respiratory sym ptoms 

Com bined responses of 

respiratory sym ptoms and 

pulmonary function 

changes 

Respiratory sym ptoms in nonasthmatics 

Hospital adm is sions 

Lead Mortalit y 

Hypertension 

Non-fatal coronary heart 

disease 

Non-fatal strokes 

IQ loss effect on lifetime 

earnings 

IQ loss effects on special 

education needs 

Health effects for individuals in 

age ranges other than those 

st udied 

Neurobehavioral function 

Other cardiovascular diseases 

Reproduct ive effect s 

Fet al effect s from m aternal 

exposure 

Delinquent and anti-social 

behavior in children 

* This analysis e stimates excess mortality using PM as an indicator of the pollutant mix to which individuals were exposed. 

31

Quan tified W elfare Effects Unqu antifie d Welfare Effects 

C hanges in crop yiel ds (for 7 crops) 

Decreased worker productivit y 

Changes in other crop yields 

Materials damage 

Effect s on forest s 

Effect s on wi ldlife 

Household s oiling 

Vi sibil ity 

Ot her materials damage 

Effect s on wi ldlife 

Vi sibil ity Crop los ses due t o aci d depos ition 

Mat erials dam age due to acid deposit ion 

Effect s on fis heries due to acidic 

deposit ion 

Effect s on forest s 

Sulfur Dioxide Vi sibil ity Crop los ses due t o aci d depos ition 

Mat erials dam age due to acid deposit ion 

Effect s on fis heries due to acidic 

deposit ion 

Effect s on forest s 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 7. Selected Welfare Effects of Criteria Pollutants. 

Pollu tant Quan tified W elfare Effects Unqu antifie d Welfare Effects 

Ozone C hanges in crop yiel ds (for 7 crops) 

Decreased worker productivit y 

Changes in other crop yields 

Materials damage 

Effect s on forest s 

Effect s on wi ldlife 

Particulate Matter/ 

TSP/ Sul fates 

Household s oiling 

Vi sibil ity 

Ot her materials damage 

Effect s on wi ldlife 

Ni trogen Oxides Vi sibil ity Crop los ses due t o aci d depos ition 

Mat erials dam age due to acid deposit ion 

Effect s on fis heries due to acidic 

deposit ion 

Effect s on forest s 

Sulfur Dioxide Vi sibil ity Crop los ses due t o aci d depos ition 

Mat erials dam age due to acid deposit ion 

Effect s on fis heries due to acidic 

deposit ion 

Effect s on forest s 

that the results of the present analysis are biased in a 

particular direction. Some significant uncertainties, 

however, may have arisen from interpretation of model 

results, underlying data, and supporting scientific studies. These assumptions and uncertainties are characterized in this report to allow the reader to understand 

the degree of uncertainty and the potential for misestimation of results. In addition, the overall results are 

presented in ranges to reflect the aggregate effect of 

uncertainty in key variables. A quantitative assessment 

of some of the uncertainties in the present study is 

presented in Chapter 7. In addition, the key uncertainties in the physical effects modeling step of this analysis are summarized in Table 12 at the end of this chapter. The remainder of this section discusses each of 

the four critical modeling procedures and associated 

assumptions. 

Mapping Populations to Monitors 

The Project Team’s method of calculating benefits of air pollution reductions required a correlation 

of air quality data changes to exposed populations. 

For pollutants with monitor-level data (i.e., SO2, O3, 

NO2

, CO), it was assumed that all individuals were 

exposed to air quality changes estimated at the nearest monitor. For PM10, historical air quality data were 

available at the county level. All individuals residing 

in a county were assumed to be exposed to that 

county’s PM10 air quality.

44 

Many counties did not contain particulate matter 

air quality monitors or did not have a sufficient number of monitor observations to provide reliable estimates of air quality. For those counties, the Project 

Team conducted additional analyses to estimate PM10 

air quality changes during the study period. For counties in the eastern 31 states, the grid cell-specific sulfate particle concentrations predicted by the RADM 

model were used to provide a scaled interpolation 

between monitored counties.45 For counties outside 

the RADM domain, an alternative method based on 

state-wide average concentrations was used. With this 

supplemental analysis, estimates were developed of 

the health effects of the CAA on almost the entire 

continental U.S. population.46 Compliance costs in44 In some counties and in the early years of the study period, particulate matter was monitored as TSP rather than as PM10. In these 

cases, PM10 was estimated by applying TSP:PM10 ratios derived from historical data. This methodology is described in Appendix C. 

45 The specific methodology is described in detail in Appendix C. 

46 While this modeling approach captures the vast majority of the U.S. population, it does not model exposure for everyone. To 

improve computational efficiency, those grid cells with populations less than 500 were not modeled; thus, the analysis covered 

somewhat more than 97 percent of the population. 

32

Chapter 5: Physical Effects 

curred in Alaska and Hawaii were included in this 

study, but the benefits of historical air pollution reductions were not. In addition, the CAA yielded benefits to Mexico and Canada that were not captured in 

this study. 

Air quality monitors are more likely to be found 

in high pollution areas rather than low-pollution areas. Consequently, mapping population to the nearest 

monitor regardless of the distance to that monitor almost certainly results in an overstatement of health 

impacts due to air quality changes for those populations. The Project Team conducted a sensitivity analysis to illustrate the importance of the “mapping to nearest monitor” assumption. For comparison to the base 

case, which modeled exposure for the 48 state population, Table 8 presents the percentage of the total 48-

state population covered in the “50 km” sensitivity 

scenario. For most pollutants in most years, 25 percent or more of the population resided more than 50 

km from an air quality monitor (or in a county without PM10 monitors). Estimated health benefits are approximately linear to population covered — that is, if 

the population modeled for a pollutant in a given year 

in the sensitivity analysis is 25 percent smaller than 

the corresponding population modeled in the base case, 

then estimated health benefits are reduced by roughly 

25 percent in the sensitivity case. This sensitivity 

analysis demonstrates that limiting the benefits analysis to reflect only those living within 50 km of a monitor or within a PM-monitored county would lead to a 

substantial underestimate of the historical benefits of 

the CAA. Since these alternative results may have led 

to severely misleading comparisons of the costs and 

benefits of the Act, the Project Team decided to adopt 

the full 48-state population estimate as the central case 

for this analysis despite the greater uncertainties and 

potential biases associated with estimating exposures 

from distant monitoring sites. 

Table 8. Percent of Population (of the Continental 

US) within 50km of a monitor (or in a County 

with PM monitors), 1970-1990. 

Pollutant 

Year PM1010 O33 NO 22 SO 22 CO 

1975 79% 56% 53% 65% 67% 

1980 80% 71% 59% 73% 68% 

1985 75% 72% 61% 73% 68% 

1990 68% 74% 62% 71% 70% 

Choice of Study 

The Project Team relied on the most recent available, published scientific literature to ascertain the 

relationship between air pollution and human health 

and welfare effects. The choice of studies, and the 

uncertainties underlying those studies, also created 

uncertainties in the results. For example, to the extent 

the published literature may collectively overstate the 

effects of pollution, EPA’s analysis will overstate the 

effects of the CAA. Such outcomes may occur because scientific research which fails to find significant relationships is less likely to be published than 

research with positive results. On the other hand, history has shown that it is highly likely that scientific 

understanding of the effects of air pollution will improve in the future, resulting in discovery of previously unknown effects. Important examples of this 

phenomenon are the substantial expected health and 

welfare benefits of reductions in lead and ambient 

particulate matter, both of which have been shown in 

recent studies to impose more severe effects than scientists previously believed. To the extent the present 

analysis misses effects of air pollution that have not 

yet been subject to adequate scientific inquiry, the 

analysis may understate the effects of the CAA. 

For some health endpoints, the peer-reviewed scientific literature provides multiple, significantly differing alternative CR functions. In fact, it is not unusual for two equally-reputable studies to differ by a 

factor of three or four in implied health impact. The 

difference in implied health effects across studies can 

be considered an indication of the degree of scientific 

uncertainty associated with measurement of that health 

effect. Where more than one acceptable study was 

available, the Project Team used CR functions from 

all relevant studies to infer health effects. That is, the 

health effect implied by each study is reported (see 

Appendix D), and a range of reported results for a 

particular health endpoint can be interpreted as a measure of the uncertainty of the estimate. 

Variance Within Studies 

Even where only one CR function was available 

for use, the uncertainty associated with application of 

that function to estimate physical outcomes can be 

evaluated quantitatively. Health effects studies provided “best estimates” of the relationship between air 

quality changes and health effects, and a measure of 

the statistical uncertainty of the relationship. In this 

analysis, the Project Team used simulation modeling 

33

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

techniques to evaluate the overall uncertainty of the 

results given uncertainties within individual studies, 

across studies examining a given endpoint, and in the 

economic valuation coefficients applied to each endpoint. The analysis estimating aggregate quantitative 

uncertainty is presented in Chapter 7. 

PM-Related Mortality 

The most serious human health impact of air pollution is an increase in incidences of premature mortality. In the present study, excess premature mortality is principally related to increased exposure to lead 

(Pb)47 and to particulate matter (PM) and associated 

non-Pb criteria pollutants.48 With respect to PM, a 

substantial body of published health science literature 

recognizes a correlation between elevated PM concentrations and increased mortality rates. However, 

there is a diversity of opinion among scientific experts regarding the reasonableness of applying these 

studies to derive quantitative estimates of premature 

mortality associated with exposure to PM. While 19 

of 21 members of the Science Advisory Board Clean 

Air Act Scientific Advisory Committee agree that 

present evidence warrants concern and implementation of a fine particle (PM2.5) standard to supplement 

the PM10 standard, they also point out that the causal 

mechanism has not been clearly established. 

For the purposes of the present study, the Project 

Team has concluded that the well-established correlation between exposure to elevated PM and premature mortality is sufficiently compelling to warrant an 

assumption of a causal relationship and derivation of 

quantitative estimates of a PM-related premature mortality effect. In addition to the assumption of causality, a number of other factors contribute to uncertainty 

in the quantitative estimates of PM-related mortality.

49 

First, although there is uncertainty regarding the shape 

of the CR functions derived from the epidemiological 

studies, the present analysis assumes the relationship 

to be linear throughout the relevant range of exposures. Second, there is significant variability among 

the underlying studies which may reflect, at least in 

part, location-specific differences in CR functions. 

Transferring CR functions derived from one or more 

specific locations to all other locations may contribute significantly to uncertainty in the effect estimate. 

Third, a number of potentially significant biases and 

uncertainties specifically associated with each of the 

two types of PM-related mortality study further contribute to uncertainty. The remainder of this section 

discusses these two groups of studies and their attendant uncertainties and potential biases. (See Appendix D for a more complete discussion of these studies 

and their associated uncertainties.) 

Short-Term Exposure Studies 

Many of the studies examining the relationship 

between PM exposure and mortality evaluate changes 

in mortality rates several days after a period of elevated PM concentrations. In general, significant correlations have been found. These “short-term exposure” or “episodic” studies are unable to address two 

important issues: (1) the degree to which the observed 

excess mortalities are “premature,” and (2) the degree 

to which daily mortality rates are correlated with longterm exposure to elevated PM concentrations (i.e., 

exposures over many years rather than a few days). 

Because the episodic mortality studies evaluate 

the mortality rate impact only a few days after a highpollution event, it is likely that many of the “excess 

mortality” cases represented individuals who were 

already suffering impaired health, and for whom the 

high-pollution event represented an exacerbation of 

an already serious condition. Based on the episodic 

studies only, however, it is unknown how many of the 

victims would have otherwise lived only a few more 

days or weeks, or how many would have recovered to 

enjoy many years of a healthy life in the absence of 

the high-pollution event. For the purpose of cost-benefit analysis, it can be important to determine whether 

a pollution event reduces the average lifespan by several days or by many years. Although the episodic 

mortality studies do not provide an estimate of the 

expected life years lost (nor do they address the health 

status of victims), some have evaluated the age of the 

excess premature mortality cases, and have estimated 

that 80 to 85 percent of the victims are age 65 or older. 

In addition to causing short-term health problems, 

air pollution (measured by elevated annual PM con47 Detailed information on methods, sources, and results of the Pb mortality analysis are presented in Appendix G. 

48 PM concentrations are highly correlated with concentrations of other criteria pollutants. It is difficult to determine which 

pollutant is the causative factor in elevated mortality rates. In this study, the Project Team has used PM as a surrogate for a mix of 

criteria pollutants. 

49 It should also be noted that some of the morbidity studies, most notably the PM/chronic bronchitis epidemiological studies, 

involve many of the same uncertainties. 

34

Chapter 5: Physical Effects 

centrations) can cause longer-term health problems 

that may lead to premature mortality. Such long-term 

changes in susceptibility to premature mortality in the 

future will be missed by efforts to correlate premature mortalities with near-term episodes of elevated 

pollution concentrations. Consequently, excess premature mortality estimates based on the results of the 

“episodic” mortality studies will underestimate the 

effect of long-term elevated pollution concentrations 

on mortality rates. 

Long-Term Exposure Studies 

The other type of PM-related mortality study involves examination of the potential relationship between long-term exposure to PM and annual mortality rates. These studies are able to avoid some of the 

weaknesses of the episodic studies. In particular, by 

investigating changes in annual (rather than daily) 

mortality rates, the long-term studies do not predict 

most cases of excess premature mortality where mortality is deferred for only a few days; also, the longterm studies are able to discern changes in mortality 

rates due to long-term exposure to elevated air pollution concentrations. Additionally, the long-term exposure studies are not limited to measuring mortalities that occur within a few days of a high-pollution 

event. Consequently, use of the results of the longterm studies is likely to result in a more complete assessment of the effect of air pollution on mortality 

risk. 

The long-term exposure studies, however, have 

some significant limitations and potential biases. Although studies that are well-executed attempt to control for those factors that may confound the results of 

the study, there is always the possibility of insufficient or inappropriate adjustment for those factors that 

affect long-term mortality rates and may be confounded with the factor of interest (e.g., PM concentrations). Prospective cohort studies have an advantage over ecologic, or population-based, studies in that 

they gather individual-specific information on such 

important risk factors as smoking. It is always possible, however, that a relevant, individual-specific risk 

factor may not have been controlled for or that some 

factor that is not individual-specific (e.g., climate) was 

not adequately controlled for. It is therefore possible 

that differences in mortality rates that have been ascribed to differences in average PM levels may be due, 

in part, to some other factor or factors (e.g., differences among communities in diet, exercise, ethnicity, 

climate, industrial effluents, etc.) that have not been 

adequately controlled for. 

Another source of uncertainty surrounding the 

prospective cohort studies concerns possible historical trends in PM concentrations and the relevant period of exposure, which is as yet unknown. TSP concentrations were substantially higher in many locations for several years prior to the cohort studies and 

had declined substantially by the time these studies 

were conducted. If this is also true for PM2.5 and PM10, 

it is possible that the larger PM coefficients reported 

by the long-term exposure studies (as opposed to the 

short-term exposure studies) reflect an upward bias. 

If the relevant exposure period extends over a decade 

or more, then a coefficient based on PM concentrations at the beginning of the study or in those years 

immediately prior to the study could be biased upward if pollution levels had been decreasing markedly for a decade or longer prior to the study. 

On the other hand, if a downward trend in PM 

concentrations continued throughout the period of the 

study, and if a much shorter exposure period is relevant (e.g., contained within the study period itself), 

then characterizing PM levels throughout the study 

by those levels just prior to the study would tend to 

bias the PM coefficient downward. Suppose, for example, that PM levels were converging across the different study locations over time, and in particular, into 

the study period. (That is, suppose PM levels were 

decreasing over time, but decreasing faster in the highPM locations than in the low-PM locations, so that at 

the beginning of the study period the interlocational 

differences in PM concentrations were smaller than 

they were a decade earlier.) Suppose also that the relevant exposure period is about one year, rather than 

many years. The Pope study characterizes the longterm PM concentration in each of the study locations 

by the median PM concentration in the location during the five year period 1979-1983. Study subjects 

were followed, however, from 1982 through 1989. If 

the difference in median PM concentrations across the 

50 study locations during the period 1979-1983 was 

greater than the difference during the period 1983-

1988, and if it is PM levels during the period 1983-

1988 that most affect premature mortality during the 

study period (rather than PM levels during the period 

1979-1983), then the study would have attributed 

interlocational differences in mortality to larger 

interlocational differences in PM concentrations than 

were actually relevant. This would result in a downward bias of the PM coefficient estimated in the study. 

35

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

The relevant exposure period is one of a cluster 

of characteristics of the mortality-PM relationship that 

are as yet unknown and potentially important. It is 

also unknown whether there is a time lag in the PM 

effect. Finally, it is unknown whether there may be 

cumulative effects of chronic exposure — that is, 

whether the relative risk of mortality actually increases 

as the period of exposure increases. 

Three recent studies have examined the relationship between mortality and long-term exposure to PM: 

Pope et al. (1995), Dockery et al. (1993), and Abbey 

et al. (1991). The Pope et al. study is considered a 

better choice of long-term exposure study than either 

of the other two studies. Pope et al. examined a much 

larger population and many more locations than either the Dockery study or the Abbey study. The 

Dockery study covered only six cities. The Abbey 

study covered a cohort of only 6,000 people in California. In particular, the cohort in the Abbey study 

was considered substantially too small and too young 

to enable the detection of small increases in mortality 

risk. The study was therefore omitted from consideration in this analysis. Even though Pope et al. (1995) 

reports a smaller premature mortality response to elevated PM than Dockery et al. (1993), the results of 

the Pope study are nevertheless consistent with those 

of the Dockery study. 

Pope et al., (1995) is also unique in that it followed a largely white and middle class population, 

decreasing the likelihood that interlocational differences in premature mortality were attributable to differences in socioeconomic status or related factors. 

Furthermore, the generally lower mortality rates and 

possibly lower exposures to pollution among this 

group, in comparison to poorer minority populations, 

would tend to bias the PM coefficient from this study 

downward, counteracting a possible upward bias associated with historical air quality trends discussed 

above. 

Another source of downward bias in the PM coefficient in Pope et al., (1995) is that intercity movement of cohort members was not considered. Migration across study cities would result in exposures of 

cohort members being more similar than would be 

indicated by assigning city-specific annual average 

pollution levels to each member of the cohort. The 

more intercity migration there is, the more exposure 

will tend toward an intercity mean. If this is ignored, 

differences in exposure levels, proxied by differences 

in city-specific annual average PM levels, will be exaggerated, resulting in a downward bias of the PM 

coefficient. This is because a given difference in mortality rates is being associated with a larger difference 

in PM levels than is actually the case. 

An additional source of uncertainty in the Pope et 

al., study arises from the PM indicator used in the 

study. The Pope et al. study examined the health effects associated with two indices of PM exposure; 

sulfate particles and fine particles (PM2.5). The PM2.5 

relationship is used in this analysis because it is more 

consistent with the air quality data selected for this 

analysis (PM10). Because we use a PM2.5 mortality relationship, air quality profiles were developed from 

the PM10 profiles generated for the entire 20 year period. The same regional information about the PM10 

components (sulfate, nitrate, organic particulate and 

primary particulate) used to develop the PM10 profiles 

was used to develop regional PM2.5/PM10 ratios. Although both urban and rural ratios are available, for 

computational simplicity, only the regional urban ratios were used to estimate the PM2.5 profiles from the 

PM10 profiles used in the analysis. This reflects the 

exposure of the majority of the modeled population 

(i.e., the urban population), while introducing some 

error in the exposure changes for the rural population. In the east and west, where the rural ratio is larger 

than the urban ratio, the change in PM2.5 exposure will 

be underestimated for the rural population. In the central region the PM2.5 change will be overestimated. 

These ratios were used in each year during 1970-1990, 

introducing another source of uncertainty in the analysis. 

After considering the relative advantages and disadvantages of the various alternative studies available 

in the peer-reviewed literature, the Project Team decided that the long-term exposure studies were preferable for the purposes of the present study, primarily 

because the long-term exposure studies appear to provide a more comprehensive estimate of the premature 

mortality incidences attributable to PM exposure. 

Among the long-term exposure studies, the Pope et 

al., (1995) study appears more likely to mitigate a key 

source of potential confounding. For these reasons, 

the CR function estimated in Pope et al., (1995) is 

considered the most reasonable choice for this analysis and is utilized in spite of the several important residual uncertainties and potential biases which are subsequently reflected in the PM-related mortality effect 

estimate. 

36

*Av erages c alculated from p rop ortions of prem ature m or talitie s by ag e 

group, from Table D-14. 

Chapter 5: Physical Effects 

Health Effects Modeling Results 

This section provides a summary of the differences 

in health effects estimated under the control and nocontrol scenarios. Because the differences in air quality between the two scenarios generally increased from 

1970 to 1990, and the affected population grew larger 

during that period, the beneficial health effects of the 

CAA increased steadily during the 1970 to 1990 period. More detailed results are presented in Appendix 

D. 

Avoided Premature Mortality Estimates 

The Project Team determined that, despite their 

limitations, the long-term particulate matter exposure 

studies provided the superior basis for estimating 

mortality effects for the purpose of benefit-cost analysis. Three prospective cohort studies were identified 

(Pope et al. (1995), Dockery et al. (1993), and Abbey 

et al. (1991)), although the Abbey study was omitted 

from consideration because the cohort in that study 

was considered insufficient to allow the detection of 

small increases in mortality risk. Exposure-response 

relationships inferred from the Pope et al. study were 

used in the health benefits model to estimate avoided 

mortality impacts of the CAA. The Pope et al. study 

was selected because it is based on a much larger population and a greater number of communities (50) than 

is the six-city Dockery et al. Study. The results of the 

Pope et al. are consistent with those of the other study, 

and are consistent with earlier ecological population 

mortality studies. See Appendix D for additional discussion of the selection of mortality effects studies. 

Table 9 presents estimated avoided excess premature mortalities for 1990 only, with the mean estimate and 90 percent confidence interval. See Appendix D for more detail on results implied by individual 

epidemiological studies, and on the temporal pattern 

of impacts.50 The model reports a range of results for 

each health endpoint. Here, the fifth percentile, mean, 

and ninety-fifth percentile estimates are used to characterize the distribution. The total number of avoided 

cases of premature mortality due to reduced exposure 

to lead (Pb) and particulate matter are presented. Additionally, avoided mortality cases are listed by age 

cohort of those who have avoided premature mortality in 1990, along with the expected remaining lifespan 

(in years) for the average person in each age cohort. 

The average expected remaining lifespan across all 

age groups is also indicated. These averages might be 

higher if data were available for PM-related mortality 

in the under 30 age group and for Pb-related mortality 

in the 5-39 age group. 

Table 9. Criteria Pollutants Health Benefits --

Distributions of 1990 Avoided Premature Mortalities 

(thousands of cases reduced) for 48 State Population. 

Remainin g 

Lif Lifee 

Exp ect ancy 

(y r s) 

Annua l Cases Av oided 

(t ho us and s) 

PoPollllu u t t a anntt Ag e g r ou p 

5t 5t hh 

%i %illee Me Mea ann 

95t h 

%i %illee 

PM2. 5 30 a nd ove r 112 184 257 

30-34 48 2 5 

35-44 38 5 11 

45-54 29 7 1 15 

55-64 21 14 23 33 

65-74 14 26 43 62 

75-84 9 32 54 76 

>84 6 24 41 59 

Av g.: 14* 

Lea d all ages 7 22 54 

infants 75 5 5 

40-44 38 0 13 

45-54 29 0 20 

55-64 21 0 18 

65-74 14 0 15 

Av g.: 38* 

TOTAL 166 205 252 

3 

8 

1

5 

2 

4 

6 

4 

*Averages calculated from proportions of premature mortalities by age 

group, from Table D-14. 

Non-Fatal Health Impacts 

The health benefits model reports non-fatal health 

effects estimates similarly to estimates of premature 

mortalities: as a range of estimates for each quantified health endpoint, with the range dependent on the 

quantified uncertainties in the underlying concentration-response functions. The range of results for 1990 

only is characterized in Table 10 with fifth percentile, 

mean, and ninety-fifth percentile estimates. All estimates are expressed as thousands of new cases avoided 

in 1990. “Lost IQ Points” represent the aggregate number of points (in thousands) across the population affected by lead concentrations in 1990. All “Hospital 

Admissions” estimates are in thousands of admissions, 

regardless of the length of time spent in the hospital. 

“Shortness of breath” is expressed as thousands of 

50 Earlier years are estimated to have had fewer excess premature mortalities. 

37

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 10. Criteria Pollutants Health Benefits -- Distributions of 1990 Non-Fatal Avoided 

En dpoint Pollutant(s) 

Incide nce (thousands of c ases reduc ed) for 48 State Population. 

Affected 

Population 

(a ge g rou p) 

An nual Effects Av oide d 

(th ousands) 

5th Un it

%ile 

Me an 95th 

%ile 

Chronic Bro nc hitis PM 

Lost IQ Points Lead 

IQ < 70 Lea d 

Hy per ten sion Lea d 

Chronic Heart Disease Lea d 

Atherothrom botic brain infarction Lead 

In itia l cerebro vascula r acc iden t Lea d 

Ho spital Admissio ns 

All Resp irato ry PM & O3 

COPD + Pneumon ia PM & O3 

Isc hem ic He art Disease PM 

Cong estive Heart Failure PM & CO 

Other Respiratory -Related Ailments 

Shortn ess of brea th, days PM 

Ac ute Bronchitis PM 

Up per & Lo wer Resp. Symptoms PM 

An y of 19 Acute Symptoms PM & O3 

Asthm a Attacks PM & O3 

In crea se in Resp irato ry Illne ss NO 2 

An y Sym ptom SO2 

Re stricted Activity and Work L oss Days 

MR A D PM & O3 

Work Lo ss Day s (WLD) PM 

all 

children 

children 

m en 20-7 4 

40-74 

40-74 

40-47 

all 

over 65 

over 65 

65 a nd over 

children 

children 

children 

18-65 

asthmatics 

all 

asthmatics 

18-65 

18-65 

493 674 886 

7, 440 10,40 0 13,00 0 

31 45 60 

9, 740 12,60 0 15,60 0 

0 22 64 

0 4 15 

0 6 19 

75 89 103 

52 62 72 

7 19 31 

28 39 50 

14,80 0 68,80 0 133, 0 00 

0 8,700 21,60 0 

5, 400 9, 500 13,40 0 

15,40 0 130, 0 00 244, 0 00 

170 850 1, 520 

4, 840 9, 800 14,00 0 

26 264 706 

107, 0 00 125, 0 00 143, 0 00 

19,40 0 22,60 0 25,60 0 

cases 

poin ts 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

cases 

da ys 

cases 

cases 

cases 

cases 

cases 

cases 

da ys 

da ys 

The following additional welfare benefits were quantified directly in economic terms: household soiling 

damage, visibility, decreased worker productivity, and agricultural benefits (measured in terms of net 

surplus). 

days: that is, one “case” represents one child experiencing shortness of breath for one day. Likewise, “Restricted Activity Days” and “Work Loss Days” are 

expressed in person-days. 

Other Physical Effects 

Human health impacts of criteria pollutants dominate quantitative analyses of the effects of the CAA, 

in part because the scientific bases for quantifying air 

quality and physical effect relationships are most advanced for health effects. The CAA yielded other benefits, however, which are important even though they 

were sometimes difficult or impossible to quantify 

fully given currently available scientific and applied 

economic information. 

Ecological Effects 

The CAA yielded important benefits in the form 

of healthier ecological resources, including: stream, 

river, lake and estuarine ecosystems; forest and wetland ecosystems; and agricultural ecosystems. These 

benefits are important because of both the intrinsic 

value of these ecological resources and the intimate 

linkage between human health and the health and vitality of our sustaining ecosystems. Given the complexity of natural and agricultural ecosystems and the 

large spatial and temporal dimensions involved, it has 

been difficult or impossible to quantify benefits fully 

given currently available scientific and applied economic information. 

Aquatic and Forest Effects 

Beyond the intrinsic value of preserving natural 

aquatic (i.e., lakes, streams, rivers, and estuaries), terrestrial (i.e., forest and grassland), and wetland ecosystems and the life they support, protection of ecosystems from the adverse effects of air pollution can 

yield significant benefits to human welfare. The historical reductions in air pollution achieved under the 

CAA probably led to significant improvements in the 

38

Chapter 5: Physical Effects 

health of ecosystems and the myriad ecological services they provide. Reductions in acid deposition (SOx 

and NOx) and mercury may have reduced adverse effects on aquatic ecosystems, including finfish, shellfish, and amphibian mortality and morbidity, reduced 

acidification of poorly buffered systems, and reduced 

eutrophication of estuarine systems. Ecological protection, in turn, can enhance human welfare through 

improvements in commercial and recreational fishing, 

wildlife viewing, maintenance of biodiversity, improvements in drinking water quality, and improvements in visibility. 

Wetlands ecosystems are broadly characterized as 

transitional areas between terrestrial and aquatic systems in which the water table is at or near the surface 

or the land is periodically covered by shallow water. 

Valuable products and services of wetlands include: 

flood control, water quality protection and improvement, fish and wildlife habitat, and landscape and biological diversity. High levels of air pollutants have 

the potential to adversely impact wetlands. Reductions 

of these pollutants due to compliance with the CAA 

have reduced the adverse effects of acidification and 

eutrophication of wetlands, which in turn has protected 

habitat and drinking water quality. 

Forest ecosystems, which cover 33 percent of the 

land in the United States, provide an extensive array 

of products and services to humans. Products include 

lumber, plywood, paper, fuelwood, mulch, wildlife 

(game), water (quality), seeds, edible products (e.g., 

nuts, syrup), drugs, and pesticides. Forest services 

include recreation, biological and landscape diversity, 

amenity functions (e.g., urban forest), reduced runoff 

and erosion, increased soil and nutrient conservation, 

pollutant sequestration (e.g., CO2

, heavy metals) and 

pollutant detoxification (e.g., organochlorines). The 

greatest adverse effect on forest systems are imposed 

by ozone. No studies have attempted to quantify the 

economic benefits associated with all product and service functions from any U.S. forest. Some studies have 

attempted to estimate the net economic damage from 

forest exposure to air pollutants by calculating hypothetical or assumed reductions in growth rates of commercial species. While quantification of forest damages remains incomplete, available evidence suggests 

that recreational, service, and non-use benefits may 

be substantial. 

For a more comprehensive discussion of the possible ecological effects of the CAA, see Appendix E. 

Quantified Agricultural Effects 

Quantification of the effects of the CAA on agriculture was limited to the major agronomic crop species including barley, corn, soybeans, peanuts, cotton, 

wheat, and sorghum. These species account for 70 

percent of all cropland in the U.S., and 73 percent of 

the nation’s agricultural receipts. Ozone is the primary 

pollutant affecting agricultural production. Nationwide 

crop damages were estimated under the control and 

no-control scenarios. Net changes in economic surplus (in 1990 dollars) annually and as a cumulative 

present value (discounted at 5%) over the period 1976-

1990 were estimated. Positive surpluses were exhibited in almost all years and were the result of the increase in yields associated with decreased ozone concentrations under the control scenario. The present 

value (in 1990) of the estimated agricultural benefits 

of the CAA ranges from $7.8 billion in the minimum 

response case to approximately $37 billion in the 

maximum response case51 (note that discounting 1976-

1990 benefits to 1990 amounts to a compounding of 

benefits). Exposure-response relationships and cultivar mix reflect historical patterns and do not account 

for possible substitution of more ozone-resistant cultivars in the no-control scenario. Thus, the upper end 

of the range of benefit calculations may overestimate 

the actual agricultural benefits of the CAA with respect to these crops. Because numerous crops are excluded from the analysis, including high value crops 

that may be sensitive to ozone, the lower end of the 

range is not likely to fully capture the agricultural 

benefits of reductions in ozone. 

Effects of Air Toxics 

In addition to control of criteria pollutants, the 

Clean Air Act resulted in control of some air toxics 

— defined as non-criteria pollutants which can cause 

adverse effects to human health and to ecological resources. Control of these pollutants resulted both from 

incidental control due to criteria pollutant programs 

and specific controls targeted at air toxics through the 

National Emission Standards for Hazardous Air Pollutants (NESHAPs) under Section 112 of the Act. 

Air toxics are capable of producing a wide variety of effects. Table 11 presents the range of potential 

human health and ecological effects which can occur 

due to air toxics exposure. For several years, the primary focus of risk assessments and control programs 

designed to reduce air toxics has been cancer. Accord51 Ranges reflect usage of alternate exposure-response functions. 

39

Other Welfare Loss o f b io lo gical diversity

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 11. Health and Welfare Effects of Hazardous Air Pollutants. 

Effect Ca tegory Qua ntified Effects Unquantified Effects Other Possible Effects 

Hum an Health Cancer M ortality 

- non utility stationary 

source 

- mo bile so urce 

Cancer M ortality 

- utility source 

- area sou rce 

No ncancer effects 

- neurological 

- respiratory 

- rep rodu ctive 

- hematop oietic 

- develop mental 

- immu nological 

- organ toxicity 

Hum an Welfare Decreased income an d 

recreation op portunities 

due to fish ad visories 

Od ors 

Decreased income 

resulting from decreased 

physical perfor man ce 

Ecological Effects on wildlife 

Effects on p lants 

Ecosystem effects 

Loss of biological 

diversity 

Effects on global climate 

Other Welfare Visib ility 

Building Deterioration 

Loss o f b io lo gical diversity 

ing to present EPA criteria, there are over 100 known 

or suspected carcinogens. EPA’s 1990 Cancer Risk 

study indicated that as many as 1,000 to 3,000 cancers annually may be attributable to the air toxics for 

which assessments were available (virtually all of this 

estimate came from assessments of about a dozen wellstudied pollutants).52 

In addition to cancer, these pollutants can cause a 

wide variety of health effects, ranging from respiratory problems to reproductive and developmental effects. There has been considerably less work done to 

assess the magnitude of non-cancer effects from air 

toxics, but one survey study has shown that some pollutants are present in the atmosphere at reference levels that have caused adverse effects in animals.53 

Emissions of air toxics can also cause adverse 

health effects via non-inhalation exposure routes. Persistent bioaccumulating pollutants, such as mercury 

and dioxins, can be deposited into water or soil and 

subsequently taken up by living organisms. The pollutants can biomagnify through the food chain and 

exist in high concentrations when consumed by humans in foods such as fish or beef. The resulting exposures can cause adverse effects in humans, and can 

also disrupt ecosystems by affecting top food chain 

species. 

Finally, there are a host of other potential ecological and welfare effects associated with air toxics, 

for which very little exists in the way of quantitative 

analysis. Toxic effects of these pollutants have the 

potential to disrupt both terrestrial and aquatic ecosystems and contribute to adverse welfare effects such 

as fish consumption advisories in the Great Lakes.54 

52 U.S. EPA, Cancer Risk from Outdoor Exposure to Air Toxics. EPA-450/1-90-004f. Prepared by EPA/OAR/OAQPS. 

53 U.S. EPA, “Toxic Air Pollutants and Noncancer Risks: Screening Studies,” External Review Draft, September, 1990. 

54 U.S. EPA, Office of Air Quality Planning and Standards. “Deposition of Air Pollutants to the Great Waters, First Report to 

Congress,” May 1994. EPA-453/R-93-055. 

40

Chapter 5: Physical Effects 

Unfortunately, the effects of air toxics emissions 

reductions could not be quantified for the present 

study. Unlike criteria pollutants, there was relatively 

little monitoring data available for air toxics, and that 

which exists covered only a handful of pollutants. 

Emissions inventories were very limited and inconsistent, and air quality modeling has only been done 

for a few source categories. In addition, the scientific 

literature on the effects of air toxics was generally 

much weaker than that available for criteria pollutants. 

Limitations in the underlying data and analyses 

of air toxics led the Project Team to exclude the available quantitative results from the primary analysis of 

CAA costs and benefits. The estimates of cancer incidence benefits of CAA air toxics control which were 

developed, but ultimately rejected, are presented in 

Appendix H. Also found in Appendix H is a list of 

research needs identified by the Project Team which, 

if met, would enable at least a partial assessment of 

air toxics benefits in future section 812 studies. 

Uncertainty In The Physical Effects 

Estimates 

As discussed above, and in greater detail in Appendix D, a number of important assumptions and 

uncertainties in the physical effects analysis may influence the estimate of monetary benefits presented 

in this study. Several of these key uncertainties, their 

potential directional bias, and the potential significance of this uncertainty for the overall results of the 

analysis are summarized in Table 12. 

41

Po tential S ource of Error 

in Physical Effects Estima te Overall Monetary Benefit Estimate 

Estimation o f PM2. 5 from modeled PM10 

and T SP dat a (to support mortality 

est imation) 

Unknown Significant. Estimated PM2. 5 profiles are used 

to calcu late most of the premature mort ality. 

Th ere is s ignificant uncertainty about how the 

fin e particle share of overall P M levels varies 

temporally and spatially throughout the 20 year 

period. 

Ex trapolation of health effect s to 

populations dist ant fro m m onitors (or 

monitored count ies in the case of P M). 

Pro bable ov erestimate. Pro bably minor. In ad dition, this adjustm ent 

avoids the u nderestim ation which would result 

by est imating effects for on ly t hose people 

living n ear m onitors. Potential overestimate 

may result to the extent air qua lity in areas 

distant from monitors is significantly better than 

in monitored areas. This di sparity should be 

quite minor for regional pollutants, such as 

ozone and fine partic ulates. 

Estimation o f degree o f life-shortening 

associat ed with PM-related premature 

mortalit y. 

Unknown. Unknown, possibly significant when using a 

value of life-years approach. Varyin g t he 

est imate of degre e of prematurity has no effect 

on the aggregate benefit est imate when a value 

of statistical life approach is used sin ce all 

incidences of premat ure mortalit y are valued 

equally. Under the alternative approach based 

on valuing i ndivi dual life-y ears, the influ ence 

of altern ativ e values for nu mbers of average 

life-years lo st may be signific ant. 

As sumption of zero lag bet ween 

exposure an d i ncidence of P M-related 

premature m ortality. 

Overest imate. Pro bably minor. T he short -term mortality 

st udies indicate that a si gnificant port ion of the 

premature m ortality associated with exposure to 

elevat ed P M concentrations is v ery short-term 

(i.e., a matter of a few days). In addition, the 

availabl e ep idem iological studies do not 

pro vide evidence of a signi ficant lag between 

exposure an d i ncidence. Th e lag is therefore 

likely to be a few years at most and application 

of reasonable discount rates over a fe w years 

would not alter th e mo netiz ed benefit estimate 

si gnificantly. 

Choice of CR fun ct ion (i.e., “acrossst udy” u ncertaint ies) Unknown. Significant. The differences in implied p hysical 

outcomes estimated by different underlying 

st udies are large. 

Uncertainty associated wit h CR 

fun ction s derived from each ind ividual 

st udy (i.e., “ within study” uncertainty) 

Unknown. Pro bably minor. 

Ex clus ion of potential UV-B attenuation 

benefits asso ci ated wit h hig her 

concentrations of trop ospheric ozone 

under th e no-con trol case. 

Overest imate. Insignificant . In addition to the incomplet e 

sc ientific ev idence that there is a U V-B 

exposure disbenefit associated specifically wit h 

tro pospheric ozone reductions, the potential 

contribut ion toward total ozone column 

attenuat ion from the tropospheric layer is 

pro bably very small. 

Ex clusion of potential substit ution of 

ozone-res ist ant cult ivars in agri culture 

analysis. 

Overest imate. Insignificant , giv en small relative contribution 

of quant ified agricultural effect s to overall 

quantified b enefit estimate. 

Ex clusion of other agricult ural effects 

(crops, pollut ant s) 

Underest imate. Unknown, possibly significant. 

Ex clus ion of effects on terrestrial, 

wetland, and aqu atic ecosy stems, an d 

forests. 

Underest imate. Unknown, possibly significant. 

No quantification of materia ls d amage Underest imate Unknown, possibly significant. 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 12. Uncertainties Associated with Physical Effects Modeling. 

Po tential S ource of Error Direction of Pote ntial Bias 

in Physical Effects Estima te 

Sig nificance Relative to Key Uncertain ties i n 

Overall Monetary Benefit Estimate 

Estimation o f PM2. 5 from modeled PM10 

and T SP dat a (to support mortality 

est imation) 

Unknown Significant. Estimated PM2. 5 profiles are used 

to calcu late most of the premature mort ality. 

Th ere is s ignificant uncertainty about how the 

fin e particle share of overall P M levels varies 

temporally and spatially throughout the 20 year 

period. 

Ex trapolation of health effect s to 

populations dist ant fro m m onitors (or 

monitored count ies in the case of P M). 

Pro bable ov erestimate. Pro bably minor. In ad dition, this adjustm ent 

avoids the u nderestim ation which would result 

by est imating effects for on ly t hose people 

living n ear m onitors. Potential overestimate 

may result to the extent air qua lity in areas 

distant from monitors is significantly better than 

in monitored areas. This di sparity should be 

quite minor for regional pollutants, such as 

ozone and fine partic ulates. 

Estimation o f degree o f life-shortening 

associat ed with PM-related premature 

mortalit y. 

Unknown. Unknown, possibly significant when using a 

value of life-years approach. Varyin g t he 

est imate of degre e of prematurity has no effect 

on the aggregate benefit est imate when a value 

of statistical life approach is used sin ce all 

incidences of premat ure mortalit y are valued 

equally. Under the alternative approach based 

on valuing i ndivi dual life-y ears, the influ ence 

of altern ativ e values for nu mbers of average 

life-years lo st may be signific ant. 

As sumption of zero lag bet ween 

exposure an d i ncidence of P M-related 

premature m ortality. 

Overest imate. Pro bably minor. T he short -term mortality 

st udies indicate that a si gnificant port ion of the 

premature m ortality associated with exposure to 

elevat ed P M concentrations is v ery short-term 

(i.e., a matter of a few days). In addition, the 

availabl e ep idem iological studies do not 

pro vide evidence of a signi ficant lag between 

exposure an d i ncidence. Th e lag is therefore 

likely to be a few years at most and application 

of reasonable discount rates over a fe w years 

would not alter th e mo netiz ed benefit estimate 

si gnificantly. 

Choice of CR fun ct ion (i.e., “acrossst udy” u ncertaint ies) Unknown. Significant. The differences in implied p hysical 

outcomes estimated by different underlying 

st udies are large. 

Uncertainty associated wit h CR 

fun ction s derived from each ind ividual 

st udy (i.e., “ within study” uncertainty) 

Unknown. Pro bably minor. 

Ex clus ion of potential UV­B attenuation 

benefits asso ci ated wit h hig her 

concentrations of trop ospheric ozone 

under th e no-con trol case. 

Overest imate. Insignificant . In addition to the incomplet e 

sc ientific ev idence that there is a U V-B 

exposure disbenefit associated specifically wit h 

tro pospheric ozone reductions, the potential 

contribut ion toward total ozone column 

attenuat ion from the tropospheric layer is 

pro bably very small. 

Ex clusion of potential substit ution of 

ozone-res ist ant cult ivars in agri culture 

analysis. 

Overest imate. Insignificant , giv en small relative contribution 

of quant ified agricultural effect s to overall 

quantified b enefit estimate. 

Ex clusion of other agricult ural effects 

(crops, pollut ant s) 

Underest imate. Unknown, possibly significant. 

Ex clus ion of effects on terrestrial, 

wetland, and aqu atic ecosy stems, an d 

forests. 

Underest imate. Unknown, possibly significant. 

No quantification of materia ls d amage Underest imate Unknown, possibly significant. 

42

6 

Economic Valuation 

Estimating the reduced incidence of physical ef- For small changes in risk, WTP and WTA are virtufects represents a valuable measure of health benefits ally identical, primarily because the budget constraints 

for individual endpoints; however, to compare or ag- normally associated with expressions of WTP are not 

gregate benefits across endpoints, the benefits must significant enough to drive a wedge between the estibe monetized. Assigning a monetary value to avoided mates. For larger risk changes, however, the WTP and 

incidences of each effect permits a summation, in terms WTA values may diverge, with WTP normally being 

of dollars, of monetized benefits realized as a result less than WTA because of the budget constraint efof the CAA, and allows that summation to be com- fect. While the underlying economic valuation literapared to the cost of the CAA. ture is based on studies which elicited expressions of 

WTP and/or WTA, the remainder of this report refers 

For the present analysis of health and welfare ben- to all valuation coefficients as WTP estimates. In some 

efits, valuation estimates were obtained from the eco- cases (e.g., stroke-related hospital admissions), neinomic literature, and are reported in dollars per case ther WTA nor WTP estimates are available and WTP 

reduced for health effects and dollars per unit of is approximated by cost of illness (COI) estimates, a


avoided damage for welfare effects.55 Similar to esti- clear underestimate of the true welfare change since


mates of physical effects provided by health studies, important value components (e.g., pain and suffering


each of the monetary values of benefits applied in this associated with the stroke) are not reflected in the out-


analysis is reported in terms of a mean value and a of-pocket costs for the hospital stay.
 probability distribution around the mean estimate. The


statistical form of the probability distribution used for For most goods, WTP can be observed by exam-


the valuation measures varies by endpoint. For ex- ining actual market transactions. For example, if a


ample, while the estimate of the dollar value of an gallon of bottled drinking water sells for one dollar, it


avoided premature mortality is described by the can be observed that at least some persons are willing


Weibull distribution, the estimate for the value of a to pay one dollar for such water. For goods that are


reduced case of acute bronchitis is assumed to be uni- not exchanged in the market, such as most environ­


formly distributed between a minimum and maximum mental “goods,” valuation is not so straightforward.


value. Nevertheless, value may be inferred from observed


behavior, such as through estimation of the WTP for

Methods for Valuation of Health mortality risk reductions based on observed sales and 

and Welfare Effects prices of safety devices such as smoke detectors. Alternatively, surveys may be used in an attempt to elicit 

In environmental benefit-cost analysis, the dollar directly WTP for an environmental improvement. 

value of an environmental benefit (e.g., a health-related improvement in environmental quality) conferred Wherever possible, this analysis uses estimates 

on a person is the dollar amount such that the person of the mean WTP of the U.S. population to avoid an 

would be indifferent between having the environmen- environmental effect as the value of avoiding that eftal benefit and having the money. In some cases, this fect. In some cases, such estimates are not available, 

value is measured by studies which estimate the dol- and the cost of mitigating or avoiding the effect is 

lar amount required to compensate a person for new used as a rough estimate of the value of avoiding the 

or additional exposure to an adverse effect. Estimates effect. For example, if an effect results in hospitalizaderived in this manner are referred to as “willingness- tion, the avoided medical costs were considered as a 

to-accept” (WTA) estimates. In other cases, the value possible estimate of the value of avoiding the effect. 

of a welfare change is measured by estimating the Finally, where even the “avoided cost” estimate is not 

amount of money a person is willing to pay to elimi- available, the analysis relies on other available methnate or reduce a current hazard. This welfare change ods to provide a rough approximation of WTP. As 

concept is referred to as “willingness-to-pay” (WTP). noted above, this analysis uses a range of values for 

most environmental effects, or endpoints. Table 13 

55 The literature reviews and valuation estimate development process is described in detail in Appendix I and in the referenced 

supporting reports. 

43

En dpoin t Pollu t a n Valua t ion (m ea n est.)

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

En dp oint Pollu t antt V alua t ion (m ea n est.) 

Mortality PM & Pb $4,80 0, 000 pe r case 

Chronic Bro nc hitis PM $26 0, 000 pe r case 

IQ Ch ang es 

Lo st IQ Poin ts Pb $3,00 0 pe r IQ po int 

IQ < 70 Pb $42, 0 00 pe r case 

Hy perten sion Pb $68 0 pe r case 

Strokes* Pb $20 0,000 

$15 0, 000 

pe r case -m ales 

pe r case -

fem ales 

Coronary Heart Disease Pb $52,0 00 pe r case 

Ho spital Admissio ns 

Ischem ic Hea rt Dise ase PM $10, 3 00 pe r case 

Cong estive Heart Failu re PM $8,30 0 pe r case 

COPD PM & O3 $8,10 0 pe r case 

Pneumonia PM & O3 $7,90 0 pe r case 

All Resp iratory PM & O3 $6,10 0 pe r case 

Re spira tory Illn ess an d Sym ptoms 

Acute Bronchitis PM $45 pe r case 

Acute A sthm a PM & O3 $32 pe r case 

Acute Re spiratory Sym ptoms PM, O3, NO2 , SO2 $18 pe r case 

Up pe r Respiratory Symptoms PM $19 pe r case 

Lo wer Respiratory Symptoms PM $12 pe r case 

Shortness of Breath PM $5.30 pe r da y 

Work Lo ss Day s PM $83 pe r da y 

Mild Restricte d Ac tivity Day s PM & O3 $38 pe r da y 

Welfare Ben efits 

Visibility De ciView $14 pe r unit ch ang e 

in DeciView 

Ho use hold Soiling PM $2.50 pe r hou seho ld 

pe r PM10 

chan ge 

Decreased Worker Produ ctivity O3 $1 ** 

Agriculture (Net Surplus) O3 Estim ated Change In 

Econom ic Surplus 

Table 13. Health and Welfare Effects Unit Valuation 

(1990 dollars). 

* Strokes are comprised of atherothrombotic brain infarctions and cerebrovascular 

accidents; both are estimated to have the same monetary value. 

** Decreased productivity valued as change in daily wages: $1 per worker per 10% 

decrease in O3. 

provides a summary of the mean unit value estimates ancy tables, the life-years saved from each statistical 

of “excess premature mortality” per time period (e.g., per year). 

The benefit, however, is the avoidance 

of small increases in the risk of mortality. If 

individuals’ WTP to avoid small increases in 

risk is summed over enough individuals, the 

value of a statistical premature death avoided 

can be inferred.56 For expository purposes, 

this valuation is expressed as “dollars per 

mortality avoided,” or “value of a statistical 

life” (VSL), even though the actual valuation 

is of small changes in mortality risk. 

The mortality risk valuation estimate 

used in this study is based on an analysis of 

26 policy-relevant value-of-life studies (see 

Table 14). Five of the 26 studies are contingent valuation (CV) studies, which directly 

solicit WTP information from subjects; the 

rest are wage-risk studies, which base WTP 

estimates on estimates of the additional compensation demanded in the labor market for 

riskier jobs. The Project Team 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 $4.8 million and standard deviation of $3.24 million, 

provided the best fit to the 26 estimates. There 

is considerable uncertainty associated with 

this approach, however, which is discussed 

in detail later in this chapter and in Appendix I. 

In addition, the Project Team developed 

alternative calculations based on a life-years 

lost approach. To employ the value of statistical life-year (VSLY) approach, the Project 

Team had to first estimate the age distribution of those lives which would be saved by 

reducing air pollution. Based on life expectused in the analysis. The full range of values can be


found in Appendix I. life saved within each age and sex cohort were calcu­


lated. To value these statistical life-years, a concep-


Mortality tual model was hypothesized which depicted the rela­


tionship between the value of life and the value of


Some forms of air pollution increase the probabil- life-years. As noted earlier in Table 9, the average


ity that individuals will die prematurely. The concen- number of life-years saved across all age groups


tration-response functions for mortality used in this for which data were available are 14 for PM-


analysis express this increase in mortality risk as cases related mortality and 38 for Pb-related mortality. The


56 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 each individual’s WTP for that risk 

reduction is $50, then the implied value of a statistical premature death avoided is $50 x 100,000 = $5 million. 

44

Stu dy Type of

Estim ate

Valua t ion

(m illion s

199 0$)

Chapter 6: Economic Valuation 

Table 14. Summary of Mortality Valuation Estimates 

(millions of $1990) 

Stu dy Typ e of 

Estim ate 

Valuat ion 

(m illion s 

199 0$) 

Kn eisne r and Leeth (1 991) (US) 

Smith and Gilb ert (1 984) 

Dilling ham (1 985) 

Butler (198 3) 

Miller and Guria (199 1) 

Moore and Viscusi (1988a) 

Viscusi, Maga t, and Hub er (1991 b) 

Ge ga x et al. (198 5) 

Ma rin and Psa charopo ulos (198 2) 

Kn eisne r and Leeth (1 991) 

(A ustralia) 

Labor M arket 

Labor M arket 

Labor M arket 

Labor M arket 

Cont. Valu e 

Labor M arket 

Cont. Valu e 

Cont. Valu e 

Labor M arket 

Labor M arket 

0.6 

0.7 

0.9 

1.1 

1.2 

2.5 

2.7 

3.3 

2.8 

3.3 

Ge rking, de Ha an, an d Schulze 

(1 988) 

Cont. Valu e 3.4 

Cousin eau, Lacroix, an d Girard 

(1 988) 

Jones-Lee (198 9) 

Dilling ham (1 985) 

Viscusi (19 78, 19 79) 

R.S. Smith (19 76) 

V. K. Smith (1 976) 

Olso n (1981 ) 

Viscusi (19 81) 

R.S. Smith (19 74) 

Moore and Viscusi (1988a) 

Kn eisne r and Leeth (1 991) (Jap an) 

He rz og and Schlottman (19 87) 

Leigh a nd Folso n (198 4) 

Leigh (1 987) 

Ga te n (1988 ) 

SOURCE: Visc usi, 19 92 

Labor Market 3.6 

Cont. Value 3.8 

Labor Market 3.9 

Labor Market 4.1 

Labor Market 4.6 

Labor Market 4.7 

Labor Market 5.2 

Labor Market 6.5 

Labor Market 7.2 

Labor Market 7.3 

Labor Market 7.6 

Labor Market 9.1 

Labor Market 9.7 

Labor Market 10 .4 

Labor Market 13 .5 

methods in this context is controversial within the 

economics profession. In general, economists prefer 

to infer WTP from observed behavior. There are times 

when such inferences are impossible, however, and 

some type of survey technique may be the only means 

of eliciting WTP. Economists’ beliefs regarding the 

reliability of such survey-based data cover a broad 

spectrum, from unqualified acceptances of the results 

of properly-conducted surveys to outright rejections 

of all survey-based valuations. 

In this analysis, unit valuations which rely exclusively on the contingent valuation method are chronic 

bronchitis, respiratory-related ailments, minor restricted activity days, and visibility. As indicated 

above, the value derived for excess premature mortality stems from 26 studies, of which five use the contingent valuation method. These five studies are within 

the range of the remaining 21 labor market studies. 

All five report mortality valuations lower than the 

central estimate used in this analysis. Excluding the 

contingent valuation studies from the mortality valuation estimate would yield a central estimate approximately ten percent higher than the 4.8 million dollar 

value reported above. The endpoints with unit valuations based exclusively on contingent valuation account for approximately 30 percent of the present value 

of total monetized benefits. Most of the CV-based 

benefits are attributable to avoided cases of chronic 

bronchitis. 

Chronic Bronchitis 

The best available estimate of WTP to avoid a 

case of chronic bronchitis (CB) comes from Viscusi 

et al.(1991). The case of CB described to the respondents in the Viscusi study, however, was described by 

the authors as a severe case. The Project Team employed an estimate of WTP to avoid a pollution-related case of CB that was based on adjusting the WTP 

to avoid a severe case, estimated by Viscusi et al. 

(1991), to account for the likelihood that an average 

case of pollution-related CB is not as severe as the 

case described in the Viscusi study. 

The central tendency estimate of WTP to avoid a 

pollution-related case of chronic bronchitis (CB) used 

in this analysis is the mean of a distribution of WTP 

estimates. This distribution incorporates the uncertainty from three sources: (1) the WTP to avoid a case 

of severe CB, as described by Viscusi et al., 1991; (2) 

the severity level of an average pollution-related case 

average for PM, in particular, differs from the 35-year 

expected remaining lifespan derived from existing 

wage-risk studies.57 

Using the same distribution of value of life estimates used above (i.e. the Weibull distribution with a 

mean estimate of $4.8 million), a distribution for the 

value of a life-year was then estimated and combined 

with the total number of estimated life-years lost. The 

details of these calculations are presented in Appendix I. 

Survey-Based Values 

Willingness-to pay for environmental improvement is often elicited through survey methods (such 

as the “contingent valuation” method). Use of such 

57 See, for example, Moore and Viscusi (1988) or Viscusi (1992). 

45

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

of CB (relative to that of the case described by Viscusi 

et al.(1991); and (3) the elasticity of WTP with respect to severity of the illness. Based on assumptions 

about the distributions of each of these three uncertain components, a distribution of WTP to avoid a 

pollution-related case of CB was derived by Monte 

Carlo methods. The mean of this distribution, which 

was about $260,000, is taken as the central tendency 

estimate of WTP to avoid a pollution-related case of 

CB. The three underlying distributions, and the generation of the resulting distribution of WTP, are described in Appendix I. 

Respiratory-Related Ailments 

In general, the valuations assigned to the respiratory-related ailments listed in Table 14 represent a 

combination of willingness to pay estimates for individual symptoms which comprise each ailment. For 

example, a willingness to pay estimate to avoid the 

combination of specific upper respiratory symptoms 

defined in the concentration-response relationship 

measured by Pope et al. (1991) is not available. However, while that study defined upper respiratory symptoms as one suite of ailments (runny or stuffy nose; 

wet cough; and burning, aching, or red eyes), the valuation literature reported individual WTP estimates for 

three closely matching symptoms (head/sinus congestion, cough, and eye irritation). The available WTP 

estimates were therefore used as a surrogate to the 

values for the precise symptoms defined in the concentration-response study. 

To capture the uncertainty associated with the 

valuation of respiratory-related ailments, this analysis incorporated a range of values reflecting the fact 

that an ailment, as defined in the concentration-response relationship, could be comprised of just one 

symptom or several. At the high end of the range, the 

valuation represents an aggregate of WTP estimates 

for several individual symptoms. The low end represents the value of avoiding a single mild symptom. 

Minor Restricted Activity Days 

An individual suffering from a single severe or a 

combination of pollution-related symptoms may experience a Minor Restricted Activity Day (MRAD). 

Krupnick and Kopp (1988) argue that mild symptoms 

will not be sufficient to result in a MRAD, so that 

WTP to avoid a MRAD should exceed WTP to avoid 

any single mild symptom. On the other hand, WTP to 

avoid a MRAD should not exceed the WTP to avoid a 

work loss day (which results when the individual experiences more severe symptoms). No studies are reported to have estimated WTP to avoid a day of minor restricted activity. Instead, this analysis uses an 

estimate derived from WTP estimates for avoiding 

combinations of symptoms which may result in a day 

of minor restricted activity ($38 per day). The uncertainty range associated with this value extends from 

the highest value for a single symptom to the value 

for a work loss day. Furthermore, the distribution acknowledges that the actual value is likely to be closer 

to the central estimate than either extreme. 

Visibility 

The value of avoided visibility impairment was 

derived from existing contingent valuation studies of 

the household WTP to improve visibility, as reported 

in the economics literature. These studies were used 

to define a single, consistent basis for the valuation of 

visibility benefits nationwide. The central tendency 

of the benefits estimate is based on an annual WTP of 

$14 per household per unit improvement in the 

DeciView index, with upper and lower bounds of $21 

and $8, respectively, on the uncertainty range of the 

estimate. 

Avoided Cost Estimates 

For some health effects, WTP estimates are not 

available, and the Project Team instead used “costs 

avoided” as a substitute for WTP. Avoided costs were 

used to value the following endpoints: hypertension, 

hospital admissions, and household soiling. 

Hypertension and Hospital Admissions 

Avoided medical costs and the avoided cost of lost 

work time were used to value hypertension (high blood 

pressure) and hospital admissions (this includes hospital admissions for respiratory ailments as well as 

heart disease, heart attacks, and strokes) . 

For those hospital admissions which were specified to be the initial hospital admission (in particular, 

hospital admissions for coronary heart disease (CHD) 

events and stroke), avoided cost estimates should consist of the present discounted value of the stream of 

medical expenditures related to the illness, as well as 

the present discounted value of the stream of lost earnings related to the illness. While an estimate of present 

discounted value of both medical expenditures and 

lost earnings was available for stroke ($200,000 for 

46

Chapter 6: Economic Valuation 

males and $150,000 for females), the best available 

estimate for CHD ($52,000) did not include lost earnings. Although no published estimates of the value of 

lost earnings due to CHD events are available, one 

unpublished study suggests that this value could be 

substantial, possibly exceeding the value of medical 

expenditures. The estimate of $52,000 for CHD may 

therefore be a substantial underestimate. The derivations of the avoided cost estimates for CHD and stroke 

are discussed in Appendix G. 

In those cases for which it is unspecified whether 

the hospital admission is the initial one or not (that is, 

for all hospital admissions endpoints other than CHD 

and stroke), it is unclear what portion of medical expenditures and lost earnings after hospital discharge 

can reasonably be attributed to pollution exposure and 

what portion might have resulted from an individual’s 

pre-existing condition even in the absence of a particular pollution-related hospital admission. In such 

cases, the estimates of avoided cost include only those 

costs associated with the hospital stay, including the 

hospital charge, the associated physician charge, and 

the lost earnings while in the hospital ($6,100 to 

$10,300, depending on the ailment for which hospitalization is required). 

The estimate of avoided cost for hypertension included physician charges, medication costs, and hospitalization costs, as well as the cost of lost work time, 

valued at the rate estimated for a work loss day (see 

discussion below). Based on this approach, the value 

per year of avoiding a case of hypertension is taken to 

equal the sum of medical costs per year plus work 

loss costs per year; the resulting value is $680 per case 

per year. 

Presumably, willingness-to-pay to avoid the effects (and treatment) of hypertension would reflect 

the value of avoiding any associated pain and suffering, and the value placed on dietary changes, etc. Likewise, the value of avoiding a health effect that would 

require hospitalization or doctor’s care would include 

the value of avoiding the pain and suffering caused 

by the health effect as well as lost leisure time, in addition to medical costs and lost work time. Consequently, the valuations for these endpoints used in this 

analysis likely represent lower-bound estimates of the 

true social values for avoiding such health effects. 

Household Soiling 

This analysis values benefits for this welfare effect by considering the avoided costs of cleaning 

houses due to particulate matter soiling. The Project 

Team’s estimate reflects the average household’s annual cost of cleaning per μg/m3 particulate matter 

($2.50). Considered in this valuation are issues such 

as the nature of the particulate matter, and the proportion of households likely to do the cleaning themselves. 

Since the avoided costs of cleaning used herein do 

not reflect the loss of leisure time (and perhaps work 

time) incurred by those who do their own cleaning, 

the valuation function likely underestimates true WTP 

to avoid additional soiling. 

Other Valuation Estimates 

Changes in Children’s IQ 

One of the major effects of lead exposure is permanently impaired cognitive development in children. 

No ready estimates of society’s WTP for improved 

cognitive ability are currently available. Two effects 

of IQ decrements can be monetized, however: reductions in expected lifetime income, and increases in 

societal expenditures for compensatory education. 

These two effects almost certainly understate the WTP 

to avoid impaired cognitive development in children, 

and probably should be considered lower bound estimates. In the absence of better estimates, however, 

the Project Team has assumed that the two monetized 

effects represent a useful approximation of WTP. 

The effect of IQ on expected lifetime income comprises a direct and an indirect effect. The direct effect 

is drawn from studies that estimate, all else being 

equal, the effect of IQ on income. The indirect effect 

occurs as a result of the influence of IQ on educational attainment: higher IQ leads to more years of 

education, and more education leads in turn to higher 

expected future income. However, this indirect benefit is mitigated, but not eliminated, by the added costs 

of the additional education and by the potential earnings forgone by the student while enrolled in school.58 

Combining the direct and indirect influences, the net 

effect of higher IQ on expected lifetime income (dis58 Theoretically, the indirect effect should be small relative to the direct effect of IQ on future earnings. The empirical research 

used to derive values for this analysis, however, implies that the indirect effect is roughly equal in magnitude to the direct effect. One 

can infer from this information that there is a market distortion of some sort present (such as imperfect knowledge of the returns to 

education), or, perhaps, that individuals make their education “investments” for purposes other than (or in addition to) “maximizing 

lifetime income.” See Appendix G for further discussion of this issue. 

47

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

counted to the present at five percent) is estimated to 

be $3,000 per additional IQ point. 

In this analysis, it is assumed that part-time compensatory education is required for all children with 

IQ less than 70. The Project Team assumed that the 

WTP to avoid cases of children with IQ less than 70 

can be approximated by the cost ($42,000 per child) 

of part-time special education in regular classrooms 

from grades one through twelve (as opposed to independent special education programs), discounted to 

the present at five percent. See Appendix G for more 

detail on valuation methods and data sources for IQ 

effects and other lead-related health impacts. 

Work Loss Days and Worker 

Productivity 

For this analysis, it was assumed that the median 

daily 1990 wage income of 83 dollars was a reasonable approximation of WTP to avoid a day of lost 

work. Although a work loss day may or may not affect the income of the worker, depending on the terms 

of employment, it does affect economic output and is 

thus a cost to society. Conversely, avoiding the work 

loss day is a benefit. 

A decline in worker productivity has been measured in outdoor workers exposed to ozone. Reduced 

productivity is measured in terms of the reduction in 

daily income of the average worker engaged in strenuous outdoor labor, estimated at $1 per 10 percent increase in ozone concentration. 

Agricultural Benefits 

Similar to the other welfare effects, the agricultural benefits analysis estimated benefits in dollars per 

unit of avoided damage, based on estimated changes 

in crop yields predicted by an agricultural sector 

model. This model incorporated agricultural price, 

farm policy, and other data for each year. Based on 

expected yields, the model estimated the production 

levels for each crop, and the economic benefits to consumers, and to producers, associated with these production levels. To the extent that alternative exposureresponse relationships were available, a range of potential benefits was calculated (see Appendix F). 

Valuation Uncertainties 

The Project Team attempted to handle most valuation uncertainties explicitly and quantitatively by 

expressing values as distributions (see Appendix I for 

a complete description of distributions employed), 

using a Monte-Carlo simulation technique to apply 

the valuations to physical effects (see Chapter 7) with 

the mean of each valuation distribution equal to the 

“best estimate” valuation. This approach does not, of 

course, guarantee that all uncertainties have been adequately characterized, nor that the valuation estimates 

are unbiased. It is possible that the actual WTP to avoid 

an air pollution-related impact is outside of the range 

of estimates used in this analysis. Nevertheless, the 

Project Team believes that the distributions employed 

are reasonable approximations of the ranges of uncertainty, and that there is no compelling reason to believe that the mean values employed are systematically biased (except for the IQ-related and avoided 

cost-based values, both of which probably underestimate WTP). 

One particularly important area of uncertainty is 

valuation of mortality risk reduction. As noted in Chapter 7, changes in mortality risk are a very important 

component of aggregate benefits, and mortality risk 

valuation is an extremely large component of the quantified uncertainty. Consequently, any uncertainty concerning mortality risk valuation beyond that addressed 

by the quantitative uncertainty assessment (i.e., that 

related to the Weibull distribution with a mean value 

of $4.8 million) deserves note. One issue merits special attention: uncertainties and possible biases related 

to the “benefits transfer” from the 26 valuation source 

studies to valuation of reductions in PM-related mortality rates. 

Mortality Risk Benefits Transfer 

Although each of the mortality risk valuation 

source studies (see Table 14) estimated the average 

WTP for a given reduction in mortality risk, the degree of reduction in risk being valued varied across 

studies and is not necessarily the same as the degree 

of mortality risk reduction estimated in this analysis. 

The transferability of estimates of the value of a statistical life from the 26 studies to the section 812 benefit analysis rests on the assumption that, within a reasonable range, WTP for reductions in mortality risk 

is linear in risk reduction. For example, suppose a study 

48

Chapter 6: Economic Valuation 

estimates that the average WTP for a reduction in 

mortality risk of 1/100,000 is 50 dollars, but that the 

actual mortality risk reduction resulting from a given 

pollutant reduction is 1/10,000. If WTP for reductions 

in mortality risk is linear in risk reduction, then a WTP 

of 50 dollars for a reduction of 1/100,000 implies a 

WTP of 500 dollars for a risk reduction of 1/10,000 

(which is ten times the risk reduction valued in the 

study). Under the assumption of linearity, the estimate 

of the value of a statistical life does not depend on the 

particular amount of risk reduction being valued. 

Although the particular amount of mortality risk 

reduction being valued in a study may not affect the 

transferability of the WTP estimate from the study to 

the benefit analysis, the characteristics of the study 

subjects and the nature of the mortality risk being valued in the study could be important. Certain characteristics of both the population affected and the mortality risk facing that population are believed to affect 

the average WTP to reduce risk. The appropriateness 

of the mean of the WTP estimates from the 26 studies 

for valuing the mortality-related benefits of reductions 

in pollutant concentrations therefore depends not only 

on the quality of the studies (i.e., how well they measure what they are trying to measure), but also on (1) 

the extent to which the subjects in the studies are similar to the population affected by changes in air pollution and (2) the extent to which the risks being valued 

are similar. 

The substantial majority of the 26 studies relied 

upon are wage-risk (or labor market) studies. Compared with the subjects in these wage-risk studies, the 

population most affected by air pollution-related mortality risk changes is likely to be, on average, older 

and probably more risk averse. Some evidence suggests that approximately 85 percent of those identified in short-term (“episodic”) studies who die prematurely from PM-related causes are over 65.59 The 

average age of subjects in wage-risk studies, in contrast, would be well under 65. 

The direction of bias resulting from the age difference is unclear. It could be argued that, because an 

older person has fewer expected years left to lose, his 

or her WTP to reduce mortality risk would be less 

than that of a younger person. This hypothesis is supported by one empirical study, Jones-Lee et al. (1985), 

which found WTP to avoid mortality risk at age 65 to 

be about 90 percent of what it is at age 40. On the 

other hand, there is reason to believe that those over 

65 are, in general, more risk averse than the general 

population, while workers in wage-risk studies are 

likely to be less risk averse than the general population. Although the list of 26 studies used here excludes 

studies that consider only much-higher-than-average 

occupational risks, there is nevertheless likely to be 

some selection bias in the remaining studies—that is, 

these studies are likely to be based on samples of 

workers who are, on average, more risk-loving than 

the general population. In contrast, older people as a 

group exhibit more risk-averse behavior. 

There is substantial evidence that the income elasticity of WTP for health risk reductions is positive 

(although there is uncertainty about the exact value of 

this elasticity). Individuals with higher incomes (or 

greater wealth) should, then, be willing to pay more 

to reduce risk, all else equal, than individuals with 

lower incomes or wealth. The comparison between 

the (actual and potential) income or wealth of the 

workers in the wage-risk studies versus that of the 

population of individuals most likely to be affected 

by changes in pollution concentrations, however, is 

unclear. One could argue that because the elderly are 

relatively wealthy, the affected population is also 

wealthier, on average, than are the wage-risk study 

subjects, who tend to be middle-aged (on average) 

blue-collar workers. On the other hand, the workers 

in the wage-risk studies will have potentially more 

years remaining in which to acquire streams of income from future earnings. In addition, it is possible 

that among the elderly it is largely the poor elderly 

who are most vulnerable to air pollution-related mortality risk (e.g., because of generally poorer health 

care). On net, the potential income comparison is unclear. 

Although there may be several ways in which jobrelated mortality risks differ from air pollution-related 

mortality risks, the most important difference may be 

that job-related risks are incurred voluntarily whereas 

air pollution-related risks are incurred involuntarily. 

There is some evidence60 that people will pay more to 

reduce involuntarily incurred risks than risks incurred 

voluntarily. If this is the case, WTP estimates based 

on wage-risk studies may be downward biased estimates of WTP to reduce involuntarily incurred air 

pollution-related mortality risks. 

59 See Schwartz and Dockery (1992), Ostro et al. (1995), and Chestnut (1995). 

60See, for example, Violette and Chestnut, 1983. 

49

Fact or Li kely Di rectio n of Bias in WT P

Estim ate

Protracted Death 

Un ce rtain, pe rh aps do wnw ard 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Finally, another important difference related to the 

nature of the risk may be that some workplace mortality risks tend to involve sudden, catastrophic events, 

whereas air pollution-related risks tend to involve 

longer periods of disease and suffering prior to death. 

Some evidence suggests that WTP to avoid a risk of a 

protracted death involving prolonged suffering and 

loss of dignity and personal control is greater than the 

WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that the mortality risks addressed in this assessment are associated with longer 

periods of illness or greater pain and suffering than 

are the risks addressed in the valuation literature, the 

WTP measurements employed in the present analysis 

would reflect a downward bias. 

The potential sources of bias introduced by relying on wage-risk studies to derive an estimate of the 

WTP to reduce air pollution-related mortality risk are 

summarized in Table 15. Among these potential biases, it is disparities in age and income between the 

subjects of the wage-risk studies and those affected 

by air pollution which have thus far motivated specific suggestions for quantitative adjustment61 ; however, the appropriateness and the proper magnitude of 

such potential adjustments remain unclear given presently available information. These uncertainties are 

particularly acute given the possibility that age and 

income biases might offset each other in the case of 

pollution-related mortality risk aversion. Furthermore, 

the other potential biases discussed above, and summarized in Table 16, add additional uncertainty regarding the transferability of WTP estimates from 

wage-risk studies to environmental policy and program assessments. 

Table 15. Estimating Mortality Risk Based on WageRisk Studies: Potential Sources and Likely Direction of 

Bias. 

Fa ct or Li kely Di rectio n of Bias in WT P 

Estim ate 

Ag e Un ce rtain, pe rh aps up wa rd 

De gree o f Risk Aversion 

In com e 

Vo lunta ry vs. 

In voluntary 

Do wnw ard 

Un ce rtain 

Do wnw ard 

Catastro phic vs. 

Protracted Death 

Un ce rtain, pe rh aps do wnw ard 

61 Chestnut, 1995; IEc, 1992. 

50

7 

Results and Uncertainty 

This chapter presents a summary of the monetized 

benefits of the CAA from 1970 to 1990, compares 

these with the corresponding costs, explores some of 

the major sources of uncertainty in the benefits estimates, and presents alternative results reflecting diverging viewpoints on two key variables: PM-related 

mortality valuation and the discount rate. 

Monetized economic benefits for the 1970 to 1990 

period were derived by applying the unit valuations 

discussed in Chapter 6 to the stream of physical effects estimated by the method documented in Chapter 

5. The range of estimates for monetized benefits is 

based on the quantified uncertainty associated with 

the health and welfare effects estimates and the quantified uncertainty associated with the unit valuations 

applied to them. Quantitative estimates of uncertainties in earlier steps of the analysis (i.e., estimation of 

compliance costs,62 emissions changes, and air quality changes) could not be adequately developed and 

are therefore not applied in the present study. As a 

result, the range of estimates for monetized benefits 

presented in this chapter is narrower than would be 

expected with a complete accounting of the uncertainties in all analytical components. However, the uncertainties in the estimates of physical effects and unit 

values are considered to be large relative to these earlier components. The characterization of the uncertainty surrounding unit valuations is discussed in detail in Appendix I. The characterization of the uncertainty surrounding health and welfare effects estimates, 

as well as the characterization of overall uncertainty 

surrounding monetized benefits, is discussed below. 

Quantified Uncertainty in the 

Benefits Analysis 

Alternative studies published in the scientific literature which examine the health or welfare consequences of exposure to a given pollutant often obtain 

different estimates of the concentration-response (CR) 

relationship between the pollutant and the effect. In 

some instances the differences among CR functions 

estimated by, or derived from, the various studies are 

substantial. In addition to sampling error, these differences may reflect actual variability of the concentration-response relationship across locations. Instead 

of a single CR coefficient characterizing the relationship between an endpoint and a pollutant in the CR 

function, there could be a distribution of CR coefficients which reflect geographic differences.63 Because 

it is not feasible to estimate the CR coefficient for a 

given endpoint-pollutant combination in each county 

in the nation, however, the national benefits analysis 

applies the mean of the distribution of CR coefficients 

to each county. This mean is estimated based on the 

estimates of CR coefficients reported in the available 

studies and the information about the uncertainty of 

these estimates, also reported in the studies. 

Based on the assumption that for each endpointpollutant combination there is a distribution of CR 

coefficients, the Project team used a Monte Carlo approach to estimate the mean of each distribution and 

to characterize the uncertainty surrounding each estimate. For most health and welfare effects, only a single 

study is considered. In this case, the best estimate of 

the mean of the distribution of CR coefficients is the 

reported estimate in the study. The uncertainty surrounding the estimate of the mean CR coefficient is 

62 Although compliance cost estimation is primarily of concern to the cost side of this analysis, uncertainty in the estimates for 

compliance costs does influence the uncertainty in the benefit estimates because compliance cost changes were used to estimate 

changes in macroeconomic conditions which, in turn, influenced the estimated changes in emissions, air quality, and physical effects. 

63 Geographic variability may result from differences in lifestyle (e.g., time spent indoors vs outdoors), deposition rates, or other 

localized factors which influence exposure of the population to a given atmospheric concentration of the pollutant. 

51

A gricu ltur e (Net Surplus) $11 $23 $35 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

best characterized by the standard error of the reported 

estimate. This yields a normal distribution, centered 

at the reported estimate of the mean. If two or more 

studies are considered for a given endpoint-pollutant 

combination, a normal distribution is derived for each 

study, centered at the mean estimate reported in the 

study. On each iteration of a Monte Carlo procedure, 

a CR coefficient is randomly selected from each of 

the normal distributions, and the selected values are 

averaged. This yields an estimate of the mean CR coefficient for that endpoint-pollutant combination. Iterating this procedure many times results in a distribution of estimates of the mean CR coefficient. 

Each estimate randomly selected from this distribution was evaluated for each county in the nation, 

and the results were aggregated into an estimate of 

the national incidence of the health or welfare effect. 

Through repeated sampling from the distribution of 

mean CR coefficients, a distribution of the estimated 

change in effect outcomes due to the change in air 

quality between the control and no-control scenarios 

was generated. 

Once a distribution of estimated outcomes was 

generated for each health and welfare effect, Monte 

Carlo methods were used again to characterize the 

overall uncertainty surrounding monetized benefits. 

For each health and welfare effect in a set of nonoverlapping effects, an estimated incidence was randomly selected from the distribution of estimated incidences for that endpoint, and a unit value was randomly selected from the corresponding distribution 

of unit values, on each iteration of the Monte Carlo 

procedure. The estimated monetized benefit for that 

endpoint produced on that iteration is the product of 

these two factors. Repeating the process many times 

generated a distribution of estimated monetized benefits by endpoint. Combining the results for the individual endpoints using the Monte Carlo procedure 

yielded a distribution of total estimated monetized 

benefits for each target year (1975, 1980, 1985 and 

1990). This technique enabled a representation of 

uncertainty in current scientific and economic opinion in these benefits estimates. 

Aggregate Monetized Benefits 

For each of the target years of the analysis, the 

monetized benefits associated with the different health 

and welfare effects for that year must be aggregated. 

These aggregate benefits by target year must then be 

aggregated across the entire 1970 to 1990 period of 

the study to yield a present discounted value of aggregate benefits for the period. The issues involved in 

each stage of aggregation, as well as the results of 

aggregation, are presented in this section. (The detailed results for the target years are presented in Appendix I.) 

Table 16. Present Value of 1970 to 1990 Monetized Benefits by Endpoint Category for 48 State 

Population (billions of $1990, discounted to 1990 at 5 percent). 

En dpoint Pollutant(s) 

Present Value 

5th %ile Mean 95th %ile 

M ortality PM 

M ortality Pb 

Chro nic Bro nchitis PM 

IQ (Lost IQ Pts. + Children w/ I Q<70) Pb 

Hy perten sion Pb 

Ho sp ital Admissio ns PM , O3, Pb, & CO 

Resp iratory-R elated Symp toms, Restricted 

Activity, & Decreased Pr oductivity 

PM , O3, NO 2, & SO2 

So iling D amage PM 

Visib ility particu lates 

A gricu ltur e (Net Surplus) O3

$2,369 

$121 

$409 

$271 

$77 

$27 

$123 

$6 

$38 

$11 

$16,63 2 

$1,339 

$3,313 

$399 

$98 

$57 

$182 

$74 

$54 

$23 

$40,59 7 

$3,910 

$10,40 1 

$551 

$120 

$120 

$261 

$192 

$71 

$35 

52

Chapter 7: Results and Uncertainty 

Table 16 presents monetized benefits for each 

quantified and monetized health and welfare endpoint 

(or group of endpoints), aggregated from 1970 to 1990. 

The mean estimate resulting from the Monte Carlo 

simulation is presented, along with the measured credible range (upper and lower fifth percentiles of the 

distribution). Aggregating the stream of monetized 

benefits across years involved compounding the stream 

of monetized benefits estimated for each year to the 

1990 present value (using a five percent discount rate). 

Since the present value estimates combine streams 

of benefits from 1970 to 1990, the calculation required 

monetized estimates for each year. However, Monte 

Carlo modeling was carried out only for the four target years (1975, 1980, 1985 and 1990). In the intervening years, only a central estimate of benefits was 

estimated for each health and welfare endpoint (by 

multiplying the central incidence estimate for the given 

year by the central estimate of the unit valuation). The 

resulting annual benefit estimates provided a temporal trend of monetized benefits across the period resulting from the annual changes in air quality. They 

Table 16 offers a comparison of benefits by health 

or welfare endpoint. The effect categories listed in 

the table are mutually exclusive, allowing the monetized benefits associated with them to be added. It 

should be noted, however, that the listed categories 

combine estimates that are not mutually exclusive. To 

avoid double counting, care was taken to treat the benefits associated with overlapping effects as alternative estimates. For example, the “Hospital Admissions” category includes admissions for specific ailments (Pneumonia and COPD) as well as the broader 

classification of “all respiratory” ailments. Clearly, 

benefits accruing from the first two represent a subset 

of the last and adding all three together would result 

in an overestimate of total monetized benefits. To avoid 

this, the sum of benefits from Pneumonia and COPD 

was treated as an alternative to the benefits estimated 

for all respiratory ailments (the sum of the first two 

was averaged with the third). This issue of doublecounting also arose for two other cases of overlapping health effects, both of which have been combined 

into the “Respiratory-Related Symptoms, Restricted 

Activity, & Decreased Productivity” category in Table 

Table 17. Total Monetized Benefits for 48 State Population (Present Value in billions of 1990$, 

discounted to 1990 at 5 percent). 

Pres ent Val ue 

5th %i le Mean 95th %il e 

TOT AL (Bi llions of 1990-value dollars ) $5,600 $22,200 $49,400 

Pres ent Val ue 

5th %i le Mean 95th %il e 

TOT AL (Bi llions of 1990-value dollars ) $5,600 $22,200 $49,400 

did not, however, characterize the uncertainty associated with the yearly estimates for intervening years. 

In an attempt to capture uncertainty associated with 

these estimates, the Project Team relied on the ratios 

of the 5th percentile to the mean and the 95th percentile to the mean in the target years. In general, these 

ratios were fairly constant across the target years, for 

a given endpoint. The ratios were interpolated between 

the target years, yielding ratios for the intervening 

years. Multiplying the ratios for each intervening year 

by the central estimate generated for that year provided estimates of the 5th and 95th percentiles, which 

were used to characterize uncertainty about the central estimate. Thus, the present value of the stream of 

benefits, including the credible range estimates, could 

be computed. 

16. First, acute bronchitis was treated as an alternative (i.e., averaged with) the combination of upper and 

lower respiratory symptoms, since their definitions of 

symptoms overlap. Second, various estimates of restricted activity, with different degrees of severity, 

were combined into a single benefit category. 

Table 17 reports the estimated total national monetized benefits attributed in this analysis to the CAA 

from 1970 to 1990. The benefits, valued in 1990 dollars, range from $5.6 to $49.4 trillion with a central 

estimate of $22.2 trillion. The Monte Carlo technique 

was used to aggregate monetized benefits across endpoints. For each of several thousand iterations, a random draw of the monetized benefits for each endpoint 

was selected from the distributions summarized in 

53

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 16 and the individual endpoint estimates were 

then summed. This resulted in the distribution of total 

national monetized benefits reported above.64 

The temporal pattern of benefits during the 1970 

to 1990 period is related to the difference in emissions between the control and no-control scenarios and 

is magnified by population growth during that period. 

As illustrated by Figure 18, quantified annual benefits increased steadily during the study period, with 

the greatest increases occurring during the late 1970s. 

The mean estimate of quantified annual benefits grew 

from 355 billion dollars in 1975 (expressed as inflation-adjusted 1990 dollars) to 930 billion dollars in 

1980, 1,155 billion dollars in 1985, and 1,248 billion 

dollars in 1990. 

Figure 19 depicts the distribution of monetized 

benefits for 1990 (similar distributions were generated for other years in the analysis period). The solid 

vertical bars in the figure represent the relative frequency of a given result in the 1990 Monte Carlo 

analysis. The largest bar, located above the “<$1,000”, 

indicates that more Monte Carlo iterations generated 

monetized benefits of $900 billion to $1 trillion than 

in any other $100 billion range bin, making this the 

modal bin. The expected value of the estimate for total monetized benefit for 1990 (i.e., the mean of the 

distribution) is $1.25 trillion. The ninety percent confidence interval, a summary description of the spread 

of a distribution, is also noted in the figure. 

Figure 18. Monte Carlo Simulation Model Results for 

Target Years (in billions of 1990 dollars). 

Total Benefits ($Billions) 

Mean 

95th% 

5th% 

Mean 

95th% 

5th% 

Mean 

95th% 

5th% 

Mean 

95th% 

5th% 

$3,000 

$2,500 

$2,000 

$1,500 

$1,000 

$500 

$0 1975 1980 1985 1990 

Figure 19. Distribution of 1990 Monetized Benefits of 

CAA (in billions of 1990 dollars). Frequency < $0 < $200 < $400 < $600 < $800 < $1,000 < $1,200 < $1,400 < $1,600 

< $1,800 < $2,000 < $2,200 < $2,400 < $2,600 < $2,800 < $3,000 

Total Monetary Benefits ($ Billions) 

95th percentile = 

mean = 

5th percent ile = 

Distribution Sum mary ($Billions) 

$2,760 

$1,250 

$329 

5th percentile Mean 95th percentile 

On initial inspection, the estimated $1.25 trillion 

value for monetized benefits in 1990 may seem implausibly large, even though 1990 is the year in which 

the differences between outcomes under the control 

and no-control scenarios are at their most extreme. 

The plausibility of this estimate may seem particularly questionable to some if one considers that the 

$1.25 trillion value for 1990 is over five percent of 

the estimated $22.8 trillion value for total 1990 assets 

of households and nonprofit organizations. Considered from this perspective, $1.25 trillion may seem to 

represent a large share of total wealth, and some might 

question whether Americans would really be willing 

to pay this much money for the reductions in risk 

achieved by the Clean Air Act and related programs, 

even if the risk in question involves premature death. 

However, in the end it is clear that such comparisons 

are overly simplistic and uninformative because they 

ignore the magnitude and nature of the welfare change 

being measured. 

First, with respect to the magnitude of the difference in estimated social welfare under the two scenarios, it is important to recognize how severe air quality conditions and health risks would be under the 

hypothetical no-control scenario. Focusing on ambient particulate matter, the pollutant responsible for the 

vast majority of the estimated monetary benefits, a 

comparison of the estimated annual mean concentrations of total suspended particulates (TSP) projected 

in the U.S. under the no-control scenario with esti64 Comparing Tables 16 and 17, it can be seen that the sum of benefits across endpoints at a given percentile level does not result 

in the total monetized benefits estimate at the same percentile level in Table 17. For example, if the fifth percentile benefits of the 

endpoints shown in Table 16 were added, the resulting total would be substantially less than $5.6 trillion, the fifth percentile value of 

the distribution of aggregate monetized benefits reported in Table 17. This is because the various health and welfare effects are treated 

as stochastically independent, so that the probability that the aggregate monetized benefit is less than or equal to the sum of the 

separate five percentile values is substantially less than five percent. 

54

Chapter 7: Results and Uncertainty 

mated annual mean TSP concentrations in other parts 

of the world65 indicates that in 1990— 

•	 60 metropolitan areas in the U.S. would have 

had higher TSP concentrations than Moscow, 

Russia 

•	 7 metropolitan areas would be worse than 

Bangkok, Thailand 

•	 6 metropolitan areas would be worse than 

Bombay, India 

•	 2 metropolitan areas would be worse than Manila, Philippines 

•	 One metropolitan area would be worse than 

Delhi, India (one of the most polluted cities 

in the world) 

Under the control scenario, TSP levels in only 3 

metropolitan areas were projected to exceed those in 

Moscow, and none exceeded levels found in the other 

foreign cities listed above. The principal reason air 

quality conditions are so poor under the no-control 

scenario is that air pollution control requirements remain fixed at their 1970 levels of scope and stringency 

while total economic activity, including polluting activity, grows by 70 percent and population grows by 

22.3 percent between 1970 and 1990. Under the severe air quality conditions projected throughout the 

U.S. in 1990 under the no-control case, an additional 

205,000 people would be projected to die prematurely 

due to the effects of particulate matter, lead, and other 

criteria pollutants. This represents a very large increase 

in the risk of premature mortality. Since the estimate 

that the average loss of life for those who actually 

succumb to PM exposure related health effects is approximately 14 years, and life-shortening due to lead 

exposure is even greater, it is no longer surprising that 

the estimated value of avoiding these severe conditions is so high. 

Second, with respect to the nature of the welfare 

change reflected in the monetized benefit estimate, 

the concern about the effects of limited budgets constraining Americans’ collective ability to pay to avoid 

these severe no-control scenario conditions is misplaced. In reality, what society actually had to pay to 

avoid these conditions is measured on the cost side of 

the analysis, which sums up the total expenditures 

made by manufacturers and others to achieve these 

air pollution reductions. The most reasonable estimate 

of the value Americans place on avoiding those severe no-control scenario conditions, however, is provided by measuring the amount of compensation 

Americans would have demanded from polluting companies and others to accept, willingly, all of that extra 

pollution and its associated risks of premature death. 

Under this concept of welfare change measurement, 

there is no inherent limit on the amount of money citizens would demand from companies to accept their 

pollution and so individual personal wealth does not 

constrain this value. 

The monetized benefit estimate presented in this 

study, therefore, does not necessarily represent an attempt to mirror what Americans would pay out of their 

own pockets to reduce air pollution from levels they 

never experienced; rather, it provides an estimate of 

the value Americans place on the protection they received against the dire air pollution conditions which 

might have prevailed in the absence of the 1970 and 

1977 Clean Air Acts and related programs. Viewed 

from this perspective, the estimated monetized benefits presented herein appear entirely plausible. 

Comparison of Monetized 

Benefits and Costs 

Table 18 presents summary quantitative results for 

the retrospective assessment. Annual results are presented for four individual years, with all dollar figures expressed as inflation-adjusted 1990 dollars. The 

final column sums the stream of costs and benefits 

from 1970 to 1990, discounted (i.e., compounded) to 

1990 at five percent. “Monetized benefits” indicate 

both the mean of the Monte Carlo analysis and the 

credible range. “Net Benefits” are mean monetized 

benefits less annualized costs for each year. The table 

also notes the benefit/cost ratios implied by the benefit ranges. The distribution of benefits changes little 

(except in scale) from year to year: The mean estimate is somewhat greater than twice the fifth percentile estimate, and the ninety-fifth percentile estimate 

is somewhat less than twice the mean estimate. The 

distribution shape changes little across years because 

the sources of uncertainty (i.e., CR functions and economic valuations) and their characterizations are unchanged from year to year. Some variability is induced 

by changes in relative pollutant concentrations over 

time, which then change the relative impact of individual CR functions. 

Several measures of “cost” are available for use 

in this analysis (see Chapter 2). The Project Team 

65 “Urban Air Pollution in Megacities of the World,” UNEP/WHO, 1992a, Published by the World Health Organization and 

United Nations Environment Program, Blackwell Publishers, Oxford, England, 1992. “City Air Quality Trends,” UNEP/WHO, 1992b, 

Published by the United Nations Environment Program, Nairobi, Kenya, 1992. 

55

y

e

o

1975 1980 1985 1990 PV 

 Monetiz ed B enefit s 

 5th percent ile 

Mean est im ate 

 95t h percent ile 

87 

355 

799 

235 

930 

2,063 

293 

1,155 

2,569 

329 

1,248 

2,762 

5,600 

22,200 

49,400 

 Annualized C ost s (5%) 14 21 25 26 523 

 Net Benefits 

 Mean benefit s - C ost s 341 909 1,130 1,220 21,700 

Benefit/Cost rat io 

 5th percent ile 

Mean est im ate 

 95t h percent ile 

6/1 

25/1 

57/1 

11/1 

44/1 

98/1 

12/1 

46/1 

103/1 

13/1 

48/1 

106/1 

11/1 

42/1 

94/1 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table 18. Quantified Uncertainty Ranges for Monetized 

Annual Benefits and Benefit/Cost Ratios, 1970-1990 (in 

Notes: PV=1990 present value reflecting compounding of costs and benefits 

from 1971 to 1990 at 5 percent. 

employs “annualized cost” as the primary cost measure because it measures cost in a fashion most analogous to the benefits estimation method. An alternative 

measure, “compliance expenditure,” is a reasonable 

cost measure. Some capital expenditures, however, 

generate a benefit stream beyond the period of the 

analysis (i.e., beyond 1990). Those post-1990 benefits 

are not, in general, included in the benefit estimates 

presented above. The annualization procedure reduces 

the bias introduced by the use of capital expenditures 

by spreading the cost of the capital investment over its 

expected life, then counting as a “cost” only those costs 

incurred in the 1970 to 1990 period. 

The macroeconomic analysis employed for this 

analysis (see Chapter 2) indicates that compliance 

1975 1980 1985 1990 

billions of 1990-value dollars). 

PV 

Monetiz ed B enefit s 

5th percent ile 

Mean est im ate 

95t h percent ile 

87 

355 

799 

235 

930 

2,063 

293 

1,155 

2,569 

329 

1,248 

2,762 

Annualized C ost s (5%) 14 21 25 26 

Net Benefits 

Mean benefit s - C ost s 341 909 1,130 1,220 

5,600 

22,200 

49,400 

523 

21,700 

Benefit/Cost rat io 

5th percent ile 

Mean est im ate 

95t h percent ile 

6/1 

25/1 

57/1 

11/1 

44/1 

98/1 

12/1 

46/1 

103/1 

13/1 

48/1 

106/1 

11/1 

42/1 

94/1 

Major Sources of Uncertainty 

The methods used to aggregate monetized benefits and characterize the uncertainty surrounding estimates of these benefits have been discussed above, 

and the resulting estimates of aggregate benefits have 

been compared to the corresponding estimates of cost. 

Additional insights into key assumptions and findings 

can, however, be obtained by further analysis of potentially important variables. 

For some factors in the present analysis, both the 

degree of uncertainty and the direction of any associated bias are unknown; for some other factors, no 

employable quantitative estimates could be used even 

though available evidence suggests a positive and 

potentially substantial value. An example of the latter 

deficiency is the lack of quantitative estimates for some 

human health effects, some human welfare effects, and 

all ecological effects. Despite the exclusion of potentially important variables, it is worthwhile to evaluate 

the relative contribution of included variables to quantifiable uncertainty in the net benefit estimate. One of 

these variables, premature mortality valuation, is also 

given special attention in the subsequent section on 

alternative results. 

The estimated uncertainty ranges for each endpoint category summarized in Table 16 reflect the measured uncertainty associated with both avoided incidence and economic valuation. The Project Team conducted a sensitivity analysis to determine the variables 

with the greatest contribution to the quantified uncertainty range. The results of this sensitivity analysis 

are illustrated in Figure 20. 

expenditures induce significant second-order effects, and it can be argued that those effects should 

be included in a comprehensive cost analysis. Benefits resulting from compliance expenditures 

should also induce second-order macroeconomic 

effects (which would, one would expect, partly or 

completely offset the estimated second-order adverse effects induced by compliance expenditures). 

Due to the sequencing of the analytical steps in 

this assessment, it was not practical to estimate 

the second-order cost and benefit impacts induced 

by the estimated health and welfare benefits. Because second-order impacts of benefits are not 

estimated, the Project Team refrained from choosing as the primary cost measure one that included 

second-order impacts, and instead employed “annualized costs” as the primary cost measure. 

$0 

$5 

$10 

$15 

$20 

$25 

$30 

$35 

$40 

$45 

$50 

s 

n 

s 

y 

s 

e 

n 

n 

n 

95th %ile 

Mean 

5th %il e 

Total Benefits Present Value ($Trillions) 

Figure 20. Uncertainty Ranges Deriving From Individual 

Uncertainty Factors All Components 

Pb Mortality 

Incidence 

Lost IQ 

Incidence 

Mortality 

Valuation 

Chr. Bronchitis 

Incidence 

Chr. Bronchitis 

Valuation 

PM2.5 Mortality 

Incidence 

MRADs 

Valuation 

Household 

Soiling Valuation 

Lost IQ 

Valuation 

56

Chapter 7: Results and Uncertainty 

In this sensitivity analysis, all the inputs to the 

Monte Carlo uncertainty analysis are held constant 

(at their mean values), allowing only one variable --

for example, the economic valuation of mortality --

to vary across the range of that variable’s uncertainty. 

The sensitivity analysis then isolates how this single 

source of uncertainty contributes to the total measured 

uncertainty in estimated aggregate benefits. The first 

uncertainty bar represents the credible range associated with the total monetized benefits of the Clean 

Air Act, as reported above. This captures the multiple 

uncertainties in the quantified benefits estimation. The 

rest of the uncertainty bars represent the quantified 

uncertainty ranges generated by single variables. As 

shown in Figure 20, the most important contributors 

to aggregate quantified uncertainty are mortality valuation and incidence, followed by chronic bronchitis 

valuation and incidence. 

Alternative Results 

The primary results of this analysis, including 

aggregate cost and benefit estimates and the uncertainty associated with them, are presented and discussed above. However, although the range of net 

benefit estimates presented reflects uncertainty in 

many important elements of the analysis, there are 

two key variables which require further discussion and 

analysis: PM-related mortality valuation and the discount rate. This additional treatment is necessary because reasonable people may disagree with the Project 

Team’s methodological choices for these two variables, and these choices might be considered ex ante 

to significantly influence the results of the study. The 

purpose of this section, therefore, is to present alternative quantitative results which reflect, separately, 

(1) an alternative approach to valuation of premature 

mortality associated with particulate matter exposure, 

and (2) alternative values for the discount rate used to 

adjust the monetary values of effects occurring in various years to a particular reference year (i.e., 1990). 

PM Mortality Valuation Based on LifeYears Lost 

The primary analytical results presented earlier 

in this chapter assign the same economic value to incidences of premature mortality regardless of the age 

and health status of those affected. Although this has 

been the traditional practice for benefit-cost studies 

conducted within the Agency, this may not be the most 

appropriate method for valuation of premature mortality caused by PM exposure. Some short-term PM 

exposure studies suggest that a significantly disproportionate share of PM-related premature mortality 

occurs among persons 65 years of age or older. Combining standard life expectancy tables with the limited available data on age-specific incidence allows 

crude approximations of the number of life-years lost 

by those who die prematurely as a result of exposure 

to PM or, alternatively, the changes in age-specific 

life expectancy of those who are exposed to PM. 

The ability to estimate, however crudely, changes 

in age-specific life expectancy raises the issue of 

whether available measures of the economic value of 

mortality risk reduction can, and should, be adapted 

to measure the value of specific numbers of life-years 

saved.66 Although the Agency has on occasion performed sensitivity calculations which adjust mortality values for those over age 65, the Agency is skeptical that the current state of knowledge and available 

analytical tools support using a life-years lost approach 

or any other approach which assigns different risk reduction values to people of different ages or circumstances. This skepticism is mirrored in the OMB guidance on implementing Executive Order 12866 pertaining to economic analysis methods, which states 

on page 31: 

While there are theoretical advantages to 

using a value of statistical life-year-extended 

approach, current research does not provide 

a definitive way of developing estimates of 

VSLY that are sensitive to such factors as 

current age, latency of effect, life years 

remaining, and social valuation of different 

risk reductions. In lieu of such information, 

there are several options for deriving the 

value of a life-year saved from an estimate of 

the value of life, but each of these methods 

has drawbacks. One approach is to use results 

from the wage compensation literature (which 

focuses on the effect of age on WTP to avoid 

risk of occupational fatality). However, these 

results may not be appropriate for other types 

of risks. Another approach is to annualize the 

VSL using an appropriate rate of discount and 

the average life years remaining. This 

approach does not provide an independent 

estimate of VSLY; it simply rescales the VSL 

estimate. Agencies should consider providing 

estimates of both VSL and VSLY, while 

recognizing the developing state of knowledge 

in this area. 

While the Agency continues to prefer an approach 

which makes no valuation distinctions based on age 

or other characteristics of the affected population, alternative results based on a VSLY approach are pre66 This issue was extensively discussed during the Science Advisory Board Council review of drafts of the present study. The 

Council suggested it would be reasonable and appropriate to show PM mortality benefit estimates based on value of statistical lifeyears (VSLY) saved as well as the value of statistical life (VSL) approach traditionally applied by the Agency to all incidences of 

premature mortality. 

57

Benefits 

Benefit Est im ation Met hod PM Tot. 

 St atist ical l ife method ($4.8M/case) 16.6 18.0 

 Life-years lost m ethod ($293,000/year) 9.1 10.1 

T otal com pliance cost --- 0.5 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

sented below. The method used to develop VSLY estimates is described briefly in Chapter 6 and in more 

detail in Appendix I. 

Table 19 summarizes and compares the results of 

the VSL and VSLY approaches. Estimated 1970 to 

1990 benefits from PM-related mortality alone and 

total assessment benefits are reported, along with total compliance costs for the same period, in 1990 dollars discounted to 1990 at five percent. The results 

indicate that the choice of valuation methodology significantly affects the estimated monetized value of 

historical reductions in air pollution-related premature mortality. However, the downward adjustment 

which would result from applying a VSLY approach 

in lieu of a VSL approach does not change the basic 

outcome of this study, viz. the estimated monetized 

benefits of the historical CAA substantially exceed 

the historical costs of compliance. 

Table 19. Alternative Mortality Benefits Mean 

Estimates for 1970 to 1990 (in trillions of 1990 

dollars, discounted at 5 percent) Compared to 

Total 1970 to 1990 Compliance Costs. 

1970 toward 1990 (see Table 18 above), benefit cost 

ratios decline as the discount rate increases (because 

earlier periods are given greater weight). Overall, the 

results of the benefit-cost assessment appear to be 

generally insensitive to the choice of discount rate. 

Table 20. Effect of Alternative Discount Rates on 

Present Value of Total Monetized Benefits/Costs 

Di scount rate 

3% 5% 7% 

Mean Est imated B enefit s 19.2 22.2 25.8 

 Annualized Cost s 0.4 0.5 0.7 

 Net Benefit s 18.8 21.7 25.1 

Benefit/Cost rat io 48/1 42/1 37/1 

Di scount rate 

3% 5% 7% 

Mean Est imated B enefit s 19.2 22.2 25.8 

Annualized Cost s 0.4 0.5 0.7 

Net Benefit s 18.8 21.7 25.1 

Benefit/Cost rat io 48/1 42/1 37/1 

for 1970 to 1990 (in trillions of 1990 dollars). 

Benefits 

Benefit Est im ation Met hod PM Tot. 

St atist ical l ife method ($4.8M/case) 16.6 18.0 

Life-years lost m ethod ($293,000/year) 9.1 10.1 

T otal com pliance cost 0.5 

Alternative Discount Rates 

In some instances, the choice of discount rate can 

have an important effect on the results of a benefitcost analysis; particularly for those analyses with relatively long time horizons for costs and/or benefits. In 

this assessment, the discount rate affects only four 

factors: IQ-related benefits estimates (especially estimates of changes in discounted lifetime income), lifetime income losses due to other health effects (e.g., 

stroke), annualized costs (i.e., amortized capital expenditures), and compounding of all costs and benefits to 1990. Table 20 summarizes the effect of alternative discount rates on the “best estimate” results of 

this analysis. Because monetized benefits exceed costs 

for all years in the analysis period, net benefits increase as the discount rate increases. Because the annual benefit/cost ratio increases as one moves from 

58

Chapter 7: Results and Uncertainty 

59

Appendix A: Cost and Macroeconomic Modeling


Introduction 

The purpose of this appendix is to describe in detail the estimation of direct compliance costs associated with the CAA and the effect of those expenditures on U.S. economic conditions from 1970 to 1990. 

The first section of this appendix describes the dynamic, general equilibrium macroeconomic model 

used to examine economy-wide effects. Two broad 

categories of models were considered for use in the 

assessment: Macroeconomic forecasting models (e.g., 

the Data Resources Inc. model of the U.S. economy), 

and general equilibrium models (e.g., Hazilla and 

Kopp [1990], and Jorgenson and Wilcoxen [1990a]). 

The project team selected the Jorgenson-Wilcoxen (J/ 

W) general equilibrium model of the United States 

for this analysis (Jorgenson and Wilcoxen [1990a]). 

There are two main reasons for choosing a dynamic 

general equilibrium approach: To capture both the 

direct and indirect economic effects of environmental regulation, and to capture the long-run dynamics 

of the adjustment of the economy. The general equilibrium framework enabled the project team to assess 

shifts in economic activity between industries, including changes in distributions of labor, capital, and other 

production factors within the economy, and changes 

in the distribution of goods and services. 

The second section describes the data sources for 

direct compliance expenditures and presents estimates 

of historical air pollution control expenditures. These 

estimates are derived primarily from EPA’s 1990 report entitled “Environmental Investments: The Cost 

of a Clean Environment”1 (hereafter referred to as Cost 

of Clean). Specific adjustments to the Cost of Clean 

stationary source and mobile source O&M data needed 

to adapt these data for use in the present study are 

also described. These adjusted expenditure estimates 

represent the compliance cost data used as inputs to 

the J/W model to determine macroeconomic effects. 

The final section presents a summary of the direct expenditure data, presents direct costs in a form 

that can be compared to the benefits estimates found 

elsewhere in the study, and discusses indirect effects 

arising from compliance expenditures estimated by 

the macroeconomic model. The indirect effects reported by the model are sectoral impacts and changes 

in aggregate measures of economic activity such as 

household consumption and gross national product. 

These indirect effects are second-order impacts of 

compliance expenditures — a parallel modeling exercise to estimate second-order economic impacts arising from the benefits of compliance (e.g., increased 

output as a result of improved longevity or fewer 

workdays lost as a result of non-fatal heart attacks) 

has not been attempted. 

Macroeconomic Modeling 

EPA analyses of the costs of environmental regulations typically quantify the direct costs of pollution 

abatement equipment and related operating and maintenance expenses. However, this approach does not 

fully account for all of the broader economic consequences of reallocating resources to the production 

and use of pollution abatement equipment. A general 

equilibrium, macroeconomic model could, in theory, 

capture the complex interactions between sectors in 

the economy and assess the full economic cost of air 

pollution control. This would be particularly useful 

for assessing regulations that may produce significant 

interaction effects between markets. Another advantage of a general equilibrium, macroeconomic framework is that it is internally consistent. The consistency 

of sectoral forecasts with realistic projections of U.S. 

economic growth is ensured since they are estimated 

within the context of a single model.2 This contrasts 

1 Environmental Investments: The Cost of a Clean Environment, Report of the Administrator of the Environmental Protection 

Agency to the Congress of the United States, EPA-230-11-90-083, November 1990. 

2 In the present study, both benefits and costs are driven by of the same macroeconomic projections from the Jorgenson/ 

Wilcoxen model, to ensure that the estimates are based on a consistent set of economic assumptions. 

A-1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

with typical EPA analyses that compile cost estimates 

from disparate sectoral and partial equilibrium models. 

The economic effects of the CAA may be overor underestimated, if general equilibrium effects are 

ignored, to the extent that sectors not directly regulated are affected. For example, it is well known that 

the CAA imposed significant direct costs on the energy industry. Economic sectors not directly regulated 

will nonetheless be affected by changes in energy 

prices. However, an examination of the broader effects of the CAA on the entire economy might reveal 

that the CAA also led to more rapid technological 

development and market penetration of environmentally “clean” renewable sources of energy (e.g., photovoltaics). These effects would partially offset adverse effects on the energy industry, and lead to a different estimate of the total economic cost to society 

of the CAA. 

The significance of general equilibrium effects in 

the context of any particular analysis is an empirical 

question. Kokoski and Smith (1987) used a computable general equilibrium model to demonstrate that 

partial-equilibrium welfare measures can offer reasonable approximations of the true welfare changes 

for large exogenous changes. In contrast, the results 

of Jorgenson and Wilcoxen (1990a) and Hazilla and 

Kopp (1990) suggest that total pollution abatement in 

the U.S. has been a major claimant on productive resources, and the effect on long-run economic growth 

may be significant. Again, such conclusions must be 

considered in light of the limitations of general equilibrium models. 

Choice of Macroeconomic Model 

The adequacy of any model or modeling approach 

must be judged in light of the policy questions being 

asked. One goal of the present study is to assess the 

effects of clean air regulations on macroeconomic 

activity. Two broad categories of macroeconomic 

models were considered for use in the assessment: 

short run, Keynesian models and long-run, general 

equilibrium models. 

Recognizing that structural differences exist between the models, one needs to focus in on the particular questions that should be answered with any 

particular model. The Congressional Budget Office 

(1990) noted: 

“Both the [Data Resources Incorporated] DRI 

and the IPCAEO models show relatively 

limited possibilities for increasing energy 

efficiency and substituting other goods for 

energy in the short run... Both models focus 

primarily on short-term responses to higher 

energy prices, and neither is very good at 

examining how the structure of the economy 

could change in response to changing energy 

prices. The [Jorgenson-Wilcoxen] model 

completes this part of the picture...”3 

One strategy for assessing the macroeconomic 

effects of the CAA would be to use a DRI-type model 

in conjunction with the Jorgenson-Wilcoxen model 

to assess both the long-term effects and the short-run 

transitions, in much the same way that the Congressional Budget Office used these models to assess the 

effects of carbon taxes. However, because of significant difficulties in trying to implement the DRI model 

in a meaningful way, the project team chose to focus 

on the long-run effects of the CAA. Structural changes 

(e.g., changes in employment in the coal sector due to 

the CAA) can be identified with the JorgensonWilcoxen model. 

Overview of the JorgensonWilcoxen Model 

The discussion below focuses on those characteristics of the Jorgenson-Wilcoxen model that have 

important implications for its use in the assessment 

of environmental regulations (see Table A-1). The J/ 

W model is a detailed dynamic general equilibrium 

model of the U.S. economy designed for medium run 

analysis of regulatory and tax policy (Jorgenson and 

Wilcoxen [1990a]). It provides projections of key 

macroeconomic variables, such as GNP and aggregate consumption, as well as energy flows between 

economic sectors. As a result, the model is particularly useful for examining how the structure of the 

economy could change in response to changes in re3 The Congressional Budget Office report (1990) refers to an older (1981) version of the Jorgenson model, not the current 

(1988) version. The approach to long-run dynamics differs between the two models. The newer Jorgenson-Wilcoxen model contains 

both the capital accumulation equation and the capital asset pricing equation. The 1981 version of the model contained only the 

capital accumulation equation. 

A-2

Appendix A: Cost and Macroeconomic Modeling 

Table A-1. Key Distinguishing Characteristics of 

the Jorgenson-Wilcoxen Model. 

� Dynamic, general equilibrium, 

macroeconomic model of the U.S. economy. 

� Econometrically estimated using historic 

data. 

� Free mobility of a single type of capital and 

labor between industries. 

� Detailed treatment of production and 

consumption. 

� Rigorous representation of savings and 

investment. 

� Endogenous model of technical change. 

� Does not capture unemployment, 

underemployment, or the costs of moving 

capital from one industry to another. 

source prices. For the purpose of this study, it has five 

key features: a detailed treatment of production and 

consumption, parameters estimated econometrically 

from historical data, an endogenous model of technical change, a rigorous representation of saving and 

investment, and free mobility of labor and capital between industries. 

The first two features, industry and consumer detail and econometric estimation, allow the model to 

capture the effects of the CAA at each point in time 

for given levels of technology and the size of the 

economy’s capital stock. A detailed treatment of production and consumption is important because the 

principal effects of the Clean Air Act fell most heavily 

on a handful of industries. The J/W model divides 

total U.S. production into 35 industries which allows 

the primary economic effects of the CAA to be captured. Econometric estimation is equally important 

because it ensures that the behavior of households and 

firms in the model is consistent with the historical 

record. 

The model’s second two features —its representations of technical change and capital accumulation— 

complement the model’s intratemporal features by 

providing specific information on how the Act affected 

technical change and the accumulation of capital. 

Many analyses of environmental regulations overlook 

or ignore intertemporal effects but these effects can 

be very important. Jorgenson and Wilcoxen (1990a) 

suggests that the largest cost of all U.S. environmental regulations together was that the regulations reduced the rate of capital accumulation. 

The model’s last feature, free mobility of a single 

type of capital and a single type of labor, is important 

because it limits the model’s ability to measure the 

short run costs of changes in policy. J/W is a fullemployment model that describes the long-run dynamics of transitions from one equilibrium to another. 

Capital and labor are both assumed to be freely mobile between sectors (that is, they can be moved from 

one industry to another at zero cost) and to be fully 

used at all times. Over the medium to long run, this is 

a reasonable assumption, but in the short run it is too 

optimistic. In particular, the model will understate the 

short run costs of a change in policy because it does 

not capture unemployment, underemployment, or the 

costs of moving capital from one industry to another. 

A single rate of return on capital exists that efficiently 

allocates the capital in each period among sectors. 

Similarly, a single equilibrium wage rate allocates 

labor throughout the economy. 

Structure of the Jorgenson-Wilcoxen 

Model 

The J/W model assesses a broad array of economic 

effects of environmental regulations. Direct costs are 

captured as increased expenditures on factors of production —capital, labor, energy and materials— that 

the various industries must make to comply with the 

regulations, as well as additional out-of-pocket expenditures that consumers must make. Indirect costs 

are captured as general equilibrium effects that occur 

throughout the economy as the prices of factors of 

production change (e.g., energy prices). Also, the rate 

of technological change can respond to changes in the 

prices of factors of production, causing changes in 

productivity (Jorgenson and Fraumeni, 1981). 

The model is divided into four major sectors: the 

business, household, government, and rest-of-theworld sectors. The business sector is further subdivided into 35 industries (see Table A-2).4 Each sector 

produces a primary product, and some produce secondary products. These outputs serve as inputs to the 

production processes of the other industries, are used 

for investment, satisfy final demands by the household and government sectors, and are exported. The 

model also allows for imports from the rest of the 

world. 

4 The 35 industries roughly correspond to a two-digit SIC code classification scheme. 

A-3

5

10

15

20

25

30

35

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table A-2. Definitions of Industries Within 

the J/W Model. 

Industry

Number Description

1 Agriculture, forestry, and 

fisheries 

2 Metal mining 

3 Coal mining 

4 Crude petroleum and natural gas 

Nonmetallic mineral mining 

6 Construction 

7 Food and kindred products 

8 Tobacco manufacturers 

9 Textile mill products 

Apparel and other textile 

products 

11 Lumber and wood products 

12 Furniture and fixtur es 

13 Paper and allied products 

14 Printing and publishing 

Chemicals and allied products 

16 Petroleum refining 

17 Rubber and plastic products 

18 Leather and leather products 

19 Stone, clay, and glass products 

Primary metals 

21 Fabricated metal products 

22 Machinery, except electrical 

23 Electrical machinery 

24 Motor vehicles 

Other transportation equipment 

26 Instruments 

27 Miscellaneous manufacturing 

28 Transportation and warehousing 

29 Communication 

Electric utilities 

31 Gas utilities

32 Trade

33 Finance, insurance, and real

estate 

34 Other services 

Government enterprises 

The Business Sector 

The model of producer behavior allocates the 

value of output of each industry among the inputs of 

the 35 commodity groups, capital services, labor services, and noncompeting imports. Output supply and 

factor demands of each sector are modeled as the results of choices made by wealth maximizing, price 

taking firms which are subject to technological constraints. Firms have perfect foresight of all future 

prices and interest rates. Production technologies are 

represented by econometrically estimated cost functions that fully capture factor substitution possibilities and industry-level biased technological change. 

Capital and energy are specified separately in the 

factor demand functions of each industry. The ability 

of the model to estimate the degree of substitutability 

between factor inputs facilitates the assessment of the 

effect of environmental regulations. A high degree of 

substitutability between inputs implies that the cost 

of environmental regulation is low, while a low degree of substitutability implies high costs of environmental regulation. Also, different types of regulations 

lead to different responses on the part of producers. 

Some regulations require the use of specific types of 

equipment. Others regulations restrict the use of particular factor inputs; for example, through restrictions 

on the combustion of certain types of fuels. Both of 

these effects can change the rate of productivity growth 

in an industry through changes in factor prices. 

The Household Sector 

In the model of consumer behavior, consumer 

choices between labor and leisure and between consumption and saving are determined. A system of individual, demographically defined household demand 

functions are also econometrically estimated. Household consumption is modeled as a three stage optimization process. In the first stage households allocate 

lifetime wealth to full consumption in current and future time periods to maximize intertemporal utility. 

Lifetime wealth includes financial wealth, discounted 

labor income, and the imputed value of leisure. Households have perfect foresight of future prices and interest rates. In the second stage, for each time period 

full consumption is allocated between goods and services and leisure to maximize intratemporal utility. 

This yields an allocation of a household’s time endowment between the labor market (giving rise to labor supply and labor income) and leisure time and 

demands for goods and services. In the third stage, 

personal consumption expenditures are allocated 

among capital, labor, noncompeting imports and the 

outputs of the 35 production sectors to maximize a 

subutility function for goods consumption. As with 

the business sector, substitution possibilities exist in 

consumption decisions. The model’s flexibility enables it to capture the substitution of nonpolluting 

products for polluting ones that may be induced by 

environmental regulations. Towards this end, purchases of energy and capital services by households 

are specified separately within the consumer demand 

functions for individual commodities. 

A-4

Appendix A: Cost and Macroeconomic Modeling 

It is important to be clear regarding the notions of 

labor supply and demand within the J/W model, and 

what is meant by “employment” throughout this report. Labor demands and supplies are represented as 

quality-adjusted hours denominated in constant dollars. The labor market clears in each period; the quantity of labor services offered by households is absorbed 

fully by the economy’s producing sectors. However, 

inferences regarding the number of persons employed 

require information on labor quality and work-hours 

per person over time and across simulations. Neither 

of these are explicitly modeled. 

The Government Sector 

The behavior of government is constrained by 

exogenously specified budget deficits. Government 

tax revenues are determined by exogenously specified tax rates applied to appropriate transactions in 

the business and household sectors. Levels of economic activity in these sectors are endogenously determined. Capital income from government enterprises 

(determined endogenously), and nontax receipts 

(given exogenously), are added to tax revenues to 

obtain total government revenues. Government expenditures adjust to satisfy the exogenous budget deficit 

constraint. 

The Rest-of-the-World Sector 

The current account balance is exogenous, limiting the usefulness of the model to assess trade competitiveness effects. Imports are treated as imperfect 

substitutes for similar domestic commodities and compete on price. Export demands are functions of foreign incomes and ratios of commodity prices in U.S. 

currency to the exchange rate. Import prices, foreign 

incomes, and tariff policies are exogenously specified. Foreign prices of U.S. exports are determined 

endogenously by domestic prices and the exchange 

rate. The exchange rate adjusts to satisfy the exogenous constraint on net exports. 

Environmental Regulation, Investment, 

and Capital Formation 

Environmental regulations have several important 

effects on capital formation. At the most obvious level, 

regulations often require investment in specific pieces 

of pollution abatement equipment. If the economy’s 

pool of savings were essentially fixed, the need to invest in abatement equipment would reduce, or crowd 

out, investment in other kinds of capital on a dollar 

for dollar basis. On the other hand, if the supply of 

savings were very elastic then abatement investments 

might not crowd out other investment at all. In the J/ 

W model, both the current account and government 

budget deficits are fixed exogenously so any change 

in the supply of funds for domestic investment must 

come from a change in domestic savings. Because 

households choose consumption, and hence savings, 

to maximize a lifetime utility function, domestic savings will be somewhat elastic. Thus, abatement investment will crowd out other investment, although 

not on a dollar for dollar basis. 

The J/W assumption that the current account does 

not change as a result of environmental regulation is 

probably unrealistic, but it is not at all clear that this 

biases the crowding out effects in any particular direction. By itself, the need to invest in abatement capital would tend to raise U.S. interest rates and draw in 

foreign savings. To the extent this occurred, crowding out would be reduced. At the same time, however, regulation reduces the profitability of domestic 

firms. This effect would tend to lower the return on 

domestic assets, leading to a reduced supply of foreign savings which would exacerbate crowding out. 

Which effect dominates is an empirical question beyond the scope of this study. 

In additional to crowding out ordinary investment, 

environmental regulation also has a more subtle effect on the rate of capital formation. Regulations raise 

the prices of intermediate goods used to produce new 

capital. This leads to a reduction in the number of capital goods which can be purchased with a given pool 

of savings. This is not crowding out in the usual sense 

of the term, but it is an important means by which 

regulation reduces capital formation.5 

The General Equilibrium 

The J/W framework contains intertemporal and 

intratemporal models (Jorgenson and Wilcoxen 

[1990c]). In any particular time period, all markets 

clear. This market clearing process occurs in response 

to any changes in the levels of variables that are speci5 Wilcoxen (1988) suggests that environmental regulation may actually lead to a “crowding in” phenomenon. Wilcoxen 

examined the effects of regulation at the firm level, and introduced costs into the model related to the installation of capital. He found 

that when firms shut down their plants to install environmental capital, they take account of the adjustment costs and often concurrently replace other older capital equipment. This effect, however, is not captured in the current version of the Jorgenson-Wilcoxen 

model. 

A-5

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

fied exogenously to the model. The interactions among 

sectors determine, for each period, aggregate domestic output, capital accumulation, employment, the 

composition of output, the allocation of output across 

different household types, and other variables. 

The model also produces an intertemporal equilibrium path from the initial conditions at the start of 

the simulation to the stationary state. (A stationary 

solution for the model is obtained by merging the 

intertemporal and intratemporal models.) The dynamics of the J/W model have two elements: An accumulation equation for capital, and a capital asset pricing 

equation. Changes in exogenous variables cause several adjustments to occur within the model. First, the 

single stock of capital is efficiently allocated among 

all sectors, including the household sector. Capital is 

assumed to be perfectly malleable and mobile among 

sectors, so that the price of capital services in each 

sector is proportional to a single capital service price 

for the economy as a whole. The value of capital services is equal to capital income. The supply of capital 

available in each period is the result of past investment, i.e., capital at the end of each period is a function of investment during the period and capital at the 

beginning of the period. This capital accumulation 

equation is backward-looking and captures the effect 

of investments in all past periods on the capital available in the current period. 

The capital asset pricing equation specifies the 

price of capital services in terms of the price of investment goods at the beginning and end of each period, the rate of return to capital for the economy as a 

whole, the rate of depreciation, and variables describing the tax structure for income from capital. The current price of investment goods incorporates an assumption of perfect foresight or rational expectations. Under this assumption, the price of investment goods in 

every period is based on expectations of future capital service prices and discount rates that are fulfilled 

by the solution of the model. This equation for the 

investment goods price in each time period is forwardlooking.6 

One way to characterize the J/W model —or any 

other neoclassical growth model— is that the shortrun supply of capital is perfectly inelastic, since it is 

completely determined by past investment. However, 

the supply of capital is perfectly elastic in the long 

run. The capital stock adjusts to the time endowment, 

while the rate of return depends only on the 

intertemporal preferences of the household sector. 

A predetermined amount of technical progress 

also takes place that serves to lower the cost of sectoral 

production. Finally, the quality of labor is enhanced, 

giving rise to higher productivity and lower costs of 

production. 

Given all of these changes, the model solves for a 

new price vector and attains a new general equilibrium. Across all time periods, the model solves for 

the time paths of the capital stock, household consumption, and prices. The outcomes represent a general equilibrium in all time periods and in all markets 

covered by the J/W model. 

Configuration of the No-control 

Scenario 

One of the difficulties in describing the no-control scenario is ascertaining how much environmental regulation would have been initiated by state and 

local governments in the absence of a federal program. 

It may reasonably be argued that many state and local 

governments would have initiated their own control 

programs in the absence of a federal role. This view 

is further supported by the fact that many states and 

localities have, in fact, issued rules and ordinances 

which are significantly more stringent and encompassing than federal minimum requirements. However, it 

may also be argued that the federal CAA has motivated a substantial number of stringent state and local 

control programs. 

Specifying the range and stringency of state and 

local programs that would have occurred in the absence of the federal CAA would be almost entirely 

speculative. For example, factors which would complicate developing assumptions about stringency and 

scope of unilateral state and local programs include: 

(i) the significance of federal funding to support state 

and local program development; (ii) the influence of 

more severe air pollution episodes which might be 

expected in the absence of federally-mandated controls; (iii) the potential emergence of pollution havens, 

as well as anti-pollution havens, motivated by local 

6 The price of capital assets is also equal to the cost of production, so that changes in the rate of capital accumulation result in an 

increase in the cost of producing investment goods. This has to be equilibrated with the discounted value of future rentals in order to 

produce an intertemporal equilibrium. The rising cost of producing investment is a cost of adjusting to a new intertemporal equilibrium path. 

A-6

Appendix A: Cost and Macroeconomic Modeling 

political and economic conditions; (iv) the influence 

of federally-sponsored research on the development 

of pollution effects information and control technologies; and (v) the need to make specific assumptions 

about individual state and local control levels for individual pollutants to allow estimation of incremental reductions attributable to federal control programs. 

Another complication associated with the no-control scenario is the treatment of air pollution control 

requirements among the major trading partners of the 

U.S. Real-world manifestation of a no-control scenario 

would imply that public health and environmental 

goals were not deemed sufficiently compelling by U.S. 

policy makers. Under these conditions, major trading 

partners of the U.S. in Japan, Europe, and Canada may 

well reach similar policy conclusions. Simply put, if 

the U.S. saw no need for air pollution controls, there 

is little reason to assume other developed industrial 

countries would have either. In this case, some of the 

estimated economic benefits of reducing or eliminating air pollution controls in the U.S. would not materialize because U.S. manufacturers would not necessarily gain a production cost advantage over foreign 

competitors. However, like the question of state and 

local programs in the absence of a federal program, 

foreign government policies under a no-control scenario would be highly speculative. 

Given the severity of these confounding factors, 

the only analytically feasible assumptions with respect 

to the no-control scenario are that (a) no new control 

programs would have been initiated after 1970 by the 

states or local governments in the absence of a federal role, and (b) environmental policies of U.S. trading partners remain constant regardless of U.S. policy. 

Elimination of Compliance Costs in the 

No-Control Case 

Industries that are affected by environmental regulations can generally respond in three ways: (i) with 

process changes (e.g., fluidized bed combustion); (ii) 

through input substitution (e.g., switching from high 

sulfur coal to low sulfur coal); and (iii) end-of-pipe 

abatement (e.g., the use of electrostatic precipitation 

to reduce the emissions of particulates by combustion equipment).7 Clean air regulations have typically 

led to the latter two responses, especially in the short 

run. End-of-pipe abatement is usually the method of 

choice for existing facilities, since modifying existing production processes can be costly. This approach 

is also encouraged by EPA’s setting of standards based 

on the notion of “best available technology” (Freeman, 1978). 

All three possible responses may lead to: (i) unanticipated losses to equity owners; (ii) changes in 

current output; and (iii) changes in long-run profitability. If firms were initially maximizing profits, then 

any of the above three responses will increase its costs. 

Fixed costs of investment will be capitalized immediately. This will result in a loss to owners of equity 

when regulations are introduced. As far as firms are 

concerned, this is just like a lump sum tax on sunk 

capital. Such effects will not affect growth or efficiency. However, regulations could also change marginal costs and therefore current output. In addition, 

they could change profits (i.e., the earnings of capital), and thus affect investment. Both of these effects 

will reduce the measured output of the economy. 

On the consumption side, environmental regulations change consumers’ expectations of their lifetime 

wealth. In the no-control scenario of this assessment, 

lifetime wealth increases. This causes an increase in 

consumption. In fact, with perfect foresight, consumption rises more in earlier time periods. This also results in a change in savings. 

Capital Costs - Stationary Sources 

To appropriately model investment in pollution 

control requires a recognition that the CAA had two 

different effects on capital markets. First, CAA regulations led to the retrofitting of existing capital stock 

in order to meet environmental standards. In the nocontrol scenario, these expenditures do not occur. Instead, the resources that were invested in pollution 

abatement equipment to retrofit existing sources are 

available to go to other competing investments. Thus, 

at each point in time, these resources might go to investments in capital in the regulated industry, or may 

go into investments in other industries, depending 

upon relative rates of return on those investments. This 

will affect the processes of capital formation and deepening. 

Second, the CAA placed restrictions on new 

sources of emissions. When making investment decisions, firms take into account the additional cost of 

pollution abatement equipment. Effectively, the 

7 Regulation may also affect the rate of investment, and change the rate of capital accumulation. 

A-7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

“price” of investment goods is higher because more 

units of capital are required to produce the same 

amount of output. In the no-control scenario, there 

are no restrictions on new sources and hence no requirements for pollution control expenditures. Effectively, the “price” of investment goods is lower. Thus, 

at each point in time, investors are faced with a lower 

price of investment goods. This results in a different 

profile for investment over time. 

Operating and Maintenance Costs - Stationary 

Sources 

In addition to purchasing pollution abatement 

equipment, firms incurred costs to run and maintain 

the pollution abatement equipment. In the no-control 

scenario, resources used to pay for these operating 

and maintenance (O&M) costs are freed up for other 

uses. The model assumes that the resources required 

to run and maintain pollution control equipment are 

in the same proportions as the factor inputs used in 

the underlying production technology. For example, 

if 1 unit of labor and 2 units of materials are used to 

produce 1 unit of output, then one-third of pollution 

control O&M costs are allocated to labor and twothirds are allocated to materials. These adjustments 

were introduced at the sector level. O&M expenditures are exclusive of depreciation charges and offset 

by any recovered costs. 

Capital Costs - Mobile Sources 

Capital costs associated with pollution control 

equipment were represented by changing costs for 

motor vehicles (sector 24) and other transportation 

equipment (sector 26). Prices (unit costs) were reduced 

in proportion to the value of the pollution control devices contained in cars, trucks, motorcycles, and aircraft. 

Operating and Maintenance - Mobile Sources 

Prices for refined petroleum products (sector 16) 

were changed to reflect the resource costs associated 

with producing unleaded and reduced lead gasoline 

(fuel price penalty), the change in fuel economy for 

vehicles equipped with pollution control devices (fuel 

economy penalty), and the change in fuel economy 

due to the increased fuel density of lower leaded and 

no lead gasoline (fuel economy credit). Third, inspection and maintenance costs and a maintenance credit 

associated with the use of unleaded and lower leaded 

(i.e., unleaded and lower leaded gasoline is less corrosive, and therefore results in fewer muffler replacements, less spark plug corrosion, and less degradation of engine oil) were represented as changes in 

prices for other services (sector 34). 

Direct Compliance Expenditures 

Data 

Sources of Cost Data 

Cost data for this study are derived primarily from 

the 1990 Cost of Clean report. EPA publishes cost 

data in response to requirements of the Clean Air and 

Clean Water Acts. The following subsections describe 

Cost of Clean data in detail, as well as adjustments 

made to the data and data from other sources. 

Cost of Clean Data 

EPA is required to compile and publish public 

and private costs resulting from enactment of the Clean 

Air Act and the Clean Water Act. The 1990 Cost of 

Clean report presents estimates of historical pollution 

control expenditures for the years 1972 through 1988 

and projected future costs for the years 1989 through 

2000. This includes federal, state, and local governments as well as the private sector. Estimates of capital costs, operation and maintenance (O&M) costs, 

and total annualized costs for five categories of environmental media, including air, water, land, chemical, and multi-media, are presented. It should be noted 

that these estimates represent direct regulatory implementation and compliance costs rather than social 

costs. The Cost of Clean relied on data from two governmental sources, the EPA and the U.S. Department 

of Commerce (Commerce). 

EPA Data 

EPA expenditures were estimated from EPA budget justification documents.8 Estimates of capital and 

operating costs resulting from new and forthcoming 

regulations were derived from EPA’s Regulatory Impact Analyses (RIAs). RIAs have been prepared prior 

to the issuance of all major regulations since 1981. 

Finally, special analyses conducted by EPA program 

offices or contractors were used when other data 

sources did not provide adequate or reliable data. 

8 The main source of data for EPA expenditures is the Justification of Appropriation Estimates for Committee on Appropriations. 

A-8

Appendix A: Cost and Macroeconomic Modeling 

Commerce Data 

Data collected by Commerce were used extensively in the Cost of Clean for estimates of historical 

pollution control expenditures made by government 

agencies other than EPA and by the private sector. 

Two Commerce agencies, the Bureau of Economic 

Analysis (BEA) and the Bureau of the Census (Census), have collected capital and operating costs for 

compliance with environmental regulations since the 

early 1970’s. Commerce is, in fact, the primary source 

of original survey data for environmental regulation 

compliance costs. Commerce publishes a number of 

documents that report responses to surveys and comprise most of the current domain of known pollution 

abatement and control costs in the United States, including: 

•	 A series of articles entitled “Pollution Abatement and Control Expenditures” published 

annually in the Survey of Current Business 

by BEA (BEA articles); 

•	 A series of documents entitled “Pollution 

Abatement Costs and Expenditures” published annually in the Current Industrial Reports by Census (PACE reports); and 

•	 A series of documents entitled Government 

Finances published annually by Census (Government Finances). 

BEA articles contain data derived from a number 

of sources, including two key agency surveys —the 

“Pollution Abatement Costs and Expenditures Survey” (PACE Survey) and the “Pollution Abatement 

Plant and Equipment Survey” (PAPE Survey)— 

which are conducted annually by Census for BEA. 

Data have been reported for 1972 through 1987.9 

PACE reports have been published annually since 

1973 with the exception of 1987. Figures for 1987 

were estimated on the basis of historical shares within 

total manufacturing. These reports contain expenditure estimates derived from surveys of about 20,000 

manufacturing establishments. Pollution abatement 

expenditures for air, water and solid waste are reported 

by state and Standard Industrial Code (SIC) at the fourdigit level. According to Census, surveys conducted 

since 1976 have not included establishments with 

fewer than 20 employees because early surveys 

showed that they contributed only about 2 percent to 

the pollution estimates while constituting more than 

10 percent of the sample size. 

Each year Census conducts a survey of state, local, and county governments; and survey results are 

published in Government Finances. Census asks government units to report revenue and expenditures, including expenditures for pollution control and abatement. 

Non-EPA Federal expenditures were estimated 

from surveys completed by federal agencies detailing 

their pollution control expenditures, which are submitted to BEA. Private sector air pollution control 

expenditures, as well as state and local government 

air pollution expenditures, were taken from BEA articles. 

Stationary Source Cost Data 

Capital Expenditures Data 

Capital expenditures for stationary air pollution 

control are made by factories and electric utilities for 

plant and equipment that abate pollutants through endof-line (EOL) techniques or that reduce or eliminate 

the generation of pollutants through changes in production processes (CIPP). For the purposes of this 

report EOL and CIPP expenditures are aggregated.10 

Table A-3 summarizes capital expenditures for stationary air pollution control, categorized as “nonfarm 

business” or “government enterprise” expenditures. 

Nonfarm business capital expenditures consist of 

plant and equipment expenditures made by 1) manufacturing companies, 2) privately and cooperatively 

owned electric utilities, and 3) other nonmanufacturing 

companies. “Government enterprise” is, according to 

BEA, an agency of the government whose operating 

costs, to a substantial extent, are covered by the sale 

of goods and services. Here, government enterprise 

means specifically government enterprise electric 

9 The most recent BEA article used as a source for air pollution control costs in the Cost of Clean was “Pollution Abatement and 

Control Expenditures, 1984-87” in Survey of Current Business, June 1989. 

10 Survey respondents to the Census annual Pollution Abatement Surveys report the difference between expenditures for CIPP 

and what they would have spent for comparable plant and equipment without pollution abatement features. Disaggregated capital 

expenditures by private manufacturing establishments can be found in annual issues of Census reports. 

A-9

Sources: a. Non-fa rm capital expe nditures fo r 1972 -87 are from Cost of Clean , T able B-1, line 2. b. No n-farm O&M exp end itures for 197 3-8 5 are from Cost of Clean , T able B-1, line 8. c. Governm ent e nterprise ca pital expenditures for 1 972-87 are from Cost of Clean, T able B-9, line 1. d. Go ve rnm ent enterp rise O&M expe nditu re s for 1973 -85 are from Cost of Clean, T able B-9, line 5. All othe r re ported e xpenditures a re EPA estimates. 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table A-3. Estimated Capital and O&M 

Expenditures for Stationary Source Air 

Pollution Control (millions of current dollars). 

No nfa rm 

Business 

Governm ent 

En terprise 

Year Cap.a O&Mb Cap.c O&M d 

1972 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

2,172 

2,968 

3,328 

3,914 

3,798 

3,811 

3,977 

4,613 

5,051 

5,135 

5,086 

4,155 

4,282 

4,141 

4,090 

4,179 

4,267 

4,760 

4,169 

1,407 

1,839 

2,195 

2,607 

3,163 

3,652 

4,499 

5,420 

5,988 

5,674 

6,149 

6,690 

6,997 

7,116 

7,469 

7,313 

7,743 

8,688 

63 

82 

104 

102 

156 

197 

205 

285 

398 

451 

508 

422 

416 

328 

312 

277 

243 

235 

226 

29 

56 

45 

58 

60 

72 

106 

148 

135 

141 

143 

147 

189 

140 

130 

161 

173 

1990 4,169 8,688 226 154 

Sources: a. Non-farm capital expenditures for 1972-87 are from Cost of Clean , Table B-1, line 2. b. Non-farm O&M expenditures for 1973-85 are from Cost of Clean , Table B-1, line 8. c. Government enterprise capital expenditures for 1972-87 are from Cost of Clean, Table B-9, line 1. d. Government enterprise O&M expenditures for 1973-85 are from Cost of Clean, Table B-9, line 5.

All other reported expenditures are EPA estimates.

utilities. Government enterprise capital expenditures 

are pollution abatement expenditures made by publicly owned electric utilities.11 

Operation and Maintenance Expenditures Data 

Stationary source O&M expenditures are made 

by manufacturing establishments, private and public 

electric utilities, and other nonmanufacturing businesses to operate air pollution abatement equipment. 

O&M expenditures for electric utilities are made up 

of two parts: 1) expenditures for operating air pollution equipment and 2) the additional expenditures associated with switching to alternative fuels that have 

lower sulfur content (fuel differential). Expenditures 

to operate air pollution abatement equipment are for 

the collection and disposal of flyash, bottom ash, sulfur and sulfur products, and other products from flue 

gases.12 O&M expenditures are net of depreciation 

and payments to governmental units, and are summarized in Table A-3. O&M data were disaggregated to 

the two digit SIC level for use in the macroeconomic 

model. 

For both capital and O&M expenditures, historical survey data were not available for each year 

through 1990 prior to publication of Cost of Clean. 

For the purpose of the section 812 analysis, EPA projected 1988-1990 capital expenditures and 1986-1990 

O&M expenditures. Those projections were used in 

the macroeconomic simulation, and have been retained 

as cost estimates to ensure consistency between the 

macroeconomic results and the direct cost estimates. 

Since completion of the macroeconomic modeling, 

however, BEA has published expenditure estimates 

through 1990. A comparison of more recent BEA estimates with the EPA projections used in the section 

812 analysis can be found in the “Uncertainties in the 

Cost Analysis” section, below. 

Recovered Costs 

“Recovered costs” are costs recovered (i.e., revenues realized) by private manufacturing establishments through abatement activities. According to instructions provided to survey participants by Census, 

recovered costs consist of 1) the value of materials or 

energy reclaimed through abatement activities that 

were reused in production and 2) revenue that was 

obtained from the sale of materials or energy reclaimed 

through abatement activities. Estimates of recovered 

costs were obtained from the PACE reports and are 

summarized in Table A-4. In this analysis, recovered 

costs were removed from total stationary source air 

pollution control O&M costs — that is, net O&M cost 

in any year would be O&M expenditures (see Table 

A-3) less recovered costs. Recovered cost data were 

disaggregated to the two digit SIC level for use in the 

macroeconomic model. 

11 BEA calculates these expenditures using numbers obtained from Energy Information Agency (EIA) Form 767 on steamelectric plant air quality control. 

12 Farber, Kit D. and Gary L. Rutledge, “Pollution Abatement and Control Expenditures: Methods and Sources for CurrentDollar Estimates,” Unpublished paper, Bureau of Economic Analysis, U.S. Department of Commerce, October 1989. 

A-10

Appendix A: Cost and Macroeconomic Modeling 

Table A-4. Estimated Recovered Costs for 

Stationary Source Air Pollution Control 

(millions of current dollars). 

* Air cost recovered as reported in PACE 

Source: "Pollution Abatement Costs and 

Expenditures" published annually in the Curr ent 

Industrial Reports by Census. 

Mobile Source Cost Data 

Costs of controlling pollution emissions from 

motor vehicles were estimated by calculating the purchase price and O&M cost premiums associated with 

vehicles equipped with pollution abatement controls 

over the costs for vehicles not equipped with such 

controls. These costs were derived using EPA analyses, including EPA RIAs, the Cost of Clean, and other 

EPA reports.13 This Appendix summarizes the section 812 mobile source compliance cost estimates and 

provides references to published data sources where 

possible. Further information on specific methods, 

analytical steps, and assumptions can be found in 

McConnell et al. (1995),14 which provides a detailed 

description of the section 812 mobile source cost estimation exercise and compares the method and reYear PAC E* Estim ated 

1972 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

750 

862 

1,000 

858 

822 

866 

767 

860 

1,103 

248 

199 

296 

389 

496 

557 

617 

750 

862 

997 

857 

822 

870 

768 

867 

987 

1,107 

1,122 

1,256 

sults to other similar analyses (including Cost of Clean 

(1990)). 

Capital Expenditures Data 

Capital expenditures for mobile source emission 

control are associated primarily with pollution abatement equipment on passenger cars, which comprise 

the bulk of all mobile sources of pollution. These capital costs reflect increasingly stringent regulatory requirements and improvements in pollution control 

technologies over time. Each of the following devices 

have been used at one time or another dating back to 

the Clean Air Act Amendments of 1965: air pumps, 

exhaust-gas recirculation valves, high altitude controls, evaporative emissions controls, and catalysts. 

The cost estimates for each component were computed 

on a per-vehicle basis by engineering cost analyses 

commissioned by EPA. The resulting per-vehicle capital costs were multiplied by vehicle production estimates to determine annual capital costs. Table A-5 

summarizes mobile source capital costs. 

Operation and Maintenance Expenditures Data 

Costs for operation and maintenance of emission 

abatement devices include the costs of maintaining 

pollution control equipment plus the cost of vehicle 

inspection/maintenance programs. Operating costs per 

vehicle were multiplied by total vehicles in use to 

determine annual cost. Mobile source O&M costs are 

made up of three factors: 1) fuel price penalty, 2) fuel 

economy penalty, and 3) inspection and maintenance 

program costs as described below. These costs are 

mitigated by cost savings in the form of maintenance 

economy and fuel density economy. Table A-6 summarizes mobile source O&M expenditures and cost 

savings by categories, with net O&M costs summarized above in Table A-5. The following sections describe the components of the mobile source O&M cost 

estimates. 

Fuel Price Penalty 

Historically, the price of unleaded fuel has been 

several cents per gallon higher than the price of leaded 

fuel. CAA costs were calculated as the difference be13 A complete listing of sources used in calculating mobile source capital and operating expenditures can be found in Environmental Investments: The Cost of a Clean Environment, Report of the Administrator of the Environmental Protection Agency to the 

Congress of the United State, EPA-230-11-90-083, November 1990. 

14 Evaluating the Cost of Compliance with Mobile Source Emission Control Requirements: Retrospective Analysis, Resources 

for the Future Discussion Paper, 1995. Note that McConnell et al. refer to the section 812 estimates as: Cost of Clean (1993, unpublished). 

A-11

b

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table A-5. Estimated Capital and 

Operation and Maintenance Expenditures 

for Mobile Source Air Pollution Control 

(millions of current dollars). 

Year Ca pital

aa O&M b 

1973 276 1,765 

1974 242 2,351 

1975 1,570 2,282 

1976 1,961 2,060 

1977 2,248 1,786 

1978 2,513 908 

1979 2,941 1,229 

1980 2,949 1,790 

1981 3,534 1,389 

1982 3,551 555 

1983 4,331 -155 

1984 5,679 -326 

1985 6,387 337 

1986 6,886 -1,394 

1987 6,851 -1,302 

1988 7,206 -1,575 

1989 7,053 -1,636 

1990 7,299 -1,816 

Sources: 

a. Capital exp.: Cost of Clean, Tables C-2 to C-9, line 3 

on each; Tables C-2A to C-9A, line 10 on each; converted 

from $1986 to current dollars. 

b. O&M exp.: EPA analyses based on sources and 

methods in: Costs and Benefits of Reducing Lead in 

Gasoline: Final Regulatory Impact Analysis, U.S. 

Environmental Protection Agency, Office of Policy 

Analysis, EPA-230-05-85-006, February 1985; and Cost 

of Clean . 

is likely to understate costs because regulatory requirements and market developments cannot be perfectly 

anticipated over time. This procedure resulted in estimates that are about ten percent less than estimates in 

other EPA reports.15 However, new process technologies that were developed in the mid-1980s were not 

reflected in either the base case or regulatory case runs. 

It is reasonable to expect that regulatory requirements 

would have encouraged development of technologies 

at a faster rate than would have occurred otherwise. 

Fuel Economy Penalty 

The fuel economy penalty benefit is the cost associated with the increased/decreased amount of fuel 

used by automobiles with air pollution control devices 

(all else being equal). An assumption that can be made 

is that the addition of devices, such as catalytic conYear 

Fu el Price 

Penal ty 

Fu el 

Econ. 

Penal ty 

Net 

I & M* 

Total 

Costs 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

91 

244 

358 

468 

568 

766 

1187 

1912 

2181 

2071 

1956 

2012 

3057 

2505 

2982 

3127 

3476 

3754 

1700 

2205 

2213 

2106 

1956 

1669 

1868 

1998 

1594 

1026 

628 

313 

118 

-40 

-158 

-210 

-318 

-481 

-26 

-98 

-289 

-514 

-738 

-1527 

-1826 

-2120 

-2386 

-2542 

-2739 

-2651 

-2838 

-3859 

-4126 

-4492 

-4794 

-5089 

1765 

2351 

2282 

2060 

1786 

908 

1229 

1790 

1389 

555 

-155 

-326 

337 

-1394 

-1302 

-1575 

-1636 

-1816 

Year Penal ty Penal ty I & M* Costs 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

91 

244 

358 

468 

568 

766 

1187 

1912 

2181 

2071 

1956 

2012 

3057 

2505 

2982 

3127 

3476 

3754 

1700 

2205 

2213 

2106 

1956 

1669 

1868 

1998 

1594 

1026 

628 

313 

118 

-40 

-158 

-210 

-318 

-481 

-26 

-98 

-289 

-514 

-738 

-1527 

-1826 

-2120 

-2386 

-2542 

-2739 

-2651 

-2838 

-3859 

-4126 

-4492 

-4794 

-5089 

1765 

2351 

2282 

2060 

1786 

908 

1229 

1790 

1389 

555 

-155 

-326 

337 

-1394 

-1302 

-1575 

-1636 

-1816 

Table A-6. O&M Costs and Credits (millions 

of c urrent dollars). 

* Inspection and maintenance costs less fuel density savings 

and maintenance savings. 

Sources: All results are presented in Jorgenson et al. (1993),

pg. A.17. FPP results are based on a petroleum refinery cost

model run for the retrospective analysis. FEP and Net I&M

are based on data and methods from Costs and Benefits of

Reducing Lead in Gasoline: Final Regulatory Impact

Analysis, U.S. Environmental Protection Agency, Office of

Policy Analysis, EPA-230-05-85-006, February 1985; and

Cost of Clean (1990). Specific analytic procedures are

summarized in McConnell et al. (1995).

tween the cost of making unleaded gasoline and leaded 

gasoline with lower lead levels and the cost of making only leaded gasoline with a lead content set at 

pre-regulatory levels. These cost estimates were developed using a linear programming model of the refinery industry. Prices of crude oil and other unfinished oils, along with the prices of refinery outputs, 

were adjusted annually according to price indices for 

imported crude oil over the period of analysis. The 

relative shares of leaded and unleaded gasoline and 

the average lead content in leaded gasoline also were 

adjusted annually according to the historical record. 

These estimates may tend to understate costs due 

to a number of biases inherent in the analysis process. 

For example, the refinery model was allowed to optimize process capacities in each year. This procedure 

15 Costs and Benefits of Reducing Lead in Gasoline: Final Regulatory Impact Analysis, U.S. Environmental Protection Agency, 

Office of Policy Analysis, EPA-230-05-85-006, February 1985. 

A-12

Appendix A: Cost and Macroeconomic Modeling 

verters, decrease automobile fuel efficiency.16 If this Maintenance Credits


assumption is true, air pollution control devices in-


crease the total fuel cost to consumers. An alternative Catalytic converters require the use of unleaded


assumption is that the use of catalytic converters has fuel, which is less corrosive than leaded gasoline. On


increased fuel economy. This increase has been at- the basis of fleet trials, the use of unleaded or lower


tributed in large measure to the feedback mechanism leaded gasoline results in fewer muffler replacements,


built into three-way catalytic converters.17 Under this less spark plug corrosion, and less degradation of en-


assumption, the decrease in total fuel cost to consum- gine oil, thus reducing maintenance costs. Mainte­


ers is considered a benefit of the program. nance credits account for the majority of the direct


(non-health) economic benefits of reducing the lead 

For the purposes of this study, sensitivity analy- concentration in gasoline. 

ses were performed using data presented in the Cost 

of Clean report. These analyses were conducted to Fuel Density Credits 

evaluate the significance of assumptions about the 

relationship between mile per gallon (MPG) values The process of refining unleaded gasoline infor controlled automobiles and MPG values for un- creases its density. The result is a gasoline that has 

controlled cars. Based on results of these and other higher energy content. Furthermore, unleaded gasoanalyses, fuel economy was assumed to be equal for line generates more deposits in engine combustion 

controlled and uncontrolled vehicles from 1976 on- chambers, resulting in slightly increased compression 

ward. This may bias the cost estimates although in an and engine efficiency. Higher energy content of ununknown direction. leaded gasoline and increased engine efficiency from 

the used of unleaded gasoline yield greater fuel 

Inspection and Maintenance Programs economy and therefore savings in refining, distribution, and retailing costs. 

Inspection and maintenance programs are administered by a number of states. Although these programs Other Direct Cost Data 

are required by the Clean Air Act, the details of administration were left to the discretion of state or lo- The Cost of Clean report includes several other 

cal officials. The primary purpose of inspection and categories of cost that are not easily classified as eimaintenance programs is to identify cars that require ther stationary source or mobile source expenditures. 

maintenance —including cars that 1) have had poor Federal and state governments incur air pollution 

maintenance, 2) have been deliberately tampered with abatement costs; additionally, federal and state govor had pollution control devices removed, or 3) have ernments incur costs to develop and enforce CAA 

used leaded gasoline when unleaded is required— and regulations. Research and development expenditures 

force the owners of those cars to make necessary re- by the federal government, state and local governpairs or adjustments.18 Expenditures for inspection and ments, and (especially) the private sector can be atmaintenance were taken from the Cost of Clean. tributed to the CAA. These data are summarized by 

year in Table A-7. 

Beneficial effects of the mobile source control 

program associated with maintenance and fuel den- Unlike the other private sector expenditure data 

sity were also identified. These cost savings were in- used for this analysis, the survey data used as a source 

cluded in this study as credits to be attributed to the for private sector R&D expenditures cannot be disagmobile source control program. Credits were estimated gregated into industry-specific expenditure totals. 

based on an EPA study,19 where more detailed expla- Consequently, private sector R&D expenditures are 

nations may be found. 

16 Memo from Joel Schwartz (EPA/OPPE) to Joe Somers and Jim DeMocker dated December 12, 1991, and entitled “Fuel 

Economy Benefits.” Schwartz states that since this analysis is relative to a no Clean Air Act baseline, not a 1973 baseline, fuel 

economy benefits are not relevant. In the absence of regulation, tuning of engines for maximum economy would presumably be 

optimal in the base case as well. 

17 Memo from Joseph H. Somers, EPA Office of Mobile Sources, to Anne Grambsch (EPA/OPPE) and Joel Schwartz (EPA/ 

OPPE) entitled “Fuel Economy Penalties for section 812 Report,” December 23, 1991. 

18 Walsh, Michael P., “Motor Vehicles and Fuels: The Problem,” EPA Journal, Vol. 17, No. 1, January/February 1991, p. 12. 

19 Schwartz, J., et al. Costs and Benefits of Reducing Lead in Gasoline: Final Regulatory Impact Analysis, U. S. Environmental 

Protection Agency, Economic Analysis Division, Office of Policy Analysis, February 1985. 

A-13

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table A-7. Other Air Pollution Control Expenditures (millions of 

current dollars). 

Year Abatement 

Regulations 

an d Monito ring 

Research 

an d Development Total 

Fed. a 

State & 

Localb Fed.c 

State & 

Locald Privatee Fed. f 

State & 

Localg 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

47 

56 

88 

105 

106 

90 

103 

95 

85 

87 

136 

115 

98 

67 

80 

65 

70 

71 

0 

0 

1 

1 

1 

0 

0 

0 

0 

0 

4 

14 

12 

14 

15 

10 

12 

13 

50 

52 

66 

69 

80 

93 

100 

122 

108 

93 

88 

101 

103 

106 

110 

120 

130 

133 

115 

131 

139 

135 

161 

183 

200 

207 

226 

230 

239 

250 

250 

307 

300 

320 

360 

343 

492 

520 

487 

562 

675 

805 

933 

851 

798 

761 

691 

665 

775 

833 

887 

934 

984 

749 

126 

100 

108 

131 

144 

146 

105 

130 

131 

126 

133 

165 

247 

217 

200 

220 

230 

231 

6 

7 

8 

6 

7 

8 

7 

5 

0 

2 

6 

4 

3 

4 

2 

1 

2 

2 

836 

866 

897 

1,009 

1,174 

1,325 

1,448 

1,410 

1,348 

1,229 

1,297 

1,314 

1,488 

1,548 

1,594 

1,670 

1,788 

1,542 

Sources: 

a. Federal government abatement expenditures: 1973-82, “Pollution Abatement and Control 

Expenditures”, Survey of Current Business (BEA) July 1986 Table 9 line 13; 1983-87, BEA 

June 1989 Table 7 line 13; 1988-90, BEA May 1995 Table 7 line 13. 

b. State and local abatement expenditures: 1973-87, Cost of Clean, Table B-9 line 2; 1988-90, 

BEA May 1995 Table 7 line 14. 

c. Federal government “regs/monitoring” expenditures: 1973-82, BEA July 1986, Table 9 line 

17; 1983-87, BEA June 1989 Table 6 line 17; 1988-90, BEA May 1995 Table 7 line 17. 

d. State and local government “regs/monitoring” expenditures: 1973-87, Cost of Clean, Table 

B-9 line 3; 1988-90, BEA May 1995 Table 7 line 18. 

e. Private sector R&D expenditures: 1973-86, BEA May 1994 Table 4 (no line #) [total R&D 

expenditures in $1987 are converted to current dollars using the GDP price deflator series found 

elsewhere in this Appendix -- netting out public sector R&D leaves private sector expenditures]; 

1987-90, BEA May 1995 Table 7 line 20. 

f. Federal government R&D expenditures: 1973-82, BEA July 1986 Table 9 line 21; 1983-87, 

BEA June 1989 Table 6 line 21; 1988-90, BEA May 1995, Table 7 line 21. 

g. State and local government R&D expenditures: 1973-87, Cost of Clean, Table B-9 line 4; 

1988-90, BEA May 1995 Table 7 line 22. 

from more recent issues of the Survey of 

Current Business (BEA). Federal government expenditures are from BEA (various 

issues). Private R&D expenditures were 

reported in Cost of Clean. Since publication of Cost of Clean, however, BEA has 

revised its private sector R&D expenditure 

series (BEA, 1994 and 1995). Since private 

R&D expenditures were not included in the 

macroeconomic modeling exercise, the revised series can be (and has been) used 

without causing inconsistency with other 

portions of the section 812 analysis. 

Assessment Results 

Compliance Expenditures and 

Costs 

Compliance with the CAA imposed 

direct costs on businesses, consumers, and 

governmental units, and triggered other 

expenditures such as governmental regulation and monitoring costs and expenditures 

for research and development by both government and industry. As shown in Table 

A-8, annual CAA compliance expenditures 

– including R&D, etc.– over the period 

from 1973 to 1990 were remarkably 

stable20 , ranging from about $20 billion to 

$25 billion in inflation-adjusted 1990 dollars (expenditures are adjusted to 1990 dollars through application of the GDP Implicit 

Price Deflator). This is equal to approximately one third of one percent of total 

domestic output during that period, with the 

percentage falling from one half of one percent of total output in 1973 to one quarter 

of one percent in 1990. 

omitted from the macroeconomic modeling exercise Although useful for many purposes, a summary 

(the macro model is industry-specific). The R&D ex- of direct annual expenditures is not the best cost meapenditures are, however, included in aggregate cost sure to use when comparing costs to benefits. Capital 

totals used in the benefit-cost analysis. expenditures are investments, generating a stream of 

benefits (and opportunity cost) over the life of the inThe Cost of Clean and the series of articles “Pol- vestment. The appropriate accounting technique to use 

lution Abatement and Control Expenditures” in the for capital expenditures in a cost/benefit analysis is to 

Survey of Current Business (various issues) are the annualize the expenditure — i.e., to spread the capidata sources for “Other Air Pollution Control Expen- tal cost over the useful life of the investment, applyditures.” State and local expenditures through 1987 ing a discount rate to account for the time value of 

are found in Cost of Clean; 1988-90 expenditures are money. 

20 While total expenditures remained relatively constant over the period, the sector-specific data presented in Tables A-3 and A-5 

above indicate that capital expenditures for stationary sources fell significantly throughout the period but that this decline was offset 

by significant increases in mobile source capital expenditures. 

A-14

Appendix A: Cost and Macroeconomic Modeling K = Capital expenditures; O&M = Operation and Maintenance expenditures. Rec. Costs = recovered costs. Total expenditures are the sum of stationary source, mobile source, and “other” expenditures, less recovered costs. Stationary source expenditures are the sum of “Nonfarm Business” and “Government Enterprise” expenditures (from Table A-3). To calculate expenditures in 1990 dollars, current year expenditures are multiplied by the ratio of the 1990 price deflator to the current year deflator. For example, 1989 expenditures are multiplied by (113.2/108.5). Source for price deflator series: Economic Report of the President, February 1995, Table B-3. 

Stationary Rec. Mobile Source TOTAL 

K O&M Costs K O&M Other EXP 

6,521 

8,360 3,936 545 756 4,838 2,290 19,635 

8,653 4,778 746 610 5,927 2,184 21,405 

9,240 5,154 895 3,612 5,250 2,063 24,425 

8,558 5,768 1,074 4,244 4,459 2,183 24,139 

8,116 6,527 1,128 4,552 3,617 2,378 24,062 

7,851 6,991 1,158 4,718 1,705 2,487 22,593 

8,465 7,959 1,296 5,083 2,124 2,503 24,837 

8,603 8,791 1,361 4,656 2,826 2,226 25,741 

8,014 8,785 1,430 5,070 1,993 1,935 24,367 

7,557 7,855 1,158 4,797 750 1,755 21,555 

5,942 8,168 1,067 5,622 (201) 1,684 20,148 

5,844 8,505 1,082 7,064 (406) 1,634 21,560 

5,359 8,617 921 7,659 404 1,785 22,903 5,142 8,477 1,013 8,044 (1,628) 1,809 20,831 

5,044 8,602 1,117 7,755 (1,474) 1,804 20,615 

4,914 8,143 1,206 7,851 (1,716) 1,819 19,805 

5,211 8,259 1,171 7,359 (1,707) 1,865 19,817 

4,395 8,842 1,256 7,312 (1,816) 1,542 19,019 

GDP 

price 

defl. 38.8 41.3 44.9 49.2 52.3 55.9 60.3 65.5 71.7 78.9 83.8 

87.2 

91 

94.4 96.9 

100 

103.9 108.5 113.2 

Stationary Rec. Mobile Source TOTAL 

K O&M Costs K O&M Other EXP 

2,235 na na na na na na 

3,050 1,436 199 276 1,765 836 7,164 

3,432 1,895 296 242 2,351 866 8,490 

4,016 2,240 389 1,570 2,282 897 10,616 3,954 2,665 496 1,961 2,060 1,009 11,153 4,008 3,223 557 2,248 1,786 1,174 11,882 4,182 3,724 617 2,513 908 1,325 12,035 4,898 4,605 750 2,941 1,229 1,448 14,371 5,449 5,568 862 2,949 1,790 1,410 16,304 5,586 6,123 997 3,534 1,389 1,348 16,983 5,594 5,815 857 3,551 555 1,299 15,957 

4,577 6,292 822 4,331 (155) 1,297 15,520 4,698 6,837 870 5,679 (326) 1,314 17,332 

4,469 7,186 768 6,387 337 1,488 19,099 4,402 7,256 867 6,886 (1,394) 1,548 17,831 

4,456 7,599 987 6,851 (1,302) 1,594 18,211 

4,510 7,474 1,107 7,206 (1,575) 1,670 18,178 

4,995 7,916 1,122 7,053 (1,636) 1,788 18,994 

4,395 8,842 1,256 7,312 (1,816) 1,542 19,019 

Table A-8. Summary of Expenditures and Conversion to 1990 Dollars (millions of dollars). 

CURRENT YEAR DOLLARS 1990 DOLLARS 

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

A-15

Stationary rec. Mobile Source 

K O&M costs K O&M other Total 

1973 3 3,936 545 4,838 2,290 ,042 

1974 194 4,778 746 98 5,927 2,184 ,435 

1975 888 5,154 895 7 5,250 2,063 ,638 

1976 630 5,768 1,074 5 4,459 2,183 ,611 

1977 317 6,527 1,128 194 3,617 2,378 ,904 

1978 968 6,991 1,158 784 1,705 2,487 ,776 

1979 598 7,959 1,296 395 2,124 2,503 ,282 

1980 277 8,791 1,361 053 2,826 2,226 ,812 

1981 967 8,785 1,430 656 1,993 1,935 ,905 

1982 610 7,855 1,158 313 750 1,755 ,125 

1983 217 8,168 1,067 934 (201) 1,684 ,734 

1984 694 8,505 1,082 564 (406) 1,634 ,909 

1985 163 8,617 921 400 404 1,785 ,447 

1986 593 8,477 1,013 924 (1,628) 1,809 ,161 

1987 005 8,602 1,117 416 (1,474) 1,804 ,237 

1988 410 8,143 1,206 831 (1,716) 1,819 ,281 

1989 804 8,259 1,171 237 (1,707) 1,865 ,288 

1990 10,222 8,842 1,256 531 (1,816) 1,542 ,066 

Source: St at ionary source capital cost s and mobile source capital costs are from 

Tables A-10 and A-11, respectively. All other costs and offset s are from Table 

A-8. 

52 0 11

1, 13

1, 17 13

2, 64 14

3, 1, 15

3, 1, 15

4, 2, 18

5, 3, 20

5, 3, 20

6, 4, 20

7, 4, 20

7, 5, 21

8, 6, 24

8, 6, 23

9, 7, 24

9, 7, 24

9, 8, 25

8, 26

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Annualization Method The Stationary Source table reports a capital expenditure of $6,521 million for 1972 (in 1990 dolFor this cost/benefit analysis, all capital expendi- lars). The cost is spread over the following twenty 

tures have been annualized at 3 percent, 5 percent, years (which is the assumed useful life of the investand 7 percent (real) rates of interest. Therefore, “an- ment) using a discount rate of five percent; thus, the 

nualized” costs reported for any given year are equal amortization factor to be used is f(20)=0.0802. Multo O&M expenditures (plus R&D, etc., expenditures, tiplying $6,521 million by 0.0802 gives an annuity of 

minus recovered costs) plus amortized capital costs $523 million. That annuity is noted on the first data 

(i.e., depreciation plus interest costs associated with row of the table, signifying that the 1972 expenditure 

the pre-existing capital stock) for that year. Station- of $6,521 million implies an annual cost of $523 milary source air pollution control capital costs are am- lion for the entire twenty-year period of 1973 to 1992 

ortized over twenty years; mobile source air pollution (the years following 1990 are not included on the 

control costs are amortized over ten years. Capital tables, since costs incurred in those years are not inexpenditures are amortized using the formula for an cluded in this retrospective assessment). The first sumannuity [that is, r/(1-(1+r)-t) , where r is the rate of mary row near the bottom of the table (labeled “SUM”) 

interest and t is the amortization period].21 Multiply- reports aggregate annualized capital costs: for 1973 

ing the expenditure by the appropriate annuity factor (the first data column), capital costs are $523 million. 

gives a constant annual cost to be incurred for t years, 

the present value of which is equal to the expenditure. Capital expenditures in 1973 amounted to $8,360 

million. Using the amortization technique explained 

Due to data limitations, the cost analysis for this above, one can compute an annualized cost of $671 

CAA retrospective starts in 1973, missing costs in- million, incurred for the twenty-year period of 1974 

curred in 1970-72. Cost of Clean, however, includes to 1993. Aggregate annualized capital costs for 1974 

stationary source capital expenditures for 1972. In this include cost flows arising from 1972 and 1973 investanalysis, amortized costs arising from 1972 

capital investments are included in the 1973-

1990 annualized costs, even though 1972 

costs are not otherwise included in the analysis. Conversely, only a portion of the (e.g.) 

1989 capital expenditures are reflected in the 

1990 annualized costs — the remainder of 

the costs are spread through the following 

two decades, which fall outside of the scope 

of this study (similarly, benefits arising from 

emission reductions in, e.g., 1995 caused by 

1990 capital investments are not captured 

by the benefits analysis). Table A-9 presents 

CAA compliance costs from 1973 to 1990, 

in 1990 dollars, with capital expenditures 

amortized at a five percent real interest rate. 

“Total” costs are the sum of stationary 

source, mobile source, and “other” costs, 

minus recovered costs. 

Tables A-10 and A-11 provide details 

of the amortization calculation (using a five 

percent interest rate) for stationary sources 

and mobile sources, respectively. Similar 

calculations were performed to derive the 

annualized cost results using discount rates 

of three percent and seven percent. 

Table A-9. Annualized Costs, 1973-1990 (millions of 1990 

dollars; capital expenditures annualized at 5 percent). 

Stationary rec. Mobile Source 

K O&M costs K O&M other Total 

1973 523 3,936 545 0 4,838 2,290 11,042 

1974 1,194 4,778 746 98 5,927 2,184 13,435 

1975 1,888 5,154 895 177 5,250 2,063 13,638 

1976 2,630 5,768 1,074 645 4,459 2,183 14,611 

1977 3,317 6,527 1,128 1,194 3,617 2,378 15,904 

1978 3,968 6,991 1,158 1,784 1,705 2,487 15,776 

1979 4,598 7,959 1,296 2,395 2,124 2,503 18,282 

1980 5,277 8,791 1,361 3,053 2,826 2,226 20,812 

1981 5,967 8,785 1,430 3,656 1,993 1,935 20,905 

1982 6,610 7,855 1,158 4,313 750 1,755 20,125 

1983 7,217 8,168 1,067 4,934 (201) 1,684 20,734 

1984 7,694 8,505 1,082 5,564 (406) 1,634 21,909 

1985 8,163 8,617 921 6,400 404 1,785 24,447 

1986 8,593 8,477 1,013 6,924 (1,628) 1,809 23,161 

1987 9,005 8,602 1,117 7,416 (1,474) 1,804 24,237 

1988 9,410 8,143 1,206 7,831 (1,716) 1,819 24,281 

1989 9,804 8,259 1,171 8,237 (1,707) 1,865 25,288 

1990 10,222 8,842 1,256 8,531 (1,816) 1,542 26,066 

Source: Stationary source capital costs and mobile source capital costs are from 

Tables A-10 and A-11, respectively. All other costs and offsets are from Table 

A-8. 

21 Using an interest rate of five percent, the factor for a twenty year amortization period is 0.0802; that for a ten year amortization period is 0.1295. 

A-16

Appendix A: Cost and Macroeconomic Modeling 

523 

687 

690 

469 

394 

523 

687 

690 

469 

394 

Table A-10. Amortization of Capital Expenditures for Stationary Sources (millions of 1990 dollars). 

EXPEND 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

SUM 523 1,194 1,888 2,630 3,317 3,968 4,598 5,277 5,967 6,610 7,217 7,694 8,163 8,593 9,005 9,410 9,804 10,222 Expenditures
K stock 6,521 14,880 23,533 32,773 41,331 49,448 57,299 65,763 74,366 82,381 89,937 95,879 101,723 107,082 112,225 117,269 122,182 127,394


79,819 79,217 

1973
1974 8,653 694 694 694 694 694 694 694 694 694 694 694 694 694 694 694 694


671 671 

1975 9,240 741 741 741 741 741 741 741 741 741 741 741 741 741 741 741 

651 651 1977


1978 7,851 630 630 630 630 630 630 630 630 630 630 630 630
1979 8,465 679 679 679 679 679 679 679 679 679 679 679 

643 643 1981


1982 7,557 606 606 606 606 606 606 606 606
1983 5,942 477 477 477 477 477 477 477 1985
1986 5,142 413 413 413 413


430 430 

1987 5,044 405 405 405 

5,211 418 

5,211 4,395 

K stock net depr. 

Int 326 734 1,144 1,569 1,943 2,281 2,589 2,912 3,223 3,487 3,709 3,830 3,929 3,986 4,012 4,015 3,991 3,961 

5,813 6,262 Depr 

Capital expenditures for each year are found in the “EXPEND” column. Expenditures are amortized over 20 years (i.e., years (t+1) to (t+20)) using a 5% real interest rate to derive a constant cost per year for the entire amortization period. The present value (in year t) of the cost flow is equal to the expenditure in year t. Annualized CAA compliance capital cost for each year (displayed in row “SUM”) is the sum of the annuities calculated for capital expenditures from previous years. The capital stock (“K stock”) in place at the start of each year is equal to the sum of expenditures from previous years. Subtracting depreciation from the capital stock leaves “K stock net depr.” Annual interest expense is 5% of net capital stock. Annual interest expense plus depreciation equals annualized compliance cost (row “SUM”). 

5,044 4,914 

80,249 80,300 

4,993 5,395 

523 523 523 523 523 523 523 523 523 523 523 523 523 523 523 523 

671 671 

687 687 687 687 687 687 687 687 687 687 687 687 

651 651 

690 690 690 690 690 690 690 690 

643 643 

469 469 469 469 

430 430 

5,359 5,142 

78,587 79,713 

4,233 4,607 

671 671 

651 651 

643 643 

5,359 430 

74,173 76,606 

671 671 

651 651 

643 643 

5,942 5,844 

3,508 3,863 

64,469 69,740 

671 671 

651 651 

8,014 643 

8,014 7,557 

2,744 3,123 

51,776 58,232 

671 671 

651 651 

8,465 8,603 

2,009 2,365 

38,869 45,612 

671 671 

8,116 651 

8,116 7,851 

1,373 1,687 

9,240 8,558 

745 1,061 

22,876 31,372 

671 671 8,360 671 

8,360 8,653 

197 460 

6,521 14,684 

1989 1990 

6,521 8,558 8,603 5,844 4,914 

4,395 

1972 1976 1980 1984 1988 

A-17

The Benefits and Costs of the Clean Air Act, 1970 to 1990 621 

1,042 

8,531 

7,312 

65,878 

42,169 

2,108 

6,423 

Capital expenditures for each year are found in the “EXPEND” column. Expenditures are amortized over 10 years (i.e., years (t+1) to (t+10)) using a 5% real interest rate to derive a constant cost per year for the entire amortization period. The present value (in year t) of the cost flow is equal to the expenditure in year t. Annualized CAA compliance capital cost for each year (displayed in row “SUM”) is the sum of the annuities calculated for capital expenditures from previous years. The capital stock (“K stock”) in place at the start of each year is equal to the sum of expenditures from the previous ten years. The sum of all previous expenditures less depreciation leaves “K stock net depr.” Annual interest expense is 5% of net capital stock. Annual interest expense plus depreciation equals annualized compliance cost (row “SUM”). 

1987
1988 7,851 1,017 1,017


1,004 1,004 

Table A-11. Amortization of Capital Expenditures for Mobile Sources (millions of 1990 dollars). 

EXPEND 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

1974 610 79 79 79 79 79 79 79 79 79 79 1975 3,612 468 468 468 468 468 468 468 468 468 468 1976 4,244 550 550 550 550 550 550 550 550 550 550 1977 4,552 590 590 590 590 590 590 590 590 590 590 1978 4,718 611 611 611 611 611 611 611 611 611 611 1979 5,083 658 658 658 658 658 658 658 658 658 658 1980 4,656 603 603 603 603 603 603 603 603 603 603 1981 5,070 657 657 657 657 657 657 657 657 657 1983
1984 7,064 915 915 915 915 915 915


728 728 

1985 7,659 992 992 992 992 992 1989 7,359 953 

1,042

8,237 

1,042 1,042 

7,359 

2,050 

6,187 

63,602 

40,997 

621 621 621 621 621 621 621 

57,266 60,469 

7,755 1,004 

7,416 7,831 

7,755 7,851 

1,842 1,951 

5,574 5,880 

728 728 

36,845 39,026 

49,419 53,466 

6,400 6,924 

7,659 8,044 

1,560 1,701 

4,840 5,223 

728 728 

31,204 34,023 

4,934 5,564 

5,622 7,064 

1,314 1,414 

3,620 4,150 

38,099 42,965 

5,622 728 

26,287 28,289 

1972
1973 756 98 98 98 98 98 98 98 98 98 98


3,656 4,313 

5,070 4,797 

1,103 1,229 

2,553 3,084 

28,232 33,302 

22,057 24,574 

2,395 3,053 

5,083 4,656 

800 974 

1,595 2,079 

18,493 23,576 

15,993 19,480 

1,194 1,784 

4,552 4,718 

8,647 12,437 

432 622 

762 1,162 

9,223 13,776 

177 645 

1,306 4,807 

65 240 

3,612 4,244 

1,367 4,979 

112 404 

98 

38 

60 

6

756 

756 

10 

0 

K stock net depr. 

4,797 8,044 7,312 

Int 

Depr 

1982 1986 1990 SExpenditures K stock 

UM 

A-18

Appendix A: Cost and Macroeconomic Modeling 

ments: that is, $523 million plus $671 million, or 

$1,194 million (see the “SUM” row). Similar calculations are conducted for every year through 1990, to 

derive aggregate annualized capital costs that increase 

monotonically from 1973 to 1990, even though capital expenditures decline after 1975.22 

An alternative calculation technique is available 

that is procedurally simpler but analytically identical 

to that outlined above. Instead of calculating an annuity for each capital expenditure (by multiplying the 

expenditure by the annuity factor f), then summing 

the annuities associated with all expenditures in previous years, one can sum all previous expenditures 

and multiply the sum (i..e., the capital stock at the 

start of the year) by f. The third summary row (labeled “K stock”) near the bottom of the amortization 

summary tables give the pollution control capital stock 

at the start of each year. For example, the stationary 

sources capital stock in place at the start of 1975 was 

$23,533 million (this is the sum of 1972, 1973, and 

1974 capital expenditures). Multiplying the capital 

stock by the annuity factor 0.0802 gives $1,888 million, which is the aggregate annualized stationary 

source capital cost for 1975. 

One can perform further calculations to decompose the annualized capital costs into “interest” and 

“financial depreciation” components.23 For example, 

at the start of 1973, the stationary source capital stock 

was $6,521 million. A five percent interest rate implies an “interest expense” for 1973 of $326 million. 

Given a 1973 annualized cost of $523 million, this 

implies a “depreciation expense” for that year of ($523 

million minus $326 million =) $197 million. For 1974, 

the existing capital stock net of “financial depreciation” was $14,684 million (that is, the $6,521 million 

in place at the start of 1973, plus the investment of 

$8,360 million during 1973, minus the depreciation 

of $197 million during 1973); five percent of $14,684 

million is the interest expense of $734 million. Since 

the annualized capital cost for 1974 is $1,194 million, depreciation expense is $460 million (i.e., the 

difference between annualized cost and the interest 

component of annualized cost). This procedure is repeated to determine interest and depreciation for each 

year through 1990 (see the last three rows of Table A11). 

The three tables above all present costs (and intermediate calculations) assuming a five percent interest rate. As noted above, the Project Team also 

employed rates of three percent and seven percent to 

calculate costs. Those calculations and intermediate 

results are not replicated here. The method employed, 

however, is identical to that employed to derive the 

five percent results (with the only difference being 

the interest rate employed in the annuity factor calculation). Table A-12 presents a summary of expenditures and annualized costs at the three interest rates. 

Table A-12. Compliance Expenditures and 

Annualized Costs, 1973-1990 ($1990 

millions). 

Annu alize d Costs 

Year Expend. at 3% at 5% at 7% 

1973 19,635 10,957 11,042 11,134 

1974 21,405 13,231 13,435 13,655 

1975 24,425 13,314 13,638 13,988 

1976 24,139 14,123 14,611 15,139 

1977 24,062 15,253 15,904 16,608 

1978 22,593 14,963 15,776 16,653 

1979 24,837 17,309 18,282 19,331 

1980 25,741 19,666 20,812 22,046 

1981 24,367 19,590 20,905 22,321 

1982 21,555 18,643 20,125 21,720 

1983 20,148 19,095 20,734 22,498 

1984 21,560 20,133 21,909 23,819 

1985 22,903 22,516 24,447 26,523 

1986 20,831 21,109 23,161 25,364 

1987 20,615 22,072 24,237 26,562 

1988 19,805 22,012 24,281 26,719 

1989 19,817 22,916 25,288 27,836 

1990 19,019 23,598 26,066 28,717 

Year Expend. at 3% at 5% at 7% 

1973 19,635 10,957 11,042 11,134 

1974 21,405 13,231 13,435 13,655 

1975 24,425 13,314 13,638 13,988 

1976 24,139 14,123 14,611 15,139 

1977 24,062 15,253 15,904 16,608 

1978 22,593 14,963 15,776 16,653 

1979 24,837 17,309 18,282 19,331 

1980 25,741 19,666 20,812 22,046 

1981 24,367 19,590 20,905 22,321 

1982 21,555 18,643 20,125 21,720 

1983 20,148 19,095 20,734 22,498 

1984 21,560 20,133 21,909 23,819 

1985 22,903 22,516 24,447 26,523 

1986 20,831 21,109 23,161 25,364 

1987 20,615 22,072 24,237 26,562 

1988 19,805 22,012 24,281 26,719 

1989 19,817 22,916 25,288 27,836 

1990 19,019 23,598 26,066 28,717 

Discounting Costs and Expenditures 

The stream of costs from 1973 to 1990 can be 

expressed as a single cost number by discounting all 

costs to a common year. In this analysis, all costs and 

benefits are discounted to 1990 (in addition, all costs 

and benefits are converted to 1990 dollars, removing 

the effects of price inflation).24 There is a broad range 

22 Similar calculations were performed for mobile source control capital costs, where the assumed amortization period is ten years. 

23 One might, for example, wish to examine the relative importance of the “time value” component of the computed capital costs. 

24 Unlike most cost-benefit analyses, where future expected costs and benefits are discounted back to the present, this exercise 

brings past costs closer to the present. That is, the discounting procedure used here is actually compounding past costs and benefits. 

A-19

3% 5% 7% 

Expendit ures 520,475 621 760,751 

Annualize d C ost s 416,804 522,906 657,003 

Annualize d.at 7% 476,329 

627,

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

of opinion in the economics profession regarding the 

appropriate discount rate to use in analyses such as 

this. Some economists believe that the appropriate rate 

is one that approximates the social rate of time preference — three percent, for example (all rates used 

here are “real”, i.e., net of price inflation impacts). 

Others believe that a rate that approximates the opportunity cost of capital (e.g., seven percent or greater) 

should be used. A third school of thought holds that 

some combination of the social rate of time preference and the opportunity cost of capital is appropriate, with the combination effected either by use of an 

intermediate rate or by use of a multiple-step procedure which uses the social rate of time preference as 

the “discount rate,” but still accounts for the cost of 

capital. The section 812 Project Team chose to use a 

range of discount rates (three, five, and seven percent) for the analysis. 

Expenditures and annualized costs discounted to 

1990 are found on Table A-13. Expenditures are discounted at all three rates; annualized costs are discounted at the rate corresponding to that used in the 

annualization procedure (i.e., the “annualized at 3%” 

cost stream is discounted to 1990 at three percent). 

The final row presents the result of an explicit combination of two rates: Capital costs are annualized at 

seven percent, then the entire cost stream is discounted 

to 1990 at three percent. 

Table A-13. Costs Discounted to 1990 ($1990 

millions). 

3% 5% 7% 

Expendit ures 520,475 627,621 760,751 

Annualize d C ost s 416,804 522,906 657,003 

Annualize d.at 7% 476,329 

Indirect Economic Effects of the CAA 

In addition to imposing direct compliance costs 

on the economy, the CAA induced indirect economic 

effects, primarily by changing the size and composition of consumption and investment flows. Although 

this analysis does not add these indirect effects to the 

direct costs and include them in the comparison to 

benefits, they are important to note. This section summarizes the most important indirect economic effects 

of the CAA, as estimated by the J/W macroeconomic 

simulation. 

GNP and Personal Consumption 

Under the no-control scenario, the level of GNP 

increases by one percent in 1990 relative to the control case (see Table A-14). During the period 1973-

1990, the percent change in real GNP rises monotonically from 0.26 percent to 1.0 percent. The increase 

Table A-14. Differences in Gross 

National Product Between the Control and 

No-control Scenarios. 

No min al % Real % 

Year Chan ge Chan ge 

1973 -0.09 0.26 

1974 -0.18 0.27 

1975 -0.10 0.44 

1976 -0.00 0.49 

1977 -0.10 0.54 

1978 -0.16 0.56 

1979 -0.16 0.63 

1980 -0.14 0.69 

1981 -0.14 0.73 

1982 -0.19 0.74 

1983 -0.19 0.78 

1984 -0.17 0.84 

1985 -0.12 0.95 

1986 -0.14 0.98 

1987 -0.15 1.01 

1988 -0.20 1.00 

1989 -0.21 0.99 

1990 -0.18 1.00 

in the level of GNP is attributable to a rapid accumulation of capital, which is driven by changes in the 

price of investment goods. The capital accumulation 

effect is augmented by a decline in energy prices relative to the base case. Lower energy prices that correspond to a world with no CAA regulations decreases 

costs and increases real household income, thus increasing consumption. 

Removing the pollution control component of new 

capital is equivalent to lowering the marginal price of 

investment goods. Combining this with the windfall 

gain of not having to bring existing capital into compliance leads to an initial surge in the economy’s rate 

of return, raising the level of real investment. The inA-20

3% 5% 7% 

Expendit ures 520 628 761 

Annualize d Cost s 417 523 657 

GNP 880 1005 1151 

Household C onsum ption 500 569 653 

HH and Gov’t Consum ption 676 769 881 

Source: Expendit ures and annualized cost s from above; 

macroeconomi c impacts from Jorgenson et al. (1993), 

Table 4.1 

Appendix A: Cost and Macroeconomic Modeling 

vestment effects are summarized in Figure A-1. More 

rapid (ordinary) capital accumulation leads to a decline in the rental price of capital services which, in 

turn, stimulates the demand for capital services by producers and consumers. The capital rental price reductions also serve to lower the prices of goods and services and, so, the overall price level. Obviously, the 

more capital intensive sectors exhibit larger price reductions.25 The price effects from investment changes 

are compounded by the cost reductions associated with 

releasing resources from the operation and maintenance of pollution control equipment and by the elimination of higher prices due to regulations on mobile 

sources. 

To households, no-control scenario conditions are


manifest as an increase in permanent future real earn­


ings which supports an increase in real consumption


in all periods and, generally, an increase in the de­


mand for leisure (see Table A-15). Households mar­


ginally reduce their offer of labor services as the in-


come effects of


higher real earn­


ings dominate the
 Table A-15. Difference in Personal substitution efConsumption Between the Control fects of lower 

and No-Control Scenarios. goods prices. 

The increase in 

No min al % Real % consumption is 

Year Chan ge Chan ge dampened by an 

increase in the 

1973 -0.02 0.33 rate of return that 

1974 

1975 

-0.01 

-0.10 

0.43 

0.24 produces greater 

19

1976 -0.10 0.39 investment (and 

1977 -0.10 0.54 personal sav1978 -0.09 0.63 ings). 

1979 -0.11 0.68 

1980 -0.12 0.71 Finally, tech1981 

1982 

-0.13 

-0.12 

0.74 

0.81 nical change is a 

very important 83 -0.13 0.85

19

1985 -0.19 0.88 ply-side adjust1986 -0.19 0.94 ments under the 

1987 -0.19 0.98 no-control sce1988 -0.17 1.03 nario. Lower fac1989 -0.17 1.04 tor prices in1990 -0.18 1.01 crease the endog84 -0.15 0.86 aspect of the sup­


enous rates of 

technical change in those industries that are factorusing. Lower rental prices for capital benefit the capital-using sectors, lower materials prices benefit the 

materials-using sectors, and lower energy prices benefit the energy-using sectors. On balance, a significant portion of the increase in economic growth is 

attributable to accelerated productivity growth. Under the no-control scenario, economic growth averages 0.05 percentage points higher over the interval 

1973-1990. The increased availability of capital accounts for 60 percent of this increase while faster productivity growth accounts for the remaining 40 percent. Thus, the principal effect arising from the costs 

associated with CAA initiatives is to slow the 

economy’s rates of capital accumulation and productivity growth. This finding is consistent with recent 

analyses suggesting a potential association between 

higher reported air, water, and solid waste pollution 

abatement costs and lower plant-level productivity in 

some manufacturing industries (Gray and Shadbegian, 

1993 and 1995). 

As with the cost and expenditure data presented 

above, it is possible to present the stream of GNP and 

consumption changes as single values by discounting 

the streams to a single year. Table A-16 summarizes 

the results of the discounting procedure, and also includes discounted expenditure and annualized cost 

data for reference. Accumulated (and discounted to 

1990) losses to GNP over the 1973-1990 period were 

half again as large as expenditures during the same 

period, and approximately twice as large as annualized costs. Losses in household consumption were 

approximately as great as annualized costs. 

Table A-16. GNP and Consumption Impacts 

Discounted to 1990 ($1990 billions). 3% 5% 7% 

Expenditures 520 628 761 

Annualized Costs 417 523 657 

GNP 880 1005 1151 

Household Consumption 500 569 653 

HH and Gov’t Consumption 676 769 881 

Source: Expenditures and annualized costs from above; 

macroeconomic impacts from Jorgenson et al. (1993), 

Table 4.1 

25 Not surprisingly, at the industry level, the principal beneficiaries in the long run of eliminating the costs associated with air 

pollution abatement are the most heavily regulated industries. The largest changes in industry prices and outputs occur in the motor 

vehicles industry. Other industries that benefit significantly from the elimination of environmental controls are refined petroleum 

products, electric utilities, and other transportation equipment. Turning to manufacturing industries, metal mining and the primary 

metals have the largest gains in output from elimination of air pollution controls. 

A-21

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

0.0% 

0.5% 

1.0% 

1.5% 

2.0% 

2.5% 

Year 

1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

Percent 

Figure A-1. Percent Difference in Real Investment Between Control and No-control Scenarios. 

Figure A-2. Percent Difference in Price of Output by Sector Between Control and No-control 

Scenario for 1990. Percent -4% 

-3% 

-2% 

-1% 

0% 

1% 

Se ctor 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 

A-22

Year Coal 

Refined 

Petroleum 

Elect ric 

Ut ilit ies 

Gas 

Ut ilit ies 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

-0.44 

-0.47 

-0.42 

-0.57 

-0.74 

-0.86 

-0.91 

-0.94 

-0.97 

-0.98 

-1.09 

-1.12 

-1.21 

-1.27 

-1.31 

-1.30 

-1.31 

-1.30 

-5.99 

-4.84 

-4.28 

-3.83 

-3.43 

-3.28 

-2.92 

-2.76 

-2.50 

-2.42 

-2.35 

-2.26 

-2.89 

-3.35 

-3.50 

-3.61 

-3.45 

-3.03 

-2.11 

-2.53 

-2.19 

-2.12 

-2.22 

-2.39 

-2.81 

-2.97 

-2.76 

-2.63 

-2.58 

-2.49 

-2.62 

-2.69 

-2.78 

-2.75 

-2.74 

-2.75 

-0.32 

-0.44 

-0.31 

-0.44 

-0.59 

-0.68 

-0.71 

-0.69 

-0.71 

-0.77 

-0.85 

-0.91 

-0.97 

-1.12 

-1.18 

-1.19 

-1.19 

-1.20 

Appendix A: Cost and Macroeconomic Modeling 

Although they have value as descriptors of the 

magnitude of changes in economic activity, neither 

GNP nor consumption changes are perfect measures 

of changes in social welfare. A better measure is 

Equivalent Variations (EVs), which measure the 

change in income that is equivalent to the change in 

(lifetime) welfare due to removal of the CAA. As part 

of its macroeconomic exercise, EPA measured the EVs 

associated with removal of the CAA. Elimination of 

CAA compliance costs (disregarding benefits) represents a welfare gain of $493 billion to $621 billion, 

depending on assumptions used in the analysis.26 This 

result does not differ greatly from the range of results 

represented by expenditures, anualized costs, and consumption changes. 

Prices 

One principal consequence of the Clean Air Act 

is that it changes prices. The largest price reductions 

accrue to the most heavily regulated industries which 

are the large energy producers and consumers (see 

Table A-17). But these are also the most capital intensive sectors and it is the investment effects that are 

the dominant influences in altering the course of the 

economy. Focusing on energy prices, under the 

no-control scenario the price of coal in 1990 declines 

by 1.3 percent, refined petroleum declines by 3.03 

Table A-17. Percentage Difference in Energy Prices 

Between the Control and No-control Scenarios. 

Year Coal 

Refined 

Petroleum 

Elect ric 

Ut ilit ies 

Gas 

Ut ilit ies 

1973 

1974 

1975 

1976 

1977 

1978 

1979 

1980 

1981 

1982 

1983 

1984 

1985 

1986 

1987 

1988 

1989 

1990 

-0.44 

-0.47 

-0.42 

-0.57 

-0.74 

-0.86 

-0.91 

-0.94 

-0.97 

-0.98 

-1.09 

-1.12 

-1.21 

-1.27 

-1.31 

-1.30 

-1.31 

-1.30 

-5.99 

-4.84 

-4.28 

-3.83 

-3.43 

-3.28 

-2.92 

-2.76 

-2.50 

-2.42 

-2.35 

-2.26 

-2.89 

-3.35 

-3.50 

-3.61 

-3.45 

-3.03 

-2.11 

-2.53 

-2.19 

-2.12 

-2.22 

-2.39 

-2.81 

-2.97 

-2.76 

-2.63 

-2.58 

-2.49 

-2.62 

-2.69 

-2.78 

-2.75 

-2.74 

-2.75 

-0.32 

-0.44 

-0.31 

-0.44 

-0.59 

-0.68 

-0.71 

-0.69 

-0.71 

-0.77 

-0.85 

-0.91 

-0.97 

-1.12 

-1.18 

-1.19 

-1.19 

-1.20 

percent, electricity from electric utilities declines by 

2.75 percent, and the price of natural gas from gas 

utilities declines by 1.2 percent. The declining price 

of fossil fuels induces substitution toward fossil fuel 

energy sources and toward energy in general. Total 

Btu consumption also increases. 

Sectoral Effects: Changes in Prices and 

Output by Industry 

At the commodity level, the effect of the CAA 

varies considerably. Figure A-2 shows the changes in 

the supply price of the 35 commodities measured as 

changes between the no-control case and the controlcase for 1990. 

In 1990, the largest change occurs in the price of 

motor vehicles (commodity 24), which declines by 

3.8 percent in the no-control case. Other prices showing significant effects are those for refined petroleum 

products (commodity 16) which declines by 3.0 percent, and electricity (commodity 30) which declines 

2.7 percent. Eight of the remaining industries have 

decreases in prices of 1.0 to 1.4 percent under the 

no-control scenario. The rest are largely unaffected 

by environmental regulations, exhibiting price decreases between 0.3 and 0.8 percent. 

To assess the intertemporal consequences of the 

CAA, consider the model’s dynamic results and the 

adjustment of prices between 1975 and 1990. Initially, 

in 1975, the biggest effect is on the price of output 

from petroleum refining (sector 16), which declines 

by 4.3 percent. But by 1990, the price of petroleum 

refining is about 3.0 percent below control scenario 

levels. In contrast, the price of motor vehicles (sector 

24) is about 2.4 percent below baseline levels in 1975, 

but falls to about 3.8 percent below baseline levels in 

1990. 

The price changes affect commodity demands, 

which in turn determine how industry outputs are affected. Figure A-3 shows percentage changes in quantities produced by the 35 industries for 1990. As noted 

earlier, the principal beneficiaries under the no-control 

scenario are the most heavily regulated industries: 

motor vehicles, petroleum refining, and electric utilities. 

In 1990, the motor vehicle sector (sector 24) shows 

the largest change in output, partly due to the fact that 

the demand for motor vehicles is price elastic. Recall 

26 Jorgenson et al., 1993. 

A-23

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

-2% 

-1% 

0% 

1% 

2% 

3% 

4% 

5% 

6% 

Se cto r 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 

Percent 

Figure A-3. Percent Difference in Quantity of Output by Sector Between Control and Nocontrol Scenario for 1990. 

Figure A-4. Percent Difference in Employment by Sector Between Control and No-control 

Scenario for 1990. Percent -2.0% 

-1.0% 

0.0% 

1.0% 

2.0% 

Sector 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 

A-24

Appendix A: Cost and Macroeconomic Modeling 

that the largest increase in prices also occurred in the 

motor vehicles sector. The 3.8 percent reduction in 

prices produces an increase in output of 5.3 percent 

relative to the base case. 

Significant output effects are also seen in the petroleum refining sector (sector 16) with a 3.2 percent 

increase, in electricity (sector 30) with a 3.0 percent 

increase, and in other transportation equipment (sector 25) with a 1.6 percent increase. The large gains in 

output for these industries are mostly due to the decline in their prices. In manufacturing, the sectors 

exhibiting the most significant output effects are metal 

mining (sector 2) with a 2.0 percent increase, and primary metals (sector 20) with a 1.8 percent increase. 

Twenty of the remaining industries exhibit increase 

in output of less than 0.9 percent after pollution controls are removed. 

While most sectors increase output under the 

no-control scenario, a few sectors decline in size in 

the absence of air pollution controls. The most notable of these are food and kindred products (sector 

7) which decline by 0.5 percent, furniture and fixtures 

(sector 12) which decline by 0.6 percent, and rubber 

and plastic products (sector 17) which decline by 0.3 

percent. These sectors are among the least capital intensive, so the fall in the rental price of capital services has little effect on the prices of outputs. Buyers 

of the commodities produced by these industries face 

higher relative prices and substitute other commodities in both intermediate and final demand. The rest 

of the sectors are largely unaffected by environmental regulations. 

Changes in Employment Across 

Industries 

The effect of the CAA on employment presents a 

much more complicated picture. Although JorgensonWilcoxen is a full-employment model and cannot be 

used to simulate unemployment effects, it is useful 

for gaining insights about changes in the patterns of 

employment across industries. Percentage changes in 

employment by sector for 1990 are presented in Figure A-4. 

For 1990, the most significant changes in the level 

of employment relative to the control scenario occur 

in motor vehicles (sector 24) which increases 1.2 percent, other transportation equipment (sector 25) which 

increases 0.8 percent, electric utilities (sector 30) 

which increases 0.7 percent, and primary metals (sector 20) which increases 0.6 percent. The level of employment is higher relative to the control case in 10 

other industries. 

For a few sectors, the no-control scenario results 

in changes in real wages which cause reductions in 

employment. The most notable reductions in employment under the no-control scenario occur in tobacco 

manufacturing (sector 8) which declines 1.2 percent, 

furniture and fixtures (sector 12) which declines 0.8 

percent, rubber and plastic products (sector 17) which 

declines 0.8 percent, food and kindred products (sector 7) which declines 0.7 percent, stone, clay and glass 

products (sector 19) which declines 0.6 percent, and 

instruments (sector 26) which declines 0.6 percent. 

These sectors are generally those in which the level 

of output was lower in 1990 relative to the control 

scenario, since they are among the least capital intensive and the fall in the rental price of capital services 

has little effect on the prices of outputs. Buyers of the 

commodities produced by these industries face higher 

relative prices and substitute other commodities in 

both intermediate and final demand. It is interesting 

to note that several of the least capital intensive sectors experience insignificant employment effects in 

the short run (1975) under the no-control scenario, 

but increasingly adverse effects over the 20-year period of analysis. These include food and kindred products, furniture and fixtures, rubber and plastic products, stone, clay and glass products, and instruments. 

Examination of the transition of employment in 

the economy from the initial equilibrium to 1990 reveals that the employment effects of the CAA on motor 

vehicles, transportation equipment, electric utilities, 

and primary metals persist over the entire period of 

analysis. Employment varies from: an increase of 1.7 

percent in 1975 to 1.2 percent in 1990 in motor vehicles; an increase of 0.7 in 1975 to 0.8 percent in 

1990 in transportation equipment; an increase of 1.2 

percent in 1975 to 0.7 percent in 1990 in electric utilities; and an increase of 0.8 percent in 1975 to 0.6 percent in 1990. 

A-25

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Uncertainties in the Cost 

Analysis 

Potential Sources of Error in the Cost 

Data 

Because of the importance of the Cost of Clean 

data for this assessment, the project team investigated 

potential sources of error due to the use of industry’s 

self-reported costs of compliance with air pollution 

abatement requirements. Concerns about the accuracy 

of responses include (1) misreporting by firms in response to federal agency surveys, and (2) omission of 

important categories of compliance cost from the data 

collected or reported by these federal agencies.27 Table 

A-18 contains a summary of the results of the analysis. This analysis is consistent with the findings of 

two recent studies comparing combined air, water, and 

solid waste pollution abatement costs, as reported in 

federal abatement cost surveys, to their observed effects on productivity levels. These studies suggest that, 

since observed productivity decreases exceed those 

expected to result from the reported abatement costs, 

there may be additional pollution abatement costs not 

captured or reported in the survey data, and that total 

abatement costs for the three manufacturing industries studied may be under-reported by as much as a 

factor of two in the most extreme case (Gray and 

Shadbegian, 1993 and 1995; Gray, 1996). 

The major finding from this analysis indicates that 

total O&M costs are likely to be under-reported due 

to exclusion of private research and development 

Table A-18. Potential Sources of Error and Their Effect on Total Costs of Compliance. 

S ource of Error Effect on Capital Costs Effect on O&M Costs 

Lack of Data at Fir m Level Un der-reported 

Percent Unk nown 

Un der-reported 

Percent Unk nown 

M isallocation of Costs: 

Inclusion o f OSH A and Other 

Re gulatory Costs 

Exclusion of Solid Waste Disposal Costs 

Related to Air Pollution Ab atement 

Ov er -rep orted 

Percent Unk nown 

— 

Ov er -rep orted 

Percent Unk nown 

Un der-reported 

Percent Unk nown 

Exclusion of Costs: 

Exclusion of Private R&D Exp enses 

Exclusion of Energy Use by Po llution 

Ab atement Devices

(a) 

Exclusion of Dep reciation Expenses

(a) 

Exclusion of Recovered Co sts 

— 

— 

— 

— 

Un der-reported by 14 to 17% 

(varies by year) 

Un der-reported by 1 to 3% 

(varies by year) 

Un der-reported by 1 to 2% 

(varies by year) 

Ov er -rep orted by 1% Plu s 

Omission of Small Firms Un der-reported by 1 to 2% Un der-reported by 1 to 2% 

NET EFFECT Un der-reported Un der-reported 

(a ) Energy outlays are part of the data on O&M costs and depreciation expenses are not. Accordingly, in the J/W model, energy outlays are 

considered along with other operating expenditures in terms of their impacts on unit costs. Depreciation is represented fully in the capital 

accumulation process, as the undepreciated capitalstock at the beginning of any period gives rise to the flow of capital services available to 

producers and consumers. 

Source: Industrial Economics, Incorporated, memorandum to Jim DeMocker, EPA/OAR, "Sources of Error in 

Reported Costs of Compliance with Air Pollution Abatement Requirements," October 16, 1991. 

27 Memorandum from Industrial Economics, Incorporated to Jim DeMocker (EPA/OAR) dated 10/16/91 and entitled “Sources 

of Error in Reported Costs of Compliance with Air Pollution Abatement Requirements.” 

A-26

O&M div ided by: 

K stock Net K O&M K stock Net K 

1973 521 6,521 3,936 0.60 0.60 

1974 14,880 14,684 4,778 0.32 0.33 

1975 23,533 22,876 5,154 0.22 0.23 

1976 32,773 31,372 5,768 0.18 0.18 

1977 41,331 38,869 6,527 0.16 0.17 

1978 49,448 45,612 6,991 0.14 0.15 

1979 57,299 51,776 7,959 0.14 0.15 

1980 65,763 58,232 8,791 0.13 0.15 

1981 74,366 64,469 8,785 0.12 0.14 

1982 82,381 69,740 7,855 0.10 0.11 

1983 89,937 74,173 8,168 0.09 0.11 

1984 95,879 76,606 8,505 0.09 0.11 

1985 101,723 78,587 8,617 0.08 0.11 

1986 107,082 79,713 8,477 0.08 0.11 

1987 112,225 80,249 8,602 0.08 0.11 

1988 117,269 80,300 8,143 0.07 0.10 

1989 122,182 79,819 8,259 0.07 0.10 

1990 127,394 79,217 8,842 0.07 0.11 

“K stock” is the accumulated undepreciated stationary 

source control cap ital stock available at the beginning of 

each y ear, from Table A-10. 

“Net K” is the stationary source control capital stock less 

dep reciation implied by amortization at 5%; from Table 

A-10. 

“O&M” is the stationary source control O&M 

expenditures; from Table A-9. 

The fin al two columns are ratios: O&M divided by cap ital 

stock; and O&M divided by net capital. 

6,

Appendix A: Cost and Macroeconomic Modeling 

(R&D) expenditures. Note, however, that although 

these costs were excluded from those used for the 

macroeconomic modeling, they were included in the 

overall direct cost estimate of the CAA; see “Other 

Direct Costs,” above. These costs are excluded from 

the macromodeling because they cannot be disaggregated by industry and, more importantly, because there 

is no information on what was purchased or obtained 

as a result of these expenditures. 

Based on the need indicated by the IEc review, 

modifications to the BEA data were made to remedy 

some of the biases noted above. In particular, recovered costs for stationary source air pollution, e.g. sulfur removed using scrubbers that is then sold in the 

chemical market, have been accounted for in the data 

set used in the model runs. 

Table A-19. Stationary Source O&M 

Expenditures as a Percentage of Capital Stock 

(millions of 1990 dollars). 

“K stock” is the accumulated undepreciated stationary

source control capital stock available at the beginning of

each year, from Table A-10.

“Net K” is the stationary source control capital stock less

depreciation implied by amortization at 5%; from Table

A-10.

“O&M” is the stationary source control O&M

expenditures; from Table A-9.

The final two columns are ratios: O&M divided by capital

stock; and O&M divided by net capital.

K stock Net K O&M 

1973 6,521 6,521 3,936 

1974 14,880 14,684 4,778 

1975 23,533 22,876 5,154 

1976 32,773 31,372 5,768 

1977 41,331 38,869 6,527 

1978 49,448 45,612 6,991 

1979 57,299 51,776 7,959 

1980 65,763 58,232 8,791 

1981 74,366 64,469 8,785 

1982 82,381 69,740 7,855 

1983 89,937 74,173 8,168 

1984 95,879 76,606 8,505 

1985 101,723 78,587 8,617 

1986 107,082 79,713 8,477 

1987 112,225 80,249 8,602 

1988 117,269 80,300 8,143 

1989 122,182 79,819 8,259 

1990 127,394 79,217 8,842 

O&M div ided by: 

K stock Net K 

0.60 0.60 

0.32 0.33 

0.22 0.23 

0.18 0.18 

0.16 0.17 

0.14 0.15 

0.14 0.15 

0.13 0.15 

0.12 0.14 

0.10 0.11 

0.09 0.11 

0.09 0.11 

0.08 0.11 

0.08 0.11 

0.08 0.11 

0.07 0.10 

0.07 0.10 

0.07 0.11 

An additional set of concerns relates directly to 

reporting of costs by firms. Some have noted an unexpected temporal pattern of stationary source control expenditures in the BEA data that might lead one 

to question the accuracy of the Census survey responses. One would expect that stationary source 

O&M expenditures over time would be roughly proportional to the accumulated stationary source control capital stock. Yet, as illustrated in Table A-19, 

O&M expenditures as a fraction of accumulated capital stock decline over time (even if one discounts the 

first few years because of the dramatic percentage 

increases in capital stock during those years). It is true 

that the ratio of O&M expenditures to the depreciated capital stock (in the far right column, labeled “net 

K”) is reasonably stable after 1981. The depreciation 

shown here, however, is a financial depreciation only, 

depicting the declining value of a piece of equipment 

over time, rather than a measure of physical asset 

shrinkage. Assuming a twenty-year useful lifetime, 

all of the stationary source control capital stock put in 

place since 1972 could conceivably still be in place in 

1990. If anything, one would expect the O&M/K ratio to increase as the capital depreciates (i.e., ages), 

until the equipment is scrapped, because aging equipment requires increasing maintenance. Consequently, 

one might infer from this information that firms have 

systematically under-reported O&M expenditures, or 

have over-reported capital expenditures. 

The apparent anomaly might be explained by an 

examination of the types of O&M expenditures reported. If more than a token percentage of O&M expenditures are unrelated to “operation and maintenance” of pollution control devices, then the observed 

O&M/K ratio would not appear unusual. 

The Census PACE survey28 required respondents 

to report air pollution abatement O&M expenses in 

the following categories: salaries and wages; fuel and 

electricity; contract work; and materials, leasing, and 

“miscellaneous.”29 In later versions of the survey, 

additional information relating to the types of expenses 

to report was provided as a guide to respondents. The 

types of expenses listed that are relevant to air pollution abatement include: 

28 Pollution Abatement Costs and Expenditures, various years. 

29 Census also requested a reporting of “depreciation” expenses as a component of O&M. BEA, however, removed depreciation 

expense from the reported O&M costs because retaining depreciation would have amounted to double-counting, since BEA also 

reported capital expenditures. 

A-27

Year capi tal O&M capi tal O&M Expend. 

EP A Estimates 

1986 4,090 116 312 140 11,658 

1987 4,179 469 277 130 12,055 

1988 4,267 313 243 161 11,984 

1989 4,760 743 235 173 12,911 

1990 4,169 688 226 154 13,237 

BEA Est imates 

1986 4,090 072 312 182 11,656 

1987 3,482 843 246 141 9,712 

1988 3,120 230 121 161 9,632 

1989 3,266 292 229 152 9,939 

1990 4,102 6,799 200 154 11,255 

7,

7,

7,

7,

8,

7,

5,

6,

6,

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

(1) operating and maintaining pollution abatement equipment; 

(2) fuel and power costs for operating pollution 

abatement equipment; 

(3) parts for pollution abatement equipment replacement and repair; 

(4) testing and monitoring of emissions; 

(5) incremental costs for consumption of environmentally preferable materials and fuels; 

(6) conducting environmental studies for development or expansion; 

(7) leasing of pollution abatement equipment; 

(8) compliance and environmental auditing; 

(9) salaries and wages for time spent completing 

environmental reporting requirements; and 

(10) developing pollution abatement operating 

procedures.30 

The magnitude of the expenditures associated with 

the first three items should be correlated with the size 

of the existing stock of air pollution abatement capital. Expenditures associated with items four through 

ten, however, should be independent of the size of the 

existing capital stock (expenditures associated with 

item seven, leasing of pollution abatement equipment, 

could be negatively correlated with the size of the 

capital stock). If items four through ten account for a 

non-negligible proportion of total O&M expenditures, 

and if respondents included these cost categories even 

though they were not explicitly listed in the survey 

instructions before 1991, then one would expect to 

see the O&M/K ratio declining during the study period. Thus, even though it is possible that O&M expenditures are underreported (or that capital expenditures are overreported), one cannot be certain. 

Mobile Source Costs 

For the section 812 analysis, EPA used the best 

available information on the estimated cost of mobile 

source air pollution control. Several other sources of 

cost estimates exist, however, including a cost series 

produced by the Department of Commerce Bureau of 

Economic Analysis (BEA). The BEA cost series is 

summarized in Table A-20. The BEA estimates differ significantly from EPA estimates, particularly with 

respect to estimates of capital costs and the “fuel price 

penalty” associated with the use of unleaded gasoline. 

EPA’s capital cost estimates are based on estimates of the cost of equipment required by mobile 

Table A-20. Comparison of EPA and BEA Stationary 

Source Expenditure Estimates (millions of current 

dollars). 

“Recovered Costs” are not included in this table.

Sources for “BEA Estimates”: for 1986, “Pollution Abatement and Control

Expenditures,” Survey of Current Business (BEA) June 1989, Table 7; for

1987-90, BEA May 1995, Table 8.

source regulations. BEA’s estimates are based on survey data from the Bureau of Labor Statistics (BLS) 

that measures the increase in the per-automobile cost 

(relative to the previous model year) due to pollution 

control and fuel economy changes for that model year. 

The difference in approach is significant: BEA’s annual capital cost estimates exceed EPA’s by a factor 

of (roughly) two. EPA may underestimate costs to the 

extent that engineering cost estimates of components 

exclude design and development costs for those components. The BLS estimates add the incremental annual costs to all past costs to derive total current-year 

costs. Such an approach overestimates costs to the 

extent that it fails to account for cost savings due to 

changes in component mixes over time. 

Some mobile source pollution control devices required the use of unleaded fuel. Unleaded gasoline is 

more costly to produce than is leaded gasoline, and 

generally has a greater retail price, thus imposing a 

cost on consumers. EPA estimated the “fuel price penalty” by using a petroleum refinery cost model to determine the expected difference in production cost between leaded and unleaded gasoline. BEA’s “fuel price 

penalty” was the difference between the retail price 

of unleaded gasoline and that of leaded gasoline. 

A detailed description of the data sources, analytic methods, and assumptions that underlie the EPA 

and BEA mobile source cost estimates can be found 

in McConnell et al. (1995). 

Pri vate se ctor Gov’t. Enterpris e Total 

Year capi tal O&M capi tal O&M Expend. 

EP A Estimates 

1986 4,090 7,116 312 140 11,658 

1987 4,179 7,469 277 130 12,055 

1988 4,267 7,313 243 161 11,984 

1989 4,760 7,743 235 173 12,911 

1990 4,169 8,688 226 154 13,237 

BEA Est imates 

1986 4,090 7,072 312 182 11,656 

1987 3,482 5,843 246 141 9,712 

1988 3,120 6,230 121 161 9,632 

1989 3,266 6,292 229 152 9,939 

1990 4,102 6,799 200 154 11,255 

30 Pollution Abatement Costs and Expenditures, 1992, pg. A-9. 

A-28

Appendix A: Cost and Macroeconomic Modeling 

Stationary Source Cost Estimate 

Revisions 

As noted above, the costs used for stationary 

sources in the macro-modeling (and retained in this 

cost analysis) were projected for several years in the 

late 1980s. Since that time, BEA has released historical expenditure estimates for those years based on 

survey data. A comparison of the expenditure series 

can be found in Table A-21. Apparently, EPA’s projections overestimated stationary source compliance 

expenditures by approximately $2 billion per year for 

the period 1987-1990. Since expenditures from all 

sources are estimated to be $18 billion -$19 billion 

(current dollars) per year during 1987-1990, this implies that EPA has overestimated compliance expenditures by more than ten percent during this period. 

Although a substantial overstatement for those years, 

the $2 billion per year overestimate would have little 

impact (probably less than two percent) on the discounted present value, in 1990 dollars, of the 1973-

1990 expenditure stream. 

Table A-21. BEA Estimates of Mobile Source Costs. 

Year 

Capital 

Exp. 

Net 

I& M* 

Fu el Price 

Penalty 

Fuel Econom y 

Penalty 

1973 013 1,104 697 

1974 118 1,380 5 1,180 

1975 131 1,520 97 1,344 

1976 802 1,420 309 1,363 

1977 371 1,289 701 1,408 

1978 935 1,136 1,209 1,397 

1979 4,634 1 636 1,792 

1980 5,563 6 217 2,320 

1981 7,529 2 996 2,252 

1982 7,663 9 518 1,876 

1983 9,526 4 235 1,582 

1984 ,900 118 4,427 1,370 

1985 ,210 165 4,995 1,133 

1986 ,368 (331) 4,522 895 

1987 ,725 (453) 3,672 658 

1988 ,157 (631) 3,736 420 

1989 ,340 (271) 1,972 183 

1990 14,521 (719) 1,370 (55) 

Year Exp. I& M* Penalty Penalty 

1973 013 1,104 697 

1974 118 1,380 5 1,180 

1975 131 1,520 97 1,344 

1976 802 1,420 309 1,363 

1977 371 1,289 701 1,408 

1978 935 1,136 1,209 1,397 

1979 4,634 1 636 1,792 

1980 5,563 6 217 2,320 

1981 7,529 2 996 2,252 

1982 7,663 9 518 1,876 

1983 9,526 4 235 1,582 

1984 ,900 118 4,427 1,370 

1985 ,210 165 4,995 1,133 

1986 ,368 (331) 4,522 895 

1987 ,725 (453) 3,672 658 

1988 ,157 (631) 3,736 420 

1989 ,340 (271) 1,972 183 

1990 14,521 (719) 1,370 (55) 

1,

1,

2,

2,

3,

3,

93 1,

72 2,

55 2,

40 3,

27 4,

11

13

14

13

16

15

1,

1,

2,

2,

3,

3,

93 1,

72 2,

55 2,

40 3,

27 4,

11

13

14

13

16

15

Endogenous Productivity Growth in the 

Macro Model 

For each industry in the simulation, the JW model 

separates price-induced changes in factor use from 

changes resulting strictly from technical change. Thus, 

simulated productivity growth for each industry has 

two components: (a) an exogenous component that 

varies over time, and (b) an endogenous component 

that varies with policy changes. Some reviewers have 

noted that, although not incorrect, use of endogenous 

productivity growth is uncommon in the economic 

growth literature. EPA conducted a sensitivity run of 

the J/W model, setting endogenous growth parameters 

to zero (i.e., removing endogenous productivity 

growth from the model).31 

Endogenous productivity growth is an important 

factor in the J/W model. For example, for the period 

1973-1990, removal of the endogenous productivity 

growth assumptions reduces household income by 2.9 

to 3.0 percent (depending on whether one uses a world 

with CAA or one without CAA as the baseline). In 

comparison, removal of CAA compliance costs results in a 0.6 to 0.7 percent change in household income (depending on whether one uses, as a baseline, 

a world with or one without endgenous productivity 

growth). That is, use of the endogenous productivity 

growth assumption has four to five times the impact 

of that of CAA compliance costs. 

Although very important to the simulated growth 

of the economy within any policy setting, the endogenous productivity growth assumption is less important across policy settings. Under the base (i.e., “with 

endogenous productivity growth”) scenario, the aggregate welfare effect (measured as EVs, see above) 

of CAA compliance costs and indirect effects is estimated to be 493 billion to 621 billion in 1990 dollars. 

If one removes the endogenous productivity growth 

assumption, the aggregate welfare effect declines to 

the range 391 billion to 494 billion in 1990 dollars 

(Jorgenson et al., 1993, pg. 6-15), a reduction of about 

twenty percent. 

* Inspection and maintenance costs less fuel density savings and 

maintenance savings. 

31 For greater detail, see Jorgenson et al., 1993. 

A-29

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Amortization Period for Stationary 

Source Plant and Equipment 

In developing annualized costs, stationary source 

capital expenditues were amortized over a twenty-year 

period. That is, it was assumed that plant and equipment would depreciate over twenty years. It is possible that stationary source plant and equipment has, 

on average, a useful lifetime significantly greater than 

twenty years. The Project Team tested the sensitivity 

of the cost analysis results to changes in stationary 

source capital amortization periods. 

Table A-22 presents total annualized compliance 

costs assuming a 40-year amortization period for stationary source capital expenditures (all other cost components are unchanged from the base analysis). All 

costs are in 1990-value dollars, ad three alternative 

discount rates are used in the annualization period. 

Table A-23 presents the results discounted to 1990, 

and compared to the base case results (i.e., using a 

twenty-year amortization period). Doubling the amortization period to 40 years decreases the 1990 present 

value of the 1973-1990 cost stream by approximately 

40 billion dollars. This represents a change of six percent to nine percent, depending on the discount rate 

employed. 

Table A-22. Annualized Costs 

Assuming 40-Year Stationary Source 

Capital Amortization Period, 1973-

1990 (millions of 1990 dollars). 

Annu alize d Costs 

Year at 3% at 5% at 7% 

1973 10,801 10,899 11,008 

1974 12,875 13,108 13,366 

1975 12,751 13,121 13,532 

1976 13,338 13,891 14,504 

1977 14,263 14,996 15,807 

1978 13,778 14,690 15,695 

1979 15,936 17,024 18,220 

1980 18,091 19,368 20,771 

1981 17,809 19,272 20,880 

1982 16,670 18,316 20,123 

1983 16,941 18,759 20,754 

1984 17,836 19,803 21,960 

1985 20,079 22,213 24,551 

1986 18,544 20,809 23,288 

1987 19,384 21,772 24,387 

1988 19,203 21,706 24,446 

1989 19,989 22,604 25,467 

1990 20,546 23,268 26,247 

Year at 3% at 5% at 7% 

1973 10,801 10,899 11,008 

1974 12,875 13,108 13,366 

1975 12,751 13,121 13,532 

1976 13,338 13,891 14,504 

1977 14,263 14,996 15,807 

1978 13,778 14,690 15,695 

1979 15,936 17,024 18,220 

1980 18,091 19,368 20,771 

1981 17,809 19,272 20,880 

1982 16,670 18,316 20,123 

1983 16,941 18,759 20,754 

1984 17,836 19,803 21,960 

1985 20,079 22,213 24,551 

1986 18,544 20,809 23,288 

1987 19,384 21,772 24,387 

1988 19,203 21,706 24,446 

1989 19,989 22,604 25,467 

1990 20,546 23,268 26,247 

Table A-23. Effect of Amortization 

Periods on Annualized Costs Discounted 

Di scount rate 

3% 5% 7% 

20-yr am ortizat ion period 417 523 657 

40-yr am ortizat ion period 379 483 617 

Di scount rate 

3% 5% 7% 

20-yr am ortizat ion period 417 523 657 

40-yr am ortizat ion period 379 483 617 

to 1990 (billions of 1990 dollars). 

A-30

Appendix A: Cost and Macroeconomic Modeling 

Cost and Macroeconomic 

Modeling References 

Chase Econometrics Associates, Inc. 1976. “The 

Macroeconomic Impacts of Federal Pollution 

Control Programs: 1976 Assessment.” Report 

prepared for the Council on Environmental 

Quality and the Environmental Protection 

Agency. 

Congressional Budget Office. 1990. Carbon Charges 

as a Response to Global Warming: The Effects of Taxing Fossil Fuels. Washington, DC, 

U.S. Government Printing Office. 

Data Resources, Inc. 1979. “The Macroeconomic 

Impacts of Federal Pollution Control Programs: 1978 Assessment,” Report prepared 

for the Environmental Protection Agency and 

the Council on Environmental Quality. 

Data Resources, Inc. 1981. “The Macroeconomic 

Impact of Federal Pollution Control Programs: 1981 Assessment,” Report prepared 

for the Environmental Protection Agency. 

July 17. 

Economic Report of the President. 1995. U.S. Government Printing Office, Washington, DC. 

February. 

Farber, Kit D. and G. Rutledge. 1989. “Pollution 

Abatement and Control Expenditures: Methods and Sources for Current-Dollar Estimates.” Unpublished Paper for U.S. Department of Commerce, Bureau of Economic 

Analysis. October. 

Freeman, A.M. 1978. “Air and Water Pollution 

Policy,” in P.R. Portney (ed.), Current Issues 

in U.S. Environmental Policy. Johns Hopkins 

University Press, Baltimore. 

Gray, Wayne B. 1996. Personal communication with 

Michael Hester of Industrial Economics, Inc. 

December 4. 

Gray, Wayne B. and Ronald J. Shadbegian. 1993. 

“Environmental Regulation and Manufacturing Productivity at the Plant Level,” Center 

for Economic Studies Discussion Paper, CES 

93-6. March. 

Gray, Wayne B. and Ronald J. Shadbegian. 1995. 

“Pollution Abatement Costs, Regulation, and 

Plant-Level Productivity,” National Bureau of 

Economic Research, Inc., Working Paper 

Series, Working Paper No. 4994. January. 

Hazilla, M. and R.J. Kopp. 1990. “Social Cost of Environmental Quality Regulations: A General 

Equilibrium Analysis,” Journal of Political 

Economy, Vol. 98, No. 4. August. 

Industrial Economics, Incorporated. 1991. “Sources 

of Error in Reported Costs of Compliance 

with Air Pollution Abatement Requirements,” 

memorandum to Jim DeMocker, EPA/OAR. 

October 16. 

Jorgenson, Dale W. and Barbara M. Fraumeni. 1989. 

“The Accumulation of Human and Nonhuman Capital, 1948-1984,” in R.E. Lipsey and 

H.S. Tice, eds., The Measurement of Saving, 

Investment, and Wealth. University of Chicago Press, Chicago, Il. 

Jorgenson, Dale W. and Barbara M. Fraumeni. 1981. 

“Relative Prices and Technical Change,” in 

E. Berndt and B. Field, eds., Modeling and 

Measuring Natural Resource Substitution. 

MIT Press, Cambridge, MA. 

Jorgenson, Dale W., Richard J. Goettle, Daniel 

Gaynor, Peter J. Wilcoxen, and Daniel T. 

Slesnick. 1993. “The Clean Air Act and the 

U.S. Economy,” Final report of Results and 

Findings to the U.S. EPA. August. 

Jorgenson, Dale W. and Peter J. Wilcoxen. 1990a. 

“Environmental Regulation and U.S. Economic Growth,” in RAND Journal of Economics, Vol. 21, No. 2, pp. 314-340. 

Jorgenson, Dale W. and Peter J. Wilcoxen. 1990c. 

“Intertemporal General Equilibrium Modeling of U.S. Environmental Regulation,” in 

Journal of Policy Modeling, Vol. 12, No. 4, 

pp. 715-744. 

Jorgenson, Dale W. and Peter J. Wilcoxen. 1993. “Energy, the Environment and Economic 

Growth,” in Handbook of Natural Resource 

and Energy Economics, Allen V. Kneese and 

James L. Sweeney, eds., Volume 3, Chapter 

27. North-Holland, Amsterdam, forthcoming. 

A-31

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Kokoski, Mary F. and V. Kerry Smith. 1987. “A General Equilibrium Analysis of Partial-Equilibrium Welfare Measures: The Case of Climate 

Change,” American Economic Review, Vol. 

77, No. 3, pp. 331-341. 

McConnell, Virginia, Margaret A. Walls, and Winston Harrington. 1995. “Evaluating the Costs 

of Compliance with Mobile Source Emission 

Control Requirements: Retrospective Analysis,” Resources for the Future Discussion Paper. 

Schwartz, Joel. 1991. “Fuel Economy Benefits.” 

Memorandum to Joe Somers and Jim 

DeMocker. December 12. 

Somers, J.H. 1991. “Fuel Economy Penalties for Section 812 Report.” Memorandum to Anne 

Grambsch and Joel Schwartz. December 23. 

U.S. Department of Commerce. Government Finances, various issues. Bureau of the Census. 

U.S. Department of Commerce. “Pollution Abatement 

and Control Expenditures,” Survey of Current 

Business, various issues. Bureau of Economic 

Analysis. 

U.S. Department of Commerce. “Pollution Abatement 

Costs and Expenditures,” Current Industrial 

Reports, various issues. Bureau of the Census. 

U.S. Environmental Protection Agency (EPA). 1985. 

Costs and Benefits of Reducing Lead in 

Gasolines: Final Regulatory Impact Analysis. Office of Policy Analysis, EPA-230-05-

85-006. February. 

U.S. Environmental Protection Agency (EPA). 1990. 

Environmental Investments: The Cost of a 

Clean Environment, Report to the Congress. 

Office of Policy, Planning and Evaluation. 

EPA-230-12-90-084. December. 

Verleger, Philip K., Jr. 1992. “Clean Air Regulation 

and the L.A. Riots,” The Wall Street Journal, 

Tuesday, May 19. p. A14. 

Walsh, M.P. 1991. “Motor Vehicles and Fuels: The 

Problem.” in EPA Journal, Vol. 17, No. 1, p. 

12. 

Wilcoxen, Peter J. 1988. The Effects of Environmental Regulation and Energy Prices on U.S. 

Economic Performance, Doctoral thesis presented to the Department of Economics at 

Harvard University, Cambridge, MA. December. 

A-32

Appendix B: Emissions Modeling


Introduction 

This appendix provides additional details of the 

methodologies used to estimate control and no-control 

scenario emissions and the results obtained by these 

methods. Methodological information and results are 

provided for each of the six principal emission sectors: industrial combustion, industrial processes, electric utilities, on-highway vehicles, off-highway vehicles, and commercial/residential sources. 

The initial section of this appendix assesses the 

emissions projections presented in this analysis by (1) 

comparing the 1970 to 1990 control scenario projections with recent EPA Trends report estimates for the 

same years and (2) comparing the 1970 to 1990 trend 

in no-control scenario projections with 1950 to 1970 

emissions as reported in Trends. The first comparison indicates that control scenario emissions projections approximate, but do not precisely match, the EPA 

Trends data. The reason for this mismatch is discussed 

below. The second comparison is useful for demonstrating that pre-1970 emissions trends would not provide a satisfactory basis for extrapolating emissions 

trends into the 1970 to 1990 period. The inability to 

simply extrapolate pre-1970 trends provides further 

justification for applying the present modeling methodologies to generate no-control scenario emissions 

projections. 

The remainder of the appendix provides further 

details of the emissions modeling conducted in support of the present analysis, and is largely adapted 

from the draft report “The Impact of the Clean Air 

Act on 1970 to 1990 Emissions; section 812 retrospective analysis,” March 1, 1995 by Pechan Associates. The draft Pechan report surveys the methodologies and results associated with the sector-specific 

emission modeling efforts by Argonne National Laboratory (ANL), ICF Resources Incorporated (ICF), Abt 

Associates (Abt), and the Environmental Law Institute (ELI). 

Comparison of Emissions 

Projections with Other EPA Data 

Control Scenario Projections Versus 

EPA Trends Projections 

The control scenario emission results are similar, 

but not identical, to official EPA historical emission 

estimates provided by the EPA National Air Pollutant Emission Trends Reports.1 Comparisons between 

the current estimates and the Trends data for SO2, NOx, 

VOC, CO, and TSP are presented in Figures B-1, B2, B-3, B-4, and B-5 respectively. More detailed tables 

providing emission estimates by sector and by target 

year for TSP, SO2

, NOx, VOC, CO, and Lead are presented in Tables B-16, B-17, B-18, B-19, B-20, and 

B-21, respectively, at the end of this appendix. 

Though the EPA Trends and the present study 

emission profiles are similar to each other, they should 

not be expected to match precisely. This is because 

the emission estimates developed for the present study 

are based on modeled macroeconomic and emission 

sector conditions. Even though the macroeconomic 

and sector models themselves are constructed and 

calibrated using historical data, modeled replications 

of historical trends would not be expected to precisely 

capture actual historical events and conditions which 

affect emissions. Relying on modeled historical scenarios is considered reasonable for the present analysis since its purpose is to estimate the differences between conditions with and without the CAA. Comparing actual historical emissions with modeled nocontrol emissions would lead to an inconsistent basis 

for comparisons between scenarios. Using models for 

both scenarios allows potential model biases to essentially cancel out. 

In general, however, these comparisons show 

close correspondence between control scenario and 

Trends estimates with the largest differences occur1

 EPA/OAQPS, “National Air Pollutant Emission Trends 1900 - 1994,” EPA-454/R-95-011, October 1995. 

B-1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

B-2 
Appendix B: Emissions Modeling 

nificant additional increases in SO2

 emissions, the rate 

of growth is markedly slower than during the 1950 to 

1970 period. 

The Trends data for 1950 to 1970 NO shown in x 

ring for VOC and CO emissions. The Trends report 

VOC estimates are generally higher than the control 

scenario estimates due to the inclusion of Waste Disposal and Recycling as a VOC source in the Trends 

report. This inconsistency is of no consequence since 

Waste Disposal and Recycling sources were essentially uncontrolled by the historical CAA and therefore do not appear as a difference between the control 

and no-control scenarios. The higher CO emission 

estimates in the Trends Report are primarily associated with higher off-highway vehicle emissions estimates. Again, since off-highway emissions do not 

change between the control and no-control scenario 

in the present analysis, this inconsistency is of no consequence. 

No-Control Scenario Projections Versus 

Historical EPA Trends Data 

Comparisons between the control scenario emissions estimates generated for the present study and 

1970 to 1990 emissions estimates obtained from the 

Trends Report are useful for assessing the reasonableness of the control scenario estimates. As indicated 

above, there is close correspondence between the control scenario and the Trends Report. It may also be 

useful to compare the pre-1970 historical emissions 

data from the Trends Report2 with the no-control scenario estimates presented herein to assess whether 

these pre-1970 trends can be reasonably extrapolated 

to the 1970 to 1990 period. In addition, examination 

of any significant changes in emissions trends between 

the pre-1970 Trends data and post-1970 no-control 

projections might indicate flaws in the emissions 

modeling conducted for the present study. 

For SO2

, the 1950 to 1970 Trends data in Figure 

B-1 demonstrate the effects of the huge increase in 

fossil fuel combustion between 1960 and 1970. This 

net increase occurred, despite the obsolescence of coalfired locomotives and reductions in coal refuse burning, largely because utility emissions nearly doubled 

between 1950 and 1960, and nearly doubled again 

between 1960 and 1970.3 Although no-control scenario projections for the post-1970 period show sigFigure B-2 indicate the steady increase in emissions 

resulting from increased combustion of natural gas 

and gasoline.4 The post-1970 emissions estimates 

derived for the present study reflect a continuation of 

this trend. 

Emissions of VOCs increased steadily over the 

1950 to 1970 period, as shown in Figure B-3, primarily due to increases in industrial production and vehicular travel.5 The no-control scenario emission estimates continue this trend throughout the 1970 to 1990 

period, with some acceleration of the rate of change 

due to the rapid increase in VMT projected under this 

scenario. 

The Trends data shown in Figure B-4 for CO indicate an overall increase between 1950 and 1970. This 

increase occurred despite significant reductions in 

emissions from stationary source fuel combustion and 

industrial processes because mobile source emissions 

nearly doubled during this period.6 Under the no-control scenario of the present study, additional reductions from stationary sources are not available to offset the transportation-related increases; therefore, the 

rate of increase in CO emissions after 1970 under the 

no-control scenario reflects the rapid increase in mobile source emissions caused by increases in vehicle 

miles traveled. 

Finally, Figure B-5 demonstrates a directional 

shift in emissions of primary particulates between the 

1950 to 1970 Trends data and the post-1970 no-control scenario. The declining trend from 1950 to 1970 

indicated by the Trends data, however, is largely due 

to reductions in use of coal-fired locomotives, reductions in residential coal-burning, coarse (i.e., visible) 

particle emissions controls installed on fossil fuel combustors and industrial processes, and reductions in 

forest fires and other open burning.7 Since the reductions achievable from these sources were largely 

2

 While 1970 to 1990 Trends data were obtained from more recent Trends reports, the 1950 to 1970 data were obtained from the 

November 1991 report since this was the last year the Trends report series included data for this period. 

3

 U.S. EPA, “National Air Pollutant Emission Estimates, 1940 - 1990”, EPA-450/4-91-026, November 1991, Table 4, p. 16. 

4

 U.S. EPA, “National Air Pollutant Emission Estimates, 1940 - 1990”, EPA-450/4-91-026, November 1991, p. 42. 

5

 U.S. EPA, “National Air Pollutant Emission Estimates, 1940 - 1990”, EPA-450/4-91-026, November 1991, p. 42. 

6

 U.S. EPA, “National Air Pollutant Emission Estimates, 1940 - 1990”, EPA-450/4-91-026, November 1991, Table 7, p. 19. 

7

 U.S. EPA, “National Air Pollutant Emission Estimates, 1940 - 1990”, EPA-450/4-91-026, November 1991, Table 3, p. 15. 

B-3

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

achieved by 1970, they are no longer available to offset the increases observed from other source categories (e.g., highway vehicles). The no-control scenario 

therefore shows a steady increase in overall emissions 

of primary particulates after 1975. 

The following sections of this appendix summarize the methodologies used to model control and nocontrol scenario emissions for each of the six major 

emission sectors. Additional details can be found in 

the supporting documents listed in the References section of this appendix. 

Industrial Boilers and Processes 

For the purposes of the retrospective analysis, the 

industrial sector was divided into two components: 

(1) boilers; and (2) industrial processes and process 

heaters. The factors affecting emissions from these 

two source types are different, and, as a result, separate methods were used to calculate control and 

no-control scenario emissions in each of the target 

years. To analyze the change in emissions from industrial boilers, ANL used the ICE model (Hogan, 

1988). This model was developed under the auspices 

of NAPAP to forecast State-level fuel choice and 

emissions from conventional, steam raising, industrial 

boilers. For the retrospective analysis of industrial 

processes and fuel use emissions from process heaters, ELI used the EPA Trends methods and the ANL 

MSCET data base (EPA, 1991; Kohout et al., 1990). 

The Trends report contains estimates of national emissions for a variety of industrial sources for the time 

period of interest. The MSCET data base provided 

the spatial distribution used to calculate State-level 

emissions. 

The distinction between industrial boilers and nonboiler industrial processes was necessitated by the 

structure of the CAA regulations and by the factors 

affecting emission levels from these two source types. 

Boilers are regulated differently from processes and 

process heaters. Emissions from industrial processes 

are primarily a function of levels of industrial activity. The emissions from fuel combustion, however, 

are a function of energy use and fuel choice as well as 

industrial activity. Fossil fuel emissions in the absence 

of the CAA are not proportional to industrial output, 

since the level of energy use is a decision variable for 

the firm in its production process. Therefore, in the 

ICE model simulations used to estimate no-control 

scenario boiler emissions, the level (and type) of energy use were determined first, and then the effects of 

emission regulation were taken into account. 

Overview of Approach 

Industrial Boilers 

ICE model inputs include fuel prices, total boiler 

fossil fuel demand by industry type, and environmental control costs. The outputs of the ICE model were 

SO2

, NOx, and TSP emissions by State, industry, and 

boiler size class. The model runs in 5-year increments 

and has a current base year of 1985. 

The model required boiler demand input data at 

the State level. Seven industry types were included in 

the ICE model: Standard Industrial Classification (SIC 

) codes 20, 22, 26, 28, 29, 33, and “other manufacturing.” ANL’s approach assumed that industrial boiler 

fuel use occurs only in the manufacturing sector. The 

model also required fuel price data in each of the target years at the Federal Region level. Prices by grade 

of coal and petroleum product, such as sulfur content 

and heating value, were used by the model to determine the cost of compliance, and to determine emissions when the regulations are not binding. 

Control costs were computed by engineering subroutines in the model. These costs were used by the 

ICE model’s fuel choice component to determine the 

effect of CAA-related costs on the market share of a 

particular fuel. This fuel choice decision only applies 

to new industrial boilers, since the cost of existing 

emission controls are not in the ICE data base and 

fuel choice is not re-evaluated for existing boilers. 

Industrial Processes and In-Process Fuel 

Combustion 

The calculation of historical emissions from industrial processes uses EPA Trends methods to estimate national emissions for the analysis years, then 

allocates these emissions to States using the State 

shares from the MSCET data base. 

MSCET uses a variety of methods to estimate historical emissions for the various industrial sectors. For 

industrial process emissions, MSCET is based on historical data on industrial activity to allocate emissions 

based on the State level distribution of the polluting 

activities. The State level distribution and benchmark 

B-4

Appendix B: Emissions Modeling 

is based on the 1985 NAPAP Inventory (EPA, 1989). 

This approach implies that the MSCET data corresponds directly to the 1985 NAPAP Inventory, and 

that, for any State, the sum of the emissions from 

Source Classification Codes (SCCs ) that comprise 

the MSCET industry sector are equal to the MSCET 

data for that State and sector. Data from Trends are 

used by MSCET to provide information on changes 

in the aggregate level of control for years other than 

the 1985 benchmark. Since no direct correspondence 

existed between the Trends data and MSCET, a relationship was developed to link MSCET sectors to 

Trends industry categories and to industry categories 

in the J/W model, which was used to change activity 

levels for the no-control scenario. 

Table B-1 shows the relationship between the sector definition used by MSCET, Trends, and the J/W 

model. The mapping from MSCET to J/W and Trends 

is used to provide the changes in aggregate activity 

and emission control for the calculation of no-control 

scenario emissions. 

Establishment of Control Scenario Emissions 

Energy use and corresponding emissions were 

broken down between boilers and non-boiler industrial processes. The latter category includes furnaces, 

kilns, internal combustion engines (e.g., compressors), 

and other non-steam types of process heat. The focus 

of this analysis is on boiler emissions, which were 

subject to increasingly stringent regulations over the 

1970 to 1990 period. (Emissions from some types of 

industrial processes were also regulated, but regulation of non-boiler sources was targeted on the emissions from the industrial process itself, not on its fuel 

combustion) For this study, ANL assumed that only 

boiler fuel use is affected by emission regulations. The 

non-steam boiler portion of industrial fuel use is not 

directly affected by the CAA. This portion of the 

emissions may be affected indirectly by changes in 

industry activity level and fuel consumption. The 

emissions from non-boiler industrial processes were 

calculated separately by ELI. 

Control Scenario Boiler Emissions 

Control scenario boiler SO2

, NOx, and TSP emissions were calculated by the ICE model. The MSCET 

data base provided an estimate of historical emissions 

for total fossil fuel combustion by industry. Since 

MSCET does not identify the two required components of boiler and non-boiler emissions, ANL defined the residual of the ICE model control scenario 

and MSCET as the non-boiler or in-process fuel use 

emissions. For the relevant study period, MSCET provided a control scenario estimate of total boiler and 

non-boiler emissions, which was used to calculate the 

control scenario State-level boiler emissions based on 

a special run of the ICE model.8 

In order to use ICE to model the historical emissions path, it was necessary to construct a new ICE 

model base year file and new user input file so that 

the model could begin its calculations from 1975 conditions. Construction of the base year file was completed in two stages, using two different data sources, 

as discussed below. The user input file has several 

elements, including energy prices and historical boiler 

fuel use; its construction is discussed in the next section. The model base year file provided the energy 

use in boilers and corresponding emission control 

regulations (State Implementation Plans –SIPs– for 

example) by several categories. These categories include: 

• State; 

• Industry group (one of seven); 

•	 Fuel type (natural gas, distillate or residual 

fuel oil, and coal); 

•	 Boiler size class (MMBTU/hr, one of eight 

categories); 

• Utilization rate (one of five categories); and 

• Air quality control region (AQCR ). 

For the purposes of ANL’s analysis, only the first 

three categories were assumed to vary. In other words, 

for each State, industry, and fuel type combination, 

the distribution of boiler size, utilization rate, and 

AQCR was assumed to be constant. Over time, however, changes in the aggregate composition of State, 

industry, and fuel type would cause corresponding 

changes in the aggregate composition of the other three 

characteristics. As mentioned previously, the current 

base year file was 1985. The retrospective analysis 

required a 1975 base year. Because of data limitations, the approach to construct a new base year was 

achieved in the following two steps: the construction 

of a 1980 interim base year file from the 1985 file, 

and then the construction of the 1975 file from the 

interim 1980 file. 

8

 MSCET does not provide State-level estimates of TSP, while ICE does. To estimate total regional TSP from fuel combustion, 

the Trends model was employed. These national emissions estimates were allocated to the States based on the State-level shares of 

TSP from the NAPAP inventory. 

B-5

The Benefits and Costs of the Clean Air Act, 1970 to 1990 Table B-1. Trends, and J/W Industrial Sectors and Identifier Codes. MSCET Category MSCET Code Trends Industry Category J/W Code J/W Industry Category Food Proc. and Agric. Operations FOODAG Cattle Feed Lots (0211) 1 Agriculture/forestry/fisheries Cotton Ginning (0724) Feed and Grain Milling (204) Grain Elevators (4421,5153) Mining Operations MINING Metallic Ore Mining (10) 2 Metal Mining Coal Mining (1211) Oil and Gas Extraction OILGAS Crude Oil Production, Storage, and Transfer (1211,4463) 3 Oil & Gas Extraction Natural Gas Production (1311) Mining Operations MINING Crushed Stone (142) 5 Nonfuel mining Sand and Gravel (144) Clays (145) Potash/Phosphate Rock Degreasing DEGRS Degreasing NA Manufacturing Misc. Industrial Processes MISIND Adhesives Indus. Organic Solvent Use, Misc. SOLV Other Organic Solvent Use Solvent Extraction Processes Surface Coating SRFCT Surface Coating NA Durable Goods Misc. Industrial Processes MISIND Lumber and Plywood (24) 11 Lumber & Wood Products Cement Production CEMNT Cement (3241) 19 Stone, Clay, & Glass Products Glass Manufacturing GLASS Glass (321,322) 19 Stone, Clay, & Glass Products Lime Manufacturing LIME Concrete, Lime, Gypsum (327) 19 Stone, Clay, & Glass Products 

Lime (3274) 

Mineral Products Processing MINRL Clay Sintering (3295) 19 Stone, Clay, & Glass Products 

Brick and Tile (3251) 

Iron and Steel Production IRNST Iron and Steel (3312) 20 Primary Metal Industries 

Ferroalloys (3313) Iron and Steel Foundries (332) 

Other Primary Metals Smelting OTHMET 20 Primary Metal Industries 

Primary Aluminum Smelting PALUM Primary Aluminum (3334) 20 Primary Metal Industries 

Primary Copper Smelting PCOPR Primary Copper (3331) 20 Primary Metal Industries 

Primary Lead and Zinc Smelting PLDZC Primary Lead and Zinc (3332,3333) 20 Primary Metal Industries 

Other Sec. Metal Smelting and Refining SECMET Primary Nonferrous Smelters (333) 20 Primary Metal Industries Other Sec. Metal Smelting and Refining SECMET Secondary Nonferrous Smelters (334,336) 20 Primary Metal Industries 

Secondary Lead Refining SLEAD Secondary Lead (3341) 20 Primary Metal Industries Food Proc. and Agric. Operations FOODAG Food and Beverages (20) 7 Food & Kindred Products Misc. Industrial Processes MISIND Textiles (22) 9 Textile Mill Products 

Paper and Pulp Mills Operations PAPER Pulp Mills (261,262) 13 Paper & Allied Products Misc. Industrial Processes MISIND Graphic Arts (27) 14 Printing & Publishing 

Printing Operations PRINT 14 Printing & Publishing Organic Chemicals Manufacture ORGCM Organic Chemicals (286) 15 Chemicals & Allied Products 

Carbon Black (2895) 

Other Chemicals Manufacture OTHCM Ammonia (2873) 15 Chemicals & Allied Products 

Nitric Acid (2873) Chemicals (28) Sulfuric Acid (2819) 

Petroleum Refining PTREF Petroleum Refining (2911) 16 Petroleum & Coal Products 

Asphalt Paving and Roofing (295) 16 Petroleum & Coal Products 

Plastics Production PLAST Plastics (2821,3079) 17 Rubber and plastic products Rubber and Misc. Plastics Manufacture RUBR Rubber Tires (3011) 17 Rubber and plastic products 

Correspondence Between Process Emissions Categories Used by MSCET, 

(1474,1475) 

B-6

Appendix B: Emissions Modeling 

Estimates of boiler fossil fuel consumption in 

1980 for each State and major fuel type were provided by Hogan (Hogan, 1988). These estimates are 

based on the assumption that the industry mix, size, 

utilization, and AQCR distribution within a State are 

constant. Through assuming this relationship, the 1985 

ICE base year was scaled to match the data for 1980, 

thus forming the 1980 interim base year data. 

To construct the 1975 base year file, the assumption of a constant industry mix for a State and fuel 

type was no longer necessary, since detailed data on 

each industry for 1980 and 1975 were available from 

PURchased Heat And Power (PURHAPS ) model data 

files (Werbos, 1983). These PURHAPS data files were 

derived from the Annual Survey of Manufactures: 

Fuels and Electric Energy Purchased for Heat and 

Power (DOC, 1991). The available data in these files 

were for total fuel use not boiler fuel use. To make 

use of these data, it was necessary to assume that the 

fraction of fuel used in boilers, for any given State 

and industry, remained constant from 1975 to 1980. 

To the extent that the fraction of boilers’ heat versus 

process heat applications is a function of the specific 

industrial production process, this assumption is reasonable. 

Based on the assumption of constant boiler fuel 

fraction of total fuel use, the ratio of 1975 to 1980 

energy use for each State, industry, and fuel type was 

applied to the corresponding record of the 1980 interim base year file to produce 1975 base year files. 

Control Scenario Industrial Process Emissions 

To estimate boiler emissions of sulfur oxides 

(SOx

), NOx, and VOC from industrial processes, data 

from Trends were used. The percentage change in 

national emissions by Trends category was applied to 

the appropriate sector from MSCET to obtain Statelevel emissions. In some cases there are several categories in Trends that match directly with MSCET 

categories (see Table B-1). In these cases, the Trends 

sectors were aggregated and the percentage change 

was computed. It was assumed that the level of control in each industry sector implied by Trends was 

uniform across States. The changes in emissions in 

each State are not equal to those at the national level, 

since the industry composition in each State varies. 

Development of Economic Driver 

Data for the Control Scenario -

Industrial Boilers and Processes 

The results of the J/W model were the primary 

source of activity in the ICE model driver data. These 

results were also used by ELI to produce the national 

results for industrial processes from Trends. Both ICE 

and Trends use the forecasted change in industrial 

activity that results under the no-control scenario. 

These data were in the form of industry specific 

changes in energy consumption and industrial output, 

for boilers and industrial processes. 

Economic Driver Data for Industrial 

Boiler Approach 

Using the 1975 base year file as a starting point, 

the ICE model estimated fuel choice and emissions 

based on a user input file containing total boiler energy demand and regional energy prices. The 1975, 

interim 1980, and original 1985 base year files contained the required information on energy demand for 

each industry group and State, so the data in these 

three files were aggregated across fuel type, and other 

boiler characteristics (for example, size). These aggregated data provided the energy demand for three 

of the target years. Since 1990 State-level data on 

energy use by industry group were not available at 

the time of the study, the NAPAP base case forecast 

for the ICE model for 1990 was used to provide the 

demand data for this year. 

The user input file for ICE also requires a price 

input for each target year. These prices were input by 

Federal Region for distillate oil, 4 grades of residual 

oil (by sulfur content), natural gas, and 11 grades of 

coal (by sulfur content and coal rank, i.e., bituminous 

and sub-bituminous). Prices for 1985 and 1990 were 

obtained from the NAPAP base case user input file. 

The prices for 1975 and 1980 are from U.S. Department of Energy (DOE) data on State-level industrial 

energy prices (DOE, 1990). Regional prices of natural gas, distillate oil, steam coal, and residual oil were 

constructed by aggregating expenditures across States 

within each region and dividing by total British thermal unit (BTU ) consumption for the years 1975, 1980, 

and 1985. Since prices by sulfur content grade are not 

reported by this DOE source, ANL assumed that the 

sulfur premium implied by the 1985 ICE model input 

file was proportional to the average price. Based on 

this assumption, the ratio of the regional coal and reB-7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

sidual oil price in 1975 and 1980 to the 1985 price 

was applied to the 1985 price in the ICE model base 

case file for each grade of fuel. To provide additional 

consistency between the NAPAP analysis and ANL’s 

study, the distillate oil and natural gas prices were 

benchmarked to the 1985 ICE model prices as well. 

One possible inconsistency arises using this procedure. The residual oil and natural gas markets are 

closely linked, particularly for industrial customers. 

These markets, specifically the gas market, underwent 

tremendous changes over the study period. To model 

the effect of these structural changes on the sulfur premiums in residual oil would require a detailed oil and 

gas supply model that was beyond the scope of this 

project. Moreover, the CAA regulations themselves 

create the potential for sulfur premiums. This potential effect of the CAA was not captured, though, because of the assumption of proportional fuel sulfur 

premiums on residual fuel oil. The relationship between market driven sulfur premiums in the coal market and the CAA was given additional consideration 

in this analysis through the use of an explicit coal supply model. 

The J/W data for industrial energy consumptions 

was supplied in the form of percentage change in cost 

shares. In order to compute the percentage change in 

the quantity of energy used, ANL used the following 

identity: 

PE × E ) + 1n (E) - 1n (PQ × Q), or(1) PQ× Q 1n (––––––) = 1n (PE

PE

 × E ) + 1n (PQ × Q) = 1n (E), or(2) PQ× Q 1n (––––––) - 1n (PE

The percentage change in E is the percentage 

change in cost share, minus the change in price, plus 

the change in value of shipments. These calculations 

were performed for each energy type and industry 

sector in the J/W model. The ICE model requires total fuel use, so the fuel specific percentages were 

weighted by historical fuel consumption to produce 

an aggregate change in fuel consumption to apply to 

the ICE model input data files.9 

ICE also uses energy prices to simulate boiler fuel 

choices. The control scenario forecasts of energy 

prices in ICE were adjusted based on the percentage 

changes in energy prices, by coal, oil and natural gas. 

This implicitly assumes that the oil and coal fuel sulfur premiums, by region, are proportional to the average national price. To test this assumption for the coal 

market, additional modeling of the coal prices was 

performed using the coal market component of the 

ARGUS model. 

It is possible that in some regions low sulfur coal 

prices to the industrial sector may be lower than the 

national average. This was not found to be the case. 

For example, in 1990, delivered regional industrial 

coal prices change by less than two-thirds of one percent. In most cases, the percentage change was near 

zero. This result appears to occur because of the highly 

regional nature of the coal market. While the artificial demand for low sulfur coal may fall, power plants 

near low sulfur coal reserves now find it advantageous 

to buy this local coal, which raises the price back to 

an equilibrium level near to that of the control scenario. This is even more likely to be true of industrial 

delivered prices, since industrial prices are more affected by transportation costs than are the utility prices. 

No additional ICE modeling was performed. 

Economic Driver Data for the Industrial 

Process Approach 

The J/W model was also used to account for activity level changes in the calculation of industrial 

process emissions under the no-control scenario. The 

correspondence between Trends, MSCET, and the J/ 

W model was used to apply changes in industrial activity in each target year to each industrial process. 

No-control Scenario Emissions 

Industrial Boiler Emissions of SO2, NOx, and TSP 

The CAA imposed different regulations, SIPs, and 

New Source Performance Standards (NSPS) that apply to industrial boilers of varying size. The primary 

effect of CAA regulations on industrial boilers was 

simulated by defining the Air Quality Control Region 

(AQCR), the resulting SIPs, and subsequent NSPS for 

boilers. The industrial boiler SIP regulations were included in the ICE base year file discussed in the previous section. Since the ICE model estimates new 

boiler emissions for each target year, the boiler NSPS 

are input through the ICE user files. Industrial NSPS 

were implemented in two phases. The 1971 regulations are imposed for the study years 1975 and 1980. 

9

 ICE uses six of the manufacturing industries from the J/W model directly. The remaining industries’ percentage changes were 

weighted to produce the “other” category. 

B-8

Appendix B: Emissions Modeling 

The 1984 NSPS revisions are imposed in the study 

years 1985 and 1990. For the no-control scenario, ANL 

set the SIPs and NSPS to a flag that indicated “no 

regulation.” 

Industrial Boiler Emissions of CO and VOC 

Two of the criteria pollutants emitted by industrial fuel combustors, CO and VOC, were not included 

as outputs of the ICE model. Therefore, CO and VOC 

emissions were analyzed separately using Trends 

methods. Control scenario CO and VOC emissions 

were taken directly from Trends. 

To estimate CO and VOC emissions from industrial combustion for the no-control scenario, fuel use 

for industrial manufacturing was adjusted, reflecting 

fuel consumption changes estimated by the J/W model. 

These changes in the level of fuel consumption by 

industrial combustion were also used in ANL’s ICE 

boiler model. Changes in industrial combustion fuel 

use by manufacturing between the control and 

no-control scenarios are reported in Table B-2. These 

estimates represent an average of several sectors, 

which were developed by ANL as part of the modeling process for ICE. 

No-control scenario emissions were computed 

using 1970 emission factors. Since there were no addTable B-2. Fuel Use Changes Between 

Control and No-control Scenarios. 

Year Fuel T ype Fuel Use C hanges 

1975 

Coal 

Oil 

Gas 

-.0 042 

+.0 311 

-.0 064 

1980 

Coal 

Oil 

Gas 

-.0 061 

+.0 107 

-.0 095 

1985 

Coal 

Oil 

Gas 

-.0 061 

+.0 089 

-.0 097 

1990 

Coal 

Oil 

Gas 

-.0 079 

+.0 091 

-.0 099 

on controls for industrial combustion VOC and CO 

emissions, it was not necessary to adjust the no-control scenario for changes in control efficiency. 

Emission estimates were regionalized using Statelevel emissions data from industrial boilers recorded 

in MSCET. For the control scenario estimates, VOCs 

were regionalized using the MSCET State-level shares 

for industrial fuel combustion. In the no-control scenario, the State-level shares were held constant. The 

control scenario emissions of CO were regionalized 

using the control scenario NOx emissions from the ICE 

model. This approach assumes that CO emissions are 

consistent with NO emissions. The no-control sce­ x 

nario CO emission estimates from industrial combustion sources were regionalized using no-control NOx 

emission estimates from industrial combustion 

sources. 

Industrial Process Emissions 

A wide range of controls were imposed on industrial processes. These emission limits are embodied 

in the assumptions of control efficiencies in theTrends 

model. Data on national no-control scenario emissions 

from industrial processes were provided by EPA. 

These data were combined with MSCET to produce 

regional-level results. 

Lead Emissions 

Estimates of lead emissions from industrial boilers and industrial processes were completed by Abt 

Associates. The methods used for calculating lead 

emissions from industrial processes and industrial 

boilers were similar. The starting point was the TRI, 

which provides air toxics emissions data for manufacturing facilities with more than 10 employees. To 

estimate lead emissions from industrial boilers and 

processes, 1990 facility-level lead emissions data were 

extracted from the TRI. These data were then adjusted 

to create estimates of lead emissions from industrial 

sources under the control and no-control scenarios for 

each of the target years. For the control scenario, lead 

emissions for 1975, 1980, and 1985 were obtained by 

extracting an emission factor and a control efficiency 

for each lead-emitting industrial process in the Trends 

data base. These emission factors and control efficiencies were multiplied by the economic activity data 

for each year for each process as reported in Trends 

to yield estimated control scenario emissions by industrial process. Each industrial process was assigned 

B-9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

a code to correspond with energy consumption data 

by industrial process compiled in the National Energy 

Accounts (NEA ) by the Bureau of Economic Analysis, and emissions were summed over all processes to 

obtain a total for each target year. 

For consistency with the other emission estimates 

in this analysis, industrial process no-control scenario 

lead emissions were adjusted for changes in industrial output, and for changes in emissions per unit of 

output due to control technology applications. Changes 

in industrial output were accounted for using results 

from the J/W model. Lead-emitting industrial processes in the Trends data base were assigned to a J/W 

sector. For each sector, the percentage change in economic output was used to adjust the economic activity data for that process from the Trends data base. 

These adjusted economic output figures were used 

with the 1970 emission factors and control efficiencies to derive the estimated no-control scenario lead 

emissions for each industrial process in each target 

year. The process-level emissions were then aggregated to the NEA-code level as in the control scenario. 

The lead emission estimates from industrial processes, by NEA code, were used to derive percentage 

changes in emissions under the control and no-control 

scenarios by NEA code for application to the TRI 

emissions data. Since TRI data are reported by SIC 

code, NEA codes were “mapped” to the appropriate 

SIC codes, and then the percentage change for each 

NEA code was used to represent the percentage change 

for all SIC codes covered by that NEA code. 

To calculate lead emissions from industrial boilers, Abt Associates developed estimates of lead emissions from industrial combustion under the CAA for 

each of the target years. The Trends data base contains national aggregate industrial fuel consumption 

data by fuel type. For each fuel type, the fuel consumption estimate was disaggregated by the share of 

that fuel used by each NEA industrial category. The 

Trends data base also contains emission factors for 

industrial fuel use, by fuel type, as well as control 

efficiencies. The lead emissions from industrial combustion for each NEA category were derived by multiplying the fuel-specific combustion estimate for each 

NEA category by the emission factor and control efficiency for that fuel type. The result was emissions 

of lead by NEA code and by fuel type. Emissions from 

all fuel types were then summed by NEA code. The 

NEA data were used to disaggregate the industrial fuel 

consumption figures, based on the assumption that the 

ICE are the same among all industries covered by a 

given NEA code. 

To estimate no-control scenario lead emissions, 

the macroeconomic effect of the CAA and the change 

in emissions per unit of output that resulted from specific pollution control mandates of the CAA were both 

taken into account. As in the control scenario, the national aggregate industrial fuel consumption estimate 

by fuel type was disaggregated by the share of that 

fuel used by each NEA industrial category. The fuel 

use was then adjusted in two ways: some NEA codes 

were specifically modeled by the ICE model, and for 

the remaining NEA codes, J/W percentage changes 

in fuel use were applied. These fuel use estimates were 

then combined with the 1970 emission factors and 

control efficiencies for industrial combustion by fuel 

type from the Trends data base to obtain no-control 

scenario combustion-related lead emissions from industrial boilers by NEA code. These estimates of total lead emissions by NEA codes were matched to 

SIC codes, and then to the data in the TRI data base. 

This approach assumed that an average emission value 

was assigned to all reporting TRI facilities in a given 

SIC code. 

Off-Highway Vehicles 

The off-highway vehicle sector includes all transportation sources that are not counted as highway vehicles. Therefore, this sector includes marine vessels, 

railroads, aircraft, and off-road internal combustion 

engines and vehicles. As a whole, off-highway vehicle emissions are a relatively small fraction of total 

national anthropogenic emissions. 

Overview of Approach 

The process used by ELI to determine the national 

level of emissions from the off- highway transportation sector is similar to the procedure outlined above 

for industrial processes. To estimate the emissions of 

criteria air pollutants from these sources under the 

no-control scenario, the historical activity levels were 

held constant, rather than attempting to calculate a 

new no-control scenario level of off-highway vehicle 

activity. This assumption was necessary since the offhighway activity indicators (amount of fuel consumed, 

and landing and take-off cycles for aircraft) do not 

B-10

Appendix B: Emissions Modeling 

have direct correspondence with a given J/W category. 

The national no-control scenario emissions of criteria 

air pollutants from these sources were simply derived 

by recalculating emissions using 1970 emission factors. 

Development of Control Scenario 

To estimate control scenario emissions, the analysis relied on Trends methods, using historical activity 

indicators, emission factors, and control efficiencies. 

Essentially, the estimates of off-highway emissions 

under the control scenario represent the historical estimates from the Trends data base. 

No-control Scenario Emissions Estimates 

The calculation of off-highway emissions for the 

no-control scenario required the Trends data to be 

adjusted to reflect changes in controls and economic 

activity in each of the target years. Linking source 

activity changes with economic activity for this section is not straightforward. The economic activity data 

for off-highway engines and vehicles are expressed 

either in terms of amount of fuel consumed, or in terms 

of landing and take-off cycles for aircraft. Neither of 

these off-highway activity indicators has a direct correspondence with a given J/W sector, making the sort 

of direct linkage between Trends categories and J/W 

sectoral outputs that was used for industrial processes 

inappropriate. 

In the absence of a link between the economic 

factors that are determinants of emissions from this 

sector and the available economic activity forecasts, 

the no-control scenario emissions of criteria air pollutants from off-highway mobile sources were estimated based on the same historical activity levels used 

for the control scenario. Although there were changes 

in sectoral output and personal income that might have 

had an effect on off-highway vehicle usage, these 

changes were deemed to be small and not likely to 

have a major effect on the emissions from this sector. 

Emission factors for each of the off-highway 

sources were also held constant at 1970 levels to calculate no-control scenario emissions for each target 

year. The national emissions of criteria air pollutants 

from these sources were then recalculated using 1970 

emission factors. 

National and State-Level Off-Highway 

Emission Estimates 

Table B-3 summarizes national-level emission 

estimates for off-highway sources. The emission estimates derived from using the methodology discussed 

above yielded results that seem counter-intuitive. The 

emissions from off-highway sources, in particular the 

emissions from aircraft, are lower in the no-control 

scenario than those projected for the control scenario 

for most pollutants. This is a result of calculating 

emissions using 1970 emission factors, since the 1970 

emission factors for aircraft are lower than the aircraft emission factors in later years. 

ELI identified several potential sources of uncertainty in the emission estimates for this sector. First, 

the assumption that the total level of off-highway vehicle fuel consumption is constant between the two 

scenarios may be flawed. Second, the use of 1970 

emission factors in the no-control scenario may fail 

to capture significant changes in technology. These 

technological changes are implicitly captured in the 

control scenario and it is possible that these technological changes may also have occurred under a 

no-control scenario. 

One possible response to the biases created by the 

use of 1970 emission factors for all years in the 

no-control scenario is to test how results might differ 

if the emission factors used for the control scenario, 

which would include technological change, were also 

used for the no-control scenario. However, using this 

treatment of emission factors, the emissions projections from the adopted methodology from non-highway sources in the no-control scenario would be identical to the emissions projections under the control 

scenario. The reason for this is that the economic activity levels were not adjusted for the calculation of 

emissions under the no-control scenario. 

In order to disaggregate the national data to a State 

level, the methodology used the MSCET data base, 

which is described earlier. Emissions of VOC, SOx, 

and NOx

 were regionalized using the State-level shares 

from the MSCET methodology. The emissions of TSP 

were regionalized by using the State-level shares for 

SO x reported by MSCET, and the emissions of CO

were regionalized using the State-level shares for NOx, 

also reported by MSCET. The potential bias that this 

introduces is likely to be small, due to the relative 

homogeneity of off-highway vehicle emission sources. 

B-11

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-3. Difference in Control and No-control Scenario Off-Highway Mobile Source 

Emissions. 

197 5 198 0 198 5 199 0 

TSP 

Control Scen ario: 268. 6 281. 1 268. 7 280. 9 

No -Control Scen ario: 260. 8 268. 8 261. 2 266. 9 

Percentage Increa se: -3 % -4 % -3 % -4 % 

NO x 

Control Scen ario: 1, 987. 6 2, 176. 7 2, 077. 5 2, 085. 9 

No -Control Scen ario: 1, 974. 6 2, 150. 5 2, 042. 7 2, 058. 9 

Percentage Increa se: -1 % -1 % -2 % -1 % 

SO2 

Control Scen ario: 364. 6 531. 1 406. 4 392. 5 

No -Control Scen ario: 363. 2 528. 6 403. 0 386. 9 

Percentage Increa se: 0% 0% -1 % -1 % 

CO 

Control Scen ario: 8, 512. 8 8, 101. 4 7, 881. 9 8, 079. 0 

No -Control Scen ario: 8, 511. 0 8, 071. 2 7, 880. 2 8, 077. 7 

Percentage Increa se: 0% 0% 0% 0% 

VO C s 

Control Scen ario: 1, 374. 9 1, 370. 8 1, 334. 8 1, 405. 0 

No -Control Scen ario: 1, 385. 9 1, 416. 1 1, 388. 6 1, 485. 8 

Percentage Increa se: 1% 3% 4% 6% 

Note: Emission estimates are expressed in thousands of short tons. Percentage increase is the differential between 

scenarios divided by the Control Scenario projection. 

As with regionalization of industrial process emissions, the State-level shares are held constant between 

the two scenarios. To the extent that the distribution 

of economic activity between States was not constant 

over the period of the analysis, holding State-level 

emission shares constant may bias the results, although 

the direction and magnitude of the potential bias is 

unknown. 

On-Highway 

This section addresses the highway vehicle portion of the transportation sector. Highway vehicle 

emissions depend on fuel type, vehicle type, technology, and extent of travel. Emissions from these vehicles have been regulated through Federal emission 

standards and enforced through in-use compliance 

programs, such as State-run emission inspection programs. Vehicle activity levels are related to changes 

in economic conditions, fuel prices, cost of regulations, and population characteristics. Emissions are a 

function of vehicle activity levels and emission rates 

per unit activity. 

TEEMS was employed by ANL to analyze the 

transportation sector. The modeling system links several models, disaggregate and aggregate, to produce 

State-level estimates of criteria pollutants. The system is subdivided into two modules: an activity/energy module and an emissions module. Each module 

contains multiple models. TEEMS has been documented in several reports and papers (Mintz and Vyas, 

1991; Vyas and Saricks, 1986; Saricks, 1985). It has 

been used for several policy analyses and assessment 

studies for DOE and NAPAP. This section presents 

an overview of the approach used to conduct the analysis of the transportation sector. Also included in this 

section is a summary of the methodology used by Abt 

Associates to estimate changes in lead emissions from 

highway vehicles in each target year. 

B-12

Appendix B: Emissions Modeling 

Overview of Approach 

TEEMS has two modules: an activity/energy 

module and an emissions module. The activity/energy 

module calculates emissions based on: (1) personal 

travel; (2) goods movement; and (3) other transportation activity inputs. 

Personal Travel 

Personal travel activity and resulting fuel consumption were calculated for each target year using 

procedures that disaggregate households by demographic and economic attributes. Economic driver 

data, developed from U.S. Government data and macroeconomic model(s) of the domestic economy, 

formed the basis for household disaggregation. Modeling procedures were employed by ANL to project 

movement of households between various attribute 

classes, and vehicle holdings were projected in terms 

of the number and type of vehicles held by each household type. National totals were then developed by 

aggregating the vehicle holding estimates for each 

household type, accounting for the number of households of that type. Travel estimates, in terms of VMT, 

were calculated using the same approach, and based 

on the VMT of each household type. The basis for 

household transportation activity projection has been 

empirically established through analysis of the 1983-

84 Nationwide Personal Transportation Survey 

(NPTS) (FHWA, 1986; Mintz and Vyas, 1991). VMT 

are projected using this empirical relationship, and estimates of the elasticity of VMT to vehicle operating 

cost are then made. Energy consumption was estimated in each target year using VMT, shares of VMT 

by vehicle type, and exogenously developed vehicle 

characteristics. 

The following three models and an accounting 

procedure were employed to develop target year personal travel activity projections: 

1. The first model projected the target year distribution of households by their attributes. 

This model employed an iterative proportional 

fitting (IPF ) technique and projected the number of households in each cell of the household matrix - each of which is defined by various categories within six household attributes. 

2. The second model projected changes in vehicle ownership resulting from changes in 

income and cost of vehicle operation. The 

model applied estimated ownership changes 

to each target year household matrix such that 

the control values within each of the household attributes, excepting vehicle ownership, 

remained unchanged. 

3. The third model estimated the composition 

of household vehicle fleet by type (cars and 

trucks), size, technology, and fuel. 

4. An accounting procedure applied VMT per 

vehicle to vehicle ownership in each combination of household attributes. VMT and energy consumption were accumulated by vehicle type, size, and fuel. 

Each of these models is described separately in 

the following subsections. 

Iterative Proportional Fitting (IPF) 

This IPF model modified a control scenario matrix of household counts. A household matrix was 

developed from the 1983 NPTS data and upgraded to 

the year 1985 using published aggregate data. The 

procedure used in constructing the 1985 household 

matrix has been documented elsewhere (Appendix B 

of Mintz and Vyas, 1991). The matrix is defined by 

six attributes: (1) residential location (central city, 

suburb, rural); (2) household income; (3) age of householder; (4) household size; (5) number of drivers; and 

(6) number of vehicles. The household matrix has 

3,072 cells, some of which are illogical (such as 1 

person, 2 drivers). Illogical cells were replaced with 

zeros. 

Household shares within each attribute in each 

target year were developed exogenously using data 

from the Bureau of the Census and selected macroeconomic model runs. The projected total of households and shares of households in each category of an 

attribute were supplied to the IPF model. The model 

modified the control scenario household matrix to 

match the specified shares and total number of households. 

The IPF model treated household distribution 

within each attribute as a set of vectors. These vectors 

were scaled to match the specified shares and household total. Following the initial scaling, a gradual scaling technique was used to move in the direction of the 

target shares. The scaling process was repeated until 

closure was achieved for all attribute classes. Since 

B-13

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

vehicle ownership levels were estimated by the vehicle ownership model (described in the next section), 

shares within the sixth household attribute (number 

of vehicles held) were not specified, leaving it uncontrolled. This flexibility of an uncontrolled attribute 

helped to facilitate the model operation. The number 

of households in each class of vehicle ownership 

within the output matrix represents distribution of 

households using the control scenario (1985) relationship of vehicle ownership to other household attributes. 

Vehicle Ownership Projection (VOP) 

The VOP model projected the changes in vehicle 

ownership resulting from changes in the number of 

licensed drivers, disposable personal income, and annual fuel cost of vehicle operation. The model is based 

on historical household ownership rates. A target perdriver ownership rate was computed using disposable 

income and fuel cost. This target rate represented desired ownership if income and fuel cost were the only 

determinants. A parameter representing ownership 

responsibilities such as acquisition effort, disposal 

effort, parking requirements, and other indirect aspects 

was applied to adjust this target. The new ownership 

rate was used to estimate the number of household 

vehicles. 

The household matrix created by the IPF model 

was revised to match the projected household vehicle 

ownership. Household shares within the first five attributes remain constant while those within the sixth 

attribute (i.e., number of vehicles) were variable. A 

deviation measure was defined and its value for each 

class within the first five attributes was minimized. A 

set of simultaneous equations was solved using 

Lagrangian multipliers. 

Projection of Vehicle Fleet Composition 

The composition of household vehicles was projected for each household matrix cell using a vehicle 

choice model called the Disaggregate Vehicle Stock 

Allocation Model (DVSAM ). Vehicles are defined 

by type (auto, light truck), size (small, mid-size, fullsize auto; small pickup, small utility/minivan, standard pickup, large utility/standard van; or any other 

size classification), fuel (gasoline, diesel, methanol, 

ethanol, or compressed natural gas), and technology 

(stratified charge, direct injection, electric, fuel cell, 

or Brayton). 

The model computed vehicle composition based 

on an individual vehicle’s utility to households and 

household needs. A menu of vehicles classified by 

the previously mentioned vehicle attributes was supplied to the model. The menu specified characteristics of each vehicle available to households. Vehicles 

were characterized by price, operating cost, seating 

capacity, curb weight, and horsepower. These variables formed the basis for computing “utility” (analogous to consumer satisfaction). The household matrix provided demographic and economic attributes 

which, when combined with vehicle usage in miles, 

define household needs. Vehicle usage (VMT) was 

computed as a function of income, number of drivers, 

and number of vehicles. A logit model was applied to 

compute vehicle ownership shares. Several model enhancements facilitated modeling of limited range vehicles, and representation of supply constraints and/ 

or regulated market penetration. 

Activity/Energy Computation 

An accounting procedure was applied to compute 

personal travel activity in terms of VMT by vehicle 

type. Control scenario VMT per vehicle estimates for 

each cell in the household matrix were developed from 

the 1983 NPTS. These rates were adjusted within the 

procedure on the basis of changes in average vehicle 

operating cost per mile for each cell. The vehicle composition projection model computes ownership shares 

and share-weighted change in vehicle operating cost. 

Elasticity values were applied to this change. 

ANL assumed that VMT per vehicle remained 

nearly unchanged for a household matrix cell over time 

(with the exception of the effect of changes in vehicle 

operating cost). In other words, variation of VMT 

across household types is far greater than within household types. VMT per household vehicle remained 

stable during the period from 1977 to 1984 (Klinger 

and Kuzmyak, 1986). Some increases were observed 

in recent years, which were attributed to lower fuel 

prices and increased household income (DOC, 1991; 

FHWA, 1992). (A portion of the increase could be 

attributed to the method of computing average VMT 

per vehicle.) The assumption that VMT per vehicle 

for each cell remained nearly constant and was elastic relative to vehicle operating cost is reasonable. As 

households move from one cell of the matrix to another, they “acquire” the VMT per vehicle rate of that 

cell. Thus, this approach accounted for changes in 

VMT per vehicle due to increased household affluence, increased rate of driver licensing, changes in 

fuel price, and changes in vehicle technology. 

B-14

Appendix B: Emissions Modeling 

Goods Movement 

Energy and activity demand resulting from movement of 24 aggregate categories of commodities is 

estimated by this subcomponent of the TEEMS activity module. Changes in commodity demand/production were provided by growth indexes by two-digit 

SIC generated by a macro model. A model that 

projects shifts in mode shares among truck, rail, marine, air, and pipeline modes was used, followed by a 

procedure to compute ton miles of travel for each 

mode, VMT by fuel type for trucks, and energy consumption by operation type for non-highway modes. 

The model used 1985 control scenario data, which 

were compiled from railroad waybill sample and publications, waterborne commerce publications, transportation statistics, and other sources. The procedure 

used in developing the 1985 control scenario freight 

data has been documented in an ANL report 

(Appendix A of Mintz and Vyas, 1991). 

This goods movement model was not used for this 

retrospective analysis because of funding and time 

constraints. A procedure to estimate truck VMT by 

fuel type was employed in its place. Published historical VMT values (FHWA, 1988; 1992) were used 

along with VMT shares by fuel and truck type from 

Truck Inventory and Use Surveys (TIUS) (DOC, 1981; 

1984; 1990). 

Other Transportation Activities 

The activity/energy module also has other models for developing activity and energy use projections 

for air, fleet automobiles, and bus modes. Fleet automobile activity estimates from an earlier study (Mintz 

and Vyas, 1991) were used while other modes were 

not analyzed. 

Lead Emissions 

Estimates of lead emissions in the transportation 

sector were developed by Abt Associates based on 

changes in reductions of lead in gasoline. This estimation required the estimates of lead in gasoline consumed over the period from 1970 to 1990 and the 

amount of lead content in gasoline that would have 

been consumed in the absence of the CAA. These 

values were calculated using the quantity of both 

leaded and unleaded gasoline sold each year and the 

lead concentration in leaded gasoline in each target 

year. Data on annual gasoline sales were taken from a 

report by ANL that presented gasoline sales for each 

State in each target year. For the control scenario, data 

on the fraction of gasoline sales represented by leaded 

gasoline were used. For the no-control scenario, all of 

the gasoline sold was assumed to be leaded. Data on 

the lead content of gasoline was obtained from ANL 

for 1975 through 1990. For 1970 through 1975, the 

analysis assumed that the 1974 lead content was used. 

Estimation of No-control Scenario 

Emissions 

TEEMS emissions projections were carried out 

by ANL in the following three steps: 

1. Development of emission factors; 

2. Allocation of highway activity to States; and 

3. Development of highway pollutant estimates. 

The following subsections describe the procedures 

used for computing highway vehicle emissions. 

Development of Emission Factors 

EPA’s MOBILE5a Mobile Source Emission Factor model was used to provide all of the highway vehicle emission factors used to estimate 1975 to 1990 

emission rates (EPA, 1994b). Documentation of the 

MOBILE5a model is found in the User’s Guide for 

the MOBILE5 model.10 

Although the actual emission factors used by ANL 

are not documented in either the original ANL TEEMS 

model report or in the Pechan summary report, the 

Project Team provided direction that defined the emission factors to be used. For the control scenario, ANL 

was directed to use the official EPA emission factors 

prevailing at the time for each target year. For example, the official EPA emission factor being used in 

1980 for on-highway vehicle NOx was to be used to 

estimate 1980 control scenario on-highway vehicle 

NO emissions. For the no-control scenario, the offi­ x 

cial EPA emission factors used to estimate emissions 

in 1970 were to be used throughout the 1970 to 1990 

period. 

It is important to note that using the 1970 on-highway vehicle emission factors to estimate no-control 

scenario emissions for the entire 1970 to 1990 period 

may bias scenario emission differentials upward. This 

is because it is possible that technological changes to 

on-highway vehicles unrelated to CAA compliance 

10 EPA/OAR/OMS, “User’s Guide to MOBILE5,” EPA-AA-AQAB-94-01, May 1994; see also 58 FR 29409, May 20, 1993. 

B-15

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

strategies may have yielded incidental reductions in 

emissions. However, EPA Office of Mobile Sources 

(EPA/OMS) experts indicate that the two major technological changes in vehicles occurring during the 

period of the analysis –electronic ignition and electronic fuel injection– would have yielded negligible 

emission reductions in the absence of catalytic converters.11 

Another potential bias is introduced by assuming 

the CAA had no substantial effect on vehicle turnover. However, two factors render this potential bias 

negligible. First and foremost, under the no-control 

scenario retired vehicles would be replaced by new 

but equally uncontrolled vehicles. Second, no-control 

scenario vehicle use is greater in terms of VMT per 

year. This means no-control scenario vehicles would 

reach the end of their service lives earlier, offsetting 

to some extent the alleged incentive to retire vehicles 

later due to costs imposed by CAA control requirements. 

Allocation of Highway Activity to States 

TEEMS’ activity module generated national activity and energy estimates. These activity totals were 

allocated to States through a regionalization algorithm 

that used time series data on historical highway activity shares by State. A trend extrapolation methodology was used that stabilizes shifts after 5 years in the 

future. For the retrospective analysis, historical highway activity shares for each target year were developed using data published by the Federal Highway 

Administration (FHWA) (FHWA, 1988; 1992). 

Development of Highway Pollutant Estimates 

Highway emission estimates were calculated in 

both scenarios for each target year using VMT estimates generated by TEEMS and emission factors from 

MOBILE5a. Control scenario activity levels were 

adjusted for the no-control scenario using economic 

forecasts and historical data. 

Control Scenario Emissions Calculation 

Control scenario data for the transportation sector were compiled from several sources. Household 

counts and shares of households by six attributes were 

obtained from various editions of the Statistical Abstracts of the United States. Household income information was obtained from the control scenario run of 

the J/W model. Fuel prices were obtained from the 

Annual Energy Review (DOE, 1992) while vehicle fuel 

economy and aggregate VMT per vehicle were obtained from Highway Statistics (FHWA, 1988; 1992). 

B-4 lists data sources for the control scenario run. 

Table B-5 shows household shares prepared for 

the IPF model. The total number of households increased from 63.4 million in 1970 to 93.3 million in 

1990. A gradual shift from rural to urban was observed 

with movement to suburbs within urban areas. The 

effect of economic downturns in 1975 and 1980 was 

an increase in share for the lowest income category; 

more households moved to the highest income group 

from 1970 to 1990, while the lower middle income 

group share expanded and the upper middle income 

share declined. The rate of household formation was 

high during the 1970’s, which resulted in increases in 

smaller and younger households. The trend in younger 

households reversed after 1980 as household formation slowed. Average household size dropped from 

3.2 in 1970 to 2.67 in 1990. The number of licensed 

drivers increased throughout the analysis period as 

more and more young people were licensed to drive. 

Data for the VOP model included disposable income per capita, fuel price, overall personal vehicle 

fuel economy, and annual usage in terms of VMT. 

Table B-6 shows these data for each year in the analysis period. 

Data preparation for the model that projected 

household vehicle composition was limited to characterization of existing technology vehicles. Seven 

vehicle size and type combinations were characterized for 1975 and 1980 while one vehicle, minivan/ 

small utility, was added for 1985 and 1990. Control 

scenario vehicle characteristics are tabulated in Table 

B-7. TEEMS’ activity and energy computation procedure was executed to produce personal vehicle travel 

and energy consumption estimates. 

Commercial truck travel was not modeled but, 

historical data published by the FHWA (FHWA, 1987; 

1991) were used. FHWA publishes truck travel by 

three categories: 1) 2-axle, 4-tire trucks; 2) single unit 

11 Telephone conversation between Jim DeMocker, EPA/OAR and EPA/OMS/Ann Arbor Laboratory staff (date unknown). 

Nevertheless, the Project Team did consider reviewing emission factors for European automobiles to attempt to estimate no-control 

scenario emission factors for 1975 through 1990 reflecting the use of electronic fuel injection and electronic ignition but no catalytic 

converter. However, the Project Team concluded that differences in fuel/air mix ratios used in Europe would probably obscure any 

differences in emission rates attributable to the use of electronic fuel injection and electronic ignition. 

B-16

Data Item Mod el Sou rce

Appendix B: Emissions Modeling 

trucks; and 3) combination trucks. All 2-axle, 4-tire 

trucks were treated as light-duty trucks. VMT by personal light trucks were subtracted from the published 

totals to arrive at commercial light truck VMT. Diesel truck VMT shares of total VMT were obtained 

from TIUS (DOC, 1981; 1984; 1990). TIUS data were 

also used to split VMT by single unit and combination trucks. All combination trucks were assumed to 

be the heaviest, class 7 and class 8, while single unit 

trucks could be of any size class 3 through 8. Gasoline and diesel VMT totals were developed for these 

heavy-duty trucks and were kept constant for the control and no-control scenarios. 

Table B-4. Sources of Data for Transportation Sector Control Scenario Activity Projection. 

Data Item Mod el Sou rc e 

Ho useh old total, popula tion, house hold 

shares by four attribu tes (locatio n, income, 

age of h ead, and hou seho ld size). 

IPF Statistic al Abstrac t of the Un ited States, editions 96th, 

98th, 103 rd, 104th, 108th, and 11 3th. 

Ho useh old sha re s by num be r of drivers. IPF Statistic al Abstr ac ts and FHWA High wa y Statistics 

provided total driv ers. The with CAA distributio n of 

hou seh olds tren de d. 

Personal and Disp osab le inc ome. VOP J/W mod el outpu t and Statistical Abstrac ts. 

Ve hicle fle et on-road fu el econom y. VOP 

DVSA M 

FHWA High wa y Statistics. 

Fuel Pric es VOP 

DVSA M 

En ergy Inf orm ation Administration 's (EIA ) Annu al 

En ergy Review. 

Ve hicle Pric e DVSA M Ward's Automotive Yearboo ks 19 75-1983, Autom otive 

Ne ws Ma rket Data Book 198 5. 

IPF - Iterative Proportional Fitting

VOP - Vehicle Ownership Projection

DVSAM - Disaggregate Vehicle Stock Allocation Model

FHWA - Federal Highway Administration

EIA - Energy Information Administration

B-17

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-5. Distribution of Households by Demographic Attributes for Control Scenario. 

Ho useho ld (M illion ) 

Pop ulation (M illion ) 

63.4 

204 .0 

71 .1 

215.5 

80 .8 

227 .2 

86 .8 

237 .9 

93.3 

249.5 

At tribute Household Percent age, by Year 

1970 1975 1980 1985 1990 

Location 

Central C ity 

Suburb s 

Rur al 

33.2 

33.6 

33.2 

32 .0 

36 .0 

32 .0 

31 .9 

37 .0 

31 .1 

31 .6 

38 .1 

30 .3 

31.4 

38.3 

30.3 

Income (199 0 $ )* 

<$ 13,000 

$13 ,0 00 - $ 33,000 

$33 ,0 00 - $ 52,500 

> $52,500 

25.9 

34.0 

27.6 

12.5 

26 .5 

37 .2 

22 .7 

13 .6 

26 .6 

37 .4 

22 .4 

13 .6 

25 .9 

37 .7 

22 .2 

14 .2 

25.5 

38.0 

22.2 

14.3 

A ge of Househ older (YR ) 

<35 

35 - 44 

45 - 64 

> = 6 5 

25.4 

18.6 

36.3 

19.7 

29 .1 

16 .7 

34 .0 

20 .2 

31 .1 

17 .3 

31 .2 

20 .4 

29 .3 

20 .1 

29 .6 

21 .0 

27.4 

22.1 

29.0 

21.5 

Ho useho ld Size 

1 

2 

3 - 4 

> = 5 

17.2 

29.0 

33.0 

20.8 

19 .5 

30 .7 

33 .0 

16 .8 

22 .7 

31 .3 

33 .2 

12 .8 

23 .7 

31 .6 

33 .5 

11 .2 

24.6 

32.2 

32.8 

10.4 

Licensed Drivers 

0 

1 

2 

> = 3 

9.1 

27.8 

48.1 

15.0 

8.5 

27 .3 

49 .2 

15 .0 

8.1 

27 .0 

50 .5 

14 .4 

7.2 

26 .2 

52 .5 

14 .1 

6.6 

26.0 

53.5 

13.9 

Note: *Approximated to 1990 dollars.

B-18

1990 10,903 1.00 20.8 9,780

Appendix B: Emissions Modeling 

Table B-6. Economic and Vehicle Usage Data for Vehicle Ownership Projection �

Control Scenario. 

Year 

Disposable Income 

per Capita (84 $) 

Fuel Price 

(84 $)/Gallon Miles/Gallon VMT /Vehicle 

1970 7,597 0.92 13.5 10,143 

1971 7,769 0.88 13.5 10,246 

1972 7,990 0.84 13.4 10,350 

1973 8,436 0.84 13.3 10,184 

1974 8,270 1.06 13.4 9,563 

1975 8,340 1.03 13.5 9,729 

1976 8,553 1.02 13.5 9,833 

1977 8,742 1.01 13.8 9,936 

1978 9,070 0.97 14.0 10,143 

1979 9,154 1.21 14.4 9,522 

1980 9,052 1.53 15.5 9,212 

1981 9,093 1.55 15.9 9,212 

1982 9,050 1.38 16.7 9,419 

1983 9,239 1.27 17.1 9,419 

1984 9,691 1.20 17.8 9,550 

1985 9,881 1.09 18.2 9,568 

1986 10,139 0.88 18.3 9,672 

1987 10,174 0.88 19.2 10,090 

1988 10,564 0.86 19.9 10,100 

1989 10,713 0.90 20.3 9,819 

1990 10,903 1.00 20.8 9,780 

B-19

No te: *A verag e fo r all veh icles of ea ch type and size. 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-7. Control Scenario Personal Characteristics.* 

197 5 19198 8 00 

VVe e h h iiclcle T e T y y ppee an d Size (S(Se e aattss)) 

Cu Cu rrbb 

We Weigigh h tt (l(lb b )) 

En gine Po Po wwe e rr (hp) Fu el 

EEc c ononom om yy 

(m p g) 

Cu rb 

Weigh t (lb ) 

En gine Po Po wwe e rr (hp) 

Fu el 

EEc c ononom om yy 

(m (m p p gg)) 

Au tom o bile 

Small (2-4) 2, 770 91 17.2 2, 535 83 19 .6 

Compa ct (4) 3, 625 115 14.6 3,335 105 16 .9 

Mid-size (5) 4, 140 128 13.3 3,730 116 15 .1 

Larg e (6) 4, 900 155 12.2 4,840 153 13 .3 

Light t ruc k 

Std. tru ck 4, 530 141 11.2 4,455 143 12 .6 

Compa ct 3, 745 108 14.2 3,580 99 15 .9 

Std. Van/Std. 5, 010 145 9.9 4,975 144 11 .4 

Utility (11 -15 ) 

Miniv an/Sm all 

Utility (7 -8) 

198 5 19199 9 00 

Ve h icle T y pe an andd S Siizzee (S(Se e aattss)) Cu rb 

Weight (lb ) 

EEn n giginene Po we r ((hp) hp) Fu Fu eell 

Ec onom y (m p g) 

Cu rb 

We Weigighh tt (lb ) 

EEn n giginene Po Po wwe e rr (hp) Fu Fu eell 

Ec onom y (m (m p p gg)) 

Au tom o bile 

Small (2-4) 2, 225 75 22.7 2, 135 75 24.9 

Compa ct (4) 2, 775 90 19.3 2, 595 90 22.0 

Mid-size (5) 3,180 108 16.8 3,050 108 19.5 

Larg e (6) 3,975 135 14.6 3,705 130 17.1 

Ligh t t ruc k 

Std. tru ck 4,160 132 13.1 4,000 128 14.1 

Compa ct 3, 495 90 17.2 3, 360 90 18.9 

Std. Van/Std. 4, 920 142 12.4 4, 765 138 12.9 

Utility (11 -15 ) 

Miniv an/Sm all 

Utility (7 -8) 4, 125 101 16.7 3, 910 108 18.2 

197 5 19198 8 00 

VVe e h h iiclcle T e T y y ppee an d Size (S(Se e aattss)) 

Cu Cu rrbb 

We Weigigh h tt (l(lb b )) 

En gine Po Po wwe e rr (hp) Fu el 

EEc c ononom om yy 

(m p g) 

Cu rb 

Weigh t (lb ) 

En gine Po Po wwe e rr (hp) 

Fu el 

EEc c ononom om yy 

(m (m p p gg)) 

Utility (11 -15 ) 

Miniv an/Sm all 

Utility (7 -8) 

198 5 19199 9 00 

Ve h icle T y pe an andd S Siizzee (S(Se e aattss)) Cu rb 

Weight (lb ) 

EEn n giginene Po we r ((hp) hp) Fu Fu eell 

Ec onom y (m p g) 

Cu rb 

We Weigighh tt (lb ) 

EEn n giginene Po Po wwe e rr (hp) Fu Fu eell 

Ec onom y (m (m p p gg)) 

Utility (11 -15 ) 

Miniv an/Sm all 

Utility (7 -8) 4, 125 101 16.7 3, 910 108 18.2 

Note: *Average for all vehicles of each type and size. 

B-20

No te: *A ppro ximate d to 1990 do llars.

Appendix B: Emissions Modeling 

Table B-8. Distribution of Households by Income Class 

for No-control Scenario. 

Household S hares (%), by Year 

At tribute 1975 1980 1985 1990 

Income (1990 $ )* 

<$ 13,000 26.3 26.2 25.3 24.7 

$13, 00 0-33,000 37.3 37.6 38.4 38.4 

$33, 00 0-52,000 22.8 22.6 22.0 22.6 

>$ 52,000 13.6 13.6 14.3 14.3 

Note: *Approximated to 1990 dollars. 

No-control Scenario Emissions 

The control scenario data were modified to reflect no-control scenario emissions using economic 

changes predicted by the J/W model, EPA, and ANL. 

The J/W model predicted a slight loss of employment 

and drop in GNP in terms of nominal dollars. However, the lower rate of inflation coincided with a real 

GNP rise. ANL’s information from the model did not 

include any indexes for converting nominal income 

to real income. ANL assumed real income changes to 

be similar to those of real GNP and modified household shares by income classes accordingly. The model 

also predicted a slight drop in refined petroleum price 

beginning in 1973. The predicted drop was the largest (5.35 percent) in 1973, reached the lowest level 

(2.16 percent) in 1984, then increased to a second peak 

(3.44 percent) in 1988, and dropped again from 1989 

to 1990. Since these changes were inconsistent with 

historical patterns of leaded and unleaded gasoline 

price change, ANL developed an estimate of changes 

in fuel price resulting from the cost of removal of lead 

from gasoline and other infrastructure costs involved 

with distributing a new grade of fuel. Subsequently, 

EPA provided a set of fuel costs for use in the analysis. Both ANL and EPA fuel prices followed a similar 

pattern, although their magnitudes differed. The 

no-control scenario was analyzed with EPA fuel 

prices. ANL also established a relationship with cost 

of regulation/emission control technology, and the 

effect of costs on vehicle price and fuel economy directly from the EPA publication Cost of A Clean Environment (EPA, 1990). These changes were used in 

the analysis. 

The IPF model was executed for target years 1975, 

1980, 1985, and 1990 using a set of revised household shares by income class. Table B-8 shows the revised shares. Comparing Table B-8 no-control scenario shares with those in Table B-5 for the control 

scenario, there seems to be a slight shift away from 

travel by the lowest income group and toward the 

middle income groups. 

The vehicle ownership projection model was executed for the above four target years using the data 

listed in Table B-9. Changes in fleet characteristics 

are summarized in Table B-10. 

B-21

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-9. Economic and Vehicle Usage Data for Vehicle Ownership 

Projection � No-control Scenario. 

Year 

Disposable 

Income per 

Ca pita (84 $) 

Fuel Price 

(84 $)/Gallon 

Miles/ 

Gallon VMT /Vehicle 

1970 7,597 0.91 13.5 10,143 

1971 7,769 0.88 13.5 10,247 

1972 7,990 0.83 13.4 10,353 

1973 8,463 0.84 13.3 10,189 

1974 8,297 1.06 13.4 9,569 

1975 8,406 1.02 13.5 9,736 

1976 8,600 1.01 13.5 9 ,85 4 

1977 8,795 1.01 13.8 9 ,96 3 

1978 9,126 0.96 14.0 10,174 

1979 9,216 1.19 14.4 9 ,55 7 

1980 9,114 1.51 15.5 9 ,23 4 

1981 9,158 1.53 16.0 9 ,23 4 

1982 9,116 1.36 16.8 9 ,44 7 

1983 9,312 1.25 17.2 9 ,45 0 

1984 9,775 1.18 17.9 9 ,58 2 

1985 9,976 1.06 18.3 9 ,60 7 

1986 10,244 0.84 18.4 9 ,73 8 

1987 10,282 0.86 19.4 10,201 

1988 10,676 0.83 20.1 10,214 

1989 10,827 0.88 20.5 9 ,90 2 

1990 11,019 0.97 21.0 9,849 

Note: The effect of reductions in vehicle price and vehicle operating cost, and increases in fuel economy 

and horsepower were reflected in the menu of the vehicle choice model (DVSAM). Vehicle weight and 

seating capacity were kept unchanged from the with CAA run. Table IV-7 shows the changes in various 

vehicle attributes. 

B-22

Appendix B: Emissions Modeling 

Table B-10. Percent Changes in Key Vehicle Characteristics Between 

the Control and No-control Scenarios. 

1975 1980 

Vehicle Price mpg HP Price mpg HP 

Small Auto -2.35 0.01 0. 59 -2.76 0.22 1.81 

Compact Auto -2.35 0.01 0. 59 -2.76 0.22 1.81 

M idsize A uto -2.35 0.01 0. 59 -2.76 0.22 1.81 

Large Auto -2.35 0.01 0. 59 -2.76 0.22 1.81 

Small Tru ck -1.30 0.01 0. 59 -2.71 0.22 1.81 

Std Truck -1.30 0.01 0. 59 -2.71 0.22 1.81 

Std Van /Util -1.30 0.01 0. 59 -2.71 0.22 1.81 

M Vn/Sm 

Utility 

1985 1990 

Vehicle Price mpg HP Price mpg HP 

Small Auto -3.25 0.62 2. 20 -2.94 0.95 2.77 

Compact Auto -3.25 0.62 2. 20 -2.94 0.95 2.77 

M idsize A uto -3.25 0.62 2. 20 -2.94 0.95 2.77 

Large Auto -3.25 0.62 2. 20 -2.94 0.95 2.77 

Small Tru ck -2.53 0.62 2. 20 -2.58 0.95 2.77 

Std Truck -2.53 0.62 2.20 -2.58 0.95 2.77 

Std Van /Util -2.53 0.62 2. 20 -2.58 0.95 2.77 

M Vn/Sm 

Utility 

-2.53 0.62 2. 20 -2.58 0.95 2.77 

Note: *Average change for each vehicle size and type combination. 

B-23

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Utilities 

The electric utility industry retrospective analysis was prepared using two different utility simulation models. ICF utilized its CEUM to estimate control and no-control scenario emissions for SO2

, TSP, 

and NO in each of the target years. ANL’s ARGUS x 

model was used to estimate electric utility CO and 

VOC emissions for the same period. This mix of modeling approaches was used because, while CEUM was 

determined to be a better tool for examining fuel shifts 

that were affected by the CAA than ARGUS, the 

CEUM model was not initially set-up to evaluate CO 

or VOC emissions. Although CEUM can be (and eventually was) configured to provide emission estimates 

for pollutants other than SO2

, NOx, and PM, ARGUS 

was already configured to provide VOC and CO emissions. However, it should also be noted that VOC and 

CO emissions from utilities are quite low, as efficient 

fuel combustion reduces both pollutants. Thus, for this 

sector, the presence or absence of the CAA would not 

produce any different VOC or CO control techniques. 

VOC and CO emission rates for this sector differ primarily based on the fuel and boiler type. Therefore, a 

simpler modeling approach was judged to be acceptable and appropriate for these two pollutants. This 

chapter presents the methodology used to estimate 

utility emissions under the control and no-control scenario using the CEUM and ARGUS models. The 

method used by Abt Associates to estimate lead emissions from utilities is also presented. 

Overview of Approach 

The CEUM model uses industry capacity data and 

specific unit-by-unit characteristics, operating costs 

data, electricity demand estimates under the control 

and no-control scenario, and historical fuel prices to 

estimate SO2

, TSP, and NOx emissions for 1980, 1985, 

and 1990. Changes in electric utility emissions, costs, 

and regional coal production were developed using 

ICF’s CEUM with a calibration to historical electricity generation, fuel use, and emissions. The ARGUS 

model, which was used by ANL to estimate utility 

VOC and CO emissions, is driven by operating costs, 

industry capacity and generation data, demand for 

coal, and unit-level operating characteristics. The J/ 

W model is used to incorporate predicted changes in 

electricity demand under the no-control scenario. Finally, Abt Associates relied upon energy use data, the 

Trends data base, and the Interim 1990 Inventory to 

calculate utility lead emissions based on coal consumption. The approaches used by each of these three 

contractors are discussed individually in the following sections. 

Establishment of Control Scenario Emissions 

A common feature of the approaches taken by ICF 

and ANL was to identify conditions that are inputs to 

the CEUM and ARGUS models, respectively, in the 

control scenario. Later in the analysis, these variables 

were revised to reflect no-control scenario conditions. 

The next section discusses the specific assumptions 

used in the CEUM analysis. 

Key Assumptions in the Development of the 

ICF Analysis 

At EPA’s direction, ICF made several assumptions in conducting this analysis for purposes of consistency with other ongoing EPA efforts assessing the 

effects of the CAA. These include the macroeconomic 

assumptions regarding the effects of the CAA on economic growth, or more specifically, electricity demand, developed from other EPA commissioned efforts. Each is described briefly below. 

Pollution Control Equipment Costs 

Only limited actual data were available for this 

analysis on the historical capital and operating costs 

of pollution control equipment. Accordingly, for this 

analysis, the actual capital and operating costs of 

scrubbers were estimated using EPA scrubber cost 

assumptions adjusted to reflect actual data from a survey of scrubbed power plants with scrubbers installed 

during the 1970s and early 1980s. For those power 

plants with actual survey data, actual capital costs were 

used. For other pre-1985 scrubbers, ICF relied on the 

average costs from the survey data. For particulate 

control equipment (primarily electrostatic precipitators, or ESPs), costs were estimated based on limited 

actual data, and a 1980 Electric Power Research Institute (EPRI ) study of ESP and baghouse costs. Based 

on this information, ESPs were estimated to cost an 

average of $50 per kilowatt (in 1991 dollars). The 

development of more detailed data on actual power 

plant pollution control costs was beyond the scope of 

ICF’s analysis. ICF concluded that such an effort 

would not significantly change the national or regional 

cost estimates developed by its approach. 

B-24

Appendix B: Emissions Modeling 

Electricity Demand and Fuel Prices 

Consistent with other EPA ongoing analyses, ICF 

assumed that the CAA resulted in a reduction in electricity demand of 3.27 percent in 1980, 2.77 percent 

in 1985, and 2.97 percent in 1990. Also consistent 

with these studies, ICF assumed that natural gas prices 

and oil prices would not be affected by the CAA. Coal 

prices were estimated to change in line with increases 

and decreases in demand for specific coal supplies 

(and consistent with ICF’s detailed modeling of coal 

supply and demand). The average prices of all residual 

oils consumed were also estimated to change due to a 

greater use of more expensive lower sulfur residual 

oils under the CAA. 

Coal, Nuclear, Hydro, and Oil/Gas Capacity 

At EPA’s direction, ICF’s approach was based 

on the assumption that no changes in the amount of 

nuclear, coal, hydro, or oil/gas stream or combined 

cycle capacity would be built or in place in 1980, 1985, 

or 1990. Given that the driving factors associated with 

the actual decisions to build new baseload capacity 

were not based solely on economics but entailed financial, regulatory, and political factors as well, the 

actual effect of the CAA on these build decisions is 

very uncertain. To the extent that more coal-fired 

power plants would be built and fewer oil/gas-fired 

power plants constructed, the actual emissions reductions associated with the CAA would be greater than 

those estimated by ICF, while the estimated costs of 

the CAA would be greater (because fewer, lower-cost, 

coal-fired power plants would be on line under the 

CAA). However, the CAA had virtually no effect on 

the costs of constructing new coal-fired power plants 

that came on line prior to about 1975 and a relatively 

moderate cost effect on coal-fired power plants that 

came on line through the early 1980s (since these 

power plants were not required to install scrubbers). 

Since a large majority of coal-fired power plant capacity came on line prior to 1975, ICF concluded that 

the effect of the CAA on the amount of total coalfired capacity was not expected to be very large. 

Natural Gas Consumption 

The analysis assumed that the amount of natural 

gas consumed under the no-control scenario could not 

exceed the actual amount of consumption in 1980, 

1985, and 1990. In part, because of natural gas price 

regulation and the oil price shocks of the 1970s, natural gas was often unavailable to electric utilities in the 

early 1980s. Since the CAA is relatively unrelated to 

the questions of supply availability and price regulation of natural gas, ICF assumed that no additional 

gas supplies would be available if the CAA had never 

been adopted. It is possible, however, that in the absence of the CAA, industrial and commercial users of 

natural gas would have used more oil or coal. To the 

extent that this would have occurred, there would have 

been more natural gas supplies available to the electric utility sector. This increase in supply would have 

resulted in an increase in the estimated costs of the 

CAA, and a corresponding decrease in the estimated 

emission reductions. ICF concluded, however, that this 

effect would not be very significant. 

State and Local Environmental Regulations 

At EPA’s direction, ICF assumed that there would 

be no State and local emission limits or other emission control requirements under the no-control scenario. Accordingly, ICF assumed that there would be 

no SO2

, NOx, or TSP emission limits under the 

no-control scenario and that all scrubbers, NOx controls, and ESPs/baghouses (at coal-fired power plants) 

were installed as a result of the CAA. (The more limited amount of particulate control equipment installed 

at oil-fired plants was assumed to have been installed 

prior to the passage of the CAA.) In the case of particulate control equipment, some ESPs and other 

equipment were installed at coal plants prior to the 

1970 CAA. To the extent that this is the case, the estimates of the costs of meeting the CAA have been 

overstated. ICF concluded, however, that the amount 

of such capacity was not substantial. 

Retirement Age 

The analysis assumed that unit retirement age was 

constant between the control and no-controls scenarios. Adoption of this assumption might bias the 

emission reduction estimates upward to the extent 

turnover rates of older (and presumably higher-emitting) units may be slower under the control scenarios, 

because more significant CAA control requirements 

focused on new units. However the vast majority of 

existing coal and oil capacity was built after 1950 and 

it is generally acknowledged that a relatively short 

technical plant lifetime would be about 40 years. As 

such, even if the no-control scenarios resulted in no 

life-extension activity, there would be virtually no 

effect over the 1970 to 1990 timeframe of the analysis. 

B-25

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

ICF 1975 Control Scenario Emissions 

The 1975 emissions under both scenarios were 

calculated differently than emissions in 1980, 1985, 

and 1990. In calculating or estimating 1975 SO2

 emissions for the control scenario (i.e., “actual” 1975), the 

weighted average emission rates at the State level, in 

the year 1975 were estimated, based on plant level 

average sulfur content of fuel deliveries from Federal 

Energy Regulatory Commission (FERC ) Form 423 

and assumed AP-42 sulfur retention in ash. These 

weighted average emission rates were then applied to 

actual State-level electric utility fuel consumption in 

the year 1975 (DOE, 1991). In the case of NOx emissions, first, an estimate of Statewide NOx emissions 

in the year 1975 was derived based on the use of the 

same NOx

 emission rates, by fuel type, as developed 

for the 1980 no-control scenario modeling runs. These 

emission rates were specific to the fuel type (coal, oil, 

or natural gas). These Statewide NOx emission rates 

or factors were then applied to actual fuel consumed 

by electric utilities in the year 1975, in order to obtain 

estimated “actual” 1975 emissions. As before, the fuel 

consumption at a State level was derived from the State 

Energy Data Report (DOE, 1991). ICF calculated the 

weighted average heat content (BTU/lb) by State from 

the 1975 FERC Form 423 data and used these figures 

with the TSP emission factors (lbs/ton) to derive emission rates by State (lbs/MMBTU). These emission 

rates were then applied to 1975 fuel consumption estimates obtained from the State Energy Data Report. 

For the control scenario 1975 estimates, ICF used the 

1975 factors. 

For the remaining target years, ICF used the results of CEUM runs that provided fuel consumption 

figures in 1980, 1985, and 1990, respectively. Emissions were then calculated using the appropriate emission factors for each year. 

ARGUS Modeling Assumptions 

The portion of the electric utility sector analysis 

conducted by ANL with the ARGUS model is described in this subsection. ARGUS contains four major components: BUILD, DISPATCH, the Emissions 

and Cost Model, and the Coal Supply and Transportation Model (CSTM). An overview of ARGUS can 

be found in Veselka et al (1990). Only the DISPATCH 

and CSTM modules were used for the present analysis. A brief description of the ARGUS components 

used in this analysis is found in the following subsections. 

DISPATCH Module 

The DISPATCH module contains a probabilistic 

production-cost model called the Investigation of 

Costs and Reliability in Utility Systems (ICARUS ). 

This module calculates reliability and cost information for a utility system. ICARUS represents detailed, 

unit-by-unit operating characteristics such as fuel cost, 

forced outage rate, scheduled maintenance, heat rate, 

and fixed and variable operating and maintenance 

(O&M ) costs. These components are used to efficiently compute system reliability (such as loss-ofload probability and unserved energy) and production 

costs. 

The input data required by ICARUS include 

monthly load duration curves, annual peak demands, 

and, for both new and existing units, unit sizes, capital costs, fixed and variable O&M costs, fuel types 

and costs, heat rates, scheduled maintenance, and 

equivalent forced outage rates. The output from 

ICARUS includes annual summaries of capacity, generation, cost, and reliability for the entire generating 

system. 

CSTM Module 

The CSTM module determines the least-cost combination, on a per BTU basis, of coal supply sources 

and transportation routes for each demand source. 

First, it estimates coal market prices based on regional 

demands for coal from all economic sectors. To generate market prices, CSTM estimates regional coal 

production patterns and coal transportation routes. The 

CSTM input data are grouped into three major categories: demand, supply, and transportation. CSTM 

uses supply curves from the Resource Allocation and 

Mine Costing (RAMC ) Model (DOE, 1982). Every 

region has a separate curve for one or more of the 60 

different coal types that may be produced in that region. CSTM modifies the original RAMC supply 

curve by dividing the single RAMC curve into two 

curves, one representing deep mines and the other representing surface mines, but still uses the same ranges 

for heating values and mine prices that define the supply curves in RAMC. Prices fluctuate as a result of 

different mining methods, size of mining operations, 

reserve characteristics, and depletion effects. 

The transportation data defines the network that 

connects 32 coal supply origins with 48 demand centers. Transportation cost is affected by distance, terrain, congestion, variable fuel costs, cost escalators 

B-26

Appendix B: Emissions Modeling 

for fuels and facility upgrades, and competition. 

CSTM first computes the production cost for each coal 

supply region and coal type. It then matches supply 

sources with transportation routes to find the lowest 

delivered costs. 

Coal demand for a particular region is based on 

the amount, geographic region, economic sector, and 

range of coal types. There are 44 domestic demand 

regions. CSTM allows demand to be met by one, or a 

combination of, different supply regions. 

The ARGUS input data for existing units are based 

on the Argonne Power Plant Inventory (APPI ). APPI 

is a data base of operating and planned generating units 

in the United States that was current through 1988 at 

the time of ANL’s analysis. This data base is updated 

annually based on information in the regional North 

American Electric Reliability Council (NERC ) reports, reports from the Energy Information Administration (EIA), and other sources. Unit operating characteristics (fixed O&M, variable O&M, heat rate, 

forced outage rate, and scheduled maintenance) are 

based on regional data as defined in the EPRI report 

on regional systems and other historic data (EPRI, 

1981). 

ANL used the 1988 inventory to generate a 1990 

inventory. The 1990 inventory was then used to generate a separate unit inventory for the target years 

1975, 1980 and 1985. The target year inventories were 

generated by removing units whose on-line year was 

greater than the target year, from their respective inventory. The regional capacity totals in these preliminary inventories were tabulated by major fuel category 

(nuclear, coal, oil and gas steam) and compared to the 

regional historic NERC totals. This review identified 

capacity differences, especially in 1975 and 1980 inventories. The original plan was to add phantom units 

to match the regional historic totals. However, based 

on the need for State-level emissions, it was decided 

that a more thorough review of the unit inventories 

was required. 

ANL’s detailed review included an examination 

of the nuclear and coal units greater than 100 megawatt equivalent (MWe) in each target year. Missing 

units, with the appropriate unit size and State code, 

were added so that the regional totals were comparable. The availability of coal units was based on the 

on-line year of the unit as reported in the EIA report 

Inventory of Power Plants in the United States (DOE, 

1986). The coal units were also checked against the 

EIA Cost and Quality Report (EIA, 1985) to verify 

the existence of flue gas desulfurization (FGD ) systems in each of the target years. The nuclear unit inventories were verified with the EIA report An Analysis of Nuclear Power Plant Operating Costs (DOE, 

1988). The review also included oil and gas steam 

units greater than 100 MWe. The total capacity of the 

oil and gas steam units were compared because many 

units switched primary fuel from oil to gas during the 

relevant time period. The oil and gas units were compared to historic inventories based on information provided by Applied Economic Research. In addition to 

thermal generation, the hydro and exchange energy 

was reviewed. For each target year, the hydro generation and firm purchase and sale capacity data was adjusted to reflect the historic levels. These two components, hydro and firm purchase and sales, are accounted for first in the loading order. If these variables are overestimated, there will be less generation 

from coal units. Likewise, if they are underestimated, 

there will be too much coal generation. The hydro and 

firm purchases and sales can vary significantly from 

year to year because of weather conditions and other 

variables. Therefore, it was important that they be 

accurately represented. 

No-control Scenario Emissions 

In order to calculate utility emissions under the 

no-control scenario, inputs to both the CEUM and 

ARGUS models were adjusted to reflect no-control 

scenario conditions. The changes made to each 

model’s base year input files are discussed separately 

in the following sections. 

ICF Estimates of SO2

, TSP, and NOx Emissions 

in the No-control Scenario 

As described earlier, ICF utilized a different methodology to calculate 1975 emission estimates. Rather 

than relying on the use of detailed modeling runs, ICF 

based the 1975 emission estimation on historic fuel 

consumption and sulfur content data in 1975. This 

subsection first outlines the process used to calculate 

no-control scenario emissions in 1975 and then presents the methods used for the remaining target years. 

1975 Utility SO2

, NOx, and TSP Emissions 

To develop State-level no-control scenario utility 

SO2

 emissions, ICF developed no-control scenario SO2 

emission rates. A reasonable surrogate for these emission rates is SO2

 rates just prior to the implementaB-27

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

tion of the SIPs under the CAA. ICF developed 1972 

rates (based on the earliest year available for FERC 

Form 423) and compared these with 1975 rates. In 

each State, the greater of 1972 or 1975 rates was used 

in the calculation of SO2

 emissions in the absence of 

the CAA. To develop State-level no-control scenario 

SO2

 emissions, no-control scenario fuel consumption 

data were needed. ICF assumed that the demand for 

electricity in 1975 would be 2.73 percent higher than 

the actual energy sales in 1975. This assumption is 

identical to the no-control scenario electricity demand 

projections derived from the J/W projections. For the 

purpose of this analysis, it was further assumed that 

this increment in demand would have been met in 1975 

from the oil and coal-fired plants in each State. The 

increase in consumption of these fuels was assumed 

to be in the same proportion as their share in the 1975 

total energy mix for electricity generation in that State. 

It was assumed that the generation of nuclear, gasfired, and other electricity generation would not 

change. A sensitivity case without an assumed electricity demand change was also calculated. (The sensitivity analysis results are presented later in this appendix.) 

For NOx

 emissions under the no-control scenario, 

it was also assumed that the 1975 electricity sales 

would have been 2.73 percent higher than was the case 

in 1975. No-control scenario TSP emissions in 1975 

were based on national emission rate numbers from 

EPA that were converted to pounds per million BTU 

using the average energy content of fuels in each State. 

No-control scenario TSP emissions were calculated 

based on 1970 emission factors (Braine, Kohli, and 

Kim, 1993). 

1980, 1985, and 1990 Utility Emissions 

For 1980, 1985, and 1990, ICF calculated 

no-control scenario emissions based on fuel consumption figures from the CEUM runs, and 1970 emission 

factors from EPA. 

Electric utility SO2

 emission estimates are approximately 10 million tons (or about 38 percent) 

lower by 1990 under the control scenario than under 

the no-control scenario. Most of this estimated difference results from the imposition of emission limits at 

existing power plants through the SIPs under the 1970 

CAA. Most of these SIPs were effective by 1980 (with 

some not fully effective until 1985). Most of the additional reductions that occurred during the 1980s were 

the result of the electric utility NSPS, which required 

trol equipment. 

the installation of 70 to 90 percent SO2

 removal conBy contrast, electric utility NOx emission estimates under the control scenario are only about 1.2 

million tons, or 14 percent, lower than under the 

no-control scenario by 1990. This occurs because, 

under the implementation of the 1970 CAA, only a 

few existing power plants were subject to NOx emission limits. Virtually all of the estimated reductions 

are the result of NOx

 NSPS, which generally required 

moderate reductions at power plants relative to uncontrolled levels. In addition, electricity demand is 

estimated to be about 3 percent lower under the control scenario. This decrease reduces the utilization of 

existing power plants and also contributes to lower 

NO emissions (and other pollutants as well). x 

Electric utility annualized costs (levelized capital, fuel, and O&M) are estimated to be $0.2 billion 

lower in 1980, $1.5 billion higher in 1985, and $1.9 

billion higher in 1990 under the control scenario. Note, 

however, that this reflects the effects of two offsetting factors: (1) the higher utility compliance costs 

associated with using lower sulfur fuels, and the increased O&M and capital costs associated with scrubbers and particulate control equipment; and (2) lower 

utility generating costs (fuel, operating and capital 

costs) associated with lower electricity demand requirements. In 1980, the increase in fuel costs due to 

higher generation requirements (under the no-control 

scenario), was larger than the decrease in capital and 

O&M costs and thus yielded a cost increase over the 

control case. 

However, lower electricity demand for the utility 

sector would translate into higher costs in other sectors (as electricity substitutes are used). This effect 

was captured to some extent by the original J/W macroeconomic modeling conducted for the present analysis. 

Average levelized U.S. electricity rate estimates 

are approximately 3 percent higher under the control 

scenario during the 1980s. Note that year by year, electric utility revenue requirements and capital expenditures (not estimated by ICF) would be estimated to 

have increased by a greater percentage particularly in 

the 1970s and early 1980s as incremental capital expenditures for scrubbers and ESPs were brought into 

the rate base. 

B-28

Appendix B: Emissions Modeling 

Significant shifts in regional coal production are 

estimated to have occurred between the control and 

no-control scenarios. High sulfur coal producing regions such as Northern Appalachia and the Midwest/ 

Central West are estimated to have lower production 

under the control scenario, while lower sulfur coal 

producing regions such as Central and Southern Appalachia are estimated to have higher coal production.12 

ARGUS No-control Scenario 

Regional fuel prices, for the thermal units, were 

based on historic information from the EIA Form 423 

data for the year 1977, 1980 and 1985. The 1977 data 

was used for 1975. Fixed and variable O&M costs 

were adjusted from the 1988 level, and all cost data 

were converted to 1985 dollars. 

The load data were based on regional historic 

NERC data for each of the target years. The shapes of 

the monthly load duration curves are the result of 

modifications based on the data in the EPRI report on 

regional systems (EPRI, 1981). The shapes were modified to match the projected 1988 monthly load factors 

for the NERC regions. These load shapes were held 

constant for all years. 

The actual peak-loads were selected from historic 

information and used with the existing load duration 

curves. The system was dispatched so that the calculated generation could be compared with historic data. 

Discrepancies were resolved by adjusting the peak 

load so that the annual generation was on target. This 

procedure was repeated for each of the target years. 

The electric utilities were expected to have an increase in generation as identified by the J/W data. 

Table B-11 identifies the increase in national level 

generation by year. The national level increase in generation was applied to each power pool. 

In addition to load changes, coal units with FGD 

equipment were modified. These units had their FGD 

equipment removed along with a 3 percent decrease 

in heat rate, a 2 percentage point decrease in forced 

outage rate, and a 50 percent decrease in their fixed 

and variable O&M costs. These changes were incorTable B-11. J/W Estimates of 

Percentage Increases in National 

Electricity Generation Under 

No-control Scenario. 

porated into the ARGUS model for each of the target 

years. Model runs were then conducted to arrive at 

estimates of VOC and CO emissions in the no-control 

scenario. 

Estimation of Lead Emissions from 

Utilities 

In order to estimate lead emissions from electric 

utilities in each of the target years, data from three 

different sources were used. Energy use data for the 

control and no-control scenarios were obtained from 

the national coal use estimates prepared for the section 812 analysis by ICF (Braine and Kim, 1993). The 

Trends data base provided emission factors and control efficiencies, and the Interim 1990 Inventory identified utility characteristics. The ICF data bases provided the amount of coal consumed for both the control and no-control scenarios in each of the target years. 

A correspondence between the Interim Inventory and 

the ICF data base was achieved through the plant name 

variable. Using emission factors for lead and control 

efficiencies for electric utilities, estimates of lead 

emissions per plant per year were calculated. These 

factors were obtained from the Trends data base. It 

was assumed that pollution control on coal-burning 

power plants under the no-control scenario would be 

the same as the pollution control level in 1970. Therefore, the control efficiency from 1970 is used as the 

basis for the no-control case. 

Year Percentage 

Increase 

1975 2.7% 

1980 3.3% 

1985 2.8% 

1990 3.0% 

12 At EPA’s direction, ICF’s analysis did not estimate the effect of shifts in non-utility coal consumption on regional coal 

production, nor did it consider the possibility that fewer new coal powerplants might have been built due to the CAA as discussed 

earlier. Both of these factors could result in a greater estimated change in total U.S. coal production than estimated herein although the 

difference is not likely to be very significant. 

B-29

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

CEUM Sensitivity Case 

In addition to comparing actual (control scenario) 

historical costs and emissions with the higher electricity demand under the no-control scenario, ICF also 

evaluated emissions in a sensitivity case without the 

CAA (i.e., under the no-control scenario) with the 

same electricity demand (versus the no-control scenario with higher demand). The purpose of this sensitivity analysis was to isolate the incremental electric 

utility compliance costs and reductions in emissions 

associated with the CAA from the lower resulting 

generation costs and emissions due to lower estimated 

electricity demand under the CAA. The incremental 

effects of the CAA when compared with this case indicate: 

•	 Estimated reductions in emissions due to the 

CAA are somewhat lower if measured against 

the sensitivity case without the CAA with the 

same electricity demand than the emissions 

without the CAA with lower demand. This 

occurs because lower electricity demand under the no-control scenario sensitivity results 

in lower utilization of existing coal and oil 

plants which, in turn, results in lower emissions. As noted above, in some sense, the 

changes in emissions represent the effects of 

electric utility compliance actions under the 

CAA, absent the effect of lower resultant demand for electricity. 

•	 When measured against the sensitivity case 

without the CAA (with the same electricity 

demand), electric utility annualized costs are 

estimated to have increased by about $5 to $6 

billion during the 1980 to 1990 period. This 

reflects the following cost factors: (1) higher 

annualized capital costs associated primarily 

with scrubbers and ESPs installed by electric 

utilities to comply with the CAA; (2) higher 

O&M costs associated with the additional air 

pollution control equipment; and (3) higher 

fuel costs associated with using lower sulfur 

coal and oil in order to meet the emission limit 

requirements of the CAA. 

Commercial/Residential 

The Commercial and Residential Simulation System (CRESS) model was developed by ANL as part 

of the Emissions and Control Costs Integrated Model 

Set and used in the NAPAP assessment (Methods for 

Modeling Future Emissions and Control Costs, State 

of Science and Technology, Report 26) (McDonald 

and South, 1984). CRESS is designed to project emissions for five pollutants: SOx

, NOx, VOC, TSP, and 

CO. The CRESS output is aggregated into residential 

and commercial subsectors related to both economic 

activity and fuel use. The introductory material provided in this appendix about CRESS describes the base 

year as being 1985. It appears in this way because 

CRESS was originally developed to operate using the 

1985 NAPAP Emission Inventory as its base year data 

set. For the five pollutants reported by CRESS, emission estimates are provided for the following sectors: 

♦ Commercial/institutional 

•	 coal, including point and area categories of 

anthracite and bituminous boilers; 

•	 liquid fuel, including boiler and space heating uses of residual, distillate, LPG, and 

other fuels; 

•	 natural gas boilers, space heaters, and internal combustion engines; 

• wood used in boilers and space heaters; and 

• other mixed or unclassified fuel use. 

♦ Residential 

•	 coal, including area sources of anthracite 

and bituminous; 

•	 liquid fuel, composed of distillate and residual oil; 

• natural gas; and 

• wood. 

♦ Miscellaneous 

• waste disposal, incineration, and open burning; and 

• other, including forest fires, managed and 

agricultural burning, structural fires, cutback asphalt paving, and internal combustion engine testing. 

In addition, VOC emissions are projected for these 

source categories: 

♦ Service stations and gasoline marketing; 

♦ Dry-cleaning point and area sources; and 

B-30

Appendix B: Emissions Modeling 

♦	 Other solvents, including architectural surface 

coating, auto-body refinishing, and consumer/ 

commercial solvent use. 

This section describes the use of CRESS to estimate control and no-control scenario emissions from 

the commercial/residential sector. 

Control Scenario Emissions 

For the NAPAP assessment, 1985 CRESS output 

corresponded to the 1985 NAPAP Inventory (EPA, 

1989), which served as the benchmark for any projections. The design of CRESS is such that emissions 

by NAPAP SCC are input for each State, then projected to future years by scaling them to economic 

data such as energy demand. In estimating emissions, 

differences in emission controls associated with new, 

replacement, and existing equipment are taken into 

account where such differences are considered significant. The basic modeling approach is shown in 

the following equation: 

Qt,b = (–––) , b × (–––) × Q0 DT Σj (f

t,j × Et,j) (3) E0 D0 

where: 

Q = emissions in year t or the base year, year 0 

E =	 emission factor for the source category b 

in the base year, or for a subcategory j subject to controls in year t (this takes into 

account changes in emission rates that may 

occur as a result of emission regulations or 

technology changes) 

D = driver data indicating activity levels in the 

base and future years 

f =	 fraction of total activity in year t differentially affected by emission controls 

The calculations are carried out in two subroutines, 

one for SO2, NOx

, TSP and CO, and one for VOC. 

Typically SO2

, NOx, TSP, and CO emissions are 

projected by multiplying the 1985 NAPAP SCC data 

or base year data by the ratio of the driver data (activity level) value in the projection year to its value in 

the base year. Because there are few controls on SOx 

or NOx


projected emissions for most sectors are proportional


to the expected activity levels. Thus,


emissions from the sources covered by CRESS, 

D 

Q t

t = Q0 × (–––) (4) D0 

There are a few source types, such as commercial/institutional boilers, for which emission controls 

are mandated. These are modeled by multiplying the 

1985 emission data by the ratio of the controlled emission factor to the base-year emission factor. Emission 

factors for each source type are weighted by the proportion of base year activity in each subsector to which 

controls are expected to apply. 

Et,n Qt,b = Q0 [gt,b + (––––) × (gt,r + gt,n)] (5) E0,b 

where: 

g =	 the fraction of base-year activity accounted 

for by existing source b, replacement 

source r, or new source n in year t 

The effective emission factor (Et,n) for the sector 

is calculated by weighing the portions of sectoral 

emissions subject to NSPS controls and those likely 

to continue at existing levels. An appropriate Internal 

Revenue Service-based rate at which new equipment 

replaces existing sources is applied to each sector in 

the model. This is done to estimate how emissions 

might change as older sources are retired and replaced 

by new sources that emit at lower rates. 

The SOx

/NOx/TSP/CO subroutine varies in new 

and replacement emission-source fractions subject to 

NSPS controls. These fractions are applied to the 

emission-source replacement rates. In addition, ratios 

for new source emission factors are varied by State. 

However, emission ratios for any pollutant/source type 

combination do not vary over the projection period. 

The VOC estimation methodology is similar, but 

allows variation in emission factors over time. Emission ratios are calculated from files of replacement 

and existing source emission factors weighted by the 

replacement rate for each sector and new source factors by State. These are input for each 5-year projection interval. For most source categories, VOC conB-31

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

trols are not envisioned, and the 1985 NAPAP emissions for the category are simply scaled proportionally to changes in the driver (activity level) data. 

For sources to which controls apply, a variation 

on the following equation is employed: 

Q0 = (––– , b) × (Et,b + gt,n × Et,n)] (6) E0 

Qt,b

In equation 6, the emission factors for new and 

existing sources are effectively weighted by the proportion of total activity in year t to which controls 

apply. 

In using CRESS for the CAA retrospective analysis, the base year was 1975. CRESS requires emissions information by State and NAPAP source category as input. Since detailed information on emission levels for 1975 by NAPAP source category were 

not available, the data were developed from a combination of sources. The procedure for calculating 1975 

emissions based on the 1985 NAPAP inventory is 

described below. The emissions module uses these 

initial values in conjunction with activity estimates to 

project control and no-control scenario emissions. 

Emissions Data 

Since the starting point for the analysis was 1975, 

emissions data by State and SCC for SO2

, NOx, VOC, 

TSP, and CO were required. Available emissions information for this year was not at the level of detail 

needed by CRESS. The 1985 NAPAP Inventory, 

which contains the necessary level of detail, in conjunction with information from EPA’s National Air 

Pollutant Emission Estimates, 1940-1990 (Trends) and 

ANL’s MSCET, was used to construct an emissions 

inventory for 1975. The model then uses these emissions as a benchmark for the analysis. 

The method for constructing the 1975 emissions 

data base was consistent for all pollutants; however, 

two different sources of emissions data were necessary in order to obtain time series information on all 

pollutants. MSCET contains monthly State-level emission estimates from 1975 to 1985 by emission source 

group for SO2

, NOx, and VOC. Therefore, MSCET 

information was used for SO2

, NOx, and VOC, while 

Trends data were used for TSP and CO. Emission 

source groups from MSCET were matched with 1985 

NAPAP Inventory SCCs. The MSCET methodology 

is benchmarked to the 1985 NAPAP Inventory and 

uses time series information from Trends in conjunction with activity information to estimate State-level 

emissions for SO2

, NOx, and VOC. Although the level 

of detail contained in the NAPAP Inventory could not 

be preserved because of the aggregation needed to 

match with MSCET emissions sources, MSCET provided the State-level spatial detail required by CRESS. 

Once the 1985 emissions by SCC and State from 

the 1985 NAPAP Inventory were matched with emission source groups and States from the MSCET data 

base, an estimate of 1975 emissions was computed 

by multiplying the 1985 NAPAP Inventory emissions 

value by the ratio of 1975 MSCET emissions to 1985 

MSCET emissions. Ratios were computed and applied 

for each combination of State, pollutant, and MSCET 

emission source group. 

This method of constructing an emissions inventory for 1975 utilizes the State estimates from MSCET, 

thus capturing the spatial shifts that occurred over the 

analysis period. It is assumed that NAPAP provides 

the most reliable point and area source information in 

terms of the level of 1985 emissions (which is also 

the assumption of the MSCET methodology). Note 

that if there were a 1-to-1 correspondence between 

MSCET and NAPAP, this method would be equivalent to using the MSCET methodology directly for 

constructing 1975 emission levels. 

A similar method was used for TSP and CO, but 

since these pollutants are not included in MSCET, the 

Trends ratio of 1975 to 1985 emissions for these two 

pollutants was used. Thus, for TSP and CO, all States 

were assumed to have experienced the same change 

in emissions as indicated by the national figures. 

It should be noted that in addition to the loss in 

spatial detail, the Trends source groups generally 

spanned several NAPAP source categories. The 

strength in the Trends information is the consistency 

of emissions estimates over time. It is considered to 

be the most reliable data for tracking changes in emissions over the time period of the analysis, and was 

therefore chosen for developing 1975 estimates for 

TSP and CO. 

The 15 source categories reported in Trends were 

matched with those in the 1985 NAPAP Inventory. 

The ratios of 1975 emissions to 1985 emissions by 

source category that were applied to the 1985 NAPAP 

emissions data are shown in B-12. The 1975 emisB-32

Tr ends Sou rce Category TSP* CO* 

Commercial/Institutional Fuel 

Combustion: 

 Coal 2.11 0.59 

 Natural Gas 1.00 0.91 

 Fuel Oil 2.35 1.43 

 Other 1.83 0.67 

Residential Fuel Combustion: 

 Coal 1.33 1.47 

 Natural Gas 1.17 1.00 

 Fuel Oil 1.11 1.76 

 Wood 0.49 0.49 

Miscellaneous: Forest Fires 0.67 0.62 

Solid Waste D isposal: 

 Incineration 3.00 0.64 

 Open Burning 1.50 1.44 

Miscellaneous Other Burning 1.00 1.33 

Industrial Processes: P aving 2.71 0.56 

Asphalt Paving and Roofing 2.71 0.56 

Miscellaneous Other 1.83 0.67 

Appendix B: Emissions Modeling 

Table B-12. Trends Source Categories and (1975 to 

1985) Scaling Factors for TSP and CO. 

Note: *These values are the ratios of 1985 Trends emissions to 

1975 Trends emissions for each source category. For example, 

the commercial/ institutional fuel combustion: coal emission 

ratio of 2.11 is computed asthe ratio of the 1975 TSP emissions 

of 40 gigagrams per year to the corresponding 1985 emissions of 

19 gigagrams per year. 

sions data estimated from the above procedure served 

as the benchmark and initial value for the CRESS 

emissions module for both scenarios. 

CAA regulation of commercial/ residential emissions was limited and largely confined to fuel combustion sources (SO2, NOx

, TSP), gasoline marketing 

(VOC), dry cleaning (VOC), and surface coating 

(VOC). NSPS regulations of small (over 29 MW capacity) fuel combustors were promulgated in 1984 and 

1986. For purposes of emissions calculations, the 

stipulated NSPS for SO2, NOx

, and TSP were incorporated into the control scenario for 1985 and 1990. 

Emission rates for source categories subject to VOC 

regulation were similarly adjusted. 

Tr ends Sou rce Category TSP* CO* 

Commercial/Institutional Fuel 

Combustion: 

Coal 2.11 0.59 

Natural Gas 1.00 0.91 

Fuel Oil 2.35 1.43 

Other 1.83 0.67 

Residential Fuel Combustion: 

Coal 1.33 1.47 

Natural Gas 1.17 1.00 

Fuel Oil 1.11 1.76 

Wood 0.49 0.49 

Miscellaneous: Forest Fires 0.67 0.62 

Solid Waste D isposal: 

Incineration 3.00 0.64 

Open Burning 1.50 1.44 

Miscellaneous Other Burning 1.00 1.33 

Industrial Processes: P aving 2.71 0.56 

Asphalt Paving and Roofing 2.71 0.56 

Miscellaneous Other 1.83 0.67 

Energy Data 

Nearly 75 percent of the source categories in 

CRESS use energy consumption by State and sector 

as the driver for the emissions calculation. State-level 

energy consumption statistics are published by EIA 

in State Energy Data Report, Consumption Estimates, 

1960-1989, and are electronically available as part of 

the State Energy Data System (SEDS ) (DOE, 1991). 

The SEDS data base contains annual energy consumption estimates by sector for the various end-use sectors: residential, commercial, industrial and transportation, and electric utilities. 

Seven fuel-type categories are used in CRESS: 

coal, distillate oil, residual oil, natural gas, liquid petroleum gas, wood, and electricity. The model assumes 

zero consumption of residual fuel oil in the residential sector and zero consumption of wood in the commercial sector. Energy consumption for each fuel-type 

was expressed in BTUs for purposes of model calculations. With the exception of wood consumption, all 

of the energy consumption statistics used in CRESS 

were obtained from SEDS. 

Residential wood consumption estimates were 

derived from two data sources. State-level residential 

sector wood consumption estimates for 1975 and 1980 

were obtained from Estimates of U.S. Wood Energy 

Consumption from 1949 to 1981 (EIA, 1982). Statelevel wood consumption, however, was not available 

for 1985 and 1990, therefore, regional information 

from an alternative publication, Estimates of U.S. 

Biofuels Consumption 1990 (EIA, 1990), was used to 

derive State-level residential wood use figures. Regional 1985 and 1990 wood consumption was distributed among States using 1981 State shares. All wood 

consumption figures were converted to BTU’s using 

an average value of 17.2 million BTU per short ton. 

Economic/Demographic Data 

Emissions from slightly more than 25 percent of 

the CRESS source categories follow State-level economic and demographic activity variables. The demographic variables used by CRESS include Statelevel population, rural population, and forest acreage. 

State population is the activity indicator for six emissions source categories for SO2

, NOx, TSP, and CO, 

and 13 VOC source categories. State population data 

were assembled from the SEDS data base. Rural population, which is the indicator of residential open burning activity, is computed as a fraction of total State 

B-33

Year Coal Refined Pe trol eum Electric 

Gas 

1975 1.48 4.76 3.62 2.42 

1980 1.50 3.84 4.26 2.12 

1985 1.98 3.90 3.88 2.41 

1990 2.23 4.33 4.18 2.77 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

population. Forest wildfires and managed open burn- Energy Data


ing activity are related to 1977 State-level forest acre-


age. The demographic information is assumed to be State-level energy demand for the residential and


invariant to CAA regulations and thus is the same in commercial sectors for the no-control scenario was


the control and no-control scenarios. estimated from the J/W model forecast. Final energy


demand estimates for the household sector were calCar stock (or vehicle population), the driver vari- culated by an EPA contractor for the purposes of the 

able for the auto body refinishing, is approximated by no-control scenario analysis. State allocation of the 

State motor vehicle registrations. Highway Statistics, national-level estimates was based on historic State 

an annual publication by the FHWA, was the source shares, i.e., this assumes that there is no change in the 

for data on State motor vehicle registrations. The three distribution of energy demand across States as a resource categories connected with gasoline marketing sult of removing regulations. In addition, the J/W 

are driven by State-level gasoline sales in gallons. State model estimates an aggregate refined petroleum catgasoline consumption was obtained from the SEDS egory and does not distinguish among liquid petrodata base. Housing starts and 10 percent of the exist- leum gas, distillate oil, and residual oil. The relative 

ing housing stock were combined to form the activity shares among these three categories of petroleum prodindicator for architectural surface coating emissions. ucts remained constant between the control and 

Housing data compiled by the U.S. Bureau of the no-control scenarios. The information on percentage 

Census were available in the Statistical Abstract of change in energy demand by fuel type as provided by 

the United States (DOC, 1975; 1977; 1982; 1983; the J/W model is listed in Table B-13. 

1987; 1993). Regional-level data for 1975 was allocated to the States based on the 1980 State distribu- The differential for commercial sector final ention. ergy demand was calculated from the combination of 

four intermediate product flow categories from the J/


No-control Scenario Emissions W forecast. The National Income and Product Ac­


counts (NIPA ) for the commercial sector correspond


Adjustments to control scenario emissions in each to J/W SIC categories 32 through 35: 

of the target years to reflect conditions under the no-control scenario were achieved 

through emission factors, energy input data, 

and economic/demographic data. The adjustments made to each of these variables to generate no-control scenario emissions are discussed individually in the following subsections. 

Emissions Data 

CAA regulation of the commercial/residential sector was minimal. For regulated 

source categories, emission factors were revised to reflect pre-regulation emission rates. 

Table B-13. Percentage Change in Real Energy Demand by 

Households from Control to No-control Scenario. 

Year Coal Refined Pe trol eum Electric Natural

Gas 

1975 1.48 4.76 3.62 2.42 

1980 1.50 3.84 4.26 2.12 

1985 1.98 3.90 3.88 2.41 

1990 2.23 4.33 4.18 2.77 

Six commercial/residential source categories were (32) Wholesale and Retail Trade;


regulated for VOC emissions: Service Stations Stage (33) Finance, Insurance, and Real Estate;


I Emissions, Service Stations Stage II Emissions, Dry (34) Other Services; and


Cleaning (perchloroethylene), Gasoline Marketed, Dry (35) Government Services.


Cleaning (solvent), and Cutback Asphalt Paving.


Commercial-Institutional boilers were regulated for Percentage change information from the J/W fore-


SO2

 and TSP and internal combustion sources were cast for energy cost shares, value of output, and enregulated for NOx emissions. All NSPS were removed ergy prices was used to calculate the differential in 

for these sources to estimate no-control scenario emis- commercial sector energy demand for the no-control 

sions levels. scenario. The energy cost share is defined as the cost 

B-34

Year Coal Petroleu m Electric Gas 

1975 -0.13 3.36 1.30 -0.80 

1980 0.31 1.90 2.06 -0.82 

1985 0.48 1.98 1.72 -0.40 

1990 0.39 2.26 1.74 -0.22 

Appendix B: Emissions Modeling 

of energy input divided by the value of the output. In 

order to calculate the percentage change in commercial sector energy demand, the change in energy price 

was subtracted from the percentage change in energy 

cost, and added to the change in the value of output. 

Each of these variables was available from the J/W 

model results. This calculation was performed for each 

of the four energy types, and each of the four NIPA 

categories. The change in commercial sector energy 

demand was obtained by taking the weighted average 

of the four NIPA categories. Since data on relative 

energy demand for NIPA categories were not readily 

available, square footage was used as a proxy for calculating the weights. These data were taken from the 

Nonresidential Buildings Energy Consumption Survey, Commercial Buildings Consumption and Expenditure 1986 (EIA, 1989). The resulting estimate for 

commercial sector changes in energy demand is provided in Table B-14. 

State-level gasoline sales is one of the activities 

forecasted by the transportation sector model. The 

percentage change in gasoline sales calculated by the 

TEEMS model was used in the no-control scenario as 

a CRESS model input. 

Table B-15. J/W Percent Differential in 

Economic Variables Used in CRESS. 

Year Co nstruction 

Motor 

Vehicles 

1975 0.70 5.04 

1980 0.14 4.79 

1985 0.41 6.07 

1990 0.29 6.25 

Table B-14. Percentage Change in Commercial Energy Demand 

from Control to No-control Scenario. 

Refined Natural 

Year Coal Petroleu m Electric Gas 

1975 -0.13 3.36 1.30 -0.80 

1980 0.31 1.90 2.06 -0.82 

1985 0.48 1.98 1.72 -0.40 

1990 0.39 2.26 1.74 -0.22 

The national-level change in commercial sector 

energy demand was allocated to the States using historic shares. Implicit is the assumption that removal 

of CAA regulations does not alter the State distribution of energy use. 

Economic/Demographic Data 

State population was assumed not to vary as a result of CAA regulations, thus only the economic variables were revised for the no-control scenario. 

No-control scenario housing starts and car stock were 

derived from J/W forecast information on construction and motor vehicles. The differential for categories 6 (construction) and 24 (motor vehicles and equipment) was applied to control scenario values to obtain no-control scenario levels. The percentage change 

from the J/W forecast is given in Table B-15. 

B-35

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-16. TSP Emissions Under the Control and No-control Scenarios by Target Year (in 

thousands of short tons). 

Di Dif fffe e r reen n ccee 

Wit h th e C AA Witho ut the CAA in 19 90 

Secto r 19197 7 55 19198 8 00 19198 8 55 199 0 19197 7 55 19198 8 00 198 5 199 0 Em issio ns 

Tran sportation: 

High way Veh icles 700 760 770 820 770 910 1, 030 1, 180 (3 0%) 

Of f-H ighway Ve hicles 270 280 270 280 260 270 260 270 5% 

Station ary Sou rc es: 

Electric Utilities 1,720 880 450 430 3, 460 4, 480 5, 180 5, 860 (9 3%) 

Ind ustrial Pro ce sses 5, 620 3,650 3, 040 3,080 11,12 0 12,00 0 11,71 0 12,96 0 (7 6%) 

Ind ustrial Boilers 740 480 250 240 780 550 360 400 (4 1%) 

Comm ercial/Re sidentia l 2, 020 2, 510 2, 680 2, 550 2, 020 2, 520 2, 700 2, 560 (1 %) 

TOTAL * 11,07 0 8, 550 7, 460 7, 390 18,41 0 20,73 0 21,25 0 23,23 0 (6 8%) 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

Table B-17. SO2 Emissions Under the Control and No-control Scenarios by Target Year (in thousands 

of short tons). 

DDi iff ffer er en en cece 

Wi Wit t hh th th ee C C AAAA Witho ut the CAA iinn 19 199090 

SeSecctto o rr 197 5 198 0 198 5 199 0 19197 7 55 19198 8 00 198 5 19199 9 00 E mE mi si ss s iio o nn ss 

Tran sportation: 

High way Veh icles 380 450 500 570 380 450 500 560 1% 

Of f-H ighw ay Ve hicles 370 530 410 390 360 530 400 390 1% 

Station ary Sou rces: 

Electric Utilities 18,67 0 17,48 0 16,05 0 16,51 0 20,69 0 25,62 0 25,14 0 26,73 0 (3 8%) 

Ind ustrial Pro ce sses 4, 530 3,420 2, 730 2, 460 5, 560 5, 940 5, 630 6, 130 (6 0%) 

Ind ustrial Boilers 3,440 3,180 2,660 2, 820 3, 910 4, 110 4, 020 4, 610 (3 9%) 

Comm ercial/Re sidentia l 1, 000 800 590 690 1, 000 810 610 710 (3 %) 

TOTAL * 28,38 0 25,86 0 22,95 0 23,44 0 31,90 0 37,46 0 36,31 0 39,14 0 (4 0%) 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

B-36

Appendix B: Emissions Modeling 

Table B-18. NOx Emissions Under the Control and No-control Scenarios by Target Year (in 

thousands of short tons). 

DDi iffffer er en en cece 

Wi Wit t hh th th ee C C AAAA Witho ut the CAA iinn 19 199090 

SeSecctto o rr 197 5 198 0 198 5 199 0 19197 7 55 19198 8 00 198 5 19199 9 00 E mE mi si ss s iio o nnss 

Tran sportation: 

High way Veh icles 8, 640 9, 340 8, 610 8, 140 9, 020 11,06 0 13,16 0 15,39 0 (4 7%) 

Off-H ighw ay Ve hicles 1, 990 2, 180 2, 080 2, 090 1, 980 2, 150 2, 040 2, 060 1% 

Station ary Sou rc es: 

Electric Utilities 5,540 6,450 6,660 7, 060 5, 740 7, 150 7, 780 8, 300 (1 5%) 

Ind ustrial Pro ce sses 750 760 690 710 760 830 790 1, 090 (3 5%) 

Ind ustrial Boilers 4,090 3,680 3,540 3, 710 4, 120 3, 660 3, 680 3, 900 (5 %) 

Comm ercial/Re sidentia l 1, 060 960 880 930 1, 060 970 890 950 (2 %) 

TOTAL * 22,06 0 23,37 0 22,46 0 22,64 0 22,68 0 25,83 0 28,35 0 31,68 0 (2 9%) 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

Table B-19. VOC Emissions Under the Control and No-control Scenarios by Target Year (in 

thousands of short tons). 

DDi iffffer er en en cece 

Wi Wit t hh th th ee C C AAAA Witho ut the CAA iinn 19 199090 

Secto r 197 5 198 0 198 5 199 0 19197 7 55 19198 8 00 198 5 19199 9 00 E mE mi si ss s iio o nnss 

Transportation: 

High way Veh icles 12,22 0 10,77 0 9, 470 7,740 14,62 0 16,46 0 19,80 0 23,01 0 (6 6%) 

Off-H ighw ay Ve hicles 1, 380 1, 370 1, 340 1, 410 1, 390 1, 420 1, 390 1, 490 (5 %) 

Station ary Sou rc es: 

Electric Utilities 20 30 30 40 20 30 30 40 (7 %) 

Ind ustrial Pro ce sses 5,910 6, 780 6,230 5, 630 6, 130 7, 930 7, 290 6, 810 (1 7%) 

Ind ustrial Boilers 150 150 150 150 150 150 140 150 0% 

Comm ercial/Re sidentia l 4, 980 5, 480 5, 820 5, 870 4, 980 5, 700 6, 080 6, 130 (4 %) 

TOTAL * 24,66 0 24,58 0 23,03 0 20,84 0 27,29 0 31,68 0 34,73 0 37,63 0 (4 5%) 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

B-37

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table B-20. CO Emissions Under the Control and No-control Scenarios by Target Year (in thousands 

of short tons). 

DDi iffffer er en en cece 

Wit h th e C AA WWiiththo o uutt th the e CCAAAA iinn 19 199090 

Secto r 19197 7 55 19198 8 00 19198 8 55 19199 9 00 197 5 198 0 198 5 19199 9 00 E mE mi si ss s iio o nnss 

Tran sportation: 

High way Veh icles 83,58 0 79,97 0 72,49 0 65,43 0 90,46 0 105, 5 30 131, 4 20 149, 2 80 (5 6%) 

Off-H ighw ay Ve hicles 8, 510 8, 100 7, 880 8, 080 8, 510 8, 070 7, 880 8, 080 0% 

Station ary Sou rc es: 

Electric Utilities 240 280 290 370 250 290 300 380 (3 %) 

Ind ustrial Pro ce sses 7,580 6, 990 4,840 5, 140 9, 240 9, 120 8, 860 10,18 0 (4 9%) 

Ind ustrial Boilers 720 710 670 740 720 710 620 740 0% 

Comm ercial/Re siden tial 10,25 0 13,13 0 14,14 0 13,15 0 10,25 0 13,17 0 14,20 0 13,21 0 0% 

TOTAL * 110, 8 80 109, 1 70 100, 3 00 92,90 0 119, 4 30 136, 8 80 163, 2 80 181, 8 60 (4 9%) 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

Table B-21. Lead (Pb) Emissions Under the Control and No-control Scenarios by Target Year (in 

thousands of short tons). 

DDiiffffer er en en cece 

Wi Wit t hh th th ee C C AAAA Witho ut the CAA iinn 19 19 9090 

SeSecctto o rr 197 5 198 0 198 5 19199 9 00 197 5 198 0 198 5 199 0 E mE mi si ss s iio o nn ss 

Tran sportation: 

High way Veh icles 180 86 22 2 203 207 214 223 (9 9%) 

Station ary Sou rc e: 

Ind ustrial Pro ce sses 3 1 1 1 7 (8 7%) 

Ind ustrial Com bustion 4 2 0 0 5 (9 6%) 

Utilities 1 1 0 0 2 (9 5%) 

TOTAL * 190 90 23 3 217 221 228 237 (9 9%) 

7 6 5 

5 5 5 

3 4 4 

Notes: The estimates of emission levels with and without the CAA were developed specifically for this section 812 analysis using 

models designed to simulate conditions in the absence of the CAA. These numbers should not be interpreted as actual historical 

emission estimates. 

*Totals may differ slightly from sums due to rounding. 

B-38

Appendix B: Emissions Modeling 

Emissions Modeling References 

Abt Associates Inc. (Abt). 1995. The Impact of the 

Clean Air Act on Lead Pollution: Emissions 

Reductions, Health Effects, and Economic 

Benefits from 1970 to 1990, Final Report, 

Bethesda, MD, October. 

Argonne National Laboratory (ANL). 1990. Current 

Emission Trends for Nitrogen Oxides, Sulfur 

Dioxide, and Volatile Organic Chemicals by 

Month and State: Methodology and Results, 

Argonne, IL, August. 

Argonne National Laboratory (ANL). 1992. Retrospective Clean Air Act Analysis: Sectoral 

Impact on Emissions from 1975 to 1990, 

(Draft), Argonne, IL, July. 

Braine, Bruce and P. Kim. 1993. Fuel Consumption 

and Emission Estimates by State, ICF Resources, Inc., Fairfax, VA, memorandum to 

Jim DeMocker, EPA. April 21. 

Braine, Bruce, S. Kohli, and P. Kim. 1993.1975 Emission Estimates with and without the Clean Air 

Act, ICF Resources, Inc., Fairfax, VA, memorandum to Jim DeMocker, EPA, April 15. 

DeMocker, J. Personal Communication with Office 

of Mobile Sources Staff, Ann Arbor, Michigan. Date unknown. 

Energy Information Administration (EIA). 1982. Estimates of U.S. Wood Energy Consumption 

from 1949 to 1981. DOE/EIA-0341, U.S. 

Department of Energy. August. 

Energy Information Administration (EIA). 1985. Cost 

and Quality of Fuels for Electric Utility 

Plants. DOE/EIA-0091(85), U.S. Department 

of Energy. 

Energy Information Administration (EIA). 1989. Nonresidential Buildings Energy Consumption 

Survey: Commercial Buildings Consumption 

and Expenditures 1986. DOE/EIA-0318(86), 

U.S. Department of Energy. May. 

Energy Information Administration (EIA). 1990. Estimates of U.S. Biofuels Consumption 1990. 

DOE/EIA-0548(90), U.S. Department of Energy. October. 

Electric Power Research Institute (EPRI). 1981. The 

EPRI Regional Systems, EPRI-P-1950-SR, 

Palo Alto, CA. 

Federal Highway Administration (FHWA). 1986. 

1983-1984 Nationwide Personal Transportation Survey, U.S. Department of Transportation, Washington, DC. 

Federal Highway Administration (FHWA). 1988. 

Highway Statistics 1987, PB89-127369, U.S. 

Department of Transportation, Washington, 

DC. 

Federal Highway Administration (FHWA). 1992. 

Highway Statistics 1991, FHWA-PL-92-025, 

U.S. Department of Transportation, Washington, DC. 

Gschwandtner, Gerhard. 1989. Procedures Document 

for the Development of National Air Pollutant Emissions Trends Report, E.H. Pechan & 

Associates, Inc., Durham, NC. December. 

Hogan, Tim. 1988. Industrial Combustion Emissions 

Model (Version 6.0) Users Manual, U.S. Environmental Protection Agency, EPA-600/8-

88-007a. 

ICF Resources, Inc. 1992. Results of Retrospective 

Electric Utility Clean Air Act Analysis - 1980, 

1985 and 1990, September 30. 

Jorgenson, D.W. and P. Wilcoxen. 1989. Environmental Regulation and U.S. Economic Growth, 

Harvard University Press, Cambridge, MA. 

Klinger, D. and J.R. Kuzmyak. 1986. Personal Travel 

in the United States, Vol. I: 1983-84 Nationwide Personal Transportation Study, U.S. 

Department of Transportation, Federal Highway Administration, Washington, DC. August. 

Kohout, Ed. 1990. Current Emission Trends for Nitrogen Oxides, Sulfur Dioxide, and Volatile 

Organic Compounds by Month and State: 

Methodology and Results,” Argonne National 

Laboratory, ANL/EAIS/TM-25, Argonne, IL. 

B-39

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Lockhart, Jim. 1992. Projecting with and without 

Clean Air Act Emissions for the Section 812 

Retrospective Analysis: A Methodology Based 

Upon the Projection System used in the Office of Air Quality Planning and Standards 

National Air Pollutant Emission Estimate 

Reports,” (Draft Report), Environmental Law 

Institute, November 16. 

McDonald, J.F. and D.W. South. 1984. The Commercial and Residential Energy Use and Emissions Simulation System (CRESS): Selection 

Process, Structure, and Capabilities, Argonne 

National Laboratory, ANL/EAIS/TM-12, 

Argonne, IL. October. 

Mintz, M.M. and A.D. Vyas. 1991. Forecast of Transportation Energy Demand through the Year 

2010, Argonne National Laboratory, ANL/ 

ESD-9, Argonne, IL. April. 

Pechan Associates. 1995. The Impact of the Clean Air 

Act on 1970 to 1990 Emissions; Section 812 

Retrospective Analysis. Draft Report. March. 

Saricks, C.L. 1985. The Transportation Energy and 

Emissions Modeling System (TEEMS): Selection Process, Structure, and Capabilities, 

Argonne National Laboratory, ANL/EESTM-295, Argonne, IL. November. 

Veselka, T.D., et al. 1990. Introduction to the Argonne 

Utility Simulation (ARGUS) Model,” Argonne 

National Laboratory, ANL/EAIS/TM-10, 

Argonne, IL. March. 

Vyas, A.D. and C.L. Saricks. 1986. Implementation 

of the Transportation Energy and Emissions 

Modeling System (TEEMS) in Forecasting 

Transportation Source Emissions for the 1986 

Assessment, Argonne National Laboratory, 

ANL/EES-TM-321, Argonne, IL. October. 

U.S. Department of Commerce (DOC). 1975. Statistical Abstract of the United States: 1975 (96th 

Edition), Bureau of the Census, Washington, 

DC, September. 

U.S. Department of Commerce (DOC). 1977. Statistical Abstract of the United States: 1977 (98th 

Edition), Bureau of the Census, Washington, 

DC, September. 

U.S. Department of Commerce (DOC). 1981. 1977 

Truck Inventory and Use Survey, Bureau of 

the Census, TC-77-T-52, Washington, DC, 

August. 

U.S. Department of Commerce (DOC). 1982. Statistical Abstract of the United States: 1982-1983 

(103rd Edition), Bureau of the Census, Washington, DC, December. 

U.S. Department of Commerce (DOC). 1983. Statistical Abstract of the United States: 1984 

(104th Edition), Bureau of the Census, Washington, DC, December. 

U.S. Department of Commerce (DOC). 1984. 1982 

Truck Inventory and Use Survey, Bureau of 

the Census, TC-82-T-52, Washington, DC, 

August. 

U.S. Department of Commerce (DOC). 1987. Statistical Abstract of the United States: 1988 

(108th Edition), Burea of the Census, Washington, DC, December. 

U.S. Department of Commerce (DOC). 1990. 1987 

Truck Inventory and Use Survey,” Bureau of 

the Census, TC87-T-52, Washington, DC, 

August. 

U.S. Department of Commerce (DOC). 1991. Annual 

Survey of Manufactures: Purchased Fuels and 

Electric Energy Used for Heat and Power by 

Industry Group, Bureau of the Census, 

M87(AS)-1, Washington, DC. 

U.S. Department of Commerce (DOC). 1993. Statistical Abstract of the United States: 1993 

(113th Edition),” Bureau of the Census, 

Washington, DC. 

U.S. Department of Energy (DOE). 1982. Documentation of the Resource Allocation and Mine 

Costing (RAMC) Model. DOE/NBB-0200. 

Energy Information Administration. 

U.S. Department of Energy (DOE). 1986. Inventory 

of Power Plants in the United States 1985. 

DOE/EIA-0095(85), Energy Information 

Administration, Washington, DC, August. 

B-40

Appendix B: Emissions Modeling 

U.S. Department of Energy (DOE). 1988. An Analysis of Nuclear Power Plant Operating Costs. 

DOE/EIA-0511(88), Energy Information 

Administration. 

U.S. Department of Energy (DOE). 1990. State Energy Price and Expenditure Report 1988. 

DOE/EIA-0376(88), Energy Information 

Administration, Washington, DC, September. 

U.S. Department of Energy (DOE). 1991. State Energy Data Report: Consumption Estimates -

1960-1989. DOE/EIA-0214(89), Energy Information Administration, Washington, DC, 

May. 

U.S. Department of Energy (DOE). 1992. Annual 

Energy Review 1991. DOE/EIA-0384(91), 

Energy Information Administration, Washington, DC. 

U.S. Environmental Protection Agency (EPA). 1985. 

Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area 

Sources, AP-42, Fourth Edition, GPO No. 

055-000-00251-7, Research Triangle Park, 

NC. September. 

U.S. Environmental Protection Agency (EPA). 1989. 

The 1985 NAPAP Emissions Inventory, EPA600/7-89-012a, Research Triangle Park, NC. 

November. 

U.S. Environmental Protection Agency (EPA). 1990. 

The Cost of a Clean Environment, EPA-230-

11-90-083. November. 

U.S. Environmental Protection Agency (EPA). 1991. 

Office of Air Quality Planning and Standards, 

National Air Pollutant Emissions Estimates, 

1940-1990, EPA-450/4-91-026, Research 

Triangle Park, NC. November. 

U.S. Environmental Protection Agency (EPA). 1992. 

1990 Toxics Release Inventory, EPA-700-S92-002, Washington, DC. 

U.S. Environmental Protection Agency (EPA). 1994a. 

National Air Pollutant Emission Trends, 

1900-1993, EPA-454/R-94-027, Office of Air 

Quality Planning and Standards, Research 

Triangle Park, NC. October. 

U.S. Environmental Protection Agency (EPA). 1994b. 

Office of Mobile Sources, User’s Guide to 

MOBILE5 (Mobile Source Emission Factor 

Model), EPA-AA-AQAB-94-01, Ann Arbor, 

MI. May. 

U.S. Environmental Protection Agency (EPA). 1995. 

National Air Pollutant Emission Trends 1900-

1994, EPA-454/R-95-011. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. October. 

Werbos, Paul J. 1983. A Statistical Analysis of What 

Drives Energy Demand: Volume III of the 

PURHAPS Model Documentation, U.S. Department of Energy, Energy Information Administration, DOE/EIA-0420/3, Washington, 

DC. 

B-41

Appendix C: Air Quality Modeling


Introduction 

This appendix describes in greater detail the various methodologies used to translate differences in 

control and no-control scenario emission estimates 

into changes in air quality conditions. Summary characterizations of the results of the air quality modeling 

efforts for 1990 are provided here and in the main 

text. Further details and discussion of key analytical 

and modeling issues can be found in a number of supporting documents. These documents, which provide 

the analytical basis for the results presented herein, 

are: 

♦	 ICF Kaiser/Systems Applications International, “Retrospective Analysis of Ozone Air 

Quality in the United States”, Final Report, 

May 1995. (Hereafter referred to as “SAI 

Ozone Report (1995).”) 

♦	 ICF Kaiser/Systems Applications International, “Retrospective Analysis of Particulate 

Matter Air Quality in the United States”, Draft 

Report, September 1992. (Hereafter referred 

to as “SAI PM Report (1992).”) 

♦	 ICF Kaiser/Systems Applications International, “Retrospective Analysis of Particulate 

Matter Air Quality in the United States”, Final Report, April 1995. (Hereafter referred to 

as “SAI PM Report (1995).”) 

♦	 ICF Kaiser/Systems Applications International, “PM Interpolation Methodology for 

the section 812 retrospective analysis”, 

Memorandum from J. Langstaff to J. 

DeMocker, March 1996. (Hereafter referred 

to as “SAI PM Interpolation Memo (1996).”) 

♦ ICF Kaiser/Systems Applications Interna-


2

, NOx


and CO Air Quality in the United States”,


tional, “Retrospective Analysis of SO

Final Report, November 1994. (Hereafter re­

(1994).”) 

ferred to as “SAI SO2, NOx and CO Report 

♦	 ICF Kaiser/Systems Applications International, “Retrospective Analysis of the Impact 

of the Clean Air Act on Urban Visibility in 

the Southwestern United States”, Final Report, October 1994. (Hereafter referred to as 

“SAI SW Visibility Report (1994).”) 

♦	 Dennis, Robin L., US EPA, ORD/NERL, 

“Estimation of Regional Air Quality and 

Deposition Changes Under Alternative 812 

Emissions Scenarios Predicted by the Regional Acid Deposition Model, RADM”, Draft 

Report, October 1995. (Hereafter referred to 

as “RADM Report (1995).”) 

The remainder of this appendix describes, for each 

pollutant or air quality effect of concern, (a) the basis 

for development of the control scenario air quality 

profiles; (b) the air quality modeling approach used 

to estimate differences in air quality outcomes for the 

control and no-control scenario and the application of 

those results to the derivation of the no-control scenario air quality profiles; (c) the key assumptions, 

caveats, analytical issues, and limitations associated 

with the modeling approach used; and (d) a summary 

characterization of the differences in estimated air 

quality outcomes for the control and no-control scenarios. 

Carbon Monoxide 

Control scenario carbon monoxide 

profiles 

As described in the preceding general methodology section, the starting point for development of control scenario air quality profiles was EPA’s AIRS daC-1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

vide a manageable charTable C-1. Summary of CO Monitoring Data. 

Data Source: SAI SO2 , NOx and CO Report (1994). 

Year 

Number of 

Monito rs 

Number of 

Co unties 

Percent 

Population 

Co vered 

Number of 

S amples 

Mean 

Number of 

S amples per 

Monito r 

1970 82 54 n/a 408,52 4 4, 982 

1975 503 246 n/a 2,667,525 5, 303 

1980 522 250 50 % 3,051,599 5, 846 

1985 472 232 n/a 3,533,286 7, 486 

1990 506 244 55 % 3,788,053 7, 486 

acterization of air quality conditions. Initially, 

two-parameter lognormal distributions were 

fitted to the profiles 

based on substantial evidence that such distributions are appropriate for 

modeling air quality 

data. However, given 

the relative importance 

of accurately modeling 

higher percentile observations (i.e., 90th percentile and higher), a 

three-parameter modeling approach was used 

tabase. Hourly CO air quality monitoring data were to isolate the effect of 

compiled for all monitors in the 48 contiguous states observations equal, or very close, to zero. In this apfor the study target years of 1970, 1975, 1980, 1985, proach one parameter defines the proportion of data 

and 1990. Although the CO monitoring network was below a cutoff close to zero and the remaining two 

sparse in 1970, by 1990 506 monitors in 244 counties parameters describe the distribution of data above the 

provided monitoring coverage for 55 percent of the cutoff value. Several other studies have already dempopulation in the conterminous U.S. Table C-1 sum- onstrated good fit to air quality modeling data with a 

marizes the CO monitoring data derived from AIRS. three-parameter gamma distribution, and both lognorAdditional data regarding the EPA Region location, mal and gamma distributions using a three-parameter 

land use category, location-setting category, and ob- approach were developed for the present study. As 

jective category of the monitors providing these data documented in the SAI SO2

, NOx, and CO Report 

are described in the SAI SO2

, NOx, and CO Report (1994), a cutoff of 0.05 ppm was applied and both the 

(1994). three-parameter lognormal and three-parameter 

gamma distributions provided a good fit to the emThe next step in constructing the control scenario pirical data. For CO, the gamma distribution provided 

air quality profiles was to calculate moving averages, the best fit.


for a variety of time periods, of the hourly CO data


for each monitor. For CO, moving averages of 1, 3, 5, The control scenario air quality profiles are avail-


7, 8, 12, and 24 hours were calculated. Daily maxi- able on diskette. The filename for the CO Control


mum concentrations observed at each monitor for each Scenario profile database is COCAA.DAT, and adopts


of these averaging periods were then calculated. Fi- the format presented in Table C-2.


nally, profiles were developed to reflect the average


and maximum concentrations for each of the seven No-control scenario carbon monoxide


averaging periods. However, profiles were only de- profiles


veloped for a given monitor when at least 10 percent


of its theoretically available samples were actually To derive comparably configured profiles repre­


available. The purpose of applying this cutoff was to senting CO air quality in the no-control scenario, conavoid inclusion of monitors for which available sample trol scenario profile means and variances were adsizes were too small to provide a reliable indication justed in proportion to the difference in emissions esof historical air quality. timated under the two scenarios. Specifically, for all 

control scenario air quality observations predicted by 

As discussed in the air quality modeling chapter the three-parameter distributions falling above the 

of the main text, development of representative dis- “near-zero” cutoff level, comparable no-control estitributions for these profiles was then necessary to pro- mates were derived by the following equation: 

C-2

Colu mn s Format Des cription 

1 - 2 Integer Year (70, 75, 80, 85, 90) 

4 - 6 Integer Averaging time (1, 3, 5, 7, 8, 12, 24 hours) 

8 - 9 Integer St ate FIPS code 

11 - 13 Integer County FIPS code 

15 - 19 Integer Monitor number (digits 6-10 of monitor id) 

21 - 30 Real Latitude 

32 - 41 Real Longitude 

43 - 44 Integer Latitude/longitude flag 

a 

46 - 55 Real (F10.3) Hourly intermittency parameter p b 

56 - 65 Real (F10.3) Hourly lognormal parameter μ 

b 

66 - 75 Real (F10.3) Hourly lognormal parameter �

b 

76 - 85 Real (F10.3) Hourly gamma parameter �

b 

86 - 95 Real (F10.3) Hourly gamma parameter �

b 

96 - 105 Real (F10.3) Daily max intermittency parameter p b 

106 - 115 Real (F10.3) Daily max lognormal parameter μ b 

116 - 125 Real (F10.3) Daily max lognormal parameter �

b 

126 - 135 Real (F10.3) Daily max gamma parameter �

b 

136 - 145 Real (F10.3) Daily max gamma parameter �

b 

Appendix C: Air Quality Modeling 

Table C-2. Format of Air Quality Profile Databases. 

Colu mn s Format Des cription 

1 - 2 Integer Year (70, 75, 80, 85, 90) 

4 - 6 Integer Averaging time (1, 3, 5, 7, 8, 12, 24 hours) 

8 - 9 Integer St ate FIPS code 

11 - 13 Integer County FIPS code 

15 - 19 Integer Monitor number (digits 6-10 of monitor id) 

21 - 30 Real Latitude 

32 - 41 Real Longitude 

43 - 44 Integer Latitude/longitude flag 

a 

46 - 55 Real (F10.3) Hourly intermittency parameter p b 

56 - 65 Real (F10.3) Hourly lognormal parameter μ 

b 

66 - 75 Real (F10.3) Hourly lognormal parameter �

b 

76 - 85 Real (F10.3) Hourly gamma parameter �

b 

86 - 95 Real (F10.3) Hourly gamma parameter �

b 

96 - 105 Real (F10.3) Daily max intermittency parameter p b 

106 - 115 Real (F10.3) Daily max lognormal parameter μ b 

116 - 125 Real (F10.3) Daily max lognormal parameter �

b 

126 - 135 Real (F10.3) Daily max gamma parameter �

b 

136 - 145 Real (F10.3) Daily max gamma parameter �

b 

a Values for flag: 1 = actual latitude/longitude values 

2 = latitude/longitude values from collocated monitor or previous monitor 

location (monitor parameter occurrence code 1) 

-9 = latitude/longitude missing (county center substituted) 

b Units of concentration are ppm for CO and ppb for SO2, NO2 and NO. 

Source: SAI SO2, NOx and CO Report (1994). 

ENC  XNC = ––– (XC - b) + b (1) 

 EC  

where 

XNC = air quality measurement for the no-control scenario,


XC = air quality measurement for the control scenario,


ENC = emissions estimated for the no-control scenario,


EC = emissions estimated for the control scenario, and


b = background concentration.


C-3

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

The adjustment for background concentration is 

made to hold ambient background concentrations of 

the pollutant constant between the control and 

no-control scenarios. To the extent background concentrations are affected by transport of anthropogenic 

pollutants from upwind sites, and to the extent upwind emissions may have been controlled under the 

control scenario, assuming a fixed background concentration represents a conservative assumption in this 

analysis. As discussed in the SAI SO2

, NOx, and CO 

Report (1994), the CO background concentration used 

for this analysis was 0.2 ppm, which equals the lowest typical concentration observed in the lower 48 

states. 

In the SAI SO2

, NOx, and CO Report (1994) documenting the CO air quality modeling effort, reference 

is made to using county-level emission estimates as 

the basis for deriving the no-control profiles. Derivation of these county-level results is described in more 

detail in the appendix on emissions estimation. It is 

important to emphasize here, however, that the countylevel CO emissions data were derived for both the 

control and no-control scenarios by simple population-weighted disaggregation of state-level emission 

totals. Although CO emission estimates were needed 

at the county level to support the ozone air quality 

modeling effort, differences in state-level emissions 

estimates are what drive the difference in the control 

and no-control air quality profiles for CO. In other 

words, the ENCAA to ECAA ratios used to derive the 

no-control profiles according to Equation (1) above 

are essentially based on state-level emissions estimates 

for CO. 

As for the control scenario air quality profiles, 

the no-control scenario air quality profiles are available on diskette. The filename for the CO No-control 

Scenario profile database is CONCAA.DAT. The 

same data format described in Table C-2 is adopted. 

Summary differences in carbon 

monoxide air quality 

While the control and no-control scenario air quality profiles are too extensive to present in their entirety in this report, a summary indication of the difference in control and no-control scenario CO concentrations is useful. Figure C-1 provides this summary characterization. Specifically, the air quality 

indicator provided is the 95th percentile observation 

of 1990 CO concentrations averaged over a 1-hour 

period. The graph shows the number of monitors for 

Figure C-1. 

for 1990 Control to No-control Scenario 95th Percentile 

1-Hour Average CO Concentrations, by Monitor. Number of Monitors 

Frequency Distribution of Estimated Ratios 

which the ratio of 1990 control to no-control scenario 

95th percentile 1-hour average concentrations falls 

within a particular range. The x-axis values in the 

graph represent the midpoint of each bin. The results 

indicate that, by 1990, CO concentrations under a nocontrol scenario would have been dramatically higher 

than control scenario concentrations. 

Key caveats and uncertainties for 

carbon monoxide 

A number of important uncertainties should be 

noted regarding the CO air quality estimates used in 

this analysis. First and foremost, CO is a highly localized, “hot spot” pollutant. As such, CO monitors are 

often located near heavily-used highways and intersections to capture the peak concentrations associated 

with mobile sources. Since this analysis relies on statelevel aggregate changes in CO emissions from all 

sources, the representativeness and accuracy of the 

predicted CO air quality changes are uncertain. There 

is no basis, however, for assuming any systematic bias 

which would lead to over- or under-estimation of air 

quality conditions due to reliance on state-wide emission estimates. 

A second source of uncertainty is the extent to 

which the three-parameter distributions adequately 

characterize air quality indicators of concern. Appendix C of the SAI SO2

, NOx, and CO Report (1994) 

presents a number of graphs comparing the fitted versus empirical data for one-hour and 12-hour averaging periods. In the case of CO, the gamma distribution appears to provide a very reasonable fit, though 

clearly some uncertainty remains. 

C-4

Monitors Counties Cove re d Sam pl es Monitor 

86 56 n/a 399,717 4,648 

847 340 n/a 4,280,303 5,053 

1,113 440 60 % 6,565,589 5,899 

926 401 n/a 6,602,615 7,130 

769 374 50 % 5,810,230 7,556 

Appendix C: Air Quality Modeling 

Finally, a central 

premise of this analysis is that changes in 

CO emissions should 

be well-correlated 

with changes in CO 

air quality. Strong 

correlation between 

the state-level emissions estimates used 

in this analysis and 

empirical air quality 

measurements would 

not be expected due to 

inconsistencies between the state-level 

scale of modeled 

emissions versus the 

Table C-3. Summary of SO2 Monitoring Data. 

Data Source: SAI SO2, NOx and CO Report (1994). 

Year 

Num ber of 

Monitors 

Num ber of 

Counties 

Percen t 

Popu lation 

Cove re d 

Num ber of 

Sam pl es 

Me an Nu m ber 

of Sam ples per 

Monitor 

1970 86 56 n/a 399,717 4,648 

1975 847 340 n/a 4,280,303 5,053 

1980 1,113 440 60 % 6,565,589 5,899 

1985 926 401 n/a 6,602,615 7,130 

1990 769 374 50 % 5,810,230 7,556 

monitor-level scale of the air quality data, and between Control scenario sulfur dioxide profiles
 the modeled control scenario emissions inventories


and actual historical air quality measurements. Under Unlike the CO monitoring network, the number
 these circumstances, it is particularly important to


focus on the primary objective of the current analy- of monitors as well as the population coverage of the


SO2

 monitoring network shrank during the 1980’s. sis, which is to estimate the difference in air quality Table C-3 summarizes the SO2

 monitoring data used outcomes between scenarios which assume the absence or presence of historical air pollution controls. as the basis for development of the control scenario


In the process of taking differences, some of the un- air quality profiles.


certainties are expected to cancel out. No attempt is As for CO, air quality profiles reflecting average
 made in the overall analysis to predict historical air values and daily maxima for 1, 3, 5, 7, 8, 12, and 24
 quality, or hypothetical air quality in the absence of hour averages were compiled from AIRS for moni­
 the Clean Air Act, in absolute terms. tors in the lower 48 states which had at least 10 per-


cent of their potential samples available. Applying a


Sulfur Dioxide cutoff of 0.1 ppb to isolate the zero and near-zero ob­


servations, three-parameter lognormal and gamma


Sulfur dioxide (SO2) emissions lead to several air distributions were fitted to these empirical profiles.


quality effects, including secondary formation of fine In the case of SO2

, the three-parameter lognormal dis­


particle sulfates, long range transport and deposition tribution was found to provide the best fit.


of sulfuric acid, and localized concentrations of gas­


eous sulfur dioxide. The first two effects are addressed The control scenario SO2 air quality profiles are


later in this appendix, under the particulate matter and available on diskette, contained in a file named


acid deposition sections. The focus of this section is SO2CAA.DAT. The same data format described in


estimation of changes in local concentrations of sul- Table C-2 is adopted.


fur dioxide.


No-control scenario sulfur dioxide 

The methodology applied to estimation of local profiles 

sulfur dioxide air quality is essentially identical to the 

one applied for carbon monoxide. As such, this sec- The no-control air quality profiles for SO2

 are 

tion does not repeat the “roll-up” modeling method- derived using Equation 1, the same equation used for 

ological description presented in the CO section, but CO. For SO2

, the background concentration was asinstead simply highlights those elements of the sulfur sumed to be zero. Although anthropogenic emissions 

dioxide modeling which differ from carbon monox- contribute only small amounts to total global atmoide. spheric sulfur, measured background concentrations 

C-5

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

for the continental U.S. range from only 0.1 to 1.3 

ppb. Background SO2

 is discussed in more detail in 

the supporting document SAI SO2

, NOx, and CO Report (1994).1 

The no-control scenario SO2 air quality profiles 

are available on diskette, contained in a file named 

SO2NCAA.DAT. The data format is described in 

Table C-2. 

Summary differences in sulfur dioxide 

air quality 

As for CO, reporting differences in control and 

no-control scenario air quality projections for each 

monitor covered in the analysis is impractical due to 

the large amount of data involved. However, Figure 

C-2 provides an illustration of scenario differences 

similar to the one provided for CO. Specifically, the 

graph shows the distribution of 1990 control to nocontrol scenario 95th percentile 1-hour average concentrations ratios at SO2

 monitors. By 1990, SO2 concentrations under the no-control scenario were substantially higher than those associated with the control scenario. 

Figure C-2. 

for 1990 Control to No-control Scenario 95th Percentile 

1-Hour Average SO2

 Concentrations, by Monitor. 

Number of Monitors 

Frequency Distribution of Estimated Ratios 

Key caveats and uncertainties for sulfur 

dioxide 

The height of stacks used to vent flue gases from 

utility and industrial fossil fuel-fired boilers has a significant effect on the dispersion of sulfur dioxide and 

on the formation and long-range transport of secondary products such as particulate sulfates. Under a nocontrol scenario, it is conceivable that some sources 

might have built taller stacks to allow higher emission rates without creating extremely high groundlevel concentrations of flue gases. On the other hand, 

it is also conceivable that, in the absence of post-1970 

air pollution control programs, sources might have 

built shorter stacks to avoid incurring the higher costs 

associated with building and maintaining taller stacks. 

To the extent facilities would have adopted different 

stack height configurations under a no-control scenario, both local exposures to sulfur dioxides (and 

other emissions from fossil fuel combustion) and longrange transport, deposition, and exposure associated 

with secondary formation products may have been 

different. However, this analysis assumes that both 

the location of individual facilities and the height and 

configuration of emission stacks are constant between 

the two scenarios. If, in fact, stack heights were raised 

under the historical case due to CAA-related concerns, 

increases in local SO2

 concentrations under the 

no-control scenario may be overestimated. However, 

this same assumption may at the same time lead to 

underestimation under the no-control scenario of longrange transport and formation of secondary particulates associated with taller stacks. For stacks built 

lower under a no-control scenario, local SO2

 exposures would have been higher and long-range effects 

lower. Finally, the comments on uncertainties for carbon monoxide apply as well to SO2

. 

Nitrogen Oxides 

Similarly to sulfur dioxide, emissions of nitrogen oxides (NOx ) –including nitrogen dioxide (NO2) 

and nitrous oxide (NO)– lead to several air quality 

effects. These effects include secondary formation of 

fine particle nitrates, formation of ground-level ozone, 

long range transport and deposition of nitric acid, and 

localized concentrations of both NO2

 and NO. The 

first three effects are addressed later in this appendix, under the particulate matter, ozone, and acid 

deposition sections. The focus of this section is estimation of changes in local concentrations of NO2

 and 

NO. 

The methodology applied to estimation of local 

nitrogen oxides air quality is essentially identical to 

the one applied for carbon monoxide and sulfur dioxide. As such, this section does not repeat the “roll-up” 

modeling methodological description presented in the 

CO section, but instead simply highlights those ele1

 SAI SO2, NOx, and CO Report (1994), page 4-9. 

C-6

Monitors Counties Covered Samples Monitor 

45 32 n/a 275,534 6,123 

308 155 n/a 1,574,444 5,112 

379 205 45 % 1,984,128 5,235 

305 182 n/a 2,142,606 7,025 

346 187 40 % 2,456,922 7,101 

Monitors Counties Cove re d Samples Monitor 

39 28 n/a 246,262 6,314 

206 94 n/a 1,101,051 5,345 

224 124 30 % 1,023,834 4,571 

139 86 n/a 956,425 6,881 

145 81 15 % 999,808 6,895 

Data Source: SAI SO2, NOx and CO Report (1994). 

Appendix C: Air Quality Modeling 

ments of the nitrogen oxides modeling which differ 

from carbon monoxide. 

Control scenario nitrogen oxides 

profiles 

After peaking around 1980, the number of NO2 

and NO monitors, their county coverage, and their 

population coverage shrank between 1980 and 1990. 

Tables C-4 and C-5 summarize, respectively, the NO2 

and NO monitoring data used as the basis for development of the control scenario air quality profiles. 

As for CO and SO2

, air quality profiles reflecting 

average values and maxima for 1, 3, 5, 7, 8, 12, and 

24 hour NO2

 and NO averages were compiled from 

AIRS for monitors in the lower 48 states which had at 

least 10 percent of their potential samples available. 

Applying a cutoff of 0.5 ppb to both NO2

 and NO to 

isolate the zero and near-zero observations, three-parameter lognormal and gamma distributions were fitted to these empirical profiles. For NO2

 and NO, the 

three-parameter gamma distribution was found to provide the best fit. 

The control scenario NO2

 and NO air quality profiles are available on diskette, contained in files named 

NO2CAA.DAT and NOCAA.DAT, respectively. The 

same data format described in Table C-2 is adopted. 

Table C-4. Summary of NO2 Monitoring Data. 

Year 

Number of 

Monitors 

Number of 

Counties 

Percent 

Population 

Covered 

Number of 

Samples 

Mean Number 

of Samples per 

Monitor 

1970 45 32 n/a 275,534 6,123 

1975 308 155 n/a 1,574,444 5,112 

1980 379 205 45 % 1,984,128 5,235 

1985 305 182 n/a 2,142,606 7,025 

1990 346 187 40 % 2,456,922 7,101 

Data Source: SAI SO2, NOx and CO Report (1994). 

Table C-5. Summary of NO Monitoring Data. 

Year 

Number of 

Monitors 

Number of 

Counties 

Percen t 

Popu lation 

Cove re d 

Number of 

Samples 

Me an Nu mber 

of Samples per 

Monitor 

1970 39 28 n/a 246,262 6,314 

1975 206 94 n/a 1,101,051 5,345 

1980 224 124 30 % 1,023,834 4,571 

1985 139 86 n/a 956,425 6,881 

1990 145 81 15 % 999,808 6,895 

Data Source: SAI SO2, NOx and CO Report (1994). 

C-7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

No-control scenario nitrogen oxides 

profiles 

The no-control air quality profiles for NO2 and 

NO are derived using Equation 1, the same equation 

used for CO and SO2. As discussed in detail in the 

SAI SO2

, NOx, and CO Report (1994),2 nitrogen oxides are emitted almost entirely from anthropogenic 

sources and they do not have long atmospheric residence times. Therefore, global background concentrations are very low, on the order of 0.1 or 0.2 ppb. 

For the present analysis, background concentrations 

of NO2

 and NO were assumed to be zero. 

The no-control scenario NO2

 and NO air quality 

profiles are available on diskette, contained in files 

named NO2NCAA.DAT and NONCAA.DAT, respectively. The data format is described in Table C-2. 

Summary differences in nitrogen oxides 

air quality 

Figure C-3 provides a summary indication of the 

differences in control and no-control scenario air quality for NO2

. As for CO and SO2, the graph shows the 

distribution of 1990 control to no-control scenario 95th 

percentile 1–hour average concentration ratios at NO2 

monitors. These ratios indicate that, by 1990, no-control scenario NO2

 concentrations were significantly 

higher than they were under the control scenario. The 

changes for NO are similar to those for NO2

. 

Figure C-3. 

for 1990 Control to No-control Scenario 95th Percentile 

1-Hour Average NO2 Concentrations, by Monitor. Number of Monitors 

Frequency Distribution of Estimated Ratios 

Key caveats and uncertainties for 

nitrogen oxides 

A number of caveats and uncertainties specific to 

modeling NOx should be noted. First, stack height and 

stack height control strategies likely to have influenced 

local concentrations of SO2

 may also have influenced 

local concentrations of NO2

 and NO. (For a fuller discussion of the stack heights issue, refer to the section 

“Key caveats and uncertainties for SO2.”) In addition, 

the earlier discussion of uncertainties resulting from 

the use of state-level emissions and the cancellation 

of uncertainties resulting from analyzing only differences or relative changes also applies to NOx

. 

Acid Deposition 

The focus of air quality modeling efforts described 

above for carbon monoxide, sulfur dioxide, and nitrogen oxides was to estimate the change in ambient 

concentrations of those pollutants as a result of 

changes in emissions. Particularly since the emissions 

modeling was driven by modeled macroeconomic 

conditions, rather than actual historical economic activity patterns, neither the emissions inventories nor 

the resultant air quality conditions developed for this 

analysis would be expected to match historical outcomes. The need to focus on relative changes, rather 

than absolute predictions, becomes even more acute 

for estimating air quality outcomes for pollutants subject to long-range transport, chemical transformation, 

and atmospheric deposition. The complexity of the 

relationships between emissions, air concentrations, 

and deposition is well-described in the following paragraph from the RADM report document developed 

by Robin Dennis of US EPA’s National Exposure 

Research Laboratory in support of the present analysis: 

“Sulfur, nitrogen, and oxidant species in the 

atmosphere can be transported hundreds to 

thousands of kilometers by meteorological 

forces. During transport the primary 

emissions, SO2

, NOx, and volatile organic 

emissions (VOC) are oxidized in the air or in 

cloud-water to form new, secondary 

compounds, which are acidic, particularly 

sulfate and nitric acid, or which add to or 

subtract from the ambient levels of oxidants, 

such as ozone. The oxidizers, such as the 

hydroxyl radical, hydrogen peroxide and 

2

 SAI SO2, NOx, and CO Report (1994), page 4-9. 

C-8

Appendix C: Air Quality Modeling 

ozone are produced by reactions of VOC and 

NOx

. The sulfur and nitrogen pollutants are 

deposited to the earth through either wet or 

dry deposition creating a load of pollutants 

to the earth’s surface... However, the 

atmosphere is partly cleansed of oxidants 

through a number of physical processes 

including deposition (e.g., ozone is removed 

by wet and dry deposition). Dry deposition 

occurs when particles settle out of the air onto 

the earth or when gaseous or fine particle 

species directly impact land, plants, or water 

or when plant stomata take up gaseous 

species, such as SO2. In wet deposition, 

pollutants are removed from the atmosphere 

by either rain or snow. In addition, fine 

particles or secondary aerosols formed by the 

gas- and aqueous-phase transformation 

processes scatter or absorb visible light and 

thus contribute to impairment of visibility.”3 

Control scenario acid deposition 

profiles 

The derivation of control scenario emission inventory inputs to the RADM model is succinctly described in this excerpt from the RADM Report (1995): 

The RADM model requires a very detailed 

emissions inventory in both time and space. 

The emissions fields are also day-specific to 

account for the temperature effects on the 

volatile organics and the wind and 

temperature effects on the plume rise of the 

major point sources. At the time of the 812 

retrospective study RADM runs, these 

inventories had been developed for 1985, 

using the 1985 NAPAP (National Acid 

Precipitation Assessment Program) 

inventory, and adjusted for point source 

The complexity and nonlinearity 

of the relationships between localized 

emissions of precursors, such as SO2 

and VOC, and subsequent regional 

scale air quality and deposition effects 

are so substantial that the simple “rollup” modeling methodology used for 

estimating local ambient concentrations of SO2

, NOx, and CO is inadequate, even for a broad-scale, aggregate assessment such as the present 

study. For sulfur deposition, and for 

a number of other effects addressed 

in subsequent sections of this appendix, a regional air quality model was 

required. After careful review of the 

capabilities, geographic coverage, 

computing intensity, and resource requirements associated with available 

regional air quality models, EPA decided to use various forms of the Regional Acid Deposition Model 

(RADM) to estimate these effects.4 

Figure C-4 shows the geographic domain of the RADM. 

Figure C-4. 

Inside the 80-km RADM Domain. 

Location of the High Resolution RADM 20-km Grid Nested 

3

 Dennis, R. RADM Report (1995), p. 1.


4

 For a detailed description of the various forms of the RADM and its evaluation history, see the Dennis, R. RADM Report (1995).


C-9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

emissions to 1988 for the Eulerian Model 

Evaluation Field Study funded by NAPAP. 

These RADM emissions inventories had 

county-level and detailed SCC and specieslevel information incorporated into them to 

provide the 80- and 20-km detail. The 812 

Study emissions are principally computed at 

the state level. While the 1985 812 Study 

emissions are close to the NAPAP inventory, 

they do not exactly match, nor do they have 

the spatial, nor economic sector, nor species 

detail within a state needed to run RADM. To 

connect the 812 Study emissions to the RADM 

emissions, the following approach was 

followed: An industry/commercial-level 

disaggregation (including mobile sources) 

was developed for the 812 emissions to allow 

different sectors in a state to change their 

emissions across time without being in lock 

step and the detailed NAPAP emissions for 

every 80- and 20-km RADM grid-cell were 

grouped by state to the same level of industry/ 

commercial aggregation for an exact 

correspondence. Then it was assumed that the 

812 Study 1985 control emissions were 

effectively the same as the 1985 NAPAP 

emissions. Relative changes in emissions 

between the 812 1985 control and any other 

scenario (e.g., 1985 no-control, or 1990 

control, or 1980 no-control, etc.) were then 

applied to the 1985 NAPAP state-level 

industry/commercial groups in the 

appropriate 80- and 20-km grid cells. Thus, 

state-level emissions for each group would 

retain the same state-level geographic pattern 

in the different scenarios years, but the mix 

across groups could change with time. In this 

way, the more detailed emissions required by 

RADM were modeled for each scenario year 

using the 812 Study emissions data sets.5 

Although the focus of the present analysis is to 

estimate the differences between the control and nocontrol scenarios, it is useful to illustrate the absolute levels of acid deposition associated with the two 

scenarios. It is particularly important to demonstrate 

the initial deposition conditions to preclude possible 

misinterpretations of the maps showing percent 

change in deposition. A relatively high percentage 

change in a particular region, for example, may occur when initial deposition is low, even when the 

change in deposition is also modest. The RADM5 Dennis, R. RADM Report (1995). 

Figure C-5. 

(Wet + Dry; in kg/ha) Under the Control Scenario. 

RADM-Predicted 1990 Total Sulfur Deposition 

Figure C-6. 

tion (Wet + Dry; in kg/ha) Under the Control Scenario. 

RADM-Predicted 1990 Total Nitrogen DeposiC-10

Appendix C: Air Quality Modeling 

Figure C-7. 

(Wet + Dry; in kg/ha) Under the No-control Scenario. 

RADM-Predicted 1990 Total Sulfur Deposition 

Figure C-8. 

tion (Wet + Dry; in kg/ha) Under the No-control Scenario. 

RADM-Predicted 1990 Total Nitrogen Deposimodeled 1990 control scenario wet and dry sulfur 

deposition pattern is shown in Figure C-5. A comparable map for nitrogen deposition is presented in 

Figure C-6. Maps of the RADM-predicted 1990 nocontrol scenario sulfur and nitrogen deposition are 

presented in Figures C-7 and C-8, respectively. 

No-control scenario acid deposition 

profiles 

Configuration of the RADM model for the 

present analysis —including allocation of emission 

inventories to model grid cells, design of meteorological cases, treatment of biogenic versus anthropogenic emissions, and temporal, spatial, and species allocation of emissions— are described in detail in the RADM Report (1995). The remainder of 

this section provides a summary description of the 

acid deposition modeling effort. 

For sulfur deposition, the RADM Engineering 

Model (RADM/EM), which focuses on sulfur compounds, was used to derive annual average total (wet 

plus dry) deposition of sulfur in kilograms sulfur 

per hectare (kg-S/ha) under both the control and 

no-control scenarios. The relative changes in annual average total sulfur deposition for each of the 

80-km RADM/EM grid cells for 1975, 1980, 1985, 

and 1990 were then compiled. 

Nitrogen deposition was calculated in a different manner. Since nitrogen effects are not included 

in the computationally fast RADM/EM, nitrogen 

deposition had to be derived from the full-scale, 

15-layer RADM runs. Because of the cost and computational intensity of the 15-layer RADM, nitrogen deposition estimates were only developed for 

1980 and 1990. As for sulfur deposition, the relative changes in annual average total (wet plus dry) 

nitrogen deposition, expressed as kg-N/ha, were calculated for each 80-km grid cell and for each of the 

two scenarios. It is important to note that ammonia 

depositin contributes significantly to total nitrogen 

deposition. However, the activities of sources associated with formation and deposition of ammonia, such as livestock farming and wildlife, were 

essentially unaffected by Clean Air Act-related control programs during the 1970 to 1990 period of 

this analysis. Therefore, ammonia deposition is held 

constant between the two scenarios. 

C-11

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Figure C-9. 

Sulfur Deposition (Wet + Dry; in kg/ha) Under the Nocontrol Scenario. 

RADM-Predicted Percent Increase in Total 

Summary differences in acid 

deposition 

Figure C-9 is a contour map showing the estimated percent increase in sulfur deposition under 

the no-control scenario relative to the control scenario for 1990. Figure C-10 provides comparable 

information for nitrogen deposition. These maps 

indicate that by 1990 acid deposition would have 

been significantly higher across the RADM domain 

under the no-control scenario. 

Examination of the percent change sulfur deposition map indicates relatively large percentage 

changes in the upper Great Lakes and the FloridaSoutheast Atlantic Coast areas. This result may appear somewhat surprising to readers familiar with 

the historical patterns of acid deposition. However, 

a review of the emission data and the control scenario sulfur deposition map reveal the reasons for 

this result. 

First, Figure C-5 shows that control scenario 

deposition rates are relatively low. As described 

above, even a small absolute increase in deposition 

leads to a large percentage increase in areas with 

low initial rates of deposition. Second, the scenario 

Figure C-10. differences in SO emission rates for these areas

Nitrogen Deposition (Wet + Dry; in kg/ha) Under the Nocontrol Scenario. 

RADM-Predicted Percent Increase in Total x 

were substantial. For example, 1990 no-control scenario total SO emissions for Michigan were ap­ x 

proximately 1.8 million tons but control scenario 

emissions for the same year were less than 600,000 

tons; a reduction of over two-thirds. Similarly, 1990 

no-control scenario emissions for Florida were over 

2.3 million tons, compared to approximately 

800,000 tons under the control scenario; also a reduction of about two-thirds. Almost 1 million tons 

of the Michigan reduction and approximately 1.3 

million tons of the Florida reduction were associated with utilities. Emission reductions of these 

magnitudes would be expected to yield significant 

reductions in rates of acid deposition. 

Key caveats and uncertainties for acid 

deposition 

Regional-scale oxidant and deposition modeling involves substantial uncertainty. This uncertainty arises from uncertainties in modeling atmospheric chemistry, incomplete meteorological data, 

normal seasonal and temporal fluctuations in atmospheric conditions, temporal and spatial variability 

C-12

Appendix C: Air Quality Modeling 

in emissions, and many other factors. Uncertainties 

specific to the RADM model, and this particular exercise, are discussed in detail in the RADM Report 

(1995). It is important, however, to highlight some of 

the potential sources of modeling uncertainty unique 

to this analysis. 

The first source of uncertainty specific to this 

analysis is associated with the spatial and geographic 

disaggregation of emissions data. As discussed in the 

RADM Report, the RADM model requires emission 

inventory inputs which are highly disaggregated over 

both time and space. The ideal emissions inventory 

fed into the RADM model includes day-specific emissions to account for temperature effects on VOCs and 

the significance of localized meteorological conditions 

around major point sources. Given the broad-scale, 

comprehensive nature of the present study, such detailed emissions inventories were not available. However, the industry/commercial-level disaggregation approach developed for the present analysis would not 

be expected to introduce any systematic bias, and the 

contribution of this disaggregation of emissions would 

not be expected to contribute significantly to the overall uncertainty of the larger analysis. 

The acid deposition estimates included in the 

present analysis are limited in that only the eastern 31 

of the 48 coterminous states are covered. Although 

acid deposition is a problem primarily for the eastern 

U.S., acid deposition does occur in states west of the 

RADM domain. The magnitude of the benefits of reducing acid deposition in these western states is likely 

to be small, however, relative to the overall benefits 

of the historical Clean Air Act. 

Particulate Matter 

Developing air quality profiles for particulate 

matter is significantly complicated by the fact that 

“particulate matter” is actually an aggregation of different pollutants with varying chemical and aerodynamic properties. Particulate species include chemically inert substances, such as wind-blown sand, as 

well as toxic substances such as acid aerosols; and 

include coarse particles implicated in household soiling as well as fine particles which contribute to human respiratory effects. In addition, emissions of both 

primary particulate matter and precursors of secondarily-formed particulates are generated by a wide variety of mobile and stationary sources, further complicating specification of particulate air quality models. Finally, particulate air quality models must take 

account of potentially significant background concentrations of atmospheric particles. 

Modeling multiple species and emission sources, 

however, is not the only major challenge related to 

particulate matter which is faced in the present study. 

Over the 1970 to 1990 period being analyzed, understanding of the relative significance of fine versus 

coarse particles evolved significantly. Up until the 

mid-1980s, particulate air quality data were collected 

as Total Suspended Particulates (TSP). However, during the 1980s, health scientists concluded that small, 

respirable particles, particularly those with an aerodynamic diameter of less than or equal to 10 microns 

(PM10), were the component of particulate matter primarily responsible for adverse human health effects. 

As of 1987, federal health-based ambient air quality 

standards for particulate matter were revised to be expressed in terms of PM10 rather than TSP. Starting in 

the mid-1980s, therefore, the U.S. began shifting away 

from TSP monitors toward PM10 monitors. As a result, neither TSP nor PM10 are fully represented by 

historical air quality data over the 1970 to 1990 period of this analysis. Furthermore, a large number of 

U.S. counties have no historical PM monitoring data 

at all, making it difficult to estimate changes in ambient concentrations of this significant pollutant for areas containing roughly 30 percent of the U.S. population. 

Given the relative significance of particulate matter to the bottom-line estimate of net benefits of the 

historical Clean Air Act, it was important to develop 

methodologies to meet each of these challenges. The 

methodologies developed and data used are described 

primarily in the two supporting documents SAI PM 

Report (1992) and SAI PM Report (1995).6 To summarize the overall approach, historical TSP data were 

broken down into principal component species, including primary particulates, sulfates, nitrates, organic 

particulates, and background particulates. Historical 

data were used for the control scenario. To derive the 

no-control profiles, the four non-background components were scaled up based on corresponding 

no-control to control scenario ratios of emissions and/ 

or modeled atmospheric concentrations. Specifically, 

the primary particulate component was scaled up by 

the ratio of no-control to control emissions of PM. 

6

 In addition, SAI memoranda and reports which supplement the results and methodologies used in this analysis are included in 

the references. 

C-13

Monitors Counties Samples Monitor 

751 245 56,804 76 

3,467 1,146 221,873 64 

3,595 1,178 234,503 65 

2,932 1,018 189,344 65 

923 410 59,184 64 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Organic constituents were scaled up by the ratio of 

no-control to control VOC emissions. In the eastern 

31 states where RADM sulfate and nitrate data were 

available, values for SO4

 and NO3 from an appropriate RADM grid cell were assigned to the relevant 

county and used to scale these components of PM. 

For the western states not covered by RADM, sulfates were scaled up by the change in SO2

 emissions 

and nitrates were scaled up the change in NOx emissions. No-control scenario profiles were then constructed by adding these scaled components to background concentrations. 

To resolve the problem of variable records of TSP 

and PM10 data, both TSP and PM10 profiles were generated for the entire 20 year period. Missing early year 

data for PM10 were derived by applying region-specific, land use category-specific PM10 to TSP ratios to 

the historical TSP data. Missing recent year TSP data 

were derived for those areas where PM10 monitors 

replaced TSP monitors by applying the reciprocal of 

the relevant PM10 to TSP ratio. The methodology is 

described in detail in the SAI PM Report (1995). 

In addition, to increase the geographic coverage 

of estimates of air quality, an interpolation methodology7 was developed to predict air quality for the control scenario in counties without measured data. PM 

concentrations were estimated by first estimating the 

components of PM (i.e., sulfate, nitrate, and organic 

particulate, and primary particulate). The methodology for developing the concentrations of components 

within a county differed depending upon whether the 

county was within or outside the RADM domain. 

ent TSP and PM10 to describe these constituents in 

counties without data. Control scenario PM profiles 

were developed by adding the RADM-estimated sulfate particulate levels to the statewide average nitrate, 

VOC, and primary particulate levels, and background. 

For counties outside the RADM domain, an alternate procedure was used. Using the primary and 

secondary particulate estimates for counties with data, 

statewide average sulfate, nitrate, VOC, and primary 

particulate concentrations were determined. Control 

scenario PM10 was predicted by adding the statewide 

averages of all primary and secondary particulate, and 

background. Using this method, all counties that did 

not have monitors and are in the same state are assigned the same PM concentration profiles. These interpolated results are clearly less certain than results 

based on actual historical monitoring data and are 

therefore presented separately. 

Control scenario particulate matter 

profiles 

The number of TSP monitors peaked in 1977 and 

declined throughout the 1980s. Table C-6 summarizes 

the daily (i.e., 24-hour average) TSP monitoring data 

used as the basis for development of the control scenario air quality profiles. Most of the TSP and PM10 

monitors collected samples every six days (i.e., 61 

samples per year). 

Daily PM10 data were also collected for each year 

between 1983 and 1990. Table C-7 summarizes the 

daily PM10 monitoring data used for the control scenario air quality profiles. 

For those counties within the 

RADM domain, the RADM modeled 

concentrations for 1980 and 1990 were Table C-6. Summary of TSP Monitoring Data. 

used to predict sulfate air quality. Relationships based on linear regressions 

that related 1980 and 1990 RADM sulfate concentrations to estimated sulfate 

particulate concentrations were calculated for counties with AIRS data. Sulfate particulate concentrations were 

then calculated for all counties in the 

domain by applying the regression results to the RADM grid cell concentration located over the county center. 

Statewide average nitrate, VOC, and 

primary particulate concentrations 

were calculated from measured ambi- Data Source: SAI PM Report (1995). 

Year 

Number of 

Monitors 

Number of 

Counties 

Number of 

Samples 

Mean Number 

of Samples per 

Monitor 

1970 751 245 56,804 76 

1975 3,467 1,146 221,873 64 

1980 3,595 1,178 234,503 65 

1985 2,932 1,018 189,344 65 

1990 923 410 59,184 64 

7 The interpolation methodology is described in detail in SAI, 1996. Memo from J. Langstaff to J. DeMocker. PM Interpolation 

Methodology for the section 812 retrospective analysis. March 1996. 

C-14

Monitors Counties Samples Monitor 

303 194 22,031 73 

1,249 556 98,904 79 

Appendix C: Air Quality Modeling 

Year 

Number of 

Monitors 

Number of 

Counties 

Number of 

Samples 

Me an Nu mber 

of Samples per 

Monitor 

1985 303 194 22,031 73 

1990 1,249 556 98,904 79 

Table C-7. Summary of PM10 Monitoring Data. 

Data Source: SAI PM Report (1995). 

allow differentiation between urban and 

rural locations for coarser particles. 

The TSP and PM10 control scenario 

profiles developed based on this methodology are available on diskette, under the filenames listed in Table C-10. 

No-control scenario 

particulate matter profiles 

To derive the no-control TSP and 

Further speciation of TSP and PM10 air quality 

data serves two purposes in the present analysis. First, 

speciation of TSP into PM10 and other fractions allows derivation of PM10:TSP ratios. Such ratios can 

then be used to estimate historical PM10 for those years 

and monitors which had TSP data but no PM10 data. 

The reciprocal ratio is also applied in this analysis to 

expand 1985 and 1990 TSP data to cover those areas 

which monitored PM10 but not TSP. The second purpose served by speciation of particulate data is, as 

described earlier, to provide a basis for scaling up 

concentrations of each species to derive no-control 

scenario TSP and PM10 profiles. 

To break the TSP and PM10 data down into component species, speciation factors were applied to the 

PM fractions with aerodynamic diameters below 2.5 

microns (PM2.5) and from 2.5 to 10 microns (PM10). 

The PM2.5 speciation factors were drawn from a National Acid Precipitation Assessment Program 

(NAPAP) report on visibility which reviewed and 

consolidated speciation data from a number of studies.8 These factors are presented in Table C-8. In the 

table, fine particle concentrations are based on particle mass measured after equilibrating to a relative 

humidity of 40 to 50 percent; and organics include 

fine organic carbon. 

To develop speciation factors for coarser particles 

review of the available literature, including Conner et 

(i.e., in the PM2.5 to PM10 range), SAI performed a 

al. (1991), Wolff and Korsog (1989), Lewis and 

Macias (1980), Wolff et al. (1983), Wolff et al. (1991), 

and Chow et al. (1994).9 These speciation factors are 

summarized in Table C-9. Data were too limited to 

PM10 air quality profiles, individual 

component species were adjusted to 

reflect the relative change in emissions or, in the case 

of sulfates and nitrates in the eastern U.S., the relative change in modeled ambient concentration. The 

following excerpt from the SAI PM Report (1995) 

describes the specific algorithm used:10 

“For the retrospective analysis, the no-CAA 

scenario TSP and PM10 air quality was 

estimated by means of the following 

algorithm: 

•	 Apportion CAA scenario TSP and PM

to size categories and species; 10 

• Adjust for background concentrations; 

•	 Use a linear scaling to adjust the nonbackground portions of primary 

particulates, sulfate, nitrate, and organic 

components based on emissions ratios of 

PM, SO2

, NOx and VOC, and Regional 

Acid Deposition Model (RADM) annual 

aggregation results for SO4

 and NO3; 

•	 Add up the scaled components to estimate 

concentrations.” 

the no-CAA scenario TSP and PM10 

The specific procedures and values used for the 

linear rollback, speciation, fine to coarse particle ratio, scaling, and background adjustment steps are described in detail in the SAI PM report (1995).11 Table 

C-11 lists the names of the electronic data files containing the TSP and PM10 profiles for the no-control 

scenario. 

8

 J. Trijonis, “Visibility: Existing and Historical Conditions--Causes and Effects,” NAPAP Report 24, 1990. 

9

 This literature review, and complete citations of the underlying studies, are presented in the SAI PM Report (1995), pp. 4-2 to 

4-6 and pp. R-1 to R-2, respectively. 

10 SAI PM Report (1995), p. 5-1. 

11 SAI PM Report (1995), pp. 5-2 to 5-15. 

C-15

Compone nt Units 

Number of 

Data Sets 

Arithmetic 

Me an 

Range of 

Valu es 

Fine particle concentration μg/m3 19 18 6 - 46 

Ammonium sulfate % Fine particles 19 52 41 - 66 

Ammonium nitrate % Fine particles 3 1 1 

Organics % Fine particles 5 24 9 - 34 

Fine particle concentration μg/m3 3 36 29 - 43 

Ammonium sulfate % Fine particles 3 55 53 - 57 

Ammonium nitrate % Fine particles 2 1 1 

Organics % Fine particles 2 24 15 - 32 

Fine particle concentration μg/m3 25 5 1 - 11 

Ammonium sulfate % Fine particles 25 35 15 - 56 

Ammonium nitrate % Fine particles 17 4 1 - 17 

Organics % Fine particles 25 27 14 - 41 

Fine particle concentration μg/m3 16 35 13 - 74 

Ammonium sulfate % Fine particles 16 16 3 - 35 

Ammonium nitrate % Fine particles 14 15 2 - 37 

Organics % Fine particles 16 42 25 - 79 

Data Sources: SAI PM Report (1995); and J. Trijonis, "Visibility: Existing and Historical Conditions--Causes and 

Effects," NAPAP Report 24, 1990. 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table C-8. Fine Particle (PM2.5) Chemical Composition by U.S. Region. 

Compone nt Units 

Number of 

Data Sets 

Arithmetic 

Me an 

Range of 

Valu es 

RURAL EAST 

Fine particle concentration μg/m3 19 18 6 - 46 

Ammonium sulfate % Fine particles 19 52 41 - 66 

Ammonium nitrate % Fine particles 3 1 1 

Organics % Fine particles 5 24 9 - 34 

URBA N EAS T 

Fine particle concentration μg/m3 3 36 29 - 43 

Ammonium sulfate % Fine particles 3 55 53 - 57 

Ammonium nitrate % Fine particles 2 1 1 

Organics % Fine particles 2 24 15 - 32 

RU RAL WEST 

Fine particle concentration μg/m3 25 5 1 - 11 

Ammonium sulfate % Fine particles 25 35 15 - 56 

Ammonium nitrate % Fine particles 17 4 1 - 17 

Organics % Fine particles 25 27 14 - 41 

URBAN WES T 

Fine particle concentration μg/m3 16 35 13 - 74 

Ammonium sulfate % Fine particles 16 16 3 - 35 

Ammonium nitrate % Fine particles 14 15 2 - 37 

Organics % Fine particles 16 42 25 - 79 

Data Sources: SAI PM Report (1995); and J. Trijonis, "Visibility: Existing and Historical Conditions--Causes and 

Effects," NAPAP Report 24, 1990. 

Summary differences in particulate 

matter air quality 

Figure C-11 provides one indication of the estimated change in particulate matter air quality between 

the control and no-control scenarios. Specifically, the 

graph provides data on the estimated ratios of 1990 

control to no-control scenario annual mean TSP concentrations in monitored counties. The X-axis values 

represent the mid-point of the ratio interval bin, and 

the Y-axis provides the number of counties falling into 

each bin. Figure C-11 indicates that annual average 

TSP concentrations would have been substantially 

higher in monitored counties under the no-control scenario. 

Key caveats and uncertainties for 

particulate matter 

There are several important caveats and uncerprofiles developed for this study. Although further 

tainties associated with the TSP and PM10 air quality 

C-16

Compone nt Units 

Se ts 

Me an Valu es 

Coarse particle concentration μg/m3 1 5.5 5.5 

Ammonium sulfate % Coarse particles 3 3 1 - 4 

Ammonium nitrate % Coarse particles 1 4 4 

Organics % Coarse particles 2 10 7 - 13.8 

Coarse particle concentration μg/m3 18 24 7.7 - 56.7 

Ammonium sulfate % Coarse particles 18 6 2.1 - 10.39 

Ammonium nitrate % Coarse particles 18 18 2.33-28.52 

Organics % Coarse particles 18 14 8.41-25.81 

Indicator Filename 

Annual Mean T SPCMEAN .DAT 

2nd Highest D aily T SPCHI2.DAT 

(X)th Percentile TSPC(X).DAT 

1 0 Annual Mean PM10CME A.DAT 

1 0 2nd Highest D aily PM10CHI2.DAT 

1 0 (X)th Percentile PM10C(X).DAT 

Appendix C: Air Quality Modeling 

Table C-9. Coarse Particle (PM2.5 to PM10) Chemical Composition by U.S. Region. 

Compone nt Units 

Number 

of Data 

Se ts 

Arithmetic 

Me an 

Range of 

Valu es 

EAS T 

Coarse particle concentration μg/m3 1 5.5 5.5 

Ammonium sulfate % Coarse particles 3 3 1 - 4 

Ammonium nitrate % Coarse particles 1 4 4 

Organics % Coarse particles 2 10 7 - 13.8 

WEST 

Coarse particle concentration μg/m3 18 24 7.7 - 56.7 

Ammonium sulfate % Coarse particles 18 6 2.1 - 10.39 

Ammonium nitrate % Coarse particles 18 18 2.33-28.52 

Organics % Coarse particles 18 14 8.41-25.81 

Data Source: SAI PM Report (1995). 

Table C-10. PM Control Scenario Air Quality Profile Filenames. 

Compone nt Indicator Filename 

TSP Annual Mean T SPCMEAN .DAT 

TSP 2nd Highest D aily T SPCHI2.DAT 

TSP (X)th Percentile TSPC(X).DAT 

PM10 Annual Mean PM10CME A.DAT 

PM10 2nd Highest D aily PM10CHI2.DAT 

PM10 (X)th Percentile PM10C(X).DAT 

Note: "(X)" refers to percentiles from 5 to 95, indicating 19 percentile data files available 

for TSP and 19 files available for PM10; for example, the filename for the 50th percentile 

TSP air quality data profile for the control scenario is named TSPC50.DAT. 

C-17

Indicator Filename 

Annual Mean T SPCNMEA .DAT 

2nd Highest D aily T SPNCHI.DAT 

(X)th Percentile T SPNC(X).DAT 

1 0 Annual Mean PM10NCME.DAT 

1 0 2nd Highest D aily PM10NCHI.DAT 

1 0 (X)th Percentile PM10NC(X).D AT 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table C-11. PM No-Control Scenario Air Quality Profile 

Filenames. 

Note: "(X)" refers to percentiles from 5 to 95, indicating 19 percentile-based data files 

available for TSP and 19 similar files available for PM10; for example, the filename for the 

50th percentile TSP air quality data profile for the no-control scenario is named 

TSPNC50.DAT. 

Compone nt Indicator Filename 

TSP Annual Mean T SPCNMEA .DAT 

TSP 2nd Highest D aily T SPNCHI.DAT 

TSP (X)th Percentile T SPNC(X).DAT 

PM10 Annual Mean PM10NCME.DAT 

PM10 2nd Highest D aily PM10NCHI.DAT 

PM10 (X)th Percentile PM10NC(X).D AT 

sions, such as using SO2

 as a surrogate for SO4 

in the western states, to roll up individual PM 

components may introduce significant uncertainty. Third, even assuming a satisfactorily 

high degree of correlation between target and 

surrogate pollutants, relying on predicted 

changes in emissions at the state level further 

compounds the uncertainty. Finally, and perhaps most important, using PM10 to TSP ratios 

derived from late 1980s monitoring data may 

lead to significant underestimation of reductions in fine particulates achieved in earlier 

years. This is because historical Clean Air Act 

programs focused extensively on controlling 

combustion sources of fine particulates. As a 

result, the share of TSP represented by PM10 

observed in the late 1980s would be lower due 

to implementation of controls on combustion 

sources. This would lead, in turn, to underestimation of baseline PM concentrations, as a 10 reductions in these uncertainties were not possible for share of TSP, in the 1970s and early 1980s. If baseline 

this study given time and resource limitations, the rela- PM10 concentrations in these early years are underes­


tive importance of particulate matter reduction con- timated, the reductions in PM10 estimated by linear


tributions towards total benefits of the Clean Air Act scaling would also be underestimated.12


highlights the importance of these uncertainties.


A number of uncertainties were introduced in the Ozone 

process of speciating and rolling up individual components of particulate matter. First, temporal and spa- Nonlinear formation processes, long-range atmotial variability in the size and chemical properties of spheric transport, multiple precursors, complex atmoparticulate emissions are substantial. These charac- spheric chemistry, and acute sensitivity to meteoroteristics change from day to day at any given loca- logical conditions combine to pose substantial diffition. Second, using changes in proxy pollutant emis- culties in estimating air quality profiles for ozone. 

Even in the context of an aggregated, national study 

such as this, the location-specific factors controlling 

ozone formation preclude the use of roll-up modeling 

based on proxy pollutants or application of state-wide 

or nation-wide average conditions. Such simplifications would yield virtually meaningless results for 

ozone. 

Figure C-11. 

Control to No-control Annual Mean TSP Concentrations, by Monitored County. Number of Counties 

Distribution of Estimated Ratios for 1990 

Ideally, large-scale photochemical grid models — 

such as the Urban Airshed Model (UAM)— would 

be used to develop control and no-control scenario 

estimates for ozone concentrations in rural and urban 

areas. Such models provide better representations of 

the effects of several important factors influencing air 

quality projections such as long-range atmospheric 

transport of ozone. However, the substantial computing time and data input requirements for such models 

precluded their use for this study.13 Instead, three sepa12 See SAI PM Report (1995), p. 5-9.


13 For a description of the extensive data inputs required to operate UAM, see SAI Ozone Report (1995), p. 1-1.


C-18

Albany, NY Fort Wayne, IN Owensboro, K Y 

Albuquerque, NM Grand Rapids, MI Parkersburg, WV 

Allentown, P A-NJ Greeley, CO Pascagoula, MS 

Altoona, P A Green Bay, WI Pensacola, FL 

Anderson, IN Greensboro, NC Peoria, IL 

Appleton, WI Greenville, SC Philadelphia, PA 

Asheville, NC Harrisburg, PA Phoenix, AZ 

Atlanta, GA Hartford, CT Portland, O R 

Atlantic City, NJ Houst on, T X Portsmouth, N H 

Auburn, M E Huntington, WV-KY Raleigh, NC 

Augusta, GA-SC Huntsville, AL Reading, PA 

Austin, TX Indianapolis, IN Reno, NV 

Baltimore, MD Iowa City, IA Richmond, VA 

Baton Rouge, LA Jackson, MS Roanoke, VA 

Beaumont, T X Jacksonville, F L Rochester, NY 

Bellingham, WA Janesville Rock Co, WI Rockford, IL 

Billings, MT Johnson City, TN-VA Sacramento, CA 

Birmingham, AL Johnstown, PA Sa lt Lake City, UT 

Boston, MA Kansas City, MO San Antonio, TX 

Boulder, CO Knoxville, T N San Diego, CA 

Canton, O H Lafayette, IN San Francisco, CA 

Cedar Rapids, IA Lafayette, LA San Joaquin Valley, CA 

Champaign, IL Lake Charles, LA Santa Barbara, CA 

Charleston, SC Lancaster, PA Sarasota, FL 

Charleston, WV Lansing, MI Scrant on, P A 

Charlotte, NC Las Cruces, N M Se attle, WA 

Chattanooga, T N-GA Las Vegas, NV Sheboygan, WI 

Chicago, IL Lexington, KY Shreveport, LA 

Cincinnati, OH Lima, OH South Bend, IN 

Cleveland, OH Lit tle Rock, AR Springfield, IL 

Colorado Springs, CO Longview, TX Springfield, MO 

Columbia, SC Los Angeles, CA Springfield, OH 

Columbus , GA-AL Louisville, KY St Louis, MO 

Columbus, OH Lynchburg, VA Steubenville, OH-W V 

Corpus Christi, TX Medford, OR Syracuse, NY 

Cumberland, MD -WV Memphis, TN Tallahassee, FL 

Dal las, T X Miami, FL Tampa, FL 

Davenport, IA-IL Minneapolis, MN-WI Terre Haute, IN 

Decatur, IL Mobile, A L Toledo, OH 

Denver, CO Monroe, LA Tucson, AZ 

Detroit, MI Montgomery, AL T ulsa, OK 

El Paso, TX Nashville, T N Utica-Rome, NY 

Erie, P A Ne w Orleans, LA Ventura County, CA 

Eugene, OR Ne w York, NY Victoria, TX 

Evansville, IN Norfolk, VA Washington, DC 

Fayet tevill e, NC Oklahoma City, OK Wheeling, WV-OH 

Fl int, MI Omaha, NE-IA Wichita, KS 

Fort Collins , CO Orange Co, CA York, PA 

Fort Smith, AR-OK Orlando, FL Youngsto wn, O H-PA 

Appendix C: Air Quality Modeling 

Table C-12. Urban Areas Modeled with OZIPM4. 

Albany, NY Fort Wayne, IN Owensboro, K Y 

Albuquerque, NM Grand Rapids, MI Parkersburg, WV 

Allentown, P A-NJ Greeley, CO Pascagoula, MS 

Altoona, P A Green Bay, WI Pensacola, FL 

Anderson, IN Greensboro, NC Peoria, IL 

Appleton, WI Greenville, SC Philadelphia, PA 

Asheville, NC Harrisburg, PA Phoenix, AZ 

Atlanta, GA Hartford, CT Portland, O R 

Atlantic City, NJ Houst on, T X Portsmouth, N H 

Auburn, M E Huntington, WV-KY Raleigh, NC 

Augusta, GA-SC Huntsville, AL Reading, PA 

Austin, TX Indianapolis, IN Reno, NV 

Baltimore, MD Iowa City, IA Richmond, VA 

Baton Rouge, LA Jackson, MS Roanoke, VA 

Beaumont, T X Jacksonville, F L Rochester, NY 

Bellingham, WA Janesville Rock Co, WI Rockford, IL 

Billings, MT Johnson City, TN-VA Sacramento, CA 

Birmingham, AL Johnstown, PA Sa lt Lake City, UT 

Boston, MA Kansas City, MO San Antonio, TX 

Boulder, CO Knoxville, T N San Diego, CA 

Canton, O H Lafayette, IN San Francisco, CA 

Cedar Rapids, IA Lafayette, LA San Joaquin Valley, CA 

Champaign, IL Lake Charles, LA Santa Barbara, CA 

Charleston, SC Lancaster, PA Sarasota, FL 

Charleston, WV Lansing, MI Scrant on, P A 

Charlotte, NC Las Cruces, N M Se attle, WA 

Chattanooga, T N-GA Las Vegas, NV Sheboygan, WI 

Chicago, IL Lexington, KY Shreveport, LA 

Cincinnati, OH Lima, OH South Bend, IN 

Cleveland, OH Lit tle Rock, AR Springfield, IL 

Colorado Springs, CO Longview, TX Springfield, MO 

Columbia, SC Los Angeles, CA Springfield, OH 

Columbus , GA-AL Louisville, KY St Louis, MO 

Columbus, OH Lynchburg, VA Steubenville, OH-W V 

Corpus Christi, TX Medford, OR Syracuse, NY 

Cumberland, MD -WV Memphis, TN Tallahassee, FL 

Dal las, T X Miami, FL Tampa, FL 

Davenport, IA-IL Minneapolis, MN-WI Terre Haute, IN 

Decatur, IL Mobile, A L Toledo, OH 

Denver, CO Monroe, LA Tucson, AZ 

Detroit, MI Montgomery, AL T ulsa, OK 

El Paso, TX Nashville, T N Utica-Rome, NY 

Erie, P A Ne w Orleans, LA Ventura County, CA 

Eugene, OR Ne w York, NY Victoria, TX 

Evansville, IN Norfolk, VA Washington, DC 

Fayet tevill e, NC Oklahoma City, OK Wheeling, WV-OH 

Fl int, MI Omaha, NE-IA Wichita, KS 

Fort Collins , CO Orange Co, CA York, PA 

Fort Smith, AR-OK Orlando, FL Youngsto wn, O H-PA 

rate modeling efforts were conducted to provide ur- run for 147 urban areas. Table C-12 lists the urban


ban and rural ozone profiles for those areas of the lower areas modeled with OZIPM4. Although it requires


48 states in which historical ozone changes attribut- substantially less input data than UAM, the OZIPM4


able to the Clean Air Act may be most significant. model provides reasonable evaluations of the relative


reactivity of ozone precursors and ozone formation


First, for urban areas the Ozone Isopleth Plotting mechanisms associated with urban air masses.14 Three


with Optional Mechanisms-IV (OZIPM4) model was to five meteorological episodes were modeled for each


14 See SAI Ozone Report (1995), p. 1-1. 

C-19

The Benefits and Costs of the Clean Air Act, 1970 to 1990

C-20

of the 147 urban areas; and for each of these, four

model runs were performed to simulate the 1980 and

1990 control and no-control scenarios. The outputs of

these model runs were peak ozone concentrations for

each of the target year-scenario combinations. The

differentials between the control and no-control scenario outputs were averaged over meteorological episodes and then applied to scale up historical air quality at individual monitors to obtain no-control case

profiles. As for the other pollutants, the control scenario profiles were derived by fitting statistical distributions to actual historical data for individual monitors.

Second, the 15-layer RADM runs for 1980 and

1990 were used to estimate the relative change in rural ozone distributions for the eastern 31 states. In addition, a limited number of 20-km grid cell high-resolution RADM runs were conducted to benchmark the

15-layer, 80-km RADM median ozone response and

to estimate high ozone response. The relative changes

in modeled median and 90th percentile rural ozone

were then assumed to be proportional to the changes

in, respectively, the median and 90th percentile ozone

concentrations. The domain of the high-resolution

RADM is shown in Figure C-4 and the general RADM

domain is shown in Figure C-12.

Finally, the SARMAP Air Quality Model

(SAQM) was run for EPA by the California Air Resources Board (CARB) to gauge the differences in

peak ozone concentrations in key California agricultural areas for 1980 and 1990. No-control profiles were

developed for ozone monitors in these areas by assuming the relative change in peak ozone concentration also applies to the median of the ozone distribution. The domain of the SAQM is shown in Figure C12.

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Figure C-12. RADM and SAQM Modeling Domains, with Rural Ozone Monitor Locations.

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Monitors Counties 

1 1 

467 240 

791 415 

719 415 

834 477 

Appendix C: Air Quality Modeling 

Control scenario ozone profiles 

For ozone, air quality profiles were developed 

from historical AIRS data and calculated for individual 

monitors based on 1, 2, 6, 12, and 24 hour averaging 

times. Profiles based on the daily maximum concentrations for these averaging times were also calculated. 

Given the significance of seasonal and diurnal ozone 

formation, twelve separate profiles of hourly ozone 

distributions were also developed for six 2-month 

periods and for daytime and nighttime hours. The 

2-month periods are January-February, March-April, 

and so forth. The diurnal/nocturnal profiles are divided 

at 7 A.M. and 7 P.M. Local Standard Time. All of 

these profiles are based on constructing 1, 2, 6, 12, 

and 24-hour moving average profiles from the hourly 

ozone data from each monitor.15 A two-parameter 

gamma distribution is then fitted to characterize each 

of these air quality profiles.16 The functional form of 

the gamma distribution, the basis for deriving the 

monitor-specific values for mean and variance, and 

an analysis of the goodness of fit to the data are presented in the SAI Ozone Report (1995). 

Table C-13 summarizes the ozone monitoring data 

used as the basis for the control scenario profiles. The 

distribution of these monitors among urban, suburTable C-13. Summary of Ozone Monitoring 

Data. 

Data Source: SAI Ozone Report (1995). 

ban, and rural locations is presented in Table C-2 of 

the SAI Ozone Report (1995). 

Given the substantial number of alternative air 

quality profiles for ozone, approximately 20 high-density disks are required to hold the profiles, even in 

compressed data format. Resource limitations therefore preclude general distribution of the actual profiles. As discussed in the caveats and uncertainties 

subsection below, however, the substantial uncertainties associated with model results for any given area 

preclude application of these profiles in contexts other 

than broad-scale, aggregated assessments such as the 

present study. The historical ozone monitoring data 

used as the basis for this study are, nevertheless, available through EPA’s Aerometric Information Retrieval 

System (AIRS). 

No-control scenario ozone profiles 

The specific modeling methodologies for the 

OZIPM4 runs —including emissions processing, development of initial and boundary conditions, meteorological conditions, simulation start and end times, 

organic reactivity, and carbon fractions— are described in detail in the SAI Ozone Report (1995). 

Assumptions and modeling procedures not otherwise 

described in the SAI report were conducted in accordance with standard EPA guidance.17 

Similarly, the RADM modeling methodology 

used to estimate changes in day-time rural ozone distributions in the eastern 31 states are described in detail in the RADM Report (1995). The referenced report also provides complete citations of the literature 

associated with development, standard application 

procedures, and evaluation of RADM by the National 

Acid Precipitation Assessment Program (NAPAP). 

To derive the no-control scenario results for key 

California agricultural areas, the California Air Resources Board and US EPA’s Region 9 office agreed 

to conduct three runs of the SAQM. For the 1990 control scenario, the 1990 SARMAP base case scenario 

adopted for California State Implementation Plan 

modeling was adopted.18 Derivation of 1990 

Year 

Number of 

Monitors 

Number of 

Counties 

1970 1 1 

1975 467 240 

1980 791 415 

1985 719 415 

1990 834 477 

15 For the nighttime profiles, only 1, 2, 6, and 12-hour averaged concentrations are derived. 

16 Normal and lognormal distributions were also developed and tested for goodness of fit; however, the gamma distribution provided 

a better representation of the concentration distribution. See SAI Ozone Report (1995), page 4-2. 

17 US EPA, Office of Air Quality Planning and Standards, “Procedures for Applying City-Specific EKMA,” EPA-450/4-89-012, 1989. 

18 Documentation of the SARMAP Air Quality Model and the SARMAP 1990 base case can be found in the SAQM references listed 

at the end of this appendix. 

C-21

Category to 1990 Con trol Ratio 1990 Control Ratio 1990 Control Ratio 

Mobile 1.344 1.955 3.178 

Area 0.820 0.901 1.106 

Point 1.284 1.439 1.232 

Mobile 1.042 1.148 1.677 

x Area 0.731 0.738 1.058 

Point 0.987 1.339 1.159 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

no-control and 1980 control and no-control scenarios 

was based on adjusting the aggregate mobile, point, 

and area source VOC and NO emissions associated x 

with each of these cases. For example, the 1980 

no-control results were derived by, first, multiplying 

the 1990 SARMAP base case mobile source VOC 

emissions by the ratio of 1980 no-control scenario to 

1990 control scenario mobile source VOC emissions 

derived for the present study. Similar adjustments were 

made for point and area sources, and for NOx

. The 

SAQM was then re-run holding fixed all other conditions associated with the 1990 SARMAP base case, 

including meteorology, activity patterns, and other 

conditions. The specific emission ratios used to modify 

the 1990 SARMAP base case are presented in Table 

C-14. The ratios themselves were derived by adding 

on-highway and off-highway emissions to represent 

the mobile source category; adding utility, industrial 

process, and industrial combustion emissions to represent point sources; and using commercial/residenresults. This is because OZIPM4 provides only the 

maximum hourly ozone concentration. However, to 

estimate all the various physical consequences of 

changes in ambient ozone concentrations, the current 

study requires estimation of the shift in the entire distribution of ozone concentrations. Since it is daytime 

ozone season concentrations which are most sensitive to changes in VOC and NOx

 emissions, the predicted shifts in the most important component of the 

ozone concentration distribution are reasonably wellfounded. The method adopted for this analysis involved applying the no-control to control peak concentration ratio to all concentrations in the distribution down to a level of 0.04 ppm. The 0.04 ppm level 

is considered at the high end of hypothetical ambient 

ozone concentrations in the absence of all anthropogenic ozone precursor emissions. A ratio of 1.0 is used 

for ozone concentrations at or near zero. The methodology is described in more detail in the SAI Ozone 

Report (1995) on page 4-6. 

Table C-14. Apportionment of Emissions Inventories for SAQM Runs. 

Source 

Category 

1980 Control 

to 1990 Con trol Ratio 

1980 No-Control to 

1990 Control Ratio 

1990 No-Control to 

1990 Control Ratio 

VOC 

Mobile 1.344 1.955 3.178 

Area 0.820 0.901 1.106 

Point 1.284 1.439 1.232 

NOx 

Mobile 1.042 1.148 1.677 

Area 0.731 0.738 1.058 

Point 0.987 1.339 1.159 

tial emissions to represent area sources. The no-control 

scenarios were then derived by adjusting the peak and 

median of the control scenario ozone distribution 

based on the ratio of SARMAP-predicted peak ozone 

concentrations under the control and no-control scenarios. 

The relative results of the control and no-control 

scenario runs of the OZIPM4, RADM, and SAQM 

models were then used to derive the no-control case 

air quality profiles. For the urban monitors relying on 

OZIPM4 results, only ozone-season daytime concentrations could be calculated directly from OZIPM4 

Estimating changes in rural ozone concentrations 

is required primarily for estimating effects on agricultural crops, trees, and other vegetation. For this 

reason, only the differences in daytime, growing season ozone concentrations are derived for the present 

study. As described in detail in the SAI Ozone Report 

(1995) on page 4-7, the no-control rural ozone profiles are calculated by, first, taking the ratio of the 

average daytime growing season ozone concentrations 

simulated by RADM or SAQM (whichever is relevant 

for that monitor). The ratio of no-control to control 

scenario average ozone concentration is then applied 

to all the hourly concentrations from that monitor. 

C-22

Appendix C: Air Quality Modeling 

Profiles based on 1, 2, 6, 12, and 24-hour averages 

are then calculated for the control case; and averages 

for daytime hours are calculated for the no-control 

case.19 Even though the control and no-control scenario off-season profiles are held constant, profiles 

for the no-control scenario are developed for all 

months of the year since the ozone season varies 

throughout the country. 

Summary differences in ozone air 

quality 

Figure C-13 presents a summary of the results of 

the 1990 OZIPM4 results for all 147 of the modeled 

urban areas. Specifically, the graph depicts a frequency distribution of the ratio of control to no-control 

scenario peak ozone. While the vast majority of simulated peak ozone concentration ratios fall below 1.00, 

eight urban areas show lower simulated peak ozone 

for the no-control scenario than for the control scenario. For these eight urban areas, emissions of precursors were higher under the no-control scenario; 

however, the high proportion of ambient NOx compared to ambient non-methane organic compounds 

(NMOCs) in these areas results in a decrease in net 

ozone production when NOx emissions increase. Figures C-14 and C-15 present frequency distributions 

for control to no-control ratios of average ozone-season daytime ozone concentrations at rural monitors 

as simulated by RADM and SAQM, respectively. 

These figures indicate that, by 1990, no-control 

scenario ozone concentrations in the modeled areas 

would have been generally higher in both urban and 

rural areas. Rural area concentrations differences are 

not as great as urban area differences due to (a) the 

differentially greater effect of CAA emission controls 

in high population density areas, and (b) potential differences in the models used for urban and rural areas. 

Ozone reductions in both rural and urban areas 

projected in this analysis are not as proportionally large 

as the estimated reductions in emissions of ozone precursors for at least four reasons. First, current knowledge of atmospheric photochemistry suggests that 

ozone reductions resulting from emissions changes 

will be proportionally smaller than the emissions reductions. Second, biogenic emissions of VOCs, an 

important ozone precursor, are significant and are held 

constant for the control and no-control scenarios of 

this analysis. Biogenic emissions are important because they contribute roughly half of the total 

19 The no-control scenario nighttime profiles are assumed to be the same as the control scenario profiles. 

C-23

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

(manmade plus natural) VOC emissions nationwide. it is important to consider the central purpose of the 

Due to this abundance of VOC loading and the inher- present study, which is to develop a reasonable estient nonlinearity of the ozone-precursor response sys- mate of the overall costs and benefits of all historical 

tem,20 historical reductions in anthropogenic VOC Clean Air Act programs. All analyses are based on 

emissions can yield minimal reductions in ozone, es- relative modeled results, and ratios of the model prepecially in rural environments. Third, this rural effect dictions for the control and no-control scenarios, rather 

also influences urban areas receiving substantial ozone than the absolute predictions. As a result of this, the 

transported in from surrounding areas. Consequently, effect of any bias in the model predictions is greatly 

the effect of emission controls placed in urban areas reduced due to partial cancellation.


often is reduced since much of the urban area ozone


is imported. Thus, the problem is truly regionalized Additional uncertainty is contributed by other


given the importance of transport, biogenic emissions limitations of the models, the supporting data, and the 

and associated urban-rural interactions, all contribut- scope of the present analysis. Relying on linear intering toward a relatively non-responsive atmospheric polation between 1970 and modeled 1980 results to 

system.21 Finally, physical process characterizations derive results for 1975, and between modeled results 

within OZIPM4 are severely limited and incapable of for 1980 and 1990 to derive results for 1985, clearly 

handling transport, complex flow phenomena, and adds to the uncertainty associated with the RADMmulti-day pollution events in a physically realistic based rural ozone estimates. Assuming that changes 

manner. Consequently, it is possible that the OZIPM4 in peak concentration predicted by OZIPM4 and 

method used herein produces negative bias tenden- SAQM can be applied to scale hourly ozone values 

cies in control estimations. Additional discussion of throughout the concentration distribution also contribuncertainties in the ozone air quality modeling is pre- utes to uncertainty. Resource and model limitations


sented in the following section. also required that night-time ozone concentrations be


held constant between the scenarios. This leads to an


Key caveats and uncertainties for ozone underestimation of the night-time component of ozone


transport. Finally, changes in rural ozone in areas not


There are a number of uncertainties in the overall covered by RADM or SAQM could not be estimated. 

analytical results of the present study contributed by As a result, potentially significant changes in ambithe ozone air quality modeling in addition to the po- ent ozone in other major agricultural areas, such as in 

tential systematic downward bias discussed above. the mid-west, could not be developed for this analyFirst, there are substantial uncertainties inherent in any sis. The Project Team considered using an emissions 

effort to model ozone formation and dispersion. These scaling (i.e., a roll-back) modeling strategy to develop 

uncertainties are compounded in the present study by crude estimates of the potential change in rural ozone 

the need to perform city-specific air quality modeling concentrations in monitored areas outside the RADM 

using OZIPM4, which is less sophisticated than an and SAQM domains. However, the Project Team conEulerian model such as the Urban Airshed Model. cluded that such estimates would be unreliable due to 

However, while the absolute ozone predictions for any the nonlinear effect on ozone of precursor emission 

given urban area provided by OZIPM4 may be quite changes. Furthermore, the team concluded that 

uncertain, the process of aggregating results for a num- baseline levels of ozone and changes in precursor 

ber of cities and meteorological episodes should sig- emissions in these areas are relatively low. The decinificantly reduce this uncertainty.22 Urban areas for sion not to spend scarce project resources on estimatwhich ozone changes may be overpredicted are offset ing ozone changes in these rural areas is further supto some degree by urban areas for which the change ported by the relatively modest change in rural ozone 

in ozone concentrations may be underpredicted. In concentrations estimated within the RADM and 

weighing the significance of this source of uncertainty, SAQM domains. 

20 Nonlinear systems are those where a reduction in precursors can result in a wide range of responses in secondary pollutants 

such as ozone. Ozone response often is “flat” or nonresponsive to reductions of VOCs in many rural areas with significant natural 

VOC emissions. Also, ozone can increase in response to increases in NOx emissions in certain localized urban areas. 

21 Both the 1990 CAA and EPA’s and the National Academy of Science’s Section 185B Report to Congress recognized the 

consequences of biogenics, transport and the need to conduct regionalized assessments, as reflected in organizational structures such 

as the Ozone Transport Commission and the North American Research Strategy for Tropospheric Ozone (NARSTO). 

22 Note that aggregating individual urban area results may reduce the effect of uncertainty in individual city projections (i.e., 

overestimated cities would offset underestimated cities). However, aggregation of individual urban area results would not reduce 

potential errors caused by systematic biases which arise due to, for example, misestimated emissions inventories. 

C-24

Appendix C: Air Quality Modeling 

Visibility 

Two separate modeling approaches were used to 

estimate changes in visibility degradation in the eastern and southwestern U.S. These are the two regions 

of the coterminous U.S. for which Clean Air Act programs were expected to have yielded the most significant reductions in visibility degradation. Visibility changes in the eastern 31 states were estimated 

based on the RADM/EM results for sulfates; and 

changes in visibility in 30 southwestern U.S. urban 

areas were calculated using a linear emissions scaling 

approach. Despite the potential significance of Clean 

Air Act-related visibility changes in southwestern U.S. 

Class I areas, such as National Parks, resource limitations precluded implementation of the analysis 

planned for these areas. 

The RADM/EM system includes a post-processor which computes various measures of visibility 

degradation associated with changes in sulfate aerosols.23 The basic approach is to allocate the light extinction budget for the eastern U.S. among various 

aerosols, including particulate sulfates, nitrates, and 

organics. The change in light extinction from sulfates 

is provided directly by RADM, thereby reflecting the 

complex formation and transport mechanisms associated with this most significant contributor to light 

extinction in the eastern U.S. Nitrates are not estimated 

directly by RADM. Instead, RADM-estimated concentrations of nitric acid are used as a surrogate to 

provide the basis for estimating changes in the particulate nitrate contribution to light extinction. The 

organic fractions were held constant between the two 

scenarios. Standard outputs include daylight distribution of light extinction, visual range, and DeciViews24 

for each of RADM’s 80-km grid cells. For the present 

study, the RADM visibility post-processor was configured to provide the 90th percentile for light extinction and the 10th percentile for visual range to represent worst cases; and the 50th percentile for both of 

these to represent average cases. More detailed documentation of the RADM/EM system and the assumptions used to configure the visibility calculations are 

presented in the RADM Report (1995). 

To estimate differences in control and no-control 

scenario visibility in southwestern U.S. urban areas, 

a modified linear rollback approach was developed 

and applied to 30 major urban areas with population 

greater than 100,000.25 For each of the 30 urban centers, seasonal average 1990 air quality data was compiled for key pollutants, including NO2

 and PM10, contributing to visibility degradation in southwestern U.S. 

coastal and inland cities. PM10 was then speciated into 

its key components using city-specific annual average PM10 profile data. After adjusting for regional — 

and for some species, city-specific— background levels, concentrations of individual light-attenuating species were scaled linearly based on changes in emissions of that pollutant or a proxy pollutant.26 Using 

the same approach used for the 1993 EPA Report to 

Congress on effects of the 1990 Clean Air Act Amendments on visibility in Class I areas, light extinction 

coefficients for each of these species were then multiplied by their respective concentrations to derive a 

city-specific light extinction budget.27 This process 

was repeated for pre-1990 control and all no-control 

scenarios by scaling 1990 results by the relative change 

in annual county-level emissions of SOx

, NOx, and 

PM. Based on the city-specific light extinction budget calculations, measures for total extinction, visual 

range, and DeciView were calculated for each scenario and target year. 

Control scenario visibility 

Unlike the other air quality conditions addressed 

in the present study, modeled visibility conditions are 

used as the basis for the control scenario rather than 

actual historical conditions. However, like the other 

air quality benefits of the historical Clean Air Act, it 

is the differences between modeled visibility outcomes 

for the control and no-control scenarios which are used 

23 A complete discussion, including appropriate references to other documents, of the RADM and RADM/EM modeling 

conducted for the present study is presented in the subsection on acid deposition earlier in this appendix. 

24 The DeciView Haze Index (dV) is a relatively new visibility indicator aimed at measuring visibility changes in terms of human 

perception. It is described in detail in the SAI SW Visibility Report (1994), pp. 4-2 to 4-3. See also Pitchford and Malm (1994) for 

the complete derivation of the DeciView index. 

25 Complete documentation of the linear scaling modeling, speciation methodologies, spatial allocation of emissions, and other 

data and assumptions are provided by the SAI SW Visibility Report (1994). 

26 For example, sulfate (SO4

) concentrations were scaled based on changes in sulfur oxide (SOx) emissions. 

27 The term “light extinction budget” refers to the apportionment of total light attenuation in an area to the relevant pollutant 

species. 

C-25

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

to estimate visibility benefits. Nevertheless, 1990 absolute levels of eastern U.S. visibility predicted by 

RADM under the control scenario are presented in 

Figure C-16 to provide a sense of initial visibility conditions. 

For the southwestern urban areas, 1990 control 

scenario annual average light extinction budget, visual range, and DeciView conditions are listed in Table 

C-15. These 1990 results are presented to give the 

reader a sense of the initial visibility conditions in 

absolute, albeit approximate, terms. 

No-control scenario visibility 

The no-control scenario visibility results for the 

eastern U.S. area covered by RADM are presented in 

Figure C-17. No-control scenario 1990 outcomes for 

the 30 southwestern U.S. urban areas are presented in 

Table C-16. 

Summary differences in visibility 

DeciView Haze Index 

The DeciView Haze Index (dV) has recently been 

proposed as an indicator of the clarity of the atmosphere that is more closely related to human perception than visual range (VR) or total extinction (bext) 

(Pitchford and Malm, 1994). It is defined by the equation: 

bext dV = 10 1ne (–––) (2) 10 

where: 

bext =	 total extinction in inverse megameters 

(Mm-1) 

This index has the value of approximately 0 when 

the extinction coefficient is equal to the scattering 

coefficient for particle-free air (Rayleigh scattering) 

and increases in value by approximately one unit for 

each 10 percent increase in bext. Since the apparent 

change in visibility is related to the percent change in 

bext (Pitchford et al., 1990), equal changes in dV correspond to approximately equally perceptible changes 

in visibility. Recent research indicates that, for most 

observers, a “just noticeable change” in visibility corresponds to an increase or decrease of about one to 

two dV units. 

Figure C-16. 

Expressed in Annual Average DeciView, for Poor Visibility 

Conditions (90th Percentile Under the Control Scenario. 

Figure C-17. 

Expressed in Annual Average DeciView, for Poor Visibility 

Conditions (90th Percentile Under the No-control Scenario. 

RADM-Predicted Visibility Degradation, 

RADM-Predicted Visibility Degradation, 

C-26

Da ta Sour ce: SAI SW Visibility Report (1994). 

Dat a So urce: SAI SW Vis ib ili ty R eport (1 994 ). 

Appendix C: Air Quality Modeling 

Table C-15. 1990 Control Scenario Visibility Table C-16. 1990 No-control Scenario Visibility Conditions for 

Conditions for 30 Southwestern U.S. Cities. 30 Southwestern U.S. Cities. 

City Ligh t 

Extin ction 

Budg et (b, 

Mm -1 ) 

Visu al 

Ra nge 

(km) 

De ci 

View 

(d V) 

Lo s Angeles, CA 197. 6 15.2 29.8 

San Be rnardin o, CA 201. 7 14.9 30.0 

Rive rside, CA 208. 3 14.4 30.4 

An aheim, CA 170. 1 17.6 28.3 

Ve ntura, CA 113. 3 26.5 24.3 

San Diego, C A 126. 9 23.6 25.4 

Santa Barbara, CA 112. 8 26.6 24.2 

Ba kersfie ld, CA 215. 1 13.9 30.7 

Fre sno, CA 211. 7 14.2 30.5 

Mod esto, CA 148. 8 20.2 27.0 

Stockton, CA 153. 1 19.6 27.3 

San Fra nc isco, CA 120. 8 24.8 24.9 

Oa kland, CA 117. 5 25.5 24.6 

San Jose , CA 154. 6 19.4 27.4 

Mon terey, CA 84.7 35.4 21.4 

Sacramento, CA 119. 1 25.2 24.8 

Re dding, C A 83.2 36.1 21.2 

Re no, NV 147. 4 20.3 26.9 

Las Ve gas, NV 157. 9 19.0 27.6 

Salt La ke City, UT 117. 5 25.5 24.6 

Prov o, UT 107. 8 27.8 23.8 

Fort Collins, CO 80.7 37.2 20.9 

Gree le y, CO 84.2 35.6 21.3 

De nver, CO 153. 4 19.6 27.3 

Colorad o Springs, CO 

83.3 36.0 21.2 

Pue blo, CO 88.1 34.1 21.8 

Alb uque rqu e, NM 91.1 32.9 22.1 

El Paso, TX 109. 3 27.5 23.9 

Tucson, AZ 85.6 35.0 21.5 

Phoenix, AZ 125. 3 23.9 25.3 

De nver, CO 

Colorad o Springs, CO 

Phoenix, AZ 125. 3 23.9 25.3 

City Ligh t Extinctio n Budg et (b, Mm-1 ) 

Visu al 

Range (km ) 

De ciView (d V) 

Lo s Angeles, CA 333. 4 9. 0 35.1 

San Be rnardin o, CA 337. 3 8. 9 35.2 

Riverside, CA 343. 2 8. 7 35.4 

An aheim, C A 286. 3 10.5 33.5 

Ve ntura, C A 194. 8 15.4 29.7 

San Diego, CA 210. 1 14.3 30.4 

Santa Barbara, CA 183. 2 16.4 29.1 

Bakersfield, CA 356. 4 8. 4 35.7 

Fre sno, CA 349. 0 8. 6 35.5 

Mod esto, CA 240. 1 12.5 31.8 

Stockton, CA 248. 1 12.1 32.1 

San Fra nc isco, CA 197. 3 15.2 29.8 

Oa kland, CA 188. 6 15.9 29.4 

San Jose , CA 253. 0 11.9 32.3 

Mon terey, CA 141. 4 21.2 26.5 

Sacramento, CA 189. 2 15.9 29.4 

Re dding, CA 128. 6 23.3 25.5 

Reno, NV 416. 6 7. 2 37.3 

Las Ve gas, NV 643. 8 4. 7 41.6 

Salt La ke City, UT 185. 8 16.1 29.2 

Prov o, UT 159. 0 18.9 27.7 

Fort Collins, CO 191. 2 15.7 29.5 

Gr ee le y, CO 117. 0 25.6 24.6 

De nver, CO 284. 4 10.5 33.5 

Colorad o Springs, CO 175. 8 17.1 28.7 

Pue blo, CO 299. 9 10.0 34.0 

Alb uquerqu e, NM 175. 8 17.1 28.7 

El Paso, TX 276. 3 10.9 33.2 

Tucson, AZ 272. 2 11.0 33.0 

Phoenix, AZ 429. 5 7. 0 37.6 

Ligh t Extinctio n Budg et (b, Mm-1 ) Visu al 

Range (km ) 

De ciView (d V) 

Phoenix, AZ 429. 5 7. 0 37.6 

Data Source: SAI SW Visibility R eport (1994). 

Data Source: SAI SW Visibility Report (1994). 

C-27

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Both VR and dV are measures of the value of b ext 

at one location in the atmosphere. Both are unaffected 

by the actual variability of the compositions and illumination of the atmosphere, so neither is closely linked 

to the human perception of a particular scene. The 

isolation of these parameters from site-specific variations and temporal fluctuations of the atmospheric illumination increases their usefulness for comparing 

the effects of air quality on visibility across a range of 

geographic locations for a range of time periods. Each 

parameter attempts to scale the bext data so that changes 

in air quality can be used to provide an indication of 

changes in the human perception of a scene. 

Modeling Results 

The differences in modeled 1990 control and 

no-control scenario visibility conditions projected by 

the RADM/EM for the eastern U.S. are presented in 

Figure C-18. The map shows the percent increase in 

modeled annual average visibility degradation under 

poor conditions for 1990 when moving from the control to the no-control scenario. The results indicate 

perceptible differences in visibility between the control and no-control scenario throughout the RADM 

domain. The relatively large increase in visibility impairment in the Gulf Coast area is a reflection of the 

Figure C-18. 

Degradation, Expressed in Annual Average DeciView, 

for Poor Visibility Conditions (90th Percentile) Under the 

No-control Scenario. 

RADM-Predicted Increase in Visibility 

significant increases in 1990 sulfate concentrations 

associated with the no-control scenario. (See the earlier discussion of effects in this region in the sections 

dealing with acid deposition.) 

The differences in modeled 1990 control and 

no-control scenario visibility conditions in the 30 

southwestern U.S. urban areas projected by linear rollback modeling are presented in Table C-17. When 

reviewing these visibility degradation differentials for 

the 30 southwestern U.S. urban areas, it is important 

to consider that while estimated differences in visual 

range were in many cases very large, changes in the 

DeciView Haze Index (dV) may be relatively small. 

This is because the perception of visibility degradation measured by dV may be small when baseline visibility is high.28 Even so, the results indicate that, by 

1990, visibility in southwestern U.S. urban areas 

would be noticeably worse under the no-control scenario. 

Key caveats and uncertainties for 

visibility 

There are several sources of uncertainty in the 

RADM and southwestern U.S. linear scaling model 

analyses. For RADM, the use of nitric acid as a surrogate for estimating changes in light-attenuating nitrate particles ignores the interaction effects of nitrates, sulfates, and ammonia. As a result, increases 

in nitrates may be overestimated by the model when 

both sulfates and nitric acid increase. However, the 

significance of this potential overestimation is mitigated to some extent by the relative insignificance 

of nitrate-related visibility degradation relative to 

sulfates which prevails in the eastern U.S. 

Several important uncertainties in the southwestern U.S. urban area visibility analysis are described in detail in the SAI SW Visibility Report 

(1994). First, the need to use seasonal average conditions leads to underestimation of extreme visibility impairment episodes associated with high humidity, since particle growth due to water absorption is highly nonlinear. Second, although the use 

of city-specific light extinction and PM speciation 

data is significantly better than reliance on regional 

averages, uncertainties in city-specific data may 

contribute to overall uncertainty in the estimates. 

However, overall uncertainty associated with these 

factors will be reduced to some extent since overestimation of visibility degradation in some cities 

28 See SAI SW Visibility Report (1994), page 5-3. 

C-28

Visu al Rang e (% ) 

De ciView (d V) 

Phoenix, AZ 243 -1 2 

Dat a Source: SAI SW Visibilit y Report (1994). 

Appendix C: Air Quality Modeling 

will be offset by underestimations in other cities. Finally, the linear scaling used to estimate the pre-1990 

control scenarios and the no-control scenarios was 

based on changes in county-wide or air basin emissions. Uncertainties associated with apportionment of 

state-wide emission changes to individual counties or 

air basins may contribute significantly to overall uncertainty in the visibility change estimates. Such apportionment is particularly difficult for SOx emission 

changes, since emission reductions achieved by the 

Clean Air Act tended to be at relatively remote utility 

and smelter plants. However, sulfates are a relatively 

minor source of light attenuation in western urban 

areas. 

An important overall limitation of the visibility 

analysis conducted for the present study is that only 

southwestern urban areas and the eastern 31 states 

were included. The Clean Air Act may have contributed toward significant reductions in visibility degradation in other areas. For example, Clean Air Act programs to reduce ambient particulate matter may have 

motivated reductions in silvicultural burning in some 

northwestern states. Perhaps the greatest deficiency 

in geographic coverage by the present study is the 

omission of visibility changes in Class I areas in the 

west. 

Table C-17. Summary of Relative Change in 

Visual Range and DeciView Between 1990 Control 

and No-control Scenario Visibility Conditions for 

30 Southwestern U.S. Cities. 

Data Source: SAI SW Visibility Report (1994). 

City Visu al Rang e (% ) 

De ciView (d V) 

Lo s Angeles, CA 69 -5 

San Be rnardin o, CA 67 -5 

Riverside, CA 65 -5 

An aheim, C A 68 -5 

Ve ntura, C A 72 -5 

San Diego, CA 65 -5 

Santa Barbara, CA 62 -5 

Bakersfield, CA 66 -5 

Fre sno, CA 65 -5 

Mod esto, CA 61 -5 

Stockton, CA 62 -5 

San Fra nc isco, CA 63 -5 

Oa kland, CA 61 -5 

San Jose , CA 64 -5 

Mon terey, CA 67 -5 

Sacramento, CA 59 -5 

Re dding, CA 55 -4 

Reno, NV 183 -1 0 

Las Ve gas, NV 308 -1 4 

Salt La ke City, UT 58 -5 

Prov o, UT 48 -4 

Fort Collins, CO 137 -9 

Gr ee le y, CO 39 -3 

De nver, CO 85 -6 

Colorad o Springs, CO 111 -7 

Pue blo, CO 240 -1 2 

Alb uquerqu e, NM 93 -7 

El Paso, TX 153 -9 

Tucson, AZ 218 -1 2 

Phoenix, AZ 243 -1 2 

C-29

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Air Quality Modeling References 

Chang. 1995. SARMAP Air Quality Model (SAQM). 

Final report to San Joaquin Valley wide Air 

Pollution Study Agency. 

DaMassa, Tanrikulu, and Ranzier. 1996. Photochemical Modeling of August 3-6, 1990, Ozone 

Episode in Central California Using the 

SARMAP Air Quality Model. Part II: Sensitivity and Diagnostic Testing. Preprints, Ninth 

Joint Conference on the Applications of Air 

Pollution Meteorology with Air Waste Management Association. January 28 - February 

2, 1996, Atlanta, Georgia. 

Dennis, R. 1995. Estimation of Regional Air Quality 

and Deposition Changes Under Alternative 

812 Emissions Scenarios Predicted by the 

Regional Acid Deposition Model, RADM. 

Draft Report for U.S. Environmental Protection Agency, ORD/NERL. October 1995. 

ICF Kaiser/Science Applications International. 1996. 

PM Interpolation Methodology for the Section 812 Retrospective Analysis. Memorandum from J. Langstaff to Jim DeMocker. 

ICF Kaiser/Systems Applications International. 1994. 

Retrospective Analysis of the Impact of the 

Clean Air Act on Urban Visibility in the Southwestern United States. Final Report. 

ICF Kaiser/Systems Applications International. 1995. 

Retrospective Analysis of Ozone Air Quality 

in the United States. Final Report. 

ICF Kaiser/Systems Applications International. 1992. 

Retrospective Analysis of Particulate Matter 

Air Quality in the United States. Draft Report. 

ICF Kaiser/Systems Applications International. 1995. 

Retrospective Analysis of Particulate Matter 

Air Quality in the United States. Final Report. 

ICF Kaiser/Systems Applications International. 1994. 

Retrospective Analysis of SO , NO and CO 2 x Air Quality in the United States. Final Report. 

ICF Resources Incorporated. 1992. Results of Retrospective Electric Utility Clean Air Act Analysis - 1980, 1985, and 1990. September 30. 

Pitchford, Marc L. and William C. Malm. 1994. “Development and Applications of a Standard 

Visual Index.” Atmospheric Environment, vol. 

28, no. 5. pp. 1049-1054. 

Seaman and Stauffer. 1995. Development and Design 

Testing of the SARMAP Meteorological 

Model. Final report to San Joaquin Valley 

wide Air Pollution Study Agency. 

Seaman, Stauffer, and Lario-Gibbs. 1995. “A MultiScale Four Dimensional Data Assimilation 

System Applied in the San Joaquin Valley 

During SARMAP. Part I: Modeling Design 

and Basic Performance Characteristics.”Journal of Applied Meteorology. Volume 34. In 

press. 

Tanrikulu, DaMassa, and Ranzieri. 1996. Photochemical Modeling of August 3-6, 1990 Ozone Episode in Central California Using the 

SARMAP Air Quality Model. Part I: Model 

Formulation, Description and Basic Performance. Preprints. Ninth Joint Conference on 

the Application of Air Pollution Meteorology 

with Air Waste Management Association. 

January 28 - February 2, 1996. Atlanta, Georgia. 

Trijonis. 1990. Visibility: Existing and Historical 

Conditions--Causes and Effects. NAPAP Report 24. 1990. 

U.S. Environmental Protection Agency (EPA). 1989. 

Procedures for Applying City-Specific EKMA. 

EPA-450/4-89-012. Office of Air Quality 

Planning and Standards. 

C-30

Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-1

Appendix D: Human Health and Welfare Effects

of Criteria Pollutants

Introduction

In responding to the mandate of section 812, EPA

conducted a comprehensive benefits analysis to identify and estimate the quantifiable health and welfare

benefits enjoyed by Americans due to improved air

quality resulting from the CAA. Health benefits resulted from avoidance of air pollution-related health

effects, such as mortality, respiratory illness, and heart

disease. Welfare benefits accrued where improved air

quality averted damage to ecological health and measurable resources, such as agricultural production,

building materials, and visibility.

This appendix presents an overview of EPA’s

approach for modeling human health and welfare effects. It provides an outline of the principles used to

guide the benefits analysis, details methods used to

quantify criteria air pollutant exposure nationwide

across the study period (1970 to 1990), and discusses

several critical conceptual and implementation issues

for using health and welfare effect information. Modeling results, estimates of avoided incidences of adverse health and welfare effects, are then presented.

Ecological and agricultural benefits are examined in

more detail in Appendices E and F, respectively. Appendix I details the approach used to translate health

and welfare effects into monetary benefits.

Principles for the Section 812

Benefits Analysis

Estimating the effects of even modest shifts in

environmental releases involves complex chemical,

environmental, biological, psychological and economic processes. The task of estimating the broad

changes associated with adoption and implementation

of the Clean Air Act challenges the limits of scientific knowledge and modeling capability to synthesize available information and techniques into a practical framework. A pragmatic plan for a comprehensive assessment must fairly reflect the complexities

and uncertainties, but still produce a policy-relevant

analysis in a timely fashion. In order to achieve this

ambitious goal, the following principles have been

used to guide the section 812 benefits assessment.

Comprehensiveness: The assessment should include as many benefit categories as are reasonably

believed to be affected by implementation of the Clean

Air Act. Comprehensiveness requires assessing effects

with which greater levels of scientific confidence are

associated, as well as less well-understood effects. The

degree of relative certainty among effects must be

carefully described in order to fairly present a broad

portrayal of the physical and social benefits accruing

to the nation from implementing the Act. In addition,

section 812 of the 1990 CAA Amendments explicitly

directs a comprehensive benefits coverage that prohibits a default assumption of zero value for identified benefits unless a zero value is supported by specific data.

Quantification Where Feasible: The central goal

of the present study is to evaluate and compare the

benefits and costs of historical CAA-related programs.

Effective comparison of the variety of human health,

welfare, and ecological benefits with the associated

compliance costs requires that these consequences be

measured in terms of a common metric. Expressing

the value of these various effects in economic terms

is the most efficient way to accomplish this objective, and is consistent with standard practices associated with economic benefit-cost analysis. Expressing

these effects in economic terms requires quantifying

and presenting estimated effects in both physical and

monetized economic terms. Pursuant to this paradigm,

the emphasis in the present study is largely on categories having direct and perceptible effects on human health. That is, the emphasis of the analysis is on

categories such as symptoms and diseases rather than

on physical changes (such as cell level changes) that

do not directly result in a decreased health status noticeable to the individual.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-2

Efficient Use of Previous Research Results: Significant research effort has been spent to understand

and quantify the complex relationships between air

pollution and human health. The present study has

relied as much as possible on available research results, making adjustments as necessary to apply the

existing results to the current analysis.

Incorporate Uncertainty: To properly convey the

results of any benefits assessment, it is important to

include an evaluation and characterization of how

much confidence the analysts have in the estimates.

Ideally this would include a formal quantitative assessment of the potential for error, and the sources,

directions, and potential significance of any resultant

biases. A method for considering and reporting uncertainty must be built into the fundamental design of

the assessment. Such a framework was developed and

applied in the present study, and was supplemented

where necessary by expert judgment regarding the

sources and potential significance of errors in each

analytical step.

General Modeling Approach

Consistent with these principles, the EPA developed an approach for quantifying the effects of reduced pollutant exposure, with particular focus on

those effect categories for which monetary benefits

could be estimated. As described previously, the study

design adopted for the section 812 assessment links a

sequence of analytical models. The macroeconomic

modeling (Appendix A) estimated economy-wide effects of CAA expenditures. These effects provided a

basis for the modeling of criteria pollutant emissions

under the two scenarios considered (the factual control scenario and the hypothetical no-control scenario),

as documented in Appendix B. The emissions estimates were used as input to the air quality models

(Appendix C). Ambient pollutant concentrations estimated by the air quality models were used as inputs

to the health and welfare benefits model, the focus of

this appendix.

The approach developed to model health and welfare benefits is known as a “reduced form” or “embedded model” approach. The concept of a reduced

form model is to use simplified versions of previously

constructed complex models to characterize the impact of a series of linked physical and socioeconomic

processes. The health and welfare benefits model is

characterized as a reduced form model because it relies on summaries of the data output from the air quality models, which rely on emissions summaries and

summaries of macroeconomic conditions, successively. Although results of the independent models

are used in series, the models themselves have not

been integrated into the health and welfare benefits

model.

In general, the reduced form health and welfare

benefits model relies on two fundamental inputs: (1)

nationwide changes in pollutant exposures across the

study period, and (2) the association between changes

in exposure and expected changes in specific health

and welfare effects. These inputs are discussed below.

Quantifying Changes in Pollutant

Exposures

Estimating changes in pollutant exposures requires characterization of nationwide air quality improvements across the study period, as well as the

populations exposed to the different levels of improvement.

Air Quality

As discussed in Appendix C, the section 812

analysis estimated ambient concentrations for both the

control and no-control scenarios for the following

pollutants and air quality parameters:

• Particulate matter, less than 10 microns in

diameter (PM10)

• Ozone (O3

)

• Nitrogen dioxide (NO2

)

• Sulfur dioxide (SO2

)

• Carbon monoxide (CO)

• Visibility measures (light extinction and

DeciView)1

• Lead (Pb)

Generally, this analysis adopted actual historical

air pollution monitoring data to represent control scenario air quality. No-control scenario profiles were

1

 While the visibility measures listed are not criteria air pollutants, they provide important measures of a significant welfare

effect resulting from air pollution, visibility degradation. Light extinction (which is related to DeciView, a haziness index) results

from light scattered by fine particles in the atmosphere, especially sulfates and ammonium nitrates. As atmospheric concentrations of

such particles increase, light is attenuated and visibility diminishes.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-3

derived by running the control and no-control scenario

emissions inventories through a suite of air quality

models and then using the differences in these modeled outcomes to adjust the historical profiles. Since

lead was treated differently than the other pollutants,

the analysis of the CAA impacts on atmospheric lead

concentrations is documented in Appendix G.

With respect to the distribution of air quality data

across the two decades considered, it should be noted

that both the number and location of monitors tracking air quality changed over time. Table D-1 depicts

the number of monitors for each pollutant across the

period of this analysis. The number of monitors generally increased throughout the 1970s and leveled off

or declined at varying points during the 1980s, depending on the pollutant.

For the section 812 modeling, the non-lead pollutants have been characterized as either county-level

or monitor-level pollutants. The distinction was important for quantifying the population exposed to different levels of air quality improvements, as discussed

below. PM10 is considered a county-level pollutant,

since historical concentrations in monitored counties

have been synthesized into a single concentration for

each county.2 In contrast, O3

, NO2, NO, SO2, and CO

were reported at specific monitor locations, given by

latitude/longitude coordinates. Finally, visibility was

treated as a county-level pollutant in the western U.S.

and a monitor-level pollutant in the eastern U.S.3 Air

quality data for PM10 and ozone were reported for each

year of the study period; data for the remaining pollutants were reported only for 1975, 1980, 1985, and

1990.

In order to reduce the volume of air quality data

necessary to describe pollutant concentrations for two

scenarios nationwide over twenty years, annual concentration profiles were reduced to frequency distributions. That is, annual pollutant concentrations for a

variety of averaging times (e.g., 1-hour, 6-hour, daily)

were summarized as a distribution of values across

the year. This approach reduced data management

requirements significantly, while adequately capturing air quality improvements between the control and

no-control scenarios.

Population Distribution

Health and some welfare benefits resulting from

air quality improvements are distributed to populations in proportion to the reduction in exposure each

enjoys. Predicting population exposures, then, is a

necessary step in estimating health effects. Doing so

for the section 812 analysis required not only an understanding of where air quality improved as a result

of the CAA, but also how many individuals were affected by varying levels of air quality improvements.

Thus, a critical component of the benefits analysis

required that the distribution of the U.S. population

nationwide be described in a manner compatible with

the air quality data. Described below is the method

used to allocate U.S. Census data to a symmetrical

grid overlying the country.

Census Data

Three years of U.S. Census data were used to represent the geographical distribution of U.S. residents:

1970, 1980, and 1990. Population data were supplied

at the census block group level, with approximately

Pollutant

Year PM10 O3 NO2 SO2 CO

1970 245 1 43 86 82

1975 1,120 321 303 827 494

1980 1,131 546 375 1,088 511

1985 970 527 305 916 458

1990 720 627 345 753 493

Pollutant

Year PM10 O3 NO2 SO2 CO

1970 245 1 43 86 82

1975 1,120 321 303 827 494

1980 1,131 546 375 1,088 511

1985 970 527 305 916 458

1990 720 627 345 753 493

Table D-1. Criteria Air Pollutant Monitors

in the U.S., 1970 - 1990.

2

 Two different measures of ambient concentrations of particulate matter were used in the United States during the period 1970

to 1990. Prior to 1987, the indicator for the National Ambient Air Quality Standard for PM was total suspended particulates (TSP). In

1987, the indicator was changed to PM10 (particles less than 10 μM in diameter). Widespread PM10 monitoring did not begin until

1985; prior to that only TSP data is available. Because the recent scientific literature reports primarily the relationship between PM10

and adverse health and welfare effects, PM10 data is preferred, if available. Where only TSP is available, PM10 concentrations were

estimated using PM10:TSP ratios that vary by area of the country and the urban/rural characterization of the area.

3

 In the western U.S., visibility was modeled using a linear-rollback model and extinction budget approach for 30 major urban

centers (SAI, 1994). The modeling results, reported in DeciView, were applied to the counties in the vicinity of the urban centers and

considered to share a common air basin. In the eastern U.S., Regional Acid Deposition Model (RADM) runs provided visibility

estimates in terms of light extinction coefficients. These were modeled across a 60 km. X 60 km. grid, approximately covering the

eastern half of the country. Since the extinction coefficients were reported at the grid cell centroids, for which the coordinates were

known, visibility in the east was treated as a monitor-level pollutant.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-4

290,000 block groups nationwide. Allocating air quality improvements to the population during intermediate years necessitated interpolation of the three years

of population data. Linear interpolation was performed

at the block group level in order to preserve the variability in growth rates throughout the country.

Gridding U.S. Population

To ease computational burden, block group population estimates were aggregated to a rectangular grid

structure. The grid, comprised of ten kilometer by ten

kilometer gridcells, spanned the entire area of the contentional United States. This grid size generated

46,885 populated gridcells throughout the U.S.

The entire population of each block group was

assumed to reside at the geographical centroid of the

block group area, the coordinates of which were available from the U.S. Bureau of the Census. Block group

populations were aggregated to gridcells according

to the block group centroids encompassed by each cell.

In addition to the population of each gridcell, the state

and county names for each gridcell were retained,

permitting aggregation of data at the state and county

level, as well as nationwide.

Allocating Exposure Estimates to the Population

Two alternative modeling strategies were used to

allocate air quality improvements to the U.S. population. They differed in terms of both the certainty of

the estimates and the geographic coverage:

Method One

Air quality improvements (difference between

control and no-control scenarios) were applied to individuals living in the vicinity of air quality monitors.

For pollutants with monitor-level data, it was assumed

that the individuals in a gridcell were exposed to air

quality changes estimated at the nearest monitor, as

long as the monitor was within 50 kilometers. Likewise, for PM10 (for which data was available at the

county level) the population of each monitored county

was assumed to be exposed to the air quality changes

reported for that county.4 The remainder of the population was excluded from the analysis.

Unfortunately, by limiting the quantitative analysis to populations within 50 km of a monitor (or within

a monitored county, for PM), a significant portion of

the U.S. population was left out of the analysis (see

Table D-2). For most pollutants in most years (excepting lead), less than three-quarters of the population lived within 50 km of a monitor (or within a PMmonitored county). Clearly, an analysis that excluded

25 percent of the population from the benefits calculations (thus implicitly assuming that the CAA had

no impact on that population) would understate the

physical effects of the CAA. Conversely, ascribing

air pollution reduction benefits to persons living great

distances from air quality monitors is a speculative

exercise, and could overstate benefits.

Method Two

As an alternative modeling strategy, air quality

improvements were applied to almost all individuals

nationwide. Where monitor data were not available

within 50 kilometers, data from the closest monitor,

regardless of distance, were used. Similarly, PM10

concentrations were extrapolated using regional air

quality models to all counties (even those for which

monitoring data was unavailable) and applied to the

populations of those counties.

Although subject to less certain air quality data,

the second alternative extrapolates pollutant exposure

estimates to almost the entire population using the

closest monitoring data available (see Table D-3).5

This second alternative was chosen as the preferred

approach in the benefits analysis. The sensitivity of

1975 1980 1985 1990 

CO 67.4% 67.9% 68.4% 70.4%

EXT 73.2% 72.3% 72.3% 72.2%

NO2 53.3% 58.8% 60.8% 61.5%

O3 55.5% 70.5% 71.5% 74.4%

PM10 78.5% 79.5% 75.8% 67.8%

SO2 64.7% 73.3% 73.0% 70.6%

Pb 100% 100% 100% 100%

1975 1980 1985 1990 

CO 67.4% 67.9% 68.4% 70.4%

EXT 73.2% 72.3% 72.3% 72.2%

NO2 53.3% 58.8% 60.8% 61.5%

O3 55.5% 70.5% 71.5% 74.4%

PM10 78.5% 79.5% 75.8% 67.8%

SO2 64.7% 73.3% 73.0% 70.6%

Pb 100% 100% 100% 100%

Table D-2. Population Coverage in the “Within

50 km” Model Runs (percent of continental U.S.

population).

4

 Since the lead (Pb) analysis, which was handled separately from that of the other criteria pollutants, did not require air quality

modeling data, the issue of proximity to monitors is irrelevant. The Pb analysis extended to 100 percent of the population.

5

 While this alternative captures the vast majority of the U.S. population, it does not model exposure for everyone. To improve

computational efficiency, those gridcells with populations less than 1,000 were not modeled; these cells account for less than five

percent of the U.S. population.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-5

the benefits estimate to the extrapolation of air quality data beyond monitored areas is explored in Appendix I.

Estimating Human Health Effects

of Exposure

It is impossible to estimate all of the physical effects that would have occurred without the Clean Air

Act. While scientific information is available that

makes it possible to estimate certain effects, many

other, potentially very important, health and welfare

effects cannot be estimated at this time. Other physical effects can be quantified, but it is impossible to

assess the economic value of those endpoints based

on the current economics literature. Table D-4 shows

the health and welfare effects for which quantitative

analysis has been prepared, as well as some of the

health effects that have not been quantified in the

analysis.

In order to translate the reductions in pollutant

exposure estimated to result from the CAA into health

benefits, it is necessary to quantify the relationship

between such exposures and adverse health effects.

As indicated below, this analysis relies on concentration-response relationships published in the scientific

literature which provide estimates of the number of

fewer individuals that incur an adverse health effect

per unit change in air quality. Such relationships are

combined with the air quality improvement and population distribution data to estimate changes in the incidence of each health endpoint. By evaluating each

concentration-response function for every gridcell

throughout the country, and aggregating the resulting

incidence estimates, it was possible to generate national estimates of avoided incidence.

It should be noted that a slightly different approach

was used to compute health effects associated with

exposure to gasoline lead. Instead of relating health

outcomes to ambient pollutant concentrations, the

concentration-response functions for lead-induced

effects link changes in health effects directly to

changes in the population’s mean blood lead level.

This value is directly related to the concentration of

lead in gasoline in a particular year. Appendix G documents both the methods used to characterize mean

blood lead levels and the approach for estimating human health effects from lead exposure.

The discussion below outlines the types of health

studies considered for this analysis, and issues critical to selecting specific studies appropriate for use in

the section 812 context. Next, details regarding use of

the results of the studies are explored. Finally, the

concentration-response functions used to model health

benefits from reductions in non-lead criteria pollutants are outlined.

Types of Health Studies

Scientific research about air pollution’s adverse

health impacts uses a broad array of methods and procedures. The research methods used to investigate the

health effects of air pollution have become considerably more sophisticated over time, and will continue

to evolve in the future. This progress is the result of

better available research techniques and data, and the

ability to focus further research more sharply on key

remaining issues based on the contributions of earlier

work.

The available health effects studies that could

potentially be used as the basis of the section 812 assessment are categorized into epidemiology studies

and human clinical studies. Epidemiological research

in air pollution investigates the association between

exposure to air pollution and observed health effects

in the study population. Human clinical studies involve examination of human responses to controlled

conditions in a laboratory setting. Research has been

conducted on health effects from exposure to pollution using each approach, and studies using these techniques have been considered in various formal regulatory proceedings. Each type of study (as it is used

1975 1980 1985 1990 

CO 97.2% 97.2% 98.7% 100.0%

EXT 75.6% 74.8% 74.7% 74.7%

NO2 97.2% 97.2% 98.7% 100.0%

O3 96.6% 97.2% 98.7% 100.0%

PM10 95.9% 95.8% 97.2% 98.5%

SO2 95.4% 95.6% 97.0% 98.4%

Pb 100% 100% 100% 100%

1975 1980 1985 1990 

CO 97.2% 97.2% 98.7% 100.0%

EXT 75.6% 74.8% 74.7% 74.7%

NO2 97.2% 97.2% 98.7% 100.0%

O3 96.6% 97.2% 98.7% 100.0%

PM10 95.9% 95.8% 97.2% 98.5%

SO2 95.4% 95.6% 97.0% 98.4%

Pb 100% 100% 100% 100%

Table D-3. Population Coverage for

“Extrapolated to All U.S.” Model Runs (percent

of continental U.S. population).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-6

Pollutant Quan tified Health Effects Unqu antified Health Effects Oth er Possible Effects

Ozone Mortality*

Respiratory symptoms

Minor restricted activity days

Respiratory restricted activity

days

Hospital admissions

Asthma attacks

Changes in pulmonary function

Chronic Sinusitis & Hay Fever

Increased airway responsiveness

to stimuli

Centroacinar fibrosis

Inflammation in the lung

Immunologic changes

Chronic respiratory diseases

Extrapulmonary effects (e.g.,

changes in structure,

function of other organs)

Particulate Matter/

TSP/ Sulfates

Mortality*

Bronchitis - Chronic and Acute

Hospital admissions

Lower respiratory illness

Upper respiratory illness

Chest illness

Respiratory symptoms

Minor restricted activity days

All restricted activity days

Days of work loss

Moderate or worse asthma status

(asthmatics)

Changes in pulmonary function Chronic respiratory diseases

other than chronic bronchitis

Inflammation in the lung

Carb on Monoxide Hospital Admissions -

 congestive heart failure

Decreased time to onset of angina

Behavioral effects

Other hospital admissions

Other cardiovascular effects

Developmental effects

Nitrogen Oxides Respiratory illness Increased airway responsiveness Decreased pulmonary function

Inflammation in the lung

Immunological changes

Sulfur Dioxide In exercising asthmatics:

Changes in pulmonary function

Respiratory symptoms

Combined responses of

respiratory symptoms and

pulmonary function changes

Respiratory symptoms in nonasthmatics

Hospital admissions

Lead Mortality

Hypertension

Non-fatal coronary heart disease

Non-fatal strokes

IQ loss effect on lifetime earnings

IQ loss effects on special

education needs

Health effects for individuals in

age ranges other than those

studied

Neurobehavioral function

Other cardiovascular diseases

Reproductive effects

Fetal effects from maternal

exposure

Delinquent and anti-social

behavior in children

 * This analysis esti mates excess mortality using PM10 as an indicator of th e po llutant mix to which 

 individu als were exposed.

 Table D-4. Hu man Health Effe cts of Criteria Pollutants.

for air pollution research) is described below, and the

relative strengths and weaknesses for the purposes of

the section 812 assessment are examined.

Epidemiological Studies

Epidemiological studies evaluate the relationship

between exposures to ambient air pollution and health

effects in the human population, typically in a “natural” setting. Statistical techniques (typically variants

of multivariate regression analysis) are used to estimate quantitative concentration-response (or exposure-response) relationships between pollution levels

and health effects.

Epidemiology studies can examine many of the

types of health effects that are difficult to study using

a clinical approach. Epidemiological results are wellsuited for quantitative benefit analyses because they

provide a means to estimate the incidence of health

effects related to varying levels of ambient air pollution without extensive further modeling effort. These

estimated relationships implicitly take into account

at least some of the complex real-world human activity patterns, spatial and temporal distributions of air

pollution, synergistic effects of multiple pollutants and

other risk factors, and compensating or mitigating

behavior by the subject population. Suspected relationships between air pollution and the effects of both
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-7

long-term and short-term exposure can be investigated

using an epidemiological approach. In addition, observable health endpoints are measured, unlike clinical studies which often monitor endpoints that do not

result in observable health effects (e.g. forced expiratory volume). Thus, from the point of view of conducting a benefits analysis, the results of epidemiological studies, combined with measures of ambient

pollution levels and the size of the relevant population, provide all the essential components for associating measures of ambient air pollution and health status for a population in the airshed being monitored.

Two types of epidemiological studies are considered for dose-response modeling: individual level

cohort studies and population level ecological studies. Cohort-based studies track individuals that are

initially disease-free over a certain period of time, with

periodic evaluation of the individuals’ health status.

Studies about relatively rare events such as cancer

incidence or mortality can require tracking the individuals over a long period of time, while more common events (e.g., respiratory symptoms) occur with

sufficient frequency to evaluate the relationship over

a much shorter time period. An important feature of

cohort studies is that information is known about each

individual, including other potential variables correlated to disease state. These variables, called confounders, are important to identify because if they are

not accounted for in the study they may produce a

spurious association between air pollution and health

effect.

A second type of study used in this analysis is a

population-level ecological study. The relationship

between population-wide health information (such as

counts for daily mortality, hospital admissions, or

emergency room visits) and ambient levels of air pollution are evaluated. One particular type of ecological study, time-series, has been used frequently in airpollution research. An advantage of the time-series

design is that it allows “the population to serve as its

own control” with regard to certain factors such as

race and gender. Other factors that change over time

(tobacco, alcohol and illicit drug use, access to health

care, employment, and nutrition) can also affect health.

However, since such potential confounding factors are

unlikely to vary over time in the same manner as air

pollution levels, or to vary over periods of months to

several years in a given community, these factors are

unlikely to affect the magnitude of the association

between air pollution and variations in short-term

human health responses.

Drawbacks to epidemiological methods include

difficulties associated with adequately characterizing

exposure, measurement errors in the explanatory variables, the influence of unmeasured variables, and correlations between the pollution variables of concern

and both the included and omitted variables. These

can potentially lead to spurious conclusions. However,

epidemiological studies involve a large number of

people and do not suffer extrapolation problems common to clinical studies of limited numbers of people

from selected population subgroups.

Human Clinical Studies

Clinical studies of air pollution involve exposing

human subjects to various levels of air pollution in a

carefully controlled and monitored laboratory situation. The physical condition of the subjects is measured before, during and after the pollution exposure.

Physical condition measurements can include general

biomedical information (e.g., pulse rate and blood

pressure), physiological effects specifically affected

by the pollutant (e.g., lung function), the onset of

symptoms (e.g., wheezing or chest pain), or the ability of the individual to perform specific physical or

cognitive tasks (e.g., maximum sustainable speed on

a treadmill). These studies often involve exposing the

individuals to pollutants while exercising, increasing

the amount of pollutants that are actually introduced

into the lungs.

Clinical studies can isolate cause-effect relationships between pollutants and certain human health

effects. Repeated experiments altering the pollutant

level, exercise regime duration and types of participants can potentially identify effect thresholds, the

impact of recovery (rest) periods, and the differences

in response among population groups. While cost considerations tend to limit the number of participants

and experimental variants examined in a single study,

clinical studies can follow rigorous laboratory scientific protocols, such as the use of placebos (clean air)

to establish a baseline level of effects and precise

measurement of certain health effects of concern.

There are drawbacks to using clinical studies as

the basis for a comprehensive benefits analysis. Clinical studies are appropriate for examining acute symptoms caused by short-term exposure to a pollutant.

While this permits examination of some important

health effects from air pollution, such as

bronchoconstriction in asthmatic individuals caused

by sulfur dioxide, it excludes studying more severe
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-8

effects or effects caused by long term exposure. Another drawback is that health effects measured in some

well-designed clinical studies are selected on the basis of the ability to measure precisely the effect, for

example forced expiratory volume, rather than a larger

symptom. The impact of some clinically measurable

but reversible health effects such as lung function on

future medical condition or lifestyle changes are not

well understood.

Ethical limits on experiments involving humans

also impose important limits to the potential scope of

clinical research. Chronic effects cannot be investigated because people cannot be kept in controlled

conditions for an extended period of time, and because these effects are generally irreversible. Participation is generally restricted to healthy subjects, or at

least to exclude people with substantial health conditions that compromise their safe inclusion in the study.

This can cause clinical studies to avoid providing direct evidence about populations of most concern, such

as people who already have serious respiratory diseases. Ethical considerations also limit the exposures

to relatively modest exposure levels, and to examining only mild health effects that do no permanent damage. Obviously for ethical reasons human clinical evidence cannot be obtained on the possible relationship

between pollution and mortality, heart attack or stroke,

or cancer.

One potential obstacle to using dose-response information from clinical research methods in a benefits assessment is the need for an exposure model.

The dose-response functions developed from clinical

research are specific to the population participating

in the study and the exposure conditions used in the

laboratory setting. It is therefore difficult to extrapolate results from clinical settings to daily exposures

faced by the whole population. For example, many

clinical studies evaluate effects on exercising individuals. Only a small portion of the population engages in

strenuous activity (manual labor or exercise) at any

time. Reflecting these fundamental differences between the laboratory setting and the “real world” imposes a formidable burden on researchers to provide

information about human activity patterns, exercise

levels, and pollution levels. This requirement adds an

additional step in the analytical process, introducing

another source of uncertainty and possible error.

To apply the clinical results to model the general

population, two decisions must be made. First, how

far can the conditions in the clinical setting be expanded? For example, if the subjects in the clinical

study were healthy male college students, should the

results be applied to the entire population, including

children? Second, how many people in the general

population are exposed to conditions similar to those

used in the clinical setting? Frequently, clinical studies are conducted at relatively high exercise levels (increasing the dose, or the quantity of pollutants actually delivered to the lungs). In the general population

few people experience these conditions very often,

and people do not reach these exercise levels with

equal frequencies during the day and night.

In addition, the analyst must determine the number of people that are exposed to the levels of ambient

conditions seen in the laboratory. Air quality varies

throughout a city and is typically reported by data from

monitors located at various places throughout the city.

However, people are not exposed to the conditions at

any one monitor all day. As people move around in

the city, they are exposed to ambient air quality conditions represented by different monitors at different

times during the day. To further compound the problem, air quality also varies between indoors and outdoors, within a car or garage, and by such factors as

proximity to a roadway or major pollution source (or

sink). The exposure model must account for the ambient conditions in the “microenvironments” that the

population actually experiences.

The issues of study subjects, exercise and microenvironments can influence the choice of clinical

studies selected for the section 812 assessment. Clinical studies that use exposure regimes and exercise levels more similar to what larger groups of the population see are easier to apply in a benefits model than

are more narrow studies. Similarly, studies that use a

diverse group of subjects are easier to apply to the

general population than are more narrow studies.

Given the major advantages of epidemiological

studies—exposures do not need to be modeled and

health effects are observed in a large, more heterogeneous population—epidemiological studies are used

as the basis for determining the majority of health effects and dose-response curves. The diverse activity

patterns, microenvironments, and pollution levels are

already considered in the aggregate through the concentration-response functions derived from epidemiological studies. Clinical studies are used if there are

health effects observed in clinical studies not observed

in epidemiological studies.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-9

Issues in Selecting Studies To Estimate

Health Effects

A number of issues arise when selecting and linking the individual components of a comprehensive

benefits analysis. The appropriate procedure for handling each issue must be decided within the context

of the current analytical needs, considering the broader

analytical framework. While more sophisticated or

robust studies may be available in some circumstances,

the potential impact on the overall analysis may make

using a simpler, more tractable approach the pragmatic

choice. In considering the overall impact of selecting

a study for use in the section 812 assessment, important factors to consider include the likely magnitude

the decision will have on the overall analysis, the balance between the overall level of analytical rigor and

comprehensiveness in separate pieces of the analysis,

and the effect on the scientific defensibility of the

overall project.

This section discusses ten critical issues in selecting health information for use in the section 812 assessment: use of peer-reviewed research, confounding factors, uncertainty, the magnitude of exposure,

duration of exposure, threshold concentrations, the

target population, statistical significance of relationships, relative risks, and the need for baseline incidence data. The previous discussion about the types

of research methods available for the health information alluded to some of these issues, as they are potentially important factors in selecting between studies using different methods. Other issues address how

scientific research is used in the overall analytical

framework.

Peer-Review of Research

Whenever possible, peer reviewed research rather

than unpublished information has been relied upon.

Research that has been reviewed by the EPA’s own

peer review processes, such as review by the Clean

Air Science Advisory Committee (CASAC) of the

Science Advisory Board (SAB), has been used whenever possible. Research reviewed by other public scientific peer review processes such as the National

Academy of Science, the National Acidic Precipitation Assessment Program, and the Health Effects Institute is also included in this category.

Research published in peer reviewed journals but

not reviewed by CASAC has also been considered for

use in the section 812 assessment, and has been used

if it is determined to be the most appropriate available study. Research accepted for publication by peer

reviewed journals (“in press”) has been considered to

have been published. Indications that EPA intends to

submit research to the CASAC (such as inclusion in a

draft Criteria Document or Staff Paper) provide further evidence that the journal-published research

should be used.

Air pollution health research is a very active field

of scientific inquiry, and new results are being produced constantly. Many research findings are first

released in University Working Papers, dissertations,

government reports, non-reviewed journals and conference proceedings. Some research is published in

abstract form in journals, which does not require peer

review. In order to use the most recent research findings and be as comprehensive as possible, unpublished

research was examined for possible use in the section

812 assessment. Any unpublished research used is

carefully identified in the report, and treated as having a higher degree of uncertainty than published results. The peer review of the section 812 assessment

by the Advisory Council on Clean Air Compliance

Analysis provides one review process for all components of the assessment, as well as for the way in which

the components have been used.

Confounding Factors

Confounding can occur when the real cause of

disease is associated with a number of factors. If only

one contributing factor is evaluated in an epidemiological study, a false association may occur. For example, in epidemiology studies of air pollution, it is

important to take into account weather conditions,

because weather is associated with both air pollution

and health outcomes. If only air pollution is evaluated, a false association between air pollution and

health could result; one may incorrectly assume that

a reduction in air pollution is exclusively responsible

for a reduction in a health outcome. Potential confounders include weather-related variables, age and

gender mix of the subject population, and pollution

emissions other than those being studied. Studies that

control for a broad range of likely confounders can

offer a more robust conclusion about an individual

pollutant, even if the statistical confidence interval is

larger due to the inclusion of more variables in the

analysis.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-10

In many cases, several pollutants in a “pollutant

mix” are correlated with each other—that is, they tend

to occur simultaneously. Therefore, although there

may be an association between a health effect and each

of several pollutants in the mix, it may not be clear

which pollutant is causally related to the health effect

(or whether more than one pollutant is causally related). This analysis includes epidemiological modeling of the health effects that have been associated

with exposure to a number of pollutants. In most cases

where the health effect is being modeled for the several correlated pollutants of interest, regression coefficients based on PM as a surrogate for the mixture

were chosen in preference to multiple pollutant models and single pollutant models. The most important

example of this occurs in estimating mortality effects.

There is substantial evidence that exposure to criteria

pollutants, either individually or collectively, is significantly associated with excess mortality. Generally,

this association is related to particulate matter. Therefore, even though particulate matter cannot be shown

to be the sole pollutant causing pollution-related excess mortality, it can be used as an indicator of the

pollutant mixture which appears to result in excess

mortality. This analysis estimates excess mortality (for

all criteria pollutants other than lead) using PM as an

indicator of the pollutant mix to which individuals

were exposed. This issue is discussed further below,

where details on estimating mortality effects are explored.

The one exception to the use of single pollutant

regression models is estimating hospital admissions.

Both PM and ozone are generally found to have a statistically significant and separate association with

hospital admissions. Using separate regressions (from

single pollutant models) for each pollutant may overstate the number of effects caused by each pollutant

alone. On the other hand, using PM as a single indicator of the pollutant mix could underestimate the total

hospital admissions caused by different mechanisms.

Separate PM and ozone coefficients for hospital admissions are selected from regression models that

consider the effects of both pollutants simultaneously.

Uncertainty

The stated goal of the section 812 assessment is

to provide a comprehensive estimate of benefits of

the Clean Air Act. To achieve this goal, information

with very different levels of confidence must be used.

Benefit categories are not to be omitted simply because they are highly uncertain or controversial, but

those benefit categories that are reasonably well understood must be distinguished from those which are

more tentative.

The ideal approach to characterizing uncertainty

is to conduct a formal quantitative uncertainty analysis. A common approach develops an estimated probability distribution for each component of the analysis. A Monte Carlo procedure draws randomly from

each of these distributions to generate an estimate of

the result. Evaluating the result for many such random combinations, creates a distribution of results that

reflects the joint uncertainties in the analysis.

The most serious obstacle to preparing a formal

quantitative uncertainty analysis is identifying all the

necessary distributions for each component of the

analysis. The Monte Carlo procedure requires that all

components of the model be rerun many times. However, the section 812 project links the outputs from

independent modeling activities. It would be impractical to simultaneously rerun the macroeconomic,

emissions, air quality, and exposure models because

of the diverse origins of the models. Therefore, instead of a complete formal uncertainty analysis, the

section 812 assessment includes a less rigorous analysis of the inherent uncertainties in the modeling effort. The uncertainty analysis combines quantitative

and qualitative elements designed to sufficiently describe the implications of the uncertainties. A primary

goal of the sensitivity/uncertainty analysis is to identify the health effects that make a sizable contribution

to the overall assessment of the monetary benefits.

There may be situations where there are significant

differences in the available information used to predict the incidence of a particular health effect (i.e.,

the uncertainty bounds are large). It is important to

alert the reader to situations where using the lower

incidence estimates may portray the health effect as

only modestly contributing to the overall total benefits, but using reasonable alternative higher estimated

incidence figures (or higher monetized values) would

substantially impact not only the monetized value of

the individual health effect, but actually make a noticeable difference in the total benefits assessment.

Consideration of the overall uncertainties inherent in the section 812 assessment has several important implications for health study selection. It was important to carefully examine the balance between the

level of uncertainties in the analysis and the need for
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-11

comprehensive coverage of all benefit categories.

There were frequently situations in which a direct

tradeoff existed between more comprehensive coverage and the restriction of the analysis to more certain

information. Also, the relationship between the uncertainty in other parts of the analysis and the uncertainty for each particular health effect was carefully

considered.

Magnitude of Exposure

One component of the section 812 analysis estimates the air pollution levels that would have occurred

in the absence of the Clean Air Act. These estimates

are larger than currently observed levels of U.S. air

pollution, and perhaps even levels currently observed

elsewhere in the world. This aspect of the analysis

poses difficulties for the application of concentrationresponse functions that have been based on exposures

at much lower pollution levels. The shape of the concentration-response function much above observed

exposures levels is unknown. It is possible that biological mechanisms affecting response that are unimportant at low levels of exposure may dominate the

form of response at higher levels, introducing

nonlinearity to the mathematical relationship. In general, studies that include exposure levels spanning the

range of interest in the section 812 assessment are

preferable to studies at levels outside of the range, or

that only include a narrow part of the range. A possible drawback to this approach is that studies which

fit this criterion have often been conducted outside

the U.S. The application of foreign studies to U.S.

populations introduces additional uncertainties regarding the representativeness of the exposed population

and the relative composition of the air pollution mix

for which the single pollutant is an indicator. These

difficult issues were considered in selecting studies

for the benefits analysis.

Duration of Exposure

Selection of health studies for the section 812 assessment must consider the need to match the health

information to the air quality modeling conducted for

the assessment. For example, information on the health

effects from short term (five minute) exposure to sulfur dioxide cannot be readily combined with information on average daily sulfur dioxide levels. In selecting studies for the benefits analysis, preference was

shown for studies whose duration of exposure matched

one of the averaging times of the air quality data.

Thresholds

Exposure-response relationships are conceptualized as either exhibiting a threshold of exposure below which adverse effects are not expected to occur,

or as having no response threshold, where any exposure level theoretically poses a non-zero risk of response to at least one segment of the population. The

methods employed by health researchers to characterize exposure-response relationships may or may not

explicitly analyze the data for the existence of a threshold. Studies may analyze relationships between health

and air pollution without considering a threshold. If a

threshold for population risk exists but is not identified by researchers, then Clean Air Act benefits could

be overestimated if CAA levels are below the threshold, because the risk reduction from the no-control

scenario could be overstated. On the other hand, if a

threshold is artificially imposed where one does not

exist, the relative benefits of the Clean Air Act may

be underestimated. In general, those studies that explicitly consider the question of a threshold (whether

a threshold is identified or not) provide stronger evidence; consideration of this question is a positive feature when selecting studies for this analysis.

Target Population

Many of the studies relevant to quantifying the

benefits of air pollution reductions have focused on

specific sensitive subpopulations suspected to be most

susceptible to the effects of the pollutant. Some of

these effects may be relevant only for the studied subpopulation; effects on other individuals are either unknown, or not expected to occur. For such studies, the

challenge of the analysis is to identify the size and

characteristics of the subpopulation and match its occurrence to exposure. Other studies have examined

specific cohorts who may be less susceptible than the

general population to health effects from air pollution (e.g., healthy workers), or who differ in age, gender, race, ethnicity or other relevant characteristics

from the target population of the benefits analysis.

Extrapolating results from studies on nonrepresentative subpopulations to the general population introduces uncertainties to the analysis, but the magnitude

of the uncertainty and its direction are often unknown.

Because of these uncertainties, benefit analyses often

limit the application of the dose-response functions

only to those subpopulations with the characteristics

of the study population. While this approach has merit

in minimizing uncertainty in the analysis, it can also
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-12

severely underestimate benefits if, in fact, similar effects are likely to occur in other populations. For these

reasons, studies that examine broad, representative

populations are preferable to studies with narrower

scope because they allow application of the functions

to larger numbers of persons without introducing additional uncertainty.

Many studies included in the section 812 analysis focus on a particular age cohort of the population

for the identification of health effects. The choice of

age group is often a matter of convenience (e.g., extensive Medicare data may be available for the elderly population) and not because the effects are, in

reality, restricted to the specific age group (even

though their incidence may vary considerably over

the life span). However, since no information is available about effects beyond the studied population, this

analysis applies the given concentration-response relationships only to those age groups corresponding to

the cohorts studied. Likewise, some studies were performed on individuals with specific occupations, activity patterns, or medical conditions because these

traits relate to the likelihood of effect. In these cases,

application of dose-response functions has been restricted to populations of individuals with these same

characteristics.

Statistical Significance of Exposure-Response

Relationships

The analysis includes as many studies related to a

given health effect as possible, except for studies inapplicable to the current analysis. For some endpoints,

the group of adequate studies yielded mixed results,

with some showing statistically significant responses

to pollutant concentrations and others with insignificant associations. Unless study methods have been

judged inadequate, dose-response functions with both

statistically significant and insignificant coefficients

have been included to characterize the possible range

of risk estimates. Excluding studies exclusively on the

basis of significance could create an upward bias in

the estimates by not reflecting research that indicates

there is a small, or even zero, relationship between

pollution and specific health effects. It should be noted,

however, that some studies that found insignificant

effects for a pollutant could not be used because they

did not report the insignificant coefficient values.

In some cases, a single study reported results for

multiple analyses, yielding both significant and nonsignificant results, depending on the nature of the input parameters (e.g., for different lag periods or concurrent exposures). In these cases, only significant

results were included.

Relative Risks

Many studies reported only a relative risk value

(defined as the ratio of the incidence of disease in two

groups exposed to two different exposure levels). The

analysis required conversion of these values to their

corresponding regression coefficients when the coefficients were not reported. When converting the relative risk to a coefficient value, the analysis used the

functional form of the regression equation reported

by the authors of the study.

The coefficients from a number of studies measured the change in the number of health effects for

the study population rather than a change per individual. These coefficients were divided by the size of

the study population to obtain an estimate of change

per individual. The coefficient could then be multiplied by the size of the population modeled in the current analysis to determine total incidence of health

effects.

Baseline Incidence Data

Certain dose-response functions (those expressed

as a change relative to baseline conditions) require

baseline incidence data associated with ambient levels of pollutants. Incidence data necessary for the calculation of risk and benefits were obtained from national sources whenever possible, because these data

are most applicable to a national assessment of benefits. The National Center for Health Statistics provided much of the information on national incidence

rates. However, for some studies, the only available

incidence information come from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence nationally.

Studies were excluded if health endpoints could

not be defined in the U.S. population. For example, in

Pope and Dockery (1992) the authors developed a

unique definition of symptomatic children in Utah

which has no correlation in the incidence data bases

which were available; consequently, the results could

not be applied to the general population.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-13

Estimating Mortality Effects

Using PM as an Indicator

There is substantial evidence that exposure to criteria pollutants, either individually or collectively, is

significantly associated with excess mortality. This

association is most closely and consistently related to

the ambient air concentrations of PM.

Several studies have found small but statistically

significant relationships between ozone and mortality, while other studies have not found a significant

relationship. There is inconclusive evidence whether

ozone has an effect independent of the effect of other

pollutants (e.g., PM or CO), has a synergistic effect

in combination with other effects, or is a confounder

in the relationship between mortality and other pollutants. For example, in a recent study HEI (1996)

found a significant and relatively stable ozone coefficient for most of the model specifications presented

in the study. However, the measured ozone effect was

largest and most significant in the winter and autumn,

when ozone levels are low.

This analysis estimates excess mortality (for all

criteria pollutants other than lead) using PM as an indicator of the pollutant mix to which individuals

were exposed. Even if particulate matter exposure

cannot be shown to be an independent causal factor

of excess mortality, it is, at a minimum, a good indicator measure of the exposure to the pollutant mixture that has been shown to be related to excess mortality. Because PM is used as an indicator, the concentration-response functions from single pollutant

models (i.e., statistical models including PM as the

only pollutant) are preferred. To the extent that ozone

is correlated with PM, the effect of ozone, either as an

independent association or acting in combination with

other pollutants, will be captured by this approach.

Estimating the Relationship Between PM and

Premature Mortality

Long-term exposure versus short-term exposure

studies and the degree of prematurity of mortality.

Both long-term exposure (cohort) studies and shortterm exposure (longitudinal or time-series) studies

have estimated the relationship between exposure to

PM and premature mortality. While there are advantages and disadvantages to each type of study (as discussed above), the long-term studies may capture more

of the PM-related premature mortality, as well as premature mortality that is more premature, than the

short-term studies.

The degree of prematurity of pollution-related

death may be an important uncertainty in the effort to

estimate the benefits of reducing pollution concentrations, as discussed in Appendix I. The willingness to

pay to save a few days of life may be significantly

less than the willingness to pay to save a few, or many,

years of life. Evidence concerning the degree of prematurity of pollution-related death would, in this case,

be crucial. Such evidence is, however, still scarce.

There is some limited evidence that the relative risk

of mortality from exposure to PM is higher for older

individuals than for younger individuals. This, combined with the fact that the baseline incidence of mortality consists disproportionately of people 65 and

over, suggests that PM-related mortality is disproportionately among older individuals. The extent to which

prematurity of death among older individuals is on

the order of days or weeks versus years, however, is

more uncertain. The short-term exposure studies can

provide little information on this. It is possible that

premature deaths on high pollution days would have

occurred only days later, if the individuals were sick

and therefore particularly susceptible. The fact that

the long-term exposure mortality studies found substantially larger relative risks, however, suggests that

not all of the premature mortality is on the order of

days or even weeks. Shortening of life of such a small

duration would not be detectable in a long-term epidemiology study, ensuring that the effects detected in

such studies must represent longer periods of life shortening. This suggests that at least some of the premature mortality associated with exposure to PM may

reduce lifespans by substantially longer amounts of

time.

Even if an individual’s PM-related premature

mortality is of very short duration, on the order of

days, however, it may be misleading to characterize

such a PM-related loss as only those few days if the

individual’s underlying susceptibility was itself exacerbated by chronic exposure to elevated levels of

pollution. Suppose, for example, that long-term exposure to elevated PM levels compromises the cardiopulmonary system, making the individual more

susceptible to mortality on peak PM days than he otherwise would have been. If this is the case, then the

underlying susceptibility would itself be either caused

by chronic exposure to elevated PM levels or exacer-
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-14

bated by it. Characterizing the individual’s loss as a

few days could, in this case, be a substantial underestimate.

In addition, the long-term studies estimate significantly more PM-related mortality than the annual

sum of the daily estimates from the short-term studies, suggesting that the short-term studies may be

missing a component of PM-related mortality that is

being observed in the long-term studies. For example,

if chronic exposure to elevated PM levels causes premature mortality that is not necessarily correlated with

daily PM peak levels, this type of mortality would be

detected in the long-term studies but not necessarily

in the short-term studies. Two of the long-term exposure studies suggest, moreover, that the association

between ambient air pollution and mortality cannot

be explained by the confounding influences of smoking and other personal risk factors.

Uncertainties surround analyses based on epidemiological studies of PM and mortality. In addition

to the uncertainty about the degree of prematurity of

mortality, there are other uncertainties surrounding

estimates based on epidemiological studies of PM and

mortality. Although epidemiological studies are generally preferred to human clinical studies, there is

nevertheless uncertainty associated with estimates of

the risk of premature mortality (and morbidity) based

on studies in the epidemiological literature. Considering all the epidemiological studies of PM and mortality, both short-term and long-term, there is significant interstudy variability as well as intrastudy uncertainty. Some of the difference among estimates

reported by different studies may reflect only sampling error; some of the difference, however, may reflect actual differences in the concentration-response

relationship from one location to another. The transferability of a concentration-response function estimated in one location to other locations is a notable

source of uncertainty.

Although there may be more uncertainty about

the degree of prematurity of mortality captured by

short-term exposure studies than by long-term exposure studies, certain sources of uncertainty associated

with long-term exposure studies require mention. Although studies that are well-executed attempt to control for those factors that may confound the results of

the study, there is always the possibility of insufficient or inappropriate adjustment for those factors that

affect long-term mortality rates and may be confounded with the factor of interest (e.g., PM concentrations). Prospective cohort studies have an advantage over ecologic, or population-based, studies in that

they gather individual-specific information on such

important risk factors as smoking. It is always possible, however, that a relevant, individual-specific risk

factor may not have been controlled for or that some

factor that is not individual-specific (e.g., climate) was

not adequately controlled for. It is therefore possible

that differences in mortality rates that have been ascribed to differences in average PM levels may be

due, in part, to some other factor or factors (e.g., differences among communities in diet, exercise,

ethnicity, climate, industrial effluents, etc.) that have

not been adequately controlled for.

Another source of uncertainty surrounding the

prospective cohort studies concerns possible historical trends in PM concentrations and the relevant period of exposure, which is as yet unknown. TSP concentrations were substantially higher in many locations for several years prior to the cohort studies and

had declined substantially by the time these studies

were conducted. If this is also true for PM10 and or

PM2.5, it is possible that the larger PM10 and or PM2.5

coefficients reported by the long-term exposure studies (as opposed to the short-term exposure studies)

reflect an upward bias. If the relevant exposure period extends over a decade or more, then a coefficient

based on PM concentrations at the beginning of the

study or in those years immediately prior to the study

could be biased upward if pollution levels had been

decreasing markedly for a decade or longer prior to

the study.

On the other hand, if a downward trend in PM

concentrations continued throughout the period of the

study, and if a much shorter exposure period is relevant (e.g., contained within the study period itself),

then characterizing PM levels throughout the study

by those levels just prior to the study would tend to

bias the PM coefficient downward.

The relevant exposure period is one of a cluster

of characteristics of the mortality-PM relationship that

are as yet unknown and potentially important. It is

also unknown whether there is a time lag in the PM

effect. Finally, it is unknown whether there may be

cumulative effects of chronic exposure — that is,

whether the relative risk of mortality actually increases

as the period of exposure increases.

Estimating the relationship between PM and premature mortality. The incidence of PM-related mortality used for estimating the benefits of the CAA is
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-15

based on the concentration-response relationship reported by one of the two recent long-term exposure

(prospective cohort) studies (Pope et al., 1995, and

Dockery et al., 1993). Because it is based on a much

larger population and many more locations than

Dockery et al. (1993), the concentration-response

function from Pope et al. (1995) was used in this analysis. The results of Pope et al. are consistent with those

of Dockery et al., which reported an even larger response, but in only six cities. Moreover, Pope et al. is

also supported by several ecological cross-sectional

studies of annual mortality based on 1960 and 1970

census data (using either TSP or sulfate as indicators

of PM), including the work of Lave and Seskin (1977)

and Lipfert (1984).

Numerous short-term exposure (time series) studies have also reported a positive and statistically significant relationship between PM and mortality. Of

the fourteen studies that estimated the relationship

between daily PM10 concentrations and daily mortality listed in Table 12-2 of the PM Criteria Document,

twelve reported positive and statistically significant

findings (Pope et al., 1992; Pope and Kalkstein, 1996;

Dockery et al., 1992; Schwartz, 1993a; Ozkaynak et

al., 1994; Kinney et al., 1995: Ito et al., 1995; Ostro et

al., 1996; Saldiva et al., 1995; Styer et al., 1995; Ito

and Thurston, 1996; Schwartz et al., 1996). While

these studies lend substantial support to the hypothesis that there is a relationship between PM10 and

mortality, they may be capturing only the portion of

that relationship involving short-term effects. For this

reason, they are considered in this analysis only as

supporting evidence to the results of the study by Pope

et al.

The Pope et al. study has several further advantages. The population followed in this study was

largely white and middle class, decreasing the likelihood that interlocational differences in premature mortality were due in part to differences in socioeconomic

status or related factors. In addition, the generally

lower mortality rates and possibly lower exposures to

pollution among this group, in comparison to poorer

minority populations, would tend to bias the PM coefficient from this study downward, counteracting a

possible upward bias associated with historical air

quality trends discussed above.

Another source of downward bias in the PM coefficient in Pope et al. is that intercity movement of

cohort members was not considered in this study.

Migration across study cities would result in exposures of cohort members being more similar than

would be indicated by assigning city-specific annual

average pollution levels to each member of the cohort. The more intercity migration there is, the more

exposure will tend toward an intercity mean. If this is

ignored, differences in exposure levels, proxied by

differences in city-specific annual median PM levels,

will be exaggerated, resulting in a downward bias of

the PM coefficient (because a given difference in mortality rates is being associated with a larger difference in PM levels than is actually the case).

In summary, because long-term exposure studies

appear to have captured more of the PM-related premature mortality, as well as premature mortality that

is more premature, they are preferable to the shortterm exposure studies. Among the long-term exposure studies, the Pope et al. study has several advantages, as discussed above, which are likely to reduce

the possibility of a key source of confounding and

increase the reliability of the concentration-response

function from that study. For these reasons, the concentration-response function estimated in this study

is considered the most reasonable choice for this analysis.

Matching PM Indices in the Air Quality Profiles

and Concentration-Response Function. The Pope et

al. study examined the health effects associated with

two indices of PM exposure: sulfate particles and fine

particles (PM2.5). The reported mortality risk ratios are

slightly larger for PM2.5 than for sulfates (1.17 versus

1.15 for a comparison between the most polluted and

least polluted cities). The PM2.5 relationship is used in

this analysis because it is more consistent with the

PM10 air quality data selected for the analysis. Estimated changes in PM2.5 air quality must be matched

with the PM2.5 mortality relationship. However, only

PM10 profiles were used for the entire 20 year period.

Therefore, the same regional information about the

PM10 components (sulfate, nitrate, organic particulate

and primary particulate) used to develop the PM10 profiles were used to develop regional PM2.5/PM10 ratios.

Although both urban and rural ratios are available,

for computational simplicity, only the regional urban

ratios were used to estimate the PM2.5 profiles from

the PM10 profiles used in the analysis. This reflects

the exposure of the majority of the modeled population (i.e., the urban population), while introducing

some error in the exposure changes for the rural population. In the east and west, where the rural ratio is

larger than the urban ratio, the change in PM2.5 exposure will be underestimated for the rural population.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-16

In the central region the PM2.5 change will be overestimated. These ratios were used in each year during

1970-1990, introducing another source of uncertainty

in the analysis. Table D-5 summarizes the PM2.5/PM10

ratios used in this analysis.

Prematurity of Mortality: Life-Years Lost as a Unit

of Measure

Perhaps the most important health effect that is

examined in this analysis is mortality. Although this

analysis does not take into account the degree of prematurity of death (that is, the ages of those individuals who die prematurely from exposure to PM are not

considered), considerable attention has been paid to

this issue and, in particular, to life-years lost as an

alternative to lives lost as a measure of the mortalityrelated effects of pollution.

Because life-years lost is of potential interest and

because there is a substantial potential for confusion

in understanding apparently disparate estimates of lifeyears lost from pollution exposure, this section attempts to present a clear discussion of the various

possible measures of life-years lost, what they depend

on, and how they are related to each other.

Because the actual number of years any particular individual is going to live cannot be known, “lifeyears lost” by an individual actually refers to an expected loss of years of life by that individual. The

expected loss of years of life by an individual depends

crucially on whether the expectation is contingent on

the individual only having been exposed to PM or on

the individual actually having died from that exposure.

An ex ante estimate of life-years lost per individual is contingent not on the individual having died

prematurely but only on the individual having been

exposed. Suppose, for example, that a 25 year old has

a life expectancy of 50 more years in the absence of

exposure and only 49 more years in the presence of

exposure. Given (chronic) exposure from the age of

25 on, the 25 year old exposed to (some elevated level

of) PM might expect a shortening of life expectancy

of one year, for example. That is one expected lifeyear lost due to chronic exposure. This is the life-years

lost that can be expected by every exposed individual.

An ex post estimate of life-years lost per individual

is contingent on the individual actually having died

from exposure to PM. When an individual dies of

exposure to PM, he is said to have lost the number of

years he would have been expected to live, calculated,

for example, from age- and gender-specific life expectancy tables. Suppose that the life expectancy of

25 year olds is 75 — that is, a 25 year old can expect

to live 50 more years. A 25 year old who dies from

exposure to PM has therefore lost 50 expected years

of life. This is the life-years lost that can be expected

by every 25 year old affected individual (i.e., every

25 year old who actually dies from exposure to PM).

Estimates of the total life-years lost by a population exposed to PM depend on several factors, including the age distribution and the size of the exposed

population, the magnitude of the change (or changes)

in PM being considered, the relative risk assumed to

be associated with each change in PM, and the length

of time exposure (i.e., the change in PM) is presumed

to occur. A population chronically exposed to a given

increase in PM will lose more life-years than a population exposed to the same increase in PM for only a

year or two.6 A population that is generally older will

lose fewer life-years, all else equal, than one that is

generally younger, because older individuals have

fewer (expected) years of life left to lose. And a population exposed to a greater increase in PM will lose

more life-years than if it were exposed to a smaller

increase in PM. Finally, the life-years lost by the population will increase as the relative risk associated with

the increase in PM increases.

Life-years lost are usually reported as averages

over a population of individuals. The population being averaged over, however, can make a crucial difEast Central West National

Urban 0.59 0.58 0.48 0.55

Rural 0.68 0.53 0.49 0.57

Table D-5. PM2.5/PM10 Ratios Used to Estimate

PM2.5 Data Used With Pope et al. (1995)

Mortality Relationship.

6

 Even in the absence of cumulative effects of exposure, exposure of a population for many years will result in a greater total

number of pollution-related deaths than exposure for only a year or two, because the same relative risk is applied repeatedly, year

after year, to the population, rather than for only a year or two.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-17

ference in the reported average life-years lost, as noted

above. The average life-years lost per exposed individual (the ex ante estimate) is just the total life-years

lost by the population of exposed individuals divided

by the number of exposed individuals. This average

will depend on all the factors that the total life-years

lost depends on except the size of the exposed population. The average life-years lost by an exposed individual is a statistical expectation. It is the average of

the numbers of life-years actually lost by each member of the exposed population. Alternatively, it can be

thought of as a weighted average of possible numbers

of years lost, where the weights are the proportions of

the population that lose each number of expected years

of life. Although those individuals who do die prematurely from exposure to PM may lose several expected

years of life, most exposed individuals do not actually die from exposure to PM and therefore lose zero

life-years. The average life-years lost per exposed individual in a population, alternatively referred to as

the average decrease in life expectancy of the exposed

population, is therefore heavily weighted towards zero.

The average number of life-years lost per individual

who dies of exposure to PM (the ex post measure of

life-years lost) is an average of the numbers of expected years of life lost by individuals who actually

died prematurely because of PM. Because everyone

who dies prematurely from exposure to PM loses some

positive number of expected years of life, this average, by definition, does not include zero.

An example of an ex ante measure of life-years

lost is given by a study in the Netherlands (WHO,

1996), which considered a cohort of Dutch males, aged

25-30, and compared the life expectancy of this cohort to what it would be in a hypothetical alternative

scenario in which these individuals are continuously

exposed to concentrations of PM2.5 that are 10 μg/m3

lower than in the actual scenario. The life expectancy

of this cohort of 25-30 year old Dutch males was calculated to be 50.21 years in the actual scenario, based

on a 1992 life table from the Netherlands. Assuming

that the relative risk of mortality associated with an

increase of 10 μg/m3

 PM2.5 is 1.1 (the average of the

relative risks of 1.14 from Dockery et al., 1993, and

1.07 from Pope et al., 1995), the study authors calculated death rates in the hypothetical “cleaner” scenario

by dividing the age-specific death rates in the actual

scenario by 1.1. Using these slightly lower death rates,

and assuming that the effect of PM does not begin

until 15 years of exposure, the authors constructed a

life table for the cohort in the hypothetical “cleaner”

scenario. Based on this new life table in a cleaner

world, the life expectancy of the cohort of 25-30 year

old Dutch males was calculated to be 51.32 years in

the hypothetical cleaner scenario. (In calculating life

expectancies in both the “dirty” scenario and the

“clean” scenario, it is assumed that any individual who

does not survive to the next 5-year age group lives

zero more years. For example, a 30 year old individual

either survives to age 35 or dies at age 30.) The change

in life expectancy for this cohort of 25-30 year old

Dutch males, due to a change in PM exposure of 10

μg/m3

 for the rest of their lives (until the age of 90),

was therefore 51.32 years - 50.21 years = 1.11 years.

That is, the average life-years lost by an exposed individual in this population, under these assumptions,

is 1.11 years.

The estimate of 1.11 years of expected life lost

depends on several things, as mentioned above. If the

study authors had used the relative risk from Pope et

al., 1995, alone, (1.07 instead of 1.1), for example,

the change in life expectancy (the ex ante measure of

life-years lost) for this cohort of 25-30 year old Dutch

males would have been 0.80 years. Similarly, changing the assumption about the duration of exposure also

changes the estimate of ex ante life-years lost. Using

a relative risk of 1.1, but assuming that exposure lasts

only during the first 5 years (i.e., that the death rate in

the first five years, from age 25 through age 30, is

lower but that after that it is the same as in the “dirty”

scenario), the average life-years lost by an exposed

individual in this population is reduced from 1.11 years

to 0.02 years.

By their construction and definitions, the average

life-years lost per exposed individual and the average

life-years lost per affected individual (i.e., per individual who dies prematurely from PM) take the same

total number of life-years lost by the exposed population and divide them by different denominators. The

average life-years lost per exposed individual divides

the total life-years lost by the total population exposed;

the average life-years lost per affected individual divides the same total life-years lost by only a small

subset of the total population exposed, namely, those

who died from PM. The average per exposed individual is therefore much smaller than the average per

affected individual. Because both types of average may

be reported, and both are valid measurements, it is

important to understand that, although the numbers

will be very dissimilar, they are consistent with each

other and are simply different measures of the estimated mortality impact of PM.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-18

To calculate the total (estimated) life-years lost

by a population, it is necessary to follow each age

cohort in the population through their lives in both

scenarios, the “dirty” scenario and the “clean” scenario, and compute the difference in total years lived

between the two scenarios, as WHO (1996) did for

the cohort of Dutch males 25-30 years old. This

method will be referred to as Method 1. In practice,

however, it is not always possible to do this. (Other

changes to the population, such as those from recruitment and immigration, for example, would make such

an exercise difficult.) An alternative method, which

approximates this, is to predict the numbers of individuals in each age category who will die prematurely

from exposure to PM (i.e., who will die prematurely

in the “dirty” scenario), and multiply each of these

numbers by the corresponding expected number of

years remaining to individuals in that age category,

determined from life expectancy tables. This method

will be referred to as Method 2. Suppose, for example,

that individuals age 25 are expected to live to age 75,

or alternatively, have an expected 50 years of life remaining. Suppose that ten 25 year olds are estimated

to die prematurely because of exposure to PM. Their

expected loss of life-years is therefore 50 years each,

or a total of 500 life-years. If the same calculation is

carried out for the individuals dying prematurely in

each age category, the sum is an estimate of the total

life-years lost by the population.

Using Method 1 (and retaining the assumptions

made by WHO, 1996), the average life-years lost per

PM-related death among the cohort of Dutch males is

calculated to be 14.28 years. Using Method 2 it is estimated to be 14.43 years.

Although this ex post measure of life-years lost is

much larger than the ex ante measure (1.11 life-years

lost per exposed individual), it only applies to those

individuals who actually die from exposure to PM.

The number of individuals in the age 25-30 Dutch

cohort example who eventually die from exposure to

PM (7,646) is much smaller than the number of individuals in the age 25-30 Dutch cohort who are exposed to PM (98,177). The total life-years lost can be

calculated either as the number of exposed individuals times the expected life-years lost per exposed individual (98,177*1.11 = 109,192.1) or as the number

of affected individuals times the expected life-years

lost per affected individual (7,646*14.28 = 109,192.1).

To further illustrate the different measures of lifeyears lost and the effects of various input assumptions on these measures, death rates from the 1992

U.S. Statistical Abstract were used to follow a cohort

of 100,000 U.S. males from birth to age 90 in a “dirty”

scenario and a “clean” scenario, under various assumptions. Death rates were available for age less than 1,

ages 1-4, and for ten-year age groups thereafter. The

ten-year age groups were divided into five-year age

groups, applying the death rate for the ten-year group

to each of the corresponding five-year age groups. Ex

ante and ex post measures of life-years lost among

those individuals who survive to the 25-29 year old

category were first calculated under the assumptions

in the WHO (1996) study. These assumptions were

that the relative risk of mortality in the “dirty” scenario versus the “clean” scenario is 1.1; that exposure

does not begin until age 25; that the effect of exposure takes fifteen years; that individuals at the beginning of each age grouping either survive to the next

age grouping or live zero more years; and that all individuals age 85 live exactly five more years. Under

these assumptions, the expected life-years lost per

exposed individual in the 25-29 year old cohort is 1.32

years. There are 96,947 exposed individuals in this

age cohort. The expected life-years lost per affected

individual (i.e., per PM-related death) is 16.44 years

(Method 1). There are 7,804 affected individuals. The

total life-years lost by individuals in this cohort is

128,329.3 (1.32*96,947 = 16.44* 7,804 = 128,329.3).

If the relative risk is changed to 1.07, the expected

life-years lost per exposed individual in the cohort of

25-29 year old U.S. males is reduced from 1.32 to

0.95 years. The expected life-years lost per affected

individual (i.e., per PM-related death) is 16.44 years

(Method 1). Using a relative risk of 1.1 but assuming

no lag (i.e., assuming that exposure starts either at

birth or at age 25 and has an immediate effect), the

expected life-years lost per exposed individual in the

25-29 year old cohort changes from 1.32 to 1.12. The

expected life-years lost per affected individual (i.e.,

per PM-related death) becomes 19.7 years (Method

1).
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-19

Estimating Morbidity Effects

In addition to mortality effects, this analysis quantifies effects for a number of non-fatal health endpoints. Several issues arise in implementing the studies selected for this analysis.

Overlapping Health Effects

Several endpoints reported in the health effects

literature overlap with each other. For example, the

literature reports relationships for hospital admissions

for single respiratory ailments (e.g. pneumonia or

chronic obstructive pulmonary disease) as well as for

all respiratory ailments combined. Similarly, several

studies quantify the occurrence of respiratory symptoms where the definitions of symptoms are not unique

(e.g., shortness of breath, upper respiratory symptoms,

and any of 19 symptoms). Measures of restricted activity provide a final example of overlapping health

endpoints. Estimates are available for pollution-induced restricted activity days, mild restricted activity

days, activity restriction resulting in work loss. This

analysis models incidence for all endpoints. Doublecounting of benefits is avoided in aggregating economic benefits across overlapping endpoints (see

Appendix I).

Studies Requiring Adjustments

Applying concentration-response relationships

reported in the epidemiological literature to the national scale benefits analysis required by section 812

required a variety of adjustments.

Normalization of coefficients by population. To

be applied nationwide, concentration-response coefficients must reflect the change in risk per person per

unit change in air quality. However, some studies report the concentration-response coefficient, , as the

change in risk for the entire studied population. For

example, Thurston et al. (1994) reported the total number of respiratory-related hospital admissions/day in

the Toronto, Canada area. To normalize the coefficient so that it might be applied universally across the

country, it was divided by the population in the geographical area of study (yielding an estimate of the

change in admissions/person/day due to a change in

pollutant levels).

Within-study meta-analysis. In some cases, studies reported several estimates of the concentrationresponse coefficient, each corresponding to a particular year or particular study area. For example, Ostro

and Rothschild (1989) report six separate regression

coefficients that correspond to regression models run

for six separate years. This analysis combined the individual estimates using a fixed coefficient metaanalysis on the six years of data.

Conversion of coefficients dependent on symptom

status during the previous day. Krupnick et al. (1990)

employed a Markov process to determine the probability of symptoms that were dependent on symptom status of the previous day. The current analysis

adjusts the regression coefficients produced by the

model in order to eliminate this dependence on previous day’s symptom status.

Concentration-Response Functions:

Health Effects

After selecting studies appropriate for the section

812 analysis, taking into account the considerations

discussed above, the published information was used

to derive a concentration-response function for estimating nationwide benefits for each health effect considered. In general, these functions combine air quality changes, the affected population and information

regarding the expected per person change in incidence

per unit change in pollutant level. The following tables

present the functions used in this analysis, incorporating information needed to apply these functions and

references for information.

Particulate Matter

The concentration-response functions used to

quantify expected changes in health effects associated with reduced exposure to particulate matter are

summarized in Table D-6. The data profiles selected

for use in this analysis are PM10. In those cases in

which PM10 was not the measure used in a study, this

analysis either converted PM10 air quality data to the

appropriate air quality data (e.g., PM2.5 or TSP) or,

equivalently, converted the pollutant coefficient from

the study to the corresponding PM10 coefficient, based

on location-specific information whenever possible.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-20

Table D-6. Summary of Concentration-Response Functions for Particulate Matter.Except where noted otherwise, the functional form iswhere “cases” refers to incidence at the first pollution level.

∆cases = cases * (eβ * ∆PM-10 – 1)
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-21
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-22
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-23
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-24
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-25
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-26

Ozone

The health effects literature includes studies of

the relationships between ozone and a variety of

non-fatal health effects. Many of these relationships

are provided by the same studies that reported the

particulate matter relationships shown above. For

some health endpoints, most notably hospital admissions, multiple studies report alternative estimates of the concentration-response relationship.

The variability between these reported estimates is

incorporated into the Monte Carlo approach used to

combine estimates of avoided health effects with

economic valuations (discussed in Appendix I).

Table D-7 documents the concentration-response

functions used in this analysis.

NOTES:a Pollutant coefficients reflect changes in health effects per change in μg/m3 PM10. b Mortality baseline incidence data for each county taken from Vital Statistics of the United States, Vol. II - Mortality, Part B, (U.S. Dept. of Health and Human Services). Incidence

rates were generated for total mortality excluding accidental deaths and adverse effects, suicide, homicide, and other external causes (ICD E800-E999). Rates calculated based on 1990population. c PM10 data converted to PM2.5 data by using national urban average PM2.5/PM10 ratio = 0.56. d Centers for Disease Control, 1992. Vital and Health Statistics, Detailed Diagnoses and Procedures, National Hospital Discharge Survey, 1990. Number of 1990 discharges divided by1990 U.S. population (248,709,873) from City and County Databook, 12th edition, 1994, U.S. Dept. of Commerce, Bureau of the Census, Washington, D.C. e Pope et al., 1991 NOTE: rates were not available from standard incidence sources and so were calculated from incidence in the study of 10-12 year olds. This may not be entirelyappropriate for older or younger individuals. Children of this age are less likely to have colds than much younger children and may be more representative of the adult population. f Dockery et al., 1989.

g Coefficient and standard error are converted from a β and s.e. for coefficient of haze (COH) to a β and s.e. for PM10. This was done by using a ratio of COH to TSP of 0.116 from the study authors (as cited in ESEERCO, 1994) and a ratio of PM10 to TSP of 0.55 (U.S. EPA, 1986). h Coefficient and standard error incorporate the stationary probabilities as described in Krupnick et al. (1990). To do this, the calculation used the transitional probabilities supplied by

the authors and presented in ESEERCO, 1994. i U.S. EPA, 1994a.

j β converted from a change in health effects per change in μg/m3 PM2.5 to a change per μg/m3 PM10 using the following relationship: 1 μg/m3 PM2.5 = 0.56 μg/m3 PM10 (ESEERCO, 1994)k Number of RADs for all acute conditions from: National Center for Health Statistics. Current Estimates from the National Health Interview Survey: United States, 1990. (Hyattsville,

MD). This number is divided by the U.S. population for 50 states for 1990 (248,709,873) and multiplied by 100,000 (to obtain the incidence per 100,000). l Based on fixed-weight meta-analysis of single-year coefficients and standard errors reported in study.

m Number of WLDs of 374,933,000 from: National Center for Health Statistics. Current Estimates from the National Health Interview Survey from 1990. (Hyattsville, MD). Series 10,

No. 181. This number is divided by the U.S. population for 50 states for 1990 (248,709,873) and multiplied by 100,000 (to obtain the incidence per 100,000).
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-27

Table D-7. Summary of Concentration-Response Functions for Ozone.Except where noted otherwise, the functional form iswhere “cases” refers to incidence at the first pollution level.

∆cases = cases * (eβ * ∆O3 – 1)
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-28
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-29
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-30
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-31
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-32
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-33

NOTES:a Pollutant coefficients expressed as a change in health effects per change in ppb O3

.

b Centers for Disease Control, 1992. Vital and Health Statistics, Detailed Diagnoses and Procedures, National Hospital Discharge Survey, 1990. Number of 1990 discharges divided by

1990 U.S. population (248,709,873) from City and County Databook, 12th edition, 1994, U.S. Dept. of Commerce, Bureau of the Census, Washington, D.C.

c Determined the incremental effect/unit O3 by incorporating stationary probabilities from transitional probabilities. ESEERCO (1994) obtained transitional probabilities for adults from

original study authors.

d U.S. EPA, 1994a.e Calculated as baseline asthma attack rate (number of attacks per person per year) divided by 365 days per year. Number of attacks per person per year = 9.9 from National Center for

Health Statistics, National Health Interview Survey, 1979 (as cited by Krupnick and Kopp, 1988).

f β coefficient and s.e. converted to 

∆ in cases/ppb O3 based on the following relationship: 1 ppb O3 = 1.11 ppb oxidants.

g Study did not report a s.e. Thus, the analysis assumed the largest s.e. possible (at p = 0.01, using a two-tailed test of significance)h Ostro and Rothschild (1989) report average annual MRADs as 7.8 per person, using data from 6 years.i β is a weighted mean using separate coefficients for six years. Each year’s coefficient was weighted by the inverse of the variance for that coefficient.j Standard error is the square root of the sum of the weights (sqrt[sum(1/vari)], where I indicates the individual year).k Ostro and Rothschild (1989) report average annual RRADs as 3.1 per person, using data from 6 years.l Obtained by determining the products of beta coefficients for other independent variables and their mean values and summing these and the constant value.m Calculated by dividing β by asymptotic t-ratio.n From Table 12 in 1992 Statistical Abstracts, the percent of individuals in the U.S. population under age 50 = 75%.o Factor to adjust for differences in concentration among microenvironments and amount of time spent in different microenvironments at heavy exercise rates.p The values, di, adjust ozone concentrations for various microenvironments (outdoor — near road, outdoor — other, and indoor) using values reported in U.S. EPA, 1993.q The values, ei, adjust the response rates by the percent of time spent in each microenvironment at the relevant exercise rates (i.e., percent of time at a fast rate is used for Avol et al.,

1984, and percent of time at a moderate rate is used for Seal et al., 1993). U.S. EPA (1993) presents information to determine ei values.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-34

Nitrogen Oxides

Nitrogen dioxide (NO2

) is the primary focus of health studies on the nitrogen oxides and serves as the basis

for this analysis. The primary pathophysiology of NO2

 in humans involves the respiratory system and the concentration-response function identified for NO2

 describes the relationships between measures of NO2 and respiratory illness.

A number of epidemiological studies of NO2

 are available; however, most have either confounded exposures (with other pollutants) or insufficient exposure quantification (e.g., exposure assessment indicates only

absence or presence of a gas stove). Most studies consider NO2

 generated by gas stoves or other combustion

sources in homes and are therefore not directly usable in concentration-response functions. However, studies by

Melia et al, 1980 and Hasselblad et al, 1992 provide a reasonable basis for development of a concentration

response function. Table D-8 presents the function obtained from their work. The function relates NO2

 to respiratory illness in children.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-35

Table D-8. Summary of Concentration-Response Functions for NO2.

NOTES:a This equation was obtained from two sources. The NO2 coefficient was reported in Hasselblad et al., 1992. The background and gender intercepts were obtained via personal

communication with V. Hasselblad 2/28/95 by Abt Associates. The equation was based on an evaluation by Hasselblad et al. of study results obtained by Melia et al. (1980). See text forfurther discussion.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-36

Carbon Monoxide

Three concentration-response relationships are available for estimating the health effects of carbon monoxide. The first relates ambient CO levels to hospital admissions for congestive heart failure (Morris et al., 1995).

The second equation (Allred et al., 1989a,b, 1991) relates the CO level in the bloodstream to the relative change

in time of onset of angina pain upon exertion. The third relates the CO level in the bloodstream to the relative

change in time of onset of silent ischemia. Due to the lack of quantitative information relating silent ischemia to

a meaningful physical health effect, this analysis uses only the first two dose-response functions shown in Table

D-9.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-37

Table D-9. Summary of Concentration-Response Functions for Carbon Monoxide.

NOTES:a Calculated as the mean of means from 3 pre-exposure treadmill tests and 1 post-exposure test (control exposure to air) (Allred et al., 1991).b American Heart Association (1991)c Allred et al. (1991)d Multiple daily events are not modeled. Although it is possible that angina attacks may occur more than once per day, the average frequency of attacks was 4.6 per week (< 1 per day).e Equation calculated from figure in U.S. EPA (1991a), p. 2-7.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-38

Sulfur Dioxide

This analysis estimated one concentration-response function for SO2

 using clinical data from two sources

on the responses of exercising asthmatics to SO2

, as measured by the occurrence of respiratory symptoms in

mild and moderate asthmatics (see Table D-10).
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-39

Table D-10. Summary of Concentration-Response Functions for Sulfur Dioxide.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-40

Estimating Welfare Effects of

Exposure

In addition to avoided incidences of adverse human health effects, the air quality improvements estimated to result from the CAA yield additional benefits, namely welfare benefits. Table D-10 indicates a

variety of benefits expected to have accrued through

the avoidance of air pollution damage to resources.

As indicated, data supporting quantified estimates of

welfare benefits are more limited than those quantifying the relationship between air pollution exposure

and human health. While evidence exists that a variety of welfare benefits result from air quality improvements, currently available data supports quantifying

only a limited number of potential effects at this time.

The Table lists the effects quantified in the section

812 analysis; each is discussed below.

Agricultural Effects

This analysis was able to quantify the benefits to

economic welfare attributable to the increased crop

yields expected from CAA-related air quality improvements. Appendix F describes the method used to estimate such benefits using reported relationships between ozone exposure and yields of a variety of commodity crops.

It should be noted that the method used to allocate monitor-level ozone concentrations to estimate

crop exposure differed from that used to estimate

ozone health effects. Instead of assigning concentrations from the nearest monitor, the agricultural benefits analysis estimated ozone concentrations for each

county nationwide. This was necessary because of two

factors specific to the agricultural analysis. First, crop

production is reported at the county level, so changes

in crop yields associated with changes in ozone levels

must be estimated for each county. Second, much of

the nation’s agricultural production of “commodity

crops” (corn, wheat, soybeans, etc.) occurs at significant distances from the location of the populationoriented ozone monitors. Thus, an algorithm was used

to assign ozone concentrations for the agricultural

analysis for the control and no-control scenarios to

county centroids based on a planar interpolation of

concentrations at the nearest three monitors. Appendix F documents the details of the triangulation of

ozone air quality data.

Pollutant Quantified Welfare Effects Unquantified Welfare Effects

Ozon e Agriculture - Changes in crop yields

(for 7 crops)

Decreased worker productivity

Changes in other crop yields

Materials damage

Ecological - effects on forests

Ecological - effects on wildlife

Particu late Matter/

TSP/ Sulfates

Materials Damage - Household

soiling

Visibility

Other materials damage

Ecological - effects on wildlife

Nitrogen Oxides Visibility Crop losses due to acid deposition

Materials damage due to acid

deposition

Effects on fisheries due to acid

deposition

Effects on forest

Sulfu r Dioxide Visibility Crop losses due to acid deposition

Materials damage due to acid

deposition

Effects on fisheries due to acid

deposition

Effects on forest

Quantified Welfare Effects Unquantified Welfare Effects

Agriculture - Changes in crop yields

(for 7 crops)

Decreased worker productivity

Changes in other crop yields

Materials damage

Ecological - effects on forests

Ecological - effects on wildlife

Materials Damage - Household

soiling

Visibility

Other materials damage

Ecological - effects on wildlife

Visibility Crop losses due to acid deposition

Materials damage due to acid

deposition

Effects on fisheries due to acid

deposition

Effects on forest

Sulfu r Dioxide Visibility Crop losses due to acid deposition

Materials damage due to acid

deposition

Effects on fisheries due to acid

deposition

Effects on forest

Table D-11. Selected Welfare Effects of Criteria Pollutants.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-41

Materials Damage

Welfare benefits also accrue from avoided air

pollution damage, both aesthetic and structural, to architectural materials and to culturally important articles. At this time, data limitations preclude the ability to quantify benefits for all materials whose deterioration may have been promoted and accelerated by

air pollution exposure. However, this analysis does

address one small effect in this category, the soiling

of households by particulate matter. Table D-11 documents the function used to associate nationwide PM10 levels with household willingness to pay to avoid

the cleaning costs incurred for each additional μg/m3

of PM-10.

Visibility

In addition to the health and welfare benefits estimated directly from reduced ambient concentrations

of individual criteria air pollutants, this analysis also

estimates the general visibility improvements attributed to improved air quality. Visibility effects are

measured in terms of changes in DeciView, a measure useful for comparing the effects of air quality on

visibility across a range of geographic locations for a

range of time periods. It is directly related to two other

common visibility measures, visual range (measured

in km) and light extinction (measured in km-1); however, it characterizes visibility in terms of perceptible

changes in haziness independent of baseline conditions.

Visibility conditions under the control and nocontrol scenarios were modeled separately for the eastern and western U.S. In the east, the Regional Acid

Deposition Model (RADM) generated extinction coefficient estimates for each of 1,330 grid cells in the

RADM domain (essentially the eastern half of the

country). The extinction coefficients were translated

to DeciView using the relationship reported in

Pitchford and Malm (1994). In the Western U.S., a

conventional extinction budget approach provided

DeciView estimates for 30 metropolitan areas (SAI,

1994). A linear rollback model provided the corresponding no-control estimates. Visibility estimates for

both portions of the country were generated for the

target years 1975, 1980, 1985, and 1990.

Table D-12 summarizes the methodology used to

predict visibility benefits attributable to the CAA.

Physical benefits for a given year are reported in terms

of the average DeciView change per person in the

modeled population.

Worker Productivity

Available data permits quantification of a final

human welfare endpoint, worker productivity. Crocker

and Horst (1981) and U.S. EPA (1994c) present evidence regarding the inverse relationship between

ozone exposure and productivity in exposed citrus

workers. This analysis applies the worker productivity relationship (reported as income elasticity with

respect to ozone) to outdoor workers in the U.S. (approximately one percent of the population). Table D12 details the form of the concentration response function.

Ecological Effects

It is likely that the air pollution reductions

achieved under the CAA resulted in improvements in

the health of aquatic and terrestrial ecosystems. To

the extent that these ecosystems provide a variety of

services (e.g., fishing, timber production, and recreational opportunities), human welfare benefits also

accrued. However, due to a lack of quantified concentration-response relationships (or a lack of information concerning affected population), ecological

effects were not quantified in this analysis. Appendix

E provides discussion of many of the important ecological benefits which may have accrued due to historical implementation of the CAA.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-42

Table D-12. Summary of Functions Quantifying Welfare Benefits.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-43

NOTES:a 1990 Censusb Visibility is measured in two ways: (1) in terms of extinction coefficient in the eastern U.S. (based on modeling of RADM domain); and (2) as DeciView (dv) in the west (modeling

of 30 western cities) (SAI, 1994).

c DeciView is a haziness index used to characterize visibility through uniform hazes.d Average daily wage, assuming an 8-hour day, by workers in the job categories listed below, taken from U.S. Bureau of the Census, Earnings by Occupation and Education, 1990.e Full- and part-time workers (total of 3,100,000) taken from U.S. Bureau of the Census, Earnings by Occupation and Education, 1990. Includes the following job categories: farm

workers; groundskeepers and gardeners, except farm; forestry workers, except logging; timber cutting and logging occupations; brickmasons and stonemasons; brickmason and stonemasonapprentices; roofers; structural metal workers; construction trades, n.e.c.; construction laborers; garbage collectors; and stevedores. Value is divided by total U.S. population.

f Average number of days worked per year, assuming an 8-hour day, by workers in the job categories listed above, taken from U.S. Bureau of the Census, Earnings by Occupation and

Education, 1990.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-44

Modeling Results

This section summarizes results of the health and

welfare effects modeling. As indicated previously, the

Project Team adopted a Monte Carlo approach in an

effort to capture uncertainty in the benefits analysis.

With respect to estimating avoided incidence of adverse health and welfare effects, two sources of variability are considered. The first is the statistical uncertainty associated with each concentration-response

relationship reported in the literature. In addition to

an estimate of a concentration-response function coefficient, studies typically report a standard error of

the reported estimate. The second source of uncertainty lies in the choice of studies, where multiple studies offer estimates for the same endpoint. Different

published results reported in the scientific literature

typically do not report identical findings; in some instances the differences are substantial. This betweenstudy variability is captured by considering the range

of estimates for a given endpoint.

Table D-13 summarizes health and welfare effects

for each study included in the analysis. The values

presented are mean estimates of the number of cases

of each endpoint avoided due to implementation of

the CAA. A distribution is associated with each mean

estimate, capturing the uncertainty inherent in the estimate of the concentration-response coefficient. The

distribution of estimated effects corresponding to a

given study was generated by randomly sampling from

the distribution of coefficients (given by the estimated

coefficient and its standard error reported in the study)

and evaluating the concentration-response function,

yielding an estimate of avoided incidence for the given

effect. This procedure was repeated many times. While

only the central estimates of the resulting distributions are presented here, the distributions were retained

for use in monetizing and aggregating economic benefits (see Appendix I).7

As shown, for some health endpoints more than

one concentration-response function was used, each

representing a different study. The alternative concentration-response functions provide differing measures of the effect. These can be used to derive a range

of possible results. In the case of lead (Pb), alternative functions were not used; rather, two analytical

procedures were implemented (labeled the “backwardlooking” and “forward looking” analyses), giving a

range of results for most Pb endpoints (see Appendix

G for discussion of Pb health effects).

The table presents the results of modeling “all U.S.

population” (although, with the exception of Pb, not

all of the 48 state population is modeled, with up to

five percent being excluded in a given year). The results depict the pattern of health effects incidence

across years. The accuracy of the scale of incidence

is less certain (due to the extrapolation of air quality

data). These results are almost certainly more accurate than the corresponding “50 km” results, but rely

on the assumption that (for a portion of the population) distant air quality monitors provide a reasonable estimate of local air quality conditions. Thus, the

results presented here are somewhat speculative. It is

likely that the estimated health effects are overstated

for that population group (20 to 30 percent of total

population in the case of PM) for which distant monitors are used. (Note, however, that the scaling of

unmonitored county PM concentrations based on regional-scale grid model projections significantly mitigates this potential overestimation in the case of PM;

see Appendix C for details). Conversely, there is an

implied zero health impact for that portion of the population (three to four percent in the case of PM) excluded from the analysis altogether, an understatement

of health impacts for that group.

The results indicate the growth of benefits over

the study period, consistent with increasing improvements in air quality between the control and no-control scenarios from 1970 to 1990.

The mortality effects documented above can be

disaggregated by age. Table D-14 indicates the estimated proportions of premature mortalities for various age groups (Pb-induced mortality estimates for

children, men, and women are grouped). Also presented is the average life expectancy for each group,

indicating the degree of prematurity of PM and Pbrelated mortality.

Table D-15 presents estimated incidence reductions for several health effects which could be quantified but not monetized for this analysis.

7

 With the exception of visibility, welfare endpoints estimated economic benefits directly and are therefore included in the

monetary benefits results presented in Appendix I.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-45

Endpoint Study Pollutant(s) 1975 1980 1985 1990

MORTALITY

Mortality (long-term exposure) Pope et al., 1995 PM10 58,764 145,884 169,642 183,539

Mortality (Pb exposure) -Male Average of Backward & Forward Pb 822 5,281 10,340 12,819

Mortality (Pb exposure) -Female Average of Backward & Forward Pb 231 1,474 2,866 3,537

Mortality (Pb exposure) -Infant Average of Backward & Forward Pb 456 2,342 3,933 4,944

CHRONIC BRONCHITIS

Chronic Bronchitis Schwartz, 1993b PM10 198,973 554,632 720,166 741,775

Abbey et al., 1993 PM10 173,571 454,309 564,753 602,990

OTHER Pb-INDUCED AILMENTS

Lost IQ Points Average of Backward & Forward Pb 1,028,492 5,031,157 8,559,426 10,378,268

IQ < 70 Average of Backward & Forward Pb 3,780 20,074 36,520 45,393

Hypertension-Men Average of Backward & Forward Pb 830,299 5,276,999 10,087,115 12,646,876

Cor. Heart Disease Average of Backward & Forward Pb 1,313 8,444 16,671 21,069

Atherothrombotic brain infarction - Men Average of Backward & Forward Pb 181 1,128 2,165 2,690

Atherothrombotic brain infarction - Women Average of Backward & Forward Pb 84 529 1,020 1,255

Initial cerebrovascular accident - Men Average of Backward & Forward Pb 260 1,635 3,154 3,926

Initial cerebrovascular accident - Women Average of Backward & Forward Pb 120 758 1,466 1,804

HOSPITAL ADMISSIONS

All Respiratory Schwartz, 1995, Tacoma PM10 & O3 32,004 77,827 95,435 106,777

Schwartz, 1996, Spokane PM10 & O3 29,393 69,449 93,137 119,290

Pope, 1991, Salt Lake Valley PM10 30,982 73,093 86,407 95,486

Schwartz, 1995, New Haven PM10 & O3 23,137 55,096 66,385 73,842

Thurston et al., 1994, Toronto PM10 & O3 13,746 32,383 39,691 46,013

COPD + Pneumonia Schwartz, 1994c PM10 & O3 21,898 53,928 64,217 70,528

Schwartz, 1996, Spokane PM10 & O3 19,769 47,294 63,116 80,113

Schwartz, 1994a PM10 & O3 16,942 40,882 49,290 55,227

Schwartz, 1994b PM10 & O3 13,006 30,679 37,434 43,410

Ischemic Heart Disease Schwartz and Morris, 1995 PM10 6,348 14,709 17,289 19,098

Congestive Heart Failure Schwartz and Morris, 1995 PM10 5,733 13,365 15,742 17,362

Morris et al., 1995 CO 3,022 8,543 17,028 21,835

OTHER RESPIRATORY-RELATED AILMENTS

- Adults

Any of 19 Acute Symptoms Krupnick et al., 1990 PM10 & O3 41,631,456 98,876,110 117,275,400 129,529,717

- Children

Shortness of breath, days Ostro et al., 1995 PM10 20,752,402 50,758,872 58,575,484 68,375,216

Acute Bronchitis Dockery et al., 1989 PM10 1,936,260 6,255,801 7,644,924 8,541,833

Lower Respiratory Symptoms Schwartz et al., 1994d PM10 2,994,048 6,100,276 6,977,680 7,804,860

Upper Respiratory Symptoms Pope et al., 1991 PM10 500,395 1,292,922 1,557,177 1,683,854

- All Ages

Asthma Attacks Ostro et al., 1991 PM10 264,430 548,306 686,953 841,916

Whittemore and Korn, 1980; O3 193 482 816 1,080

EPA ,1983

Increase in Respiratory Illness Hasselblad, 1992 NO2 729,306 2,686,813 6,113,639 9,776,267

Any Symptom Linn et al. (1987, 1988, 1990) SO2 104,896 319,192 282,846 265,650

RESTRICTED ACTIVITY AND WORK LOSS DAYS

RAD Ostro, 1987 PM10 19,170,337 47,445,314 56,939,271 62,187,720

MRAD Ostro and Rothschild, 1989 PM10 & O3 60,871,610 155,799,151 190,333,140 209,924,785

RRAD Ostro and Rothschild, 1989 PM10 & O3 47,669,732 237,799,482 176,850,171 174,329,691

Work Loss Days Ostro, 1987 PM10 6,966,775 17,213,581 20,648,906 22,562,752

HUMAN WELFARE

Household Soiling Damage ESEERCO, 1994 PM10 direct economic valuation

Visibility - East (DeciView chg. per person) Pitchford and Malm, 1994 DeciView 0.4 1.4 1.9 2.0

Visibility - West (DeciView chg. per person) Pitchford and Malm, 1994 DeciView 2.4 4.9 5.0 6.0

Decreased Worker Productivity Crocker & Horst, 1981 and EPA, 1994cO3 direct economic valuation

Agriculture (Net Surplus) Minimum Estimate O3 direct economic valuation

Maximum Estimate O3 direct economic valuation

Table D-13. Criteria Pollutants Health Effects -- Extrapolated to 48 State U.S. Population (Cases per year -

mean estimates).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-46

Proportion of Premature Mortalities by Ageaa

Age Group PMbb Pbcc

Forward (Backward)dd

Life Expectancy

(years)

Infants 33% (20%) 75

5-30

30-34 2% 48

35-39 4% 38

40-44 11% (13%)

45-54 6% 21% (25%) 29

55-64 13% 22% (27%) 21

65-74 24% 12% (15%) 14

75-84 29% 9

85+ 22% 6

100% 100%

Age Group PMbb

Forward (Backward)dd (years)

Infants 33% (20%) 75

5-30

30-34 2% 48

35-39 4% 38

40-44 11% (13%)

45-54 6% 21% (25%) 29

55-64 13% 22% (27%) 21

65-74 24% 12% (15%) 14

75-84 29% 9

85+ 22% 6

100% 100%

Table D-14. Mortality Distribution by Age: Proportion of PM- and

Pb-related Premature Mortalities and Associated Life Expectancies.

Notes:

a

 Distribution of premature mortalities across ages is fairly consistent across years.

b

 PM-related mortality incidence estimated only for individuals 30 years and older,

consistent with the population studied by Pope et al., 1995.

c

 Pb-related mortality incidence was estimated for infants, women aged 45-74, and men in

three age groups (40-54, 55-64, 65-74), each with a distinct concentration-response

relationship.

d

 Forward (backward) analysis holds other lead sources at constant 1970 (1990) levels -

see Appendix G. Values may not sum to 100% due to rounding.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-47

Endpoint Endpoint Study Pollutant Pollutant 1975 1980 1985 1985 1990 Units

Pulm onary Function Pulm onary Function

Decrem ents Decrem ents

 Decreased FEV by 15 % 

 or more

Avol et al. 1984 & Seal 

et al. 1993

O3 53 121 196 312 million person-days with

decreased FEV (per year)

 Decreased FEV by 20 % 

 or more

Avol et al. 1984 & Seal 

et al. 1993

O3 39 87 141 224 million person-days with

decreased FEV (per year)

Chronic Sinusitis and Hay

Fever

Portney and Mullahy,

1990

O3 6 8 8 9 million cases/year

Tim e to Onset of Angina

Pain

Allred, et al., 1989a,b,

1991

CO 0.1% 0.4% 0.7% 0.8% fractional increase in

time to onset of angina

attack

Pain 1991

CO 0.1% 0.4% 0.7% 0.8%

attack

Table D-15. Quantified Benefits Which Could Not Be Monetized -- Extrapolated to the

Entire 48 State Population.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-48

Human Health and Welfare

Effects References

Abbey, D.E, F. Petersen, P. K. Mills, and W. L.

Beeson. 1993. “Long-term Ambient Concentrations of Total Suspended Particulates,

Ozone, and Sulfur Dioxide and Respiratory

Symptoms in a Nonsmoking Population.”

Archives of Environmental Health 48(1): 33-

46.

Allred, E.N., E.R. Bleecker, B.R. Chaitman, T.E.

Dahms, S.O. Gottlieb, J.D. Hackney, M.

Pagano, R.H. Selvester, S.M. Walden, and J.

Warren. 1989a. “Short-term Effects of Carbon Monoxide Exposure on the Exercise Performance of Subjects with Coronary Heart

Disease.” New England Journal of Medicine

321: 1426-1432.

Allred, E.N., E.R. Bleecker, B.R. Chaitman, T.E.

Dahms, S.O. Gottlieb, J.D. Hackney, D.

Hayes, M. Pagano, R.H. Selvester, S.M.

Walden, and J. Warren. 1989b. Acute Effects

of Carbon Monoxide Exposure on Individuals with Coronary Heart Disease. Health Effects Institute, Cambridge, MA. Research

Report No 25.

Allred, E.N., E.R. Bleecker, B.R. Chaitman, T.E.

Dahms, S.O. Gottlieb, J.D. Hackney, M.

Pagano, R.H. Selvester, S.M. Walden, and J.

Warren. 1991. “Effects of Carbon Monoxide

on Myocardial Ischemia.” Environmental

Health Perspectives 91:89-132.

American Heart Association. 1991. 1992 Heart and

Stroke Facts.

Avol, E.L., W.S. Linn, T.G. Venet, D.A. Shamoo, and

J.D. Hackney. 1984. “Comparative Respiratory Effects of Ozone and Ambient Oxidant

Pollution Exposure During Heavy Exercise.”

Journal of the Air Pollution Control Association 34: 804-809.

Centers for Disease Control (CDC). 1992. Vital and

Health Statistics, Detailed Diagnoses and Procedures, National Hospital Discharge Survey,

1990. (Series 13, Number 113).

Crocker T. D., and R. L. Horst, Jr. 1981. “Hours of

Work, Labor Productivity, and Environmental Conditions: a Case Study.” The Review of

Economics and Statistics 63:361-368.

Dockery, D.W., F.E. Speizer, D.O. Stram, J.H. Ware,

J.D. Spengler, and B.G. Ferris, Jr. 1989. “Effects of Inhalable Particles on Respiratory

Health of Children.” Am. Rev. Respir. Dis.

139: 587-594.

Dockery, D.W., et al. 1993. “An Association Between

Air Pollution and Mortality in Six U.S. Cities.” The New England Journal of Medicine

329 (24): 1753-1759.

Dockery, D.W., J. Schwartz, and J.D. Spengler. 1992.

Air Pollution and Daily Mortality: Associations with Particulates and Acid Aerosols.

Environ. Res. 59: 362-373.

Empire State Electric Energy Research Corporation

(ESEERCO). 1994. New York State Environmental Externalities Cost Study. Report 2:

Methodology. Prepared by: RCG/Hagler,

Bailly, Inc., November.

Hasselblad, V., D.M. Eddy, and D.J. Kotchmar. 1992.

“Synthesis of Environmental Evidence: Nitrogen Dioxide Epidemiology Studies.” J. Air

Waste Mgmt. Assoc 42: 662-671.

Health Effects Institute (HEI). 1996. Air Pollution and

Mortality in Philadelphia, 1974-1988. Contributors: Samet, J.M., S.L. Zeger, K. Berhane,

and J. Xu.

Ito, K. and G.D. Thurston. 1996. Daily PM10

/Mortality Associations: An Investigation of At-Risk

Sub-Populations. J. Exposure Anal. Environ.

Epidemiol.: in press.

Ito, K, P. Kinney, and G.D. Thurston. 1995. Variations on PM10

 Concentrations Within Two

Metropolitan Areas and Their Implications for

Health Effects Analyses. In: Phalen, R.F. and

D.V. Bates, eds. Proceedings of the Colloquium on Particulate Air Pollution and Human Mortality and Morbidity, Part II; January 1994; Irvine, CA. Inhalation Toxical. 7:

735-745.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-49

Kinney, P.L., K. Ito, and G.D. Thurston. 1995. A Sensitivity Analysis of Mortality/PM10

 Associations in Los Angeles. In: Phalen, R.F. and

D.V. Bates, eds. Proceedings of the Colloquium on Particulate Air Pollution and Human Mortality and Morbidity; January 1994;

Irvine, CA. Inhalation Toxicol. 7: 59-69.

Krupnick, A.J. and R.J. Kopp. 1988. “The Health and

Agricultural Benefits of Reductions in Ambient Ozone in the United States.” Resources

for the Future Discussion Paper QE88-10,

Washington, DC. August.

Krupnick A.J., W. Harrington, and B. Ostro. 1990.

“Ambient Ozone and Acute Health Effects:

Evidence from Daily Data.” Journal of Environmental Economics and Management 18:1-

18.

Lave, L.B. and E.P. Seskin. 1977. Air Pollution and

Human Health. Johns Hopkins University

Press for Resources for the Future. Baltimore

and London.

Linn, W.S., E.L. Avol, R. Peng, D.A. Shamoo, and

J.D. Hackney. 1987. “Replicated Dose-Response Study of Sulfur Dioxide Effects in

Normal, Atopic, and Asthmatic Volunteers.”

Am Rev Respir Dis. 136: 1127-34.

Linn, W.S., E.L. Avol, D.A. Shamoo, R. Peng, C.E.

Spier, M.N. Smith, and J.D. Hackney. 1988.

“Effect of Metaproterenol Sulfate on Mild

Asthmatics’ Response to Sulfur Dioxide Exposure and Exercise.” Archives of Environmental Health 43(6): 399-406.

Linn, W.S., D.A. Shamoo, R. Peng, K.W. Clark, E.L.

Avol, and J.D. Hackney. 1990. “Responses

to Sulfur Dioxide and Exercise by Medication-Dependent Asthmatics: Effect of Varying Medication Levels.” Archives of Environmental Health 45(1): 24-30.

Lipfert, F.W. 1984. “Air Pollution and Mortality:

Specification Searches Using SMSA-based

Data.” J. Environ. Econ. Manage. 11: 208-

243.

Manuel, E.H., R.L. Horst, K.M. Brennan, W.N. Lanen,

M.C. Duff, and J.K. Tapiero. 1982. Benefits

Analysis of Alternative Secondary National

Ambient Air Quality Standards for Sulfur Dioxide and Total Suspended Particulates, Volumes I-IV. Prepared for U.S. Environmental

Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle

Park, NC. [Cited in ESEERCO, 1994].

McClelland, G., W. Schulze, D. Waldman, J. Irwin,

D. Schenk, T. Stewart, L. Deck, and M.

Thayer. 1991. Valuing Eastern Visibility: a

Field Test of the Contingent Valuation Method

(Draft). Prepared for U.S. Environmental Protection Agency, Washington, D.C.

Melia, R.J.W., C du V Florey, S. Chinn, B.D.

Goldstein, A.G.F. Brooks, H.H. John, D.

Clark, I.B. Craighead, and X. Webster. 1980.

“The Relation Between Indoor Air Pollution

from Nitrogen Dioxide and Respiratory Illness in Primary Schoolchildren.” Clinical

Respiratory Physiology 16:7P-8P.

Melia, R.J.W., C du V Florey, R.W. Morris, B.D.

Goldstein, H.H. John, D. Clark, I.B.

Craighead, and J.C. Mackinlay. 1982. “Childhood Respiratory Illness and the Home Environment: II. Association Between Respiratory

Illness and Nitrogen Dioxide, Temperature,

and Relative Humidity.” International Journal of Epidemiology 11: 164-169.

Melia, R.J.W., C du V. Florey, and Y. Sittampalarm.

1983. The Relationship Between Respiratory

Illness in Infants and Gas Cooking in the UK:

a Preliminary Report. Air Quality Vth World

Congress: Proceedings of the International

Union of Air Pollution Prevention Associations. SEPIC (APPA), Paris France, pp. 263-

269.

Morris, R.D., E.N. Naumova, and R.L. Munasinghe.

1995. “Ambient Air Pollution and Hospitalization for Congestive Heart Failure Among

Elderly People in Seven Large U.S. Cities.”

American Journal of Public Health 85(10):

1361-1365.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-50

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: a Specification Test.” J. Environ.

Econ. Manage. 14: 87-98.

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution

and Acute Respiratory Morbidity: An Observational Study of Multiple Pollutants.” Environmental Research 50:238-247.

Ostro, B.D., M.J. Lipsett, M.B. Wiener, and J.C.

Selner. 1991. “Asthmatic Responses to Airborne Acid Aerosols.” American Journal of

Public Health 81: 694-702.

Ostro, B.D., J.M. Sanchez, C. Aranda, and G.S.

Eskeland. 1995. “Air Pollution and Mortality: Results of a Study of Santiago, Chile.” J.

Exposure Anal. Environ. Epidemiol., submitted.

Ostro, B.D., J.M. Sanchez, C. Aranda, and G.S.

Eskeland. 1996. Air Pollution and Mortality:

Results from a Study of Santiago, Chile. In:

Lippmann, M. ed. Papers from the ISEA-ISEE

Annual Meeting; September 1994; Research

Triangle Park, NC. J. Exposure Anal. Environ.

Epidemiol.: in press.

Özkaynak, H., J. Xue, P. Severance, R. Burnett, and

M. Raizenne. 1994. Associations Between

Daily Mortality, Ozone, and Particulate Air

Pollution in Toronto, Canada. Presented at:

Colloquium on Particulate Air Pollution and

Human Mortality and Morbidity: Program

and Abstracts; January; Irvine, CA. Irvine,

CA: University of California Irvine, Air Pollution Health Effects Laboratory; p. P1.13;

report no. 94-02.

Pitchford, M.L. and W.C. Malm. 1994. “Development

and Applications of a Standard Visual Index.”

Atmospheric Environment 28(5): 1049-1054.

Pope, C.A., III. 1991. “Respiratory Hospital Admissions Associated with PM10

 Pollution in Utah,

Salt Lake, and Cache Valleys.” Arch. Environ.

Health 46 (2): 90-97.

Pope, C.A., III, and D.W. Dockery. 1992. “Acute

Health Effects of PM10 Pollution on Symptomatic and Asymptomatic Children.” Am.

Rev. Respir. Dis. 145: 1123-1128.

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and

M.E. Raizenne. 1991. “Respiratory Health

and PM10

 Pollution: a Daily Time Series

Analysis.” Am. Rev. Respir. Dis. 144: 668-

674.

Pope, C.A., III, and L.S. Kalkstein. 1996. Synoptic

Weather Modeling and Estimates of the Exposure-Response Relationship Between Daily

Mortality and Particulate Air Pollution.

Environ. Health Perspect. 104: in press.

Pope, C.A., III, J. Schwartz, and M.R. Ransom. 1992.

Daily Mortality and PM10

 Pollution in Utah

Valley. Arch. Environ. Health 47: 211-217.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W.

Dockery, J.S. Evans, F.E. Speizer, and C.W.

Heath, Jr. 1995. Particulate Air Pollution as a

Predictor of Mortality in a Prospective Study

of U.S. Adults. Am. J. Respir. Crit. Care Med.

151: 669-674.

Portney, P.R. and J. Mullahy. 1990. “Urban Air Quality and Chronic Respiratory Disease.” Regional Science and Urban Economics 20: 407-

418.

Roger, L.J., H.R. Kehrl, M. Hazucha, and D.H.

Horstman. 1985. “Bronchoconstriction in

Asthmatics Exposed to Sulfur Dioxide During Repeated Exercise.” J. Appl. Physiol.

59(3): 784-791.

Saldiva, P.H.N., Pope, C.A., III, J. Schwartz, D.W.

Dockery, A.J. Lichtenfels, J.M. Salge, I.

Barone, and G.M. Bohm. 1995. Air Pollution

and Mortality in Elderly People: A Time-Series Study in São Paulo, Brazil. Arch. Environ.

Health 50: 159-163.

Samet, J.M., Lambert, W.E., Skipper, B.J., Cushing,

A.H., Hunt, W.C., Young, S.A., McLaren,

L.C., Schwab, M., and J.D. Spengler. 1993.

“Nitrogen Dioxide and Respiratory Illnesses

in Infants.” Am Rev. Respir. Dis. 148: 1258-

1265.

Schwartz, J. 1993a. Air Pollution and Daily Mortality in Birmingham, Alabama. Am. J.

Epidemiol. 137: 1136-1147.
Appendix D: Human Health and Welfare Effects of Criteria Pollutants

D-51

Schwartz, J. 1993b. “Particulate Air Pollution and

Chronic Respiratory Disease.” Environmental Research 62: 7-13.

Schwartz, J. 1994a. “Air Pollution and Hospital Admissions in Elderly Patients in Birmingham,

Alabama.” American Journal of Epidemiology 139:589-98.

Schwartz, J. 1994b. “Air Pollution and Hospital Admissions for the Elderly in Detroit, Michigan.”

American Journal of Respiratory Care Med

150:648-55.

Schwartz, J. 1994c. “PM10

, Ozone and Hospital Admissions for the Elderly in Minneapolis-St.

Paul, Minnesota.” Archives of Environmental Health 49(5): 366-374.

Schwartz, J. 1994d. “Acute Effects of Summer Air

Pollution on Respiratory Symptom Reporting in Children.” Am J Respir Crit Care Med.

150: 1234-1242.

Schwartz, J. 1995. “Short Term Fluctuations in Air

Pollution and Hospital Admissions of the Elderly for Respiratory Disease.” Thorax 50:531-

538.

Schwartz, J. 1996. “Air Pollution and Hospital Admissions for Respiratory Disease.” Epidemiology 7(1): 1-9.

Schwartz, J., D.W. Dockery, L.M. Neas. 1996a. Is

Daily Mortality Associated Specifically with

Fine Particles? J. Air Waste Manage. Assoc.:

accepted.

Schwartz, J. and R. Morris. 1995. “Air Pollution and

Cardiovascular Hospital Admissions.” Am. J.

Epidemiol. 142: 23-35.

Seal, E., W.F. McDonnell, D.E. House, S.A. Salaam,

P.J. Dewitt, S.O. Butler, J. Green, and L.

Raggio. 1993. “The Pulmonary Response of

White and Black Adults to Six Concentrations

of Ozone.” Am. Rev. Respir. Dis. 147: 804-

810.

Styer, P., N. McMillan, F. Gao, J. Davis, and J. Sacks.

1995. The Effect of Airborne Particulate

Matter in Daily Death Counts. Environ.

Health Perspect. 103: 490-497.

Systems Application International (SAI). 1994. Retrospective Analysis of the Impact of the Clean

Air Act on Urban Visibility in the Southwestern United States. Prepared for the U.S. Environmental Protection Agency, Office of Air

and Radiation. October 31.

Thurston, G., K. Ito, C. Hayes, D. Bates, and M.

Lippmann. 1994. “Respiratory Hospital Admission and Summertime Haze Air Pollution

in Toronto, Ontario: Consideration of the Role

of Acid Aerosols.” Environmental Research

65: 271-290.

U.S. Environmental Protection Agency (U.S. EPA).

1985. Costs and Benefits of Reducing Lead

in Gasoline: Final Regulatory Impact Analysis. Office of Policy Analysis. Washington,

DC. EPA-230-05-85-006.

U.S. Environmental Protection Agency (U.S. EPA).

1986. Air Quality Criteria for Particulate

Matter: Updated Assessment of Scientific and

Technical Information Addendum to the 1982

OAQPS Staff Paper. Prepared by the Office

of Air Quality Planning and Standards, Research Triangle Park, North Carolina. EPA

450/05-86-012.

U.S. Environmental Protection Agency (U.S. EPA).

1991a. Acid Rain Benefit Assessment: Draft

Plan for the [Section 812] 1992 Assessment,

Acid Rain Division, Washington, DC.

U.S. Environmental Protection Agency (U.S. EPA).

1991b. Air Quality Criteria for Carbon Monoxide. EPA-600/8-90/045F, U.S. Environmental Protection Agency, Office of Health

and Environmental Assessment, Environmental Criteria and Assessment Office; Research

Triangle Park, NC.

U.S. Environmental Protection Agency, 1993. Documentation for Oz-One Computer Model (Version 2.0). Office of Air Quality Planning and

Standards. Prepared by: Mathtech, Inc., under EPA Contract No. 68D830094, WA 59.

July.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

D-52

U.S. Environmental Protection Agency (U.S. EPA).

1993b. External Draft, Air Quality Criteria for

Ozone and Related Photochemical Oxidants.

Volume II. Office of Health and Environmental Assessment, Environmental Criteria and

Assessment Office, Research Triangle Park,

NC; EPA/600/AP-93/004b. 3v.

U.S. Environmental Protection Agency (U.S. EPA).

1994a. Review of the National Ambient Air

Quality Standards for Sulfur Oxides: Assessment of Scientific and Technical Information.

Supplement to the 1986 OAQPS Staff Paper

Addendum. Air Quality Management Division, Office of Air Quality Planning and Standards. Research Triangle Park, NC. EPA Report No. EPA-452/R-94-013.

U.S. Environmental Protection Agency (U.S. EPA).

1994b. Supplement to the Second Addendum

(1986) to Air Quality Criteria for Particulate

Matter and Sulfur Oxides (1982): Assessment

of New Findings on Sulfur Dioxide Acute Exposure Health Effects in Asthmatics (External Review Draft). Environmental Criteria and

Assessment Office. Research Triangle Park,

NC. EPA/600/AP-93/002.

U.S. Environmental Protection Agency (U.S. EPA).

1994c. Documentation for Oz-One Computer

Model (Version 2.0). Office of Air Quality

Planning and Standards. Prepared by:

Mathtech, Inc., under EPA Contract No.

68D30030, WA 1-29. August.

Ware J.H., D.W. Dockery, A. Sprio III, F.E. Speizer,

and B.G. Ferris, Jr. 1984. “Passive Smoking,

Gas Cooking, and Respiratory Health of Children Living in Six Cities.” American Review

of Respiratory Disease 129:366-374.

Watson, W. and J. Jaksch. 1982. “Air Pollution:

Household Soiling and Consumer Welfare

Losses.” Journal of Environmental Economics and Management. 9: 248-262.

Whittemore, A. S., and E. L. Korn. 1980. “Asthma

and Air Pollution in the Los Angeles Area.”

American Journal of Public Health 70:687-

696.

World Health Organization (WHO). 1996. Final Consultation on Updating and Revision of the Air

Quality Guidelines for Europe. Bilthoven,

The Netherlands 28-31 October, 1996 ICP

EHH 018 VD96 2.11.
Appendix E: Ecological Effects of Criteria Pollutants

E-1

Appendix E: Ecological Effects of Criteria

Pollutants

Introduction

Benefits to human welfare from air pollution reductions achieved under the CAA can be expected to

arise from likely improvements in the health of aquatic

and terrestrial ecosystems and the myriad of ecological services they provide. For example, improvements

in water quality stemming from a reduction in acid

deposition-related air pollutants (e.g., SOx

 and NOx)

could benefit human welfare through enhancements

in certain consumptive services such as commercial

and recreational fishing, as well as non-consumptive

services such as wildlife viewing, maintenance of

biodiversity, and nutrient cycling. Increased growth

and productivity of U.S. forests could result from reduced emissions of ozone-forming precursors, particularly VOCs and NOx, and thus may yield benefits from

increased timber production; greater opportunities for

recreational services such as hunting, camping, wildlife observation; and nonuse benefits such as nutrient

cycling, temporary CO2

 sequestration, and existence

value.

In this Appendix, the potential ecological benefits

from CAA pollutant controls are discussed in the context of three types of ecosystems: aquatic, wetland,

and forest. In describing the potential ecological benefits of the CAA, it is clearly recognized that this discussion is far from being comprehensive in terms of

the types and magnitude of ecological benefits that

may actually have occurred from the implementation

of the CAA. Rather, this discussion reflects current

limitations in understanding and quantifying the linkages which exist between air quality and ecological

services, in addition to limitations in the subsequent

valuation of these services in monetary terms. This

discussion also does not cover potential benefits from

improvements in other ecological services, namely agriculture and visibility, which are discussed and quantified in other sections of this report. This appendix

is dedicated to a qualitative evaluation of ecological

benefits. However, where possible, the existing body

of scientific literature is drawn upon in an attempt to

provide insights to the possible magnitude of benefits

that may have resulted from CAA-related improvements of selected ecological services. It is important

to note that the inability to fully value ecological services results in a significant undervaluation of the

ecological benefits of air pollution reductions. This

undervaluation should not be interpreted as a devaluation.

Benefits From Avoidance of

Damages to Aquatic Ecosystems

Aquatic ecosystems (lakes, streams, rivers, estuaries, coastal areas) provide a diverse range of services that benefit the welfare of the human population. Commercially, aquatic ecosystems provide a

valuable food source to humans (e.g., commercial fish

and shellfish harvesting), are used for the transportation of goods and services, serve as important drinking water sources, and are used extensively for irrigation and industrial processes (e.g., cooling water, electrical generation). Recreationally, water bodies provide important services that include recreational fishing, boating, swimming, and wildlife viewing. They

also provide numerous indirect services such as nutrient cycling, and the maintenance of biological diversity.

Clearly, these and other services of aquatic ecosystems would not be expected to be equally responsive to changes in air pollution resulting from the

implementation of the CAA. The available scientific

information suggests that the CAA-regulated pollutants that can be most clearly linked to effects on

aquatic resources include SOx

 and NOx (through acid

deposition and increases in trace element

bioavailability), NOx (through eutrophication of nitrogen-limited water bodies), and mercury (through

changes in atmospheric deposition). Potential benefits from each of these processes (acid deposition,

eutrophication, mercury accumulation in fish) are

described separately in the following sections.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-2

Acid Deposition

Background

Acid deposition refers to the depositing of strong

acids (e.g., H2

SO4, HNO3) and weak acids ((NH4)2SO4,

NH4

NO3) from the atmosphere to the earth’s surface.

Acid deposition can occur in the wet or dry form and

can adversely affect aquatic resources through the

acidification of water bodies and watersheds. Acidification of aquatic ecosystems is of primary concern

because of the adverse effects of low pH and associated high aluminum concentrations on fish and other

aquatic organisms. Low pH can produce direct effects on organisms, through physiological stress and

toxicity processes, and indirect effects, mediated by

population and community changes within aquatic

ecosystems. Acidification can affect many different

aquatic organisms and communities. As pH decreases

to 5.5, species richness in the phytoplankton, zooplankton, and benthic invertebrate communities decreases.1 Additional decreases in pH affect species

richness more significantly, and may sometimes affect overall biomass.2 Table E-1 presents descriptions of the biological effects of acidification at different pH levels. In evaluating the severity of biological changes due to acidification, the reversibility

of any changes is an important consideration; biological populations and communities may not readily recover from improved water quality under certain circumstances. Researchers have addressed acidification effects through many different experimental protocols, including laboratory bioassays, particularly

concerning pH, aluminum, and calcium; manipulative whole-system acidification studies in the field;

and comparative, nonmanipulative field studies.

Although acidification affects phytoplankton,

zooplankton, benthic invertebrates, fish, amphibians,

and waterfowl, most acidification research has concentrated on fish populations.3 Aluminum, which can

be toxic to organisms, is soluble at low pH and is

leached from watershed soils by acidic deposition.4

Acidification may affect fish in several ways. The

direct physiological effects of low pH and high aluminum include increased fish mortality, decreased

growth, and decreased reproductive potential. The

mechanism of toxicity involves impaired ion regulation at the gill.5 Population losses occur frequently

because of recruitment failure,6 specifically due to

increased mortality of early life stages.7 Changes at

other trophic levels may affect fish populations by

altering food availability.8 Fish in poorly buffered,

low pH water bodies may accumulate higher levels of

mercury, a toxic metal, than in less acidic water bodies, due to increased mercury bioavailability. The

primary consequence of mercury accumulation appears to be hazardous levels to humans and wildlife

who consume fish, rather than direct harm to aquatic

organisms (discussed further below).

The CAA-regulated pollutants that are likely to

have the greatest effect on aquatic ecosystems through

acid deposition and acidification are SO2

 and NOx. In

the atmosphere, SO2

 and NOx react to form sulfate

and nitrate particulates, which may be dry-deposited;

also the pollutants may react with water and be wetdeposited as dilute sulfuric and nitric acids. SO2

 is

considered the primary cause of acidic deposition,

contributing 75 to 95 percent of the acidity in rainfall

in the eastern United States.9

Current Impacts of Acid Deposition

Effects on Water Chemistry

The effects of acid deposition and resulting acidification of water bodies was intensively studied as part

of a 10-year, congressionally-mandated study of acid

rain problems in the United States.10 Based on the

NAPAP study, it is estimated that 4 percent of the

lakes and 8 percent of the streams in acid-sensitive

1

 J. Baker et al., NAPAP SOS/T 13, 1990; Locke, 1993.

2

 J. Baker et al., NAPAP SOS/T 13, 1990.

3

 NAPAP, 1991.

4

 J. Baker et al., NAPAP SOS/T 13, 1990.

5

 J. Baker et al., NAPAP SOS/T 13, 1990.

6

 Rosseland, 1986.

7

 J. Baker et al., NAPAP SOS/T 13, 1990.

8

 Mills et al., 1987.

9

 NAPAP, 1991.

10 NAPAP, 1991.
Appendix E: Ecological Effects of Criteria Pollutants

E-3

pH

Decrease Decrease

Biological Effects Biological Effects

6.5 to 6.0 6.5 to 6.0 Small decrease in species richness of phytoplankton, zooplankton, and benthic invertebrate communities

resulting from the loss of some acid-sensitive species, but no measurable change in total com munity abundance

or production.

Som e adverse effects (decreased reproductive success) may occur for acid-sensitive fish species (e.g., fathead

minnow, striped bass).

6.0 to 5.5 6.0 to 5.5 Loss of sensitive species of minnows and dace, such as blacknose dace and fathead minnow; in some waters

decreased reproductive success of lake trout and walleye.

Distinct decrease in the species richness and change in species com position of the phytoplankton, zooplankton,

and benthic invertebrate communities.

Loss of a number of common invertebrate species from the zooplankton and benthic invertebrate com munities,

including zooplankton species such as Diaptomus silicis, Mysis relicta, Epischura lacustris; many species of

snails, clams, mayflies, and am phipods, and some crayfish.

Visual accumulations of filam entous green algae in the littoral zone of many lakes and in som e stream s.

5.5 to 5.0 5.5 to 5.0 Loss of several important sport fish species, including lake trout, walleye, rainbow trout, and sm allmouth bass;

as well as additional non-game species such as creek chub.

Continued shift in the species composition and decline in species richness of the phytoplankton, periphyton,

zooplankton, and benthic invertebrate communities; decreases in the total abundance and biom ass of benthic

invertebrates and zooplankton may occur in som e waters.

Loss of several additional invertebrate species com mon in oligotrophic waters, including Daphnia galeata

mendotae, Diaphanosoma leuchtenbergianum, Asplancha priodonta; all snails, most species of clams, and many

species of mayflies, stoneflies, and other benthic invertebrates.

Inhibition of nitrification.

Further increase in the extent and abundance of filamentous green algae in lake littoral areas and streams.

5.0 to 4.5 5.0 to 4.5 Loss of m ost fish species, including most important sport fish species such as brook trout and Atlantic salmon.

Measurable decline in the whole-system rates of decomposition of som e forms of organic m atter, potentially

resulting in decreased rates of nutrient cycling.

Substantial decrease in the number of species of zooplankton and benthic invertebrates, including loss of all

clams and many insects and crustaceans; measurable decrease in the total community biomass of zooplankton

and benthic invertebrates in most waters.

Further decline in the species richness of the phytoplankton and periphyton communities.

Reproductive failure of som e acid-sensitive species of am phibians such as spotted salamanders, Jefferson

salamanders, and the leopard frog.

Source: Baker, J. et al. (NAPAP SOS/T 13, 1990), p. 13-173.

Table E-1. Summary of Biological Changes with Surface Water Acidification.

regions of the U.S. are chronically acidic due to natural and anthropogenic causes. NAPAP defines acidic

conditions as occurring when the acid neutralizing

capacity11 (ANC) is below 0 μeq/L. Furthermore, approximately 20 percent of the streams and lakes in

these regions are considered to be extremely susceptible to acidity (defined as ANC <50 μeq/L) and

slightly more than half show some susceptibility to

acidification (defined as ANC <200 μeq/L).

In terms of the role of acid deposition as a causal

mechanism for the acidification of water bodies, it is

estimated that 75 percent of the 1,181 acidic lakes

and 47 percent of the 4,668 streams studied under

11 ANC is expressed in units of microequivalents per liter (μeq/L), where an equivalent ANC is the capacity to neutralize one

mole of H+ ions. Generally, waters with an ANC < 0 have corresponding pH values of less than 5.5 (L. Baker et al., NAPAP SOS/T

9, 1990).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-4

NAPAP receive their dominant source of acid anions

from atmospheric deposition (see Table E-2). On a

regional basis, the importance of acid deposition varies considerably, which is believed to result from regional differences in SOx

 and NOx emissions and differences in the biogeochemistry of individual watersheds. For acidic lakes (ANC <0), the regions that

appear most likely to be influenced by acid deposition include the Adirondacks and Mid-Atlantic Highland region, with acid deposition cited as the dominant source of acidity in 100 percent of the acidic lakes

studied (Table E-2). This is in stark contrast to the

West region, where none of the acidic lakes studied

were dominated by acid deposition (notably, the

sample size of lakes for this region was small to begin with). For acidic streams, the Mid-Atlantic Highland region contains the greatest proportion of streams

whose acidic inputs are dominated by acid deposition

(56 percent). This contrasts with acidic streams of

Florida, where the vast majority (79 percent) are acidic

primarily due to organic acids, rather than acid deposition.

Effects on Fish Habitat Quality

By combining information on relevant water

chemistry parameters (pH, aluminum, calcium), fish

toxicity models, and historical and current distributions of fish populations in the lakes and streams included in the National Surface Water Survey (NSWS),

NAPAP investigators estimated the proportion of

water bodies with water chemistry conditions that are

unsuitable for survival of various fish species.12 In

the Adirondack region, where the acidic lakes are

dominated by acid deposition, it is estimated that ten

percent of the lakes are unsuitable for the survival of

acid-tolerant fish species such as brook trout; twenty

percent of the lakes are estimated to be unsuitable for

Region Number of

Acidic

Waters

Deposition

Dominated 

(%)

Organic

Dominated 

(%)

Acid Mine

Drainage

Dominated 

(%)

Watershed

Sulfate

Dominated

(%)

LAKES

New England 173 79 21 --- ---

Adirondacks 181 100 --- --- ---

Mid-Atlantic Highlands 88 100 --- --- ---

Southeastern Highlands --- --- --- --- ---

Florida 477 59 37 --- 4

Upper Midwest 247 73 24 --- 3

West 15 --- --- --- 100

All Lakes 1,181 75 22 --- 3

STREAMS

Mid-Atlantic Highlands 2,414 56 --- 44 ---

Mid-Atlantic Coastal Plain 1,334 44 54 --- 2

Southeastern Highlands 243 50 --- 50 ---

Florida 677 21 79 --- ---

All Streams 4,668 47 27 26 <1

1

 Source: NAPAP 1991 (Table 2.2-3, p. 28).

Region Number of

Acidic

Waters

Deposition

Dominated 

(%)

Organic

Dominated 

(%)

Acid Mine

Drainage

Dominated 

(%)

Watershed

Sulfate

Dominated

(%)

LAKES

New England 173 79 21 --- ---

Adirondacks 181 100 --- --- ---

Mid-Atlantic Highlands 88 100 --- --- ---

Southeastern Highlands --- --- --- --- ---

Florida 477 59 37 --- 4

Upper Midwest 247 73 24 --- 3

West 15 --- --- --- 100

All Lakes 1,181 75 22 --- 3

STREAMS

Mid-Atlantic Highlands 2,414 56 --- 44 ---

Mid-Atlantic Coastal Plain 1,334 44 54 --- 2

Southeastern Highlands 243 50 --- 50 ---

Florida 677 21 79 --- ---

All Streams 4,668 47 27 26 <1

1

 Source: NAPAP 1991 (Table 2.2-3, p. 28).

Table E-2. Comparison of Population of Acidic National Surface Water Survey (NSWS) by

Chemical Category.1

12 NAPAP, 1991.
Appendix E: Ecological Effects of Criteria Pollutants

E-5

the survival of acid-sensitive species such as minnows.

About two percent and six percent of the lakes in the

New England region are estimated to be unsuitable

for acid-tolerant and acid-sensitive fish species, respectively. A greater proportion of streams in the MidAtlantic Highland region are estimated to be unsuitable for acid-tolerant and acid-resistant fish species

(18 percent and 30 percent, respectively); however,

about 44 percent of streams surveyed in this region

are thought to be heavily influenced by acid mine

drainage (Table E-2).

Economic Damages to Recreational Fishing

In an effort to assess some of the impacts from

existing levels of acid deposition to public welfare,

NAPAP investigated the current economic damages

associated with acid deposition to trout anglers of New

York, Maine, Vermont, and New Hampshire. The

general approach used consisted of linking the catch

per unit effort (CPUE) for four species of trout at individual lakes (estimated using participation survey

data) to the relevant water quality conditions at these

lakes (namely, the acid stress index or ASI). Using

historical water quality data, critical water quality

conditions (i.e., the ASI values) were estimated for

lakes in the absence of acid deposition and compared

to current conditions reflecting the presence of acid

deposition. Using two types of travel cost models, the

Random Utility Model (RUM) and Hedonic travelcost model (HTCM), estimates of the willingness to

pay (WTP) per trip of sampled trout anglers were obtained. Aggregate estimates of the WTP were obtained

across the populations of trout anglers using statistical weighting factors. Finally, the difference in total

WTP between the current (acid deposition) scenario

and the historical (acid deposition-free) scenarios was

determined.

The resulting estimates of economic damages to

trout anglers in the four state region are relatively

small. Specifically, damage estimates range from $0.3

million to $1.8 million (in 1989 dollars) for the hedonic travel-cost and random utility models, respectively. By many accounts, these estimates can be considered to underestimate actual damages to anglers in

these states. First, data limitations precluded the development of meaningful WTP estimates for brook

trout anglers, which may be a significant component

of trout fishing in these areas. Second, resource constraints necessitated exclusion of a large population

of trout anglers (i.e., those residing in New York City).

Third, the economic damage estimates were limited

to trout anglers, thus excluding potentially similar if

not greater economic damages to anglers fishing for

other coldwater or warmwater fish species. In addition, the NAPAP analysis was performed in the context of recreational fishing in lakes, thereby excluding potentially important welfare impacts from recreational fishing in streams. Finally, these estimates do

not address non-use values of lakes in this region.

Benefits From Acid Deposition Avoidance Under

the CAA

It is currently estimated that in the absence of

pollution reductions achieved under the Clean Air Act,

total sulfur emissions to the atmosphere would have

increased by nearly sixteen million tons by 1990, a 40

percent increase above 1990 levels estimated with

CAA controls remaining in place.13 Based on atmospheric transport and deposition modeling, this increase in sulfur emissions corresponds to an approximate 25 to 35 percent increase in total sulfur deposition (wet & dry) in large portions of the northeastern

portion of the United States.14 Given sulfur emission

and deposition changes of this magnitude, and the

importance of sulfur emissions in contributing to acid

deposition, one would expect some benefits to human

welfare to be achieved as a result of improved quality

of aquatic ecosystems. To date, however, no formal

benefits assessment of CAA-avoided acid deposition

impacts has been conducted for aquatic ecosystems.

Nevertheless, past benefit assessments involving acid

deposition impacts on aquatic ecosystems provide

some opportunity to gain insights into the relative

magnitude of certain aquatic-based benefits that may

be achieved through pollution reductions under the

CAA.15

Recreational Fishing

NAPAP evaluated the impact of changes in acid

deposition on use values of aquatic ecosystems (i.e.,

recreational fishing).16 In their integrated assessment,

NAPAP valued the impacts of three different sulfur13 U.S. EPA, 1995; Table B-2.

14 U.S. EPA 1995, p. 3-10.

15 See, for example, NAPAP, 1991.

16 NAPAP, 1991.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-6

induced acid deposition scenarios to trout anglers from

NY, VT, NH and ME.17 The three scenarios evaluated were:

1. No change in acid deposition.

2. A 50 percent reduction in acid deposition.

3. A 30 percent increase in acid deposition.

As described above, equations were developed by

NAPAP to estimate the catch per hour for species at

each lake as a function of the ASI value for each lake

and of the technique of the fishers. Baseline and predicted changes in CPUE were evaluated for all lakes

modeled in the region. Willingness-to-pay estimates

for CPUE per trip were derived for the baseline and

sulfur emission scenarios using two travel-cost models, a random utility model and a hedonic travel cost

model. These willingness-to-pay estimates were then

combined with the results of a participation model

that predicted the total number of trips taken by trout

anglers. Total welfare changes were determined over

a 50 year period (from 1990 to 2040).

At current levels of acid deposition, NAPAP estimates that trout anglers in these four states will experience annual losses by the year 2030 of $5.3 or $27.5

million (in 1989 dollars) for the random utility model

and hedonic travel cost model, respectively (see Table

E-3). If acid deposition increases by 30 percent, which

roughly corresponds to the 25 to 35 percent increase

predicted for the northeast U.S. in the absence of CAA

sulfur controls,18 the resulting economic losses to trout

anglers in 2030 would range from $10 million to nearly

$100 million annually (in 1989 dollars) for the RUM

and HTCM, respectively. If deposition decreases by

50 percent, annual benefits to recreational anglers are

estimated to be $14.7 million (RUM) or $4.2 million

(HTCM).

While an estimation of CAA-related benefits to

trout anglers based on the 30 percent increase in acid

deposition scenario has some appeal, a strict transfer

of these benefits to the section 812 retrospective analysis is hindered by several factors. First, the NAPAP

benefits estimates are projected for future conditions

(the year 2030). Therefore, the extent to which the

NAPAP benefits reflect conditions and benefits in

1990 (the focus of the section 812 retrospective assessment) is unclear. Second, the NAPAP and CAA

section 812 analyses operate from different baselines

(1990 for the NAPAP study versus 1970-1990 for the

section 812 study). However, the NAPAP estimates

of annual benefits of $10 to $100 million provide a

rough benchmark for assessing the likely magnitude

of the avoided damages to an important and sensitive

recreational fishery in a four-state area most impacted

by surface water acidification from atmospheric deposition.

Eutrophication

Eutrophication is the process by

which aquatic systems respond to nutrient enrichment. The most common

nutrients involved in eutrophication are

nitrogen and phosphorous (and related

chemical species). When water bodies receive excessive amounts of nutrients, adverse impacts on their resident species and on ecosystem functions can occur from excessive algal

growth and the reduction in dissolved

oxygen caused by decaying algal biomass. Under highly eutrophic conditions, excessive nutrients can cause

depleted oxygen levels that result in

subsequent loss of economically important benthic organisms (shellfish),

fish kills, and changes in phytoplankton, zooplankton, and fish community

Study Use

Value

Scenario Modeled Meth od Annual Benefits

NAPAP 

(1991)

Trout

Fishing 

No change in acid

deposition

RUM

HT CM

-$5.3 million

- $27.5 million

(NY,

ME, VT,

NH)

50% decrease in acid

deposition

RUM

HT CM

$14.4 million

$4.2 million

30% increase in acid

deposition

RUM

HT CM

-$10.3 million

-$97.7 million

No new emission

reductions after 1985

RUM

HT CM

-$5.5 million

-$3.5 million

10 million ton reduction

of SO2 from 1980 levels

by 2000

RUM

HT CM

$9.7 million

$4.4 million

Study

Value

Scenario Modeled Meth od Annual Benefits

NAPAP 

(1991)

Trout

Fishing 

No change in acid

deposition

RUM

HT CM

-$5.3 million

- $27.5 million

(NY,

ME, VT,

NH)

50% decrease in acid

deposition

RUM

HT CM

$14.4 million

$4.2 million

30% increase in acid

deposition

RUM

HT CM

-$10.3 million

-$97.7 million

No new emission

reductions after 1985

RUM

HT CM

-$5.5 million

-$3.5 million

10 million ton reduction

of SO2 from 1980 levels

by 2000

RUM

HT CM

$9.7 million

$4.4 million

Table E-3. Results from Benefits Assessments of Aquatic

Ecosystem Use Values from Acid Deposition Avoidance.

17 NAPAP, 1991; p. 383-384.

18 U.S. EPA, 1995.
Appendix E: Ecological Effects of Criteria Pollutants

E-7

composition.19 Nuisance algal blooms can have numerous economic and biological costs, including water quality deterioration affecting biological resources,

toxicity to vertebrates and higher invertebrates, and

decreased recreational and aesthetic value of waters.20

Although severe eutrophication is likely to adversely

affect organisms, especially fish, a moderate increase

in nutrient levels may also increase fish stocks, by

increasing productivity in the food chain.21

Atmospheric Deposition and Eutrophication

The deposition of NOx

 in aquatic systems and their

watersheds is one source of nitrogen that may contribute to eutrophication. The relative importance of

NOx

 deposition as a contributor to aquatic eutrophication depends on the extent to which the productivity of an aquatic ecosystem is limited by nitrogen availability and the relative importance of nitrogen deposition compared to other internal and external sources

of nitrogen to the aquatic ecosystem. Furthermore,

the vulnerability of aquatic ecosystems to eutrophication is known to vary seasonally and spatially, although these systems are affected by nutrient deposition throughout the year. In general, freshwater ecosystems appear to be more often limited by phosphorus, rather than nitrogen, and are not as likely to be

heavily impacted by nitrogen deposition compared to

some estuarine and coastal ecosystems.22 In contrast

to acidification of streams and lakes, eutrophication

from atmospheric deposition of nitrogen is more commonly found in coastal and estuarine ecosystems,

which are more frequently nitrogen-limited.23

Unfortunately, there is limited information with

regard to the relative importance of atmospheric deposition as a nitrogen source in many estuarine and marine ecosystems. Estimates of the importance of atmospheric nitrogen deposition are difficult to make

because of uncertainties in estimating deposition, especially dry deposition, as well as watershed nitrogen

retention.24 Paerl (1993) reviews the importance of

atmospheric nitrogen deposition as a contributor to

eutrophication of coastal ecosystems; he concludes

that 10 to 50 percent of the total nitrogen loading to

coastal waters is from direct and indirect atmospheric

deposition. Estimates for the economically important Chesapeake Bay indicate that about 25 to 40 percent of the nitrogen loadings to the bay occur via atmospheric deposition.25 Hinga et al. (1991) estimate

that anthropogenic deposition provides 11 percent of

total anthropogenic inputs of nitrogen in Narragansett

Bay, 33 percent for the New York Bight, and 10 percent for New York Bay. Fisher and Oppenheimer

(1991) estimate that atmospheric nitrogen provides

23 percent of total nitrogen loading to Long Island

Sound and 23 percent to the lower Neuse River in

North Carolina. Information on the importance of

atmospheric nitrogen deposition for most other U.S.

coastal ecosystems is not available in the literature.

Episodic atmospheric inputs of nitrogen may be an

important source of nitrogen to nutrient-poor marine

ecosystems, such as the North Atlantic near Bermuda

and the North Sea.26

Valuing Potential Benefits from Eutrophication

Avoidance Under the CAA

It is currently estimated that in the absence of

pollution reductions achieved under the Clean Air Act,

total nitrogen emissions to the atmosphere would have

increased by nearly 90 million tons by 1990, a twofold increase above 1990 levels estimated with CAA

controls remaining in place.27 However, the ability

to determine the potential economic benefit from such

a reduction in nitrogen emissions is heavily constrained by gaps in our current biological and economic knowledge base of aquatic ecosystems.

One water body that has received much study in

the area of nitrogen-induced eutrophication is Chesapeake Bay. As previously discussed, it is estimated

that atmospheric deposition of nitrogen contributes

approximately 25 percent to the total nitrogen load19 Paerl, 1993.

20 Paerl, 1988.

21 Hansson and Rudstam, 1990; Rosenberg et al., 1990; Paerl, 1993.

22 Hecky and Kilham, 1988; Vitousek and Howarth, 1991.

23 U.S. EPA, 1993; Paerl, 1993.

24 U.S. EPA, 1993.

25 U.S. EPA, 1994.

26 Owens et al., 1992.

27 U.S. EPA, 1995; Table B-3.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-8

ings to the bay.28 In deposition terms, an estimated

15 to more than 25 percent increase in total nitrogen

deposition has been forecast in the Chesapeake Bay

watershed by 1990 in the absence of CAA pollution

controls.29 These results are based on an estimated

40,000 tons of atmospherically deposited nitrogen (as

nitrate and ammonia) to Chesapeake Bay in 1985,30

which means a 20 percent increase in atmospheric

deposition would amount to approximately 8,000 additional tons.

One indirect method available to gauge the potential economic relevance of avoidance of such atmospheric nitrogen loadings to Chesapeake Bay is

through the avoidance cost of nitrogen controls. However, such an assessment is difficult due to the site,

facility, and treatment-specific variation in treatment

costs. For example, Camacho (1993) reviewed nitrogen treatment costs for chemical treatment of water

from important point sources (mostly public owned

treatment works) and found that costs ranged from

$9,600 to $20,600 per ton (annual costs, 1990 dollars), depending on the facility evaluated. Biological

treatment of nitrogen from point sources was far more

expensive, varying from $4,000 to $36,000 per ton.

For control of non-point source loading, values of nitrogen removal practices ranged from $1,000 to

$285,000 per ton.31 Taking chemical addition as one

possible example, the avoided costs of treatment of

8,000 tons of nitrogen would range from about $75

million to about $170 million annually (in 1990 dollars).

Mercury

Mercury, in the form of methyl mercury, is a neurotoxin of concern and can accumulate in tissue of

fish to levels that are hazardous to humans and aquaticfeeding wildlife in the U.S. In relation to the section

812 CAA retrospective analysis, mercury is of interest for two reasons. First, potential benefits to human

welfare may have occurred as a result of mercury

emission controls implemented under EPA’s National

Emission Standards for Hazardous Air Pollutants

(NESHAP). Second, experimental and observational

evidence suggests that acidification of water bodies

enhances mercury accumulation in fish tissues.32

Therefore, CAA-mandated reductions in sulfur and

nitrogen oxide emissions and subsequent acid deposition may have resulted in indirect benefits from a

reduction in mercury accumulation in fish and subsequent improvements to human health and welfare.

The accumulation of mercury to hazardous levels

in fish has become a pervasive problem in the U.S.

and Canada. A rapid increase in advisories occurred

during the 1980s, including a blanket advisory affecting 11,000 lakes in Michigan.33 The Ontario Ministries of Environment and Natural Resources (1990)

recommend fish consumption restrictions for 90 percent of the walleye populations, 80 percent of smallmouth bass populations, and 60 percent of lake trout

populations in 1,218 Ontario lakes because of mercury accumulation. In many instances, mercury has

accumulated to hazardous levels in fish in highly remote water bodies that are free from direct aqueous

discharges of mercury.34 Mass balance studies have

shown that atmospheric deposition of mercury can

account for the accumulation of mercury in fish to

high levels in lakes of these remote regions.35 The

potential impacts of mercury on the health of humans

and fish-eating (piscivorous) wildlife has lead EPA

to recently establish water quality criteria to protect

piscivorous species in the Great Lakes.36

Although mercury accumulation in fish via atmospheric deposition is now widely recognized as a potential hazard to human health and certain wildlife

species, studies establishing quantitative linkages between sources of mercury emissions, atmospheric

deposition of mercury, and subsequent accumulation

in fish are lacking. Thus at the present time, we are

unable to quantify potential benefits from CAAavoided mercury accumulation in fish of U.S. water

28 U.S. EPA, 1993.

29 U.S. EPA 1995, Figure C-6.

30 NERA, 1994.

31 Shuyler, 1992.

32 Bloom et al., 1991; Watras and Bloom, 1992; Miskimmin et al., 1992; Spry and Wiener, 1991; Wiener et al., 1990.

33 Watras et al., 1994.

34 Glass et al., 1990; Sorenson et al., 1990; Grieb et al. 1990; Schofield et al. 1994.

35 Fitzgerald et al. ,1991.

36 U.S. EPA, 1995.
Appendix E: Ecological Effects of Criteria Pollutants

E-9

bodies. Given the pervasiveness of the mercury problem with CAA-pollution controls, potential benefits

to human health and welfare from avoidance of further mercury related damages to aquatic ecosystems

could be substantial.

It should also be noted that atmospheric deposition is a major contributor to surface water loads of

other toxic pollutants as well. For example, scientists

believe that about 35 to 50 percent of the annual loadings of a variety of toxic chemicals to the Great Lakes

may be from the air; for lead, atmospheric deposition

currently accounts for an estimated 95 percent of the

total load in the Great Lakes.37 CAA-related reductions in air emissions of toxic pollutants (such as lead)

undoubtedly reduced the loading of these chemicals

to the Great Lakes and other water bodies; the magnitude of the benefits of reducing these exposures to

humans and wildlife is not known.

Benefits from Avoided Damages

to Wetland Ecosystems

Introduction

This review addresses the effects of air pollutants

on wetland ecosystems; the focus is on acidification

and nutrient loading. Valuable service flows of wetland ecosystems include flood control, water quality

protection and improvement, wildlife and fish habitat, and biodiversity. The limited scientific evidence

suggests that air pollutants may most affect

biodiversity, in particular because of nutrient loading

through nitrogen deposition.

Wetlands are broadly characterized as transitional

areas between terrestrial and aquatic systems in which

the water table is at or near the surface or the land is

periodically covered by shallow water.38 Types of

wetlands include swamps (forested wetlands), marshes

(herbaceous vegetation), and peatlands, which are

wetlands that accumulate partially decayed vegetative matter due to limited decomposition.39 Peatlands

include bogs and fens. Bogs receive water solely from

precipitation, are generally dominated by Sphagnum

moss, and are low in nutrients. Fens receive water

from groundwater and precipitation, contain more

marsh-like vegetation, and have higher pH and nutrient levels than bogs.40 Most of the limited work on

the effects of atmospheric deposition on wetlands has

been done in peatlands, specifically in Europe, where

levels of atmospheric deposition are generally much

higher than in the U.S.

The air pollutants of greatest concern with respect

to effects on wetland ecosystems are oxides of nitrogen (NOx

) and oxides of sulfur (SOx), primarily sulfur dioxide (SO2

). Air pollutants may affect wetland

ecosystems by acidification of vulnerable wetlands

and by increasing nutrient levels. Acidification in

vulnerable wetlands may affect vegetation adversely,

as appears to have occurred in Europe. In wetlands

where nitrogen levels are low, increased nitrogen

deposition may alter the dynamics of competition

between plant species. Species adapted to low-nitrogen levels, including many endangered species, may

decrease in abundance.41

Effects of Acidification

Limited evidence suggests that acidic deposition

and decreased pH may harm certain wetland plants,

alter competitive relations between wetland plants and

cause changes in wetland drainage and water retention.

Work concerning the possible acidification of

peatlands is inconclusive. Acidic deposition is unlikely to result in displacement of base cations from

cation exchange sites in bogs, and therefore it will not

cause a drop in pH.42 Peatland sediments are low in

Al3+, so mobilization of toxic aluminum is not a concern as it is in forest soils and aquatic ecosystems.43

Acidification might affect certain fen ecosystems.

Gorham et al. (1984) have hypothesized that acidic

deposition could leach base cations from mineral-poor

fens and decrease pH levels. This could result in a

37U.S. EPA, 1994.

38 Cowardin et al., 1979.

39 Mitsch and Gosselink, 1986.

40 Mitsch and Gosselink, 1986.

41 U.S. EPA, 1993.

42 Gorham et al., 1984.

43 Turner et al., NAPAP SOS/T 10, 1990.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-10

transition to bog vegetation such as Sphagnum and

away from sedge meadow vegetation. At this time,

this remains a hypothesis; however, pH did not decrease in a mineral-poor Ontario fen during a fouryear period in which researchers experimentally increased acidic deposition.44

In European wetlands affected by high levels of

deposition for many years, acidic deposition has seriously affected wetland vegetation. Roelofs (1986)

reports that acidification of heath pools in the Netherlands has caused a change in species composition with

Sphagnum and rushes replacing the original vegetation. Likewise, significant declines in Sphagnum in

British bogs have occurred in areas affected by 200

years of atmospheric pollution, including nitrogen

deposition.45 It is unclear how such changes have affected wetland service flows apart from the effects on

biodiversity; however, water retention has decreased

and significant erosion has occurred in seriously perturbed British bogs near Manchester and Liverpool.46

Effects of Nutrient Loading

Atmospheric deposition may affect wetlands by

increasing the level of nutrients, particularly nitrogen,

in wetlands. Sulfur is not a limiting nutrient in

peatlands,47 but nitrogen commonly limits plant

growth.48 The effects of increased nitrogen levels in

wetlands include an increased threat to endangered

plant species and possible large-scale changes in plant

populations and community structure. Endangered

and threatened plant species are common in wetlands,

with wetland species representing 17 percent of the

endangered plant species in the U.S. (U.S. EPA, 1993).

These plants are often specifically adapted to low nitrogen levels; examples include isoetids49 and insectivorous plants.50 In eastern Canadian wetlands, nationally rare species are most common in infertile

sites.51 When nitrogen levels increase, other species

adapted to higher levels of nitrogen may competitively

displace these species. Thus, NOx

 emissions that increase nitrogen levels in nitrogen-poor wetlands may

increase the danger of extinction for threatened and

endangered species.

By changing competitive relations between plant

species, increased nitrogen deposition may broadly

affect community structure in certain wetlands. Common species that thrive in nitrogen-poor wetlands may

become less abundant. Many nitrogen-poor bogs in

the northern U.S. are dominated by Sphagnum species. These species capture low levels of nitrogen from

precipitation. Increased nitrogen levels may directly

harm Sphagnum and cause increased nitrogen to be

available to vascular plants that may out compete Sphagnum.

52 Studies in Great Britain have documented

large declines in Sphagnum moss because of atmospheric pollution;53 nitrogen loading may play an important role in these declines. However, Rochefort et

al. (1990) document limited effects of fertilization

from experimentally-increased NO3

-

 and SO4

2- deposition on an Ontario mineral-poor fen over a four-year

period, apart from initially increased Sphagnum

growth. Thus, increased nitrogen loading might adversely or beneficially affect wetland plants depending on baseline nitrogen concentrations in the wetland, atmospheric nitrogen loading, and species requirements for and sensitivity to nitrogen.

Increases in nitrogen levels due to NOx

 emissions

will have the greatest effect on wetlands that are extremely nitrogen-limited and that receive small

amounts of nitrogen naturally. Since bogs, including

Sphagnum bogs, receive little surface water runoff,

they get most of their nutrient and water loadings

through precipitation. These bogs may receive a total

of approximately 10 kg nitrogen per hectare per year

(kg N/ha/yr), which is one to two orders of magnitude

less nitrogen than other freshwater wetlands and

44 Rochefort et al., 1990.

45 Lee et al., 1986.

46 Lee et al., 1986.

47 Turner et al., NAPAP SOS/T 10, 1990.

48 U.S. EPA, 1993.

49 Boston, 1986.

50 Moore et al., 1989.

51 Moore et al., 1989; Wisheu and Keddy, 1989.

52 Lee & Woodin 1988, Aerts et al., 1992.

53 Ferguson et al., 1984; Lee et al., 1986.
Appendix E: Ecological Effects of Criteria Pollutants

E-11

saltmarshes receive.54 As atmospheric deposition of

nitrogen has been estimated to be at least 5.5 to 11.7

kg N/ha/yr,55 changes in NOx

 emissions would most

likely affect these bogs. The results of a model by

Logofet and Alexandrov (1984) suggest that a treeless, nutrient-poor bog may undergo succession to a

forested bog because of the input of greater than 7 kg

N/ha/yr.

As in freshwater wetlands, significantly increased

nitrogen deposition to coastal wetlands will increase

productivity and alter the competitive relationships

between species.56 However, studies showing this

increased productivity have used 100 to 3000 kg N/

ha/yr.57 Therefore, limited changes in NOx

 emissions

may not affect coastal wetland productivity.

Summary of Wetland Ecosystem Effects

The effects of air pollutants on wetlands have received little attention, in contrast to the large body of

work on the effects of acid rain on aquatic and forest

ecosystems. Little evidence exists suggesting that

acidification due to atmospheric deposition is a major threat to wetlands. In particular, peatlands are

naturally acidic, although mineral-poor fens may be

at risk from acidification. Nitrogen loading may alter

community composition in wetlands naturally low in

nutrients, such as bogs. Nitrogen loading may threaten

rare species adapted to low nitrogen levels. In Britain

and The Netherlands, heavy atmospheric deposition

over a long period appears to have caused serious declines in Sphagnum in peatlands.

Air pollutants appear to most seriously threaten

rare and endangered species, biodiversity, and community composition in wetlands, particularly bogs.

These changes are difficult to associate with changes

in economic value; even the qualitative nature of the

effects is uncertain. Air pollutants may not significantly affect such important wetland service flows as

flood control, water quality protection, and wildlife

habitat in most wetlands, so the impacts on the more

readily monetized aspects of the economic value of

wetlands may be limited.

Benefits from Avoided Damages

to Forests

Introduction

Forests occupy 33 percent of the land mass in the

U.S. (some 738 million acres) and provide a wealth

of services to the U.S. population.58 Notable services

provided by forests include timber production, recreational opportunities such as hunting, camping, hiking, and wildlife observation, water quality protection, nutrient removal and cycling, flood control, erosion control, temporary carbon sequestration, preservation of diversity, and existence values. In 1991,

hunting participation alone accounted for 236 million

recreation days that included 214 million person trips

with estimated expenditures valued at $12.3 billion.59

The Clean Air Act-regulated pollutants of greatest concern with respect to effects on forest ecosystems are oxides of sulfur (SOx

), primarily sulfur dioxide (SO2

), oxides of nitrogen (NOx), and volatile

organic compounds (VOCs). While extremely high

ambient concentrations of SO2

 and NOx may directly

affect vegetation, such effects are uncommon in the

U.S.;60 the indirect effects of these pollutants are of

greater concern. Specifically, emissions of SO2

 and

NOx

 are known to contribute to acid deposition in

portions of the United States, with SO2

 contributing

75 to 95 percent of the acidity in rainfall in the eastern U.S.61 Acid deposition is of concern to forests

primarily from the acidification of soils (i.e., by reducing seed germination, altering nutrient and heavy

metal availability). Direct foliar damage can occur

from precipitation with extremely low pH levels (i.e.,

3.0-3.6 and below), although these levels are lower

than ambient levels in the U.S.62 VOCs and NOx

 are

54 U.S. EPA, 1993.

55 U.S. EPA, 1993.

56 U.S. EPA, 1993.

57 U.S. EPA, 1993.

58 Powell et al. 1993.

59 U.S. DOI, 1993.

60 Shriner et al., NAPAP SOS/T 18, 1990.

61 NAPAP, 1991.

62 Shriner et al., NAPAP SOS/T 18, 1990.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-12

important precursors to ozone formation, which can

affect leaf photosynthesis and senescence and decrease

cold hardiness, thereby causing deleterious impacts

on tree growth, survival and reproduction. Deposition of NOx

 may also alter the nutrient balance of forest soils, which in turn might alter the competitive

relationships between tree species and affect species

composition and diversity.63

Current Air Pollutant Effects on Forests

Acid Deposition Impacts

In 1985, NAPAP organized the Forest Response

Program (FRP) to evaluate the significance of forest

damage caused by acidic deposition, the causal relationships between air pollutants and forest damage,

and the dynamics of these relationships regionally.

Research was focussed on four forest regions: Eastern Spruce-Fir, Southern Commercial Forests, Eastern Hardwoods, and Western Conifers. With the exception of high-elevation spruce-fir forests, the available evidence suggests that acidic deposition does not

currently affect these forests and that observed declines in sugar maple and southern pines are not due

to acidic deposition.64

Circumstantial evidence suggests that acidic deposition may affect high-elevation spruce-fir forests in

the northeastern U.S. These forests have extensive

contact with acidic cloud water.65 Experimental evidence suggests that acidic deposition may affect cold

hardiness in red spruce, an important component of

the spruce-fir forest. Significant declines in red spruce

growth and in its importance in the forest have occurred in New York and northern New England. The

proximate cause of death of red spruce in the region

is pathogens and insects; acidic deposition may interact with these biological stresses and with weatherinduced stress to produce adverse effects in red spruce.

Ozone may also play a role in red spruce decline in

this region.66 Available evidence suggests that soil

aluminum and soil pH levels have not affected red

spruce adversely.67

Ozone Impacts

Experimental Evidence

For practical reasons, the majority of experimental evidence linking ozone exposure to damage to tree

species has been derived from studies of individual

plants, especially seedling and branch studies.68 Results from these studies suggest that ozone exposure

can reduce photosynthesis and increase senescence in

leaves. Subsequently, such effects from ozone may

alter the carbohydrate allocation to plant tissues such

as roots, which may affect plant growth and cold hardiness. Decreases in cold tolerance may be particularly important for trees in northern latitudes and high

elevations. Recent work on quantifying the relationship between ozone exposure and plant responses suggest that seedlings of aspen, ponderosa pine, black

cherry, tulip poplar, sugar maple, and eastern white

pine seedlings may experience biomass reductions of

approximately 10 percent at or near ambient ozone

exposures.69 Because trees are perennials, the effect

of even a 1-2 percent per year loss in seedling biomass (versus 10 to 20 percent yield loss in crops), if

compounded over multiple years under natural field

conditions of competition for resources, could be severe.

Although indicative of short-term relative response to ozone exposure, results from these experiments are unable to provide reliable information on

the long-term effects of ozone on forests. This limitation arises because the effects of ozone on forests

will depend on both the response of individual plants

to ozone exposure and the response of populations of

plants, which interact with their environment. Population response will be altered by the varying intraspecific genetic susceptibility to ozone. Individual plant

response will also be affected by many environmental factors, including insect pests, pathogens, plant

symbionts, competing plants, moisture, temperature,

light, and other pollutants. Consistent evidence on

the interaction of ozone with other environmental factors is lacking. Furthermore, most experimental stud63 U.S. EPA, 1993.

64 Barnard et al., NAPAP SOS/T 16, 1990; NAPAP, 1991.

65 Barnard et al., NAPAP SOS/T 16, 1990.

66 Shriner et al., NAPAP SOS/T 18, 1990.

67 Barnard et al., NAPAP SOS/T 16, 1990.

68 U.S. EPA, 1996a.

69 Hogsett et al. , 1995.
Appendix E: Ecological Effects of Criteria Pollutants

E-13

ies have only studied exposure for one growing season; effects on forest species may occur over decades.70 Therefore, considerable uncertainties occur

in scaling across individuals of different ages, from

individuals to populations and communities, and

across time.

Observational Evidence

Studies of the forests of the San Bernardino Mountains provide the strongest case for linking ozone exposure to damages to an entire forest ecosystem. These

forests have been exposed to extremely high ambient

ozone levels over the past 50 years due to their proximity to the Los Angeles area. The area has been extensively studied regarding the effects of ozone, as

described in U.S. EPA (1996a). The ecosystem has

been seriously affected by ozone pollution, with the

climax-dominant, but ozone-sensitive ponderosa pine

and Jeffrey pine declining in abundance, replaced by

more ozone-tolerant species. These sensitive species

have experienced decreased growth, survival, and reproduction, and susceptibility to insects. The effects

of ozone on these species have resulted in other ecosystem effects, including the buildup of a large litter

layer, due to increased needle senescence. The decline of the fire-tolerant ponderosa and Jeffrey pines

may seriously affect the fire ecology of the ecosystem, with fire-sensitive species becoming more common. Ozone concentrations have been declining in

recent decades, and crown injury of ponderosa and

Jeffrey pine has decreased. However, the two species

have continued to decline in abundance, as measured

by total basal area, compared with other species over

the period 1974 to 1988.71 The nature of community

dynamics, particularly in mixed species, uneven aged

stands, indicates that subtle long-term forest responses

(e.g., shifts in species composition) to elevated levels

of a chronic stress like exposure to ozone are more

likely than wide-spread community degradation.72

Limited field studies have been completed in other

forest ecosystems. Foliar injury has been observed in

the Jefferson and George Washington National Forests and throughout the Blue Ridge Mountains, including areas of the Shenandoah National Park.73 In

the Great Smoky Mountains National Park, surveys

made in the summers from 1987 through 1990 found

95 plant species exhibited foliar injury symptoms consistent with those thought to be caused by ozone.74

Foliar ozone injury has also been documented in National Parks and Forests in the Sierra Nevada mountains.75

Growth and productivity of seedlings have been

reported to be affected by ozone for numerous species in the Blue Ridge Mountains of Virginia. In the

Shenandoah National Park, Duchelle et al. (1982,

1983) found that tulip poplar, green ash, sweet gum,

black locust, as well as several evergreen species (e.g.,

Eastern hemlock, Table Mountain pine, pitch pine,

and Virginia pine), common milkweed, and common

blackberry all demonstrated growth suppression of

seedlings. Except for the last two species mentioned,

almost no visible injury symptoms accompanied the

growth reductions. Studies of mature trees in the

Appalachian Mountains also indicate that injury associated with exposure to ozone and other oxidants

has been occurring for many years.76 Researchers have

also found that major decreases in growth occurred

for both symptomatic and asymptomatic trees during

the 1950s and 1960s in the Western U.S.77 The adverse response of a number of fruit and nut trees to

ozone exposure has been reported.78

Monitoring by the USDA Forest Service shows

that growth rates of yellow pine in the Southeast have

been decreasing over the past two decades in natural

stands but not in pine plantations.79 Solid evidence

linking this growth reduction to air pollutants is lack70 U.S. EPA, 1996a.

71 Miller et al., 1989 and Miller et al., 1991.

72 Shaver et al., 1994

73 Hayes and Skelly, 1977; Skelly et al., 1984

74 Neufeld, et al., 1992

75 Peterson and Arbaugh, 1992

76 Benoit et al., 1982

77 Peterson et al., 1987; Peterson and Arbaugh, 1988, 1992; Peterson et al., 1991

78 McCool and Musselman, 1990; Retzlaff et al., 1991, 1992a, b

79 NAPAP, 1991.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-14

ing, although ozone, in particular, may be a factor.80

Ambient ozone levels in the region are high enough

to damage sensitive tree species, including pine seedlings during experimental exposure.81 Due to the commercial importance of yellow pine, the economic impacts of ozone on forest ecosystems in this area could

be significant if ozone is affecting growth.

Although the ecosystem effects occurring in the

San Bernardino forest ecosystem have occurred at very

high ozone exposures, lower ozone exposure elsewhere in the U.S. may still affect forests. The EPA

Ozone Staff Paper82 assessed the risk to vegetation,

including forests, under current ambient air quality.

Using a GIS approach, it was found that under the

base year (1990) air quality, a large portion of California and a few localized areas in North Carolina and

Georgia have seasonal ozone levels above those which

have been reported to produce greater than 17 percent

biomass loss in 50 percent of studied tree seedling

species. A broader multistate region in the east is

estimated to have air quality sufficient to cause 17

percent biomass loss in seedlings, while at least a third

of the country, again mostly in the eastern U.S., most

likely has seasonal exposure levels which could allow up to 10 percent yield loss in 50 percent of studied seedlings. The Staff Paper did not present monetized benefits because of lack of exposure-response

functions.83

Even small changes in the health of ozone-sensitive species may affect competition between sensitive and tolerant species, changing forest stand dynamics.84 Depending on the sensitivities of individual

competing species, this could affect timber production either positively or negatively, and affect community composition and, possibly, ecosystem processes.

Endangered species

Ozone effects may also reduce the ability of affected areas to provide habitats to endangered species. For example, two listed endangered plant species, the spreading aven and Roan Mountain bluet,

are currently found at a small number of sites in eastern Tennessee and western North Carolina — forested

areas where ozone-related injury is of concern.85 In

addition, ozone-related effects on individual ozonesensitive species that provide unique support to other

species can have broader impacts. For example, one

such species is the common milkweed, long known

for its sensitivity to ozone and usefulness as an indicator species of elevated ozone levels, as well as being the sole food of the monarch butterfly larvae.

Thus, a major risk associated with of the loss of milkweed foliage for a season is that it might have significant indirect effects on the monarch butterfly population. A large number of studies have shown that

ozone-sensitive vegetation exists over much of the

U.S., with many native species located in forests and

Class I areas, which are federally mandated to preserve certain air quality related values.

Valuation of Benefits From CAAAvoided Damages to Forests

Background

To quantitatively assess the economic benefits of

avoided damages of relevant CAA pollutants to forests, it is necessary to link estimated changes in air

pollution to measures of forest health and conditions

that can be readily quantified in economic terms. For

commercial timber production, this would require

quantifying the relationship between atmospheric

deposition and measures of forest productivity such

as timber yield. For assessing recreational benefits,

linkages would have to be drawn between air pollution and vulnerable factors that influence forest-based

recreation (e.g., site-characteristics such as canopy

density, type of tree species, degree of visible tree

damage, etc.). While important strides have been

made in establishing these linkages (e.g., NAPAP

modeling of air pollution effects on forest soil chemistry and tree branch physiology), critical gaps in our

ability to predict whole tree and forest responses to

air pollution changes have precluded the establishment of such quantitative linkages.86 Critical knowl80 NAPAP, 1991.

81 NAPAP, 1991.

82 U.S. EPA, 1996b

83 U.S. EPA, 1996b.

84 U.S. EPA, 1996a.

85 U.S. EPA, 1996b

86 NAPAP, 1991.
Appendix E: Ecological Effects of Criteria Pollutants

E-15

edge gaps exist in our ability to extrapolate experimental results from seedling and branch studies to

whole tree and forest responses, to account for key

growth processes of mature trees, to integrate various

mechanisms by which air pollution can affect trees

(e.g., soil acidification, nitrification, and direct foliar

damage, winter stress, etc.), and to account for the

interaction of other stressors on forest health and dynamics (susceptibility to insect damage, drought, disease, fire, nutrient and light competition, etc.).

Despite these constraints to quantifying economic

benefits from air pollution reductions on forest ecosystems, relevant studies that have attempted to value

air pollution damages on forests are reviewed and

summarized below. In some cases, the relationship

between air pollution and forest response is estimated

using expert judgement (e.g., for NAPAP assessment

from various growth scenarios). In other cases, damage estimates reflect current impacts of air pollution

on forests, and the dose-response relationship is absent. In the aggregate, this summary provides some

insight into possible CAA-related benefits from

avoided damages to a select and narrowly focussed

group of forest services, but, because of severe data

constraints, does not provide an estimate of the overall range of forest-based benefits possible under the

CAA.

Commercial Timber Harvesting

The economic impact of hypothetical growth reductions in northeastern and southeastern trees (both

hardwood and softwood species) was intensively studied under NAPAP.87 Growth reductions ranging from

5 to 10 percent over a 5 to 10 year period, depending

on the species and location, were assumed to occur as

a result of all forms of air pollution based on expert

opinion derived from a survey by deSteigner and Pye

(1988). Timber market responses to these hypothesized growth declines were modeled until the year

2040 using a revised version of the Timber Assessment Market Model (TAMM90) and the Aggregate

Timberland Assessment System (ATLAS), which was

used to simulate timber inventories on private timberland in the United States. Economic welfare outputs included changes in consumer and producer surplus and changes in revenue to southeast stumpage

owners. Results indicate that annualized reductions

in consumer and producer surplus would total $0.5

billion by the year 2000 and $3 billion by the year

2040 (in 1967 dollars). Simulated effects on stumpage owners’ revenues were minimal ($10 to $20 million).

In an attempt to estimate the net economic damages from ozone effects on selected U.S. forests,

NAPAP studied the effect of various assumed reductions in growth rates of commercial southeastern pine

forests (both natural and planted).88 For both planted

and natural plus planted pines, the following changes

in growth rates were assumed to occur: a two percent

increase, no change, a two percent decrease, a five

percent decrease, and a ten percent decrease. The two

to five percent growth reductions were considered as

possible outcomes from current ozone induced damage to southeastern forests, although no quantitative

linkage between ozone exposure and damages was

established. The ten percent growth reduction scenario was primarily included for evaluating model

sensitivity to severe changes in growth and was considered out of the range of likely ozone damage estimates. The TAMM and ATLAS models were again

used to simulate timber market responses under

baseline and hypothesized growth change scenarios

from 1985 to 2040. Results indicate that annual

changes in total economic surplus (i.e., the sum of

consumer and producer surplus and timber owner revenues in 1989 dollars) would range from an increase

of $40 million (for the two percent increase in growth

scenario) to a decrease of $110 million (for the ten

percent decrease in growth scenario) for planted and

natural pine model simulations.

In the context of estimated benefits from avoidance of other damages in the absence of the Clean Air

Act from 1970 to 1990,89 the magnitude of economic

damages estimated to the commercial timber industry are comparatively small. For example, economic

damage estimates range up to $3 billion annually for

five to ten percent growth rate reductions in northeast

and southeast forests, and just $110 million for southeastern pines. However, in the context of damages to

forest-based services as a whole, the NAPAP-derived

commercial timber damage estimates should be

viewed as representing a lower bound estimate for a

variety of reasons. First, these damage estimates

exclude other categories of possible forest-based ben87 Haynes and Kaiser, NAPAP SOS/T 27 Section B, 1990.

88 NAPAP, 1991.

89 Most notably avoided human health effects, which are estimated on the order of $300 to $800 billion annually.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-16

efits, including recreational and non-use values. Second, even within the context of timber-related damages, the NAPAP forest-damage studies focused on a

portion of U.S. forests (northeastern and southeastern

U.S.); a much greater geographic range of forests could

become susceptible to timber-related damages in the

absence of CAA controls. Finally, the NAPAP damage estimates consider only two types of tree species:

planted and naturally grown pines, although these species are economically important. Damages to other

commercially harvested tree species, such as mixed

pine and hardwood forests, are therefore excluded.

Non-marketed Forest Services

In an effort to address the potential benefits resulting from avoidance of acid deposition-induced

damages to non-marketed forest-based services (e.g.,

recreation use, existence value), an extensive review

of the economic literature was conducted under the

auspices of NAPAP.90 From their review, NAPAP

could not identify any single study or model that could

be reliably used to quantify economic benefits from

avoided acid deposition-caused damages to non-marketed forest services (such as recreational use) on a

regional or national basis. The primary limitation in

many of the studies reviewed was the absence of a

quantitative linkage between the value of a recreational

user day and important site characteristics which could

be tied to air pollution effects. In addition, most studies were narrowly focused geographically to specific

sites and did not attempt to value system-wide (larger

scale) damages that could result from acid deposition

over an entire region. Since the availability of nearby

substitution sites will affect the recreational value for

a given site, the benefits from such site-specific studies may not reflect actual economic damages incurred

from wide-scale air pollution impacts on forests. The

inability of studies to consider additional crowding at

unaffected sites in addition to changes in recreational

participation rates as a function of air pollution damages was also recognized as an important limitation.

Despite not being able to quantitatively assess the

benefits from avoided acid deposition-induced damages to nonmarket forest services, several important

concepts emerge from NAPAP’s review of recreational benefits, that bear relevance to the section 812

retrospective analysis. First, several studies were identified that established a relationship between key forest site characteristics and the value of recreational

participation. For example, Brown et al. (1989) used

contingent valuation to evaluate the relationship between scenic beauty ratings and willingness of

recreationalists to pay at pictured sites. Based on their

interviews with over 1400 recreationalists at ten different sites in Arizona, positive correlations were established between scenic beauty rankings determined

from one group of recreationalists and willingness to

pay to recreate determined by a separate group of

recreationalists (r2

 ranged from 0.27 to 0.98 depending on ranking). In another study, Walsh et al. (1989)

developed a functional relationship between reduction of recreational benefits and tree density changes

that reflected varying levels of insect damage at six

campgrounds in the Front Range of the Colorado

Rockies. By using both contingent valuation and travel

cost models, Walsh et al. (1989) were able to show

that 10 percent, 20 percent, and 30 percent decreases

in tree densities reduces the total recreational benefits

at their sites by 7 percent, 15 percent and 24 percent,

respectively. Although results from these studies are

limited to the sites from which they were derived, they

do support the intuition that the degree of visible damage to forests is to some extent correlated with the

magnitude of damages to forest-based recreation expected. This finding supports the notion that the avoidance of damages to forest ecosystems from CAA-induced pollution controls (albeit currently unquantified)

have likely benefited forest-based recreation in the

U.S.

In addition to establishing relationships between

recreational value and visible damage to forest sites,

there is evidence linking air pollution (ozone) effects

on forests to economic damages to non-use values of

forests. For example, D.C. Peterson et al. (1987) valued ozone-induced damages to forests surrounding the

Los Angeles area. Using contingent valuation methods, D.C. Peterson et al. (1987) surveyed

recreationalists (a random survey of households in the

San Bernardino, Los Angles and Orange counties) and

residents (a sample of property owners within the San

Bernardino and Angeles national forests) for their

willingness to pay to prevent forest scenes from degrading one step on a “forest quality ladder” depicting various levels of ozone-induced damages. The

mean willingness to pay to protect further degradation was $37.61 and $119.48 per household for

recreationalists and residents, respectively. Annual

damages to Los Angeles area residences from a onestep drop on the forest quality ladder were estimated

between $27 million and $147 million.

90 Rosenthal, NAPAP SOS/T 27 Section B, 1990.
Appendix E: Ecological Effects of Criteria Pollutants

E-17

These estimates cannot be directly translated into

a rough estimate of the potential non-use values of

avoided forest damages. Considering the limited size

of the population generating the estimated benefits of

forest degradation, however, they do provide evidence

that the recreational and non-use benefits may substantially exceed the commercial timber values.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-18

Ecosystem Effects References

Aerts, R., B. Wallen, and N. Malmer. 1992. Growthlimiting nutrients in Sphagnum-dominated

bogs subject to low and high atmospheric nitrogen supply. Journal of Ecology 80: 131-

140.

Baker, L.A., P.R. Kaufmann, A.T. Herlihy, J.M.

Eilers, D.F. Brakke, M.E. Mitch, R.J. Olson,

R.B. Cook, B.M. Ross-Todd, J.J. Beauchamp,

C.B. Johnson, D.D. Brown, and D.J. Blick.

1990. Current Status of Surface Water AcidBase Chemistry. NAPAP SOS/T Report 9,

In: Acidic Deposition: State of Science and

Technology, Volume II, National Acid Precipitation Assessment Program, 722 Jackson

Place NW, Washington, D.C. 20503.

Baker, J.P., D.P. Bernard, S.W. Christensen, M.J. Sale,

J. Freda, K. Heltcher, D. Marmorek, L. Rowe,

P. Scanlon, G. Suter, W. Warren-Hicks, and

P. Welbourn. 1990. Biological effects of

changes in surface water acid-base chemistry. NAPAP SOS/T Report 13, In: Acidic

Deposition: State of Science and Technology,

Volume II, National Acid Precipitation Assessment Program, 722 Jackson Place NW,

Washington, D.C. 20503.

Barnard, J.E., A.A. Lucier, R.T. Brooks, A.H. Johnson,

P.H. Dunn, and D.F. Karnosky. 1990.

Changes in forest health and productivity in

the United States and Canada. NAPAP SOS/

T Report 16, In: Acidic Deposition: State of

Science and Technology, Volume III, National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington,

D.C. 20503.

Benoit, L. F., J. M. Skelly, L. D. Moore, and L. S.

Dochinger. 1982. Radial growth reductions

in Pinus strobus L. correlated with foliar

ozone sensitivity as an indicator of ozone-induced losses in eastern forests. Can. J. For.

Res. 12:673-678.

Bloom N. S., C. J. Watras, and J. P. Hurley. 1991.

Impact of acidification on the methylmercury

cycle of remote seepage lakes. Water Air Soil

Pollution 56:477-491.

Boston, H.L. 1986. A discussion of the adaptations

for carbon acquisition in relation to the growth

strategy of aquatic isoetids. Aquatic Botany

26: 259-270.

Brown, T.C., M.T. Richards, and T.C. Daniel. 1989.

Scenic Beauty and Recreation Value: Assessing the Relationship. In: J. Vining, ed., Social Science and Natural Resources Recreation Management, Westview Press, Boulder,

Colorado.

Camacho, R. 1993. Financial Cost Effectiveness of

Point and Nonpoint Source Nutrient Reduction Technologies in the Chesapeake Bay

Basin. Report No. 8 of the Chesapeake Bay

Program Nutrient Reductio Strategy Reevaluation. Washington D.C.: U.S. Environmental

Protection Agency, February.

Cowardin, L.M., V. Carter, F.C. Golet, and E.T.

LaRoe. 1979. Classification of wetlands and

deepwater habitats of the United States. U.S.

Fish & Wildlife Service Pub. FWS/OBS-79/

31, Washington, D.C., 103 pp.

deSteigner, J. E. and J. M. Pye. 1988. Using scientific

opinion to conduct forestry air pollution economic analysis. In: A. Jobstl, ed., Proceedings of the Symposium on the Economic Assessment of Damage Caused to Forests by Air

Pollutants. IUFRO Working Party S4.04-02,

September 13-17, 1988. Gmunden, Austria.

Duchelle, S. F., J. M. Skelly, T. L. Sharick, B. I.

Chevone, Y. Yang, and J. E. Nellessen. 1983.

Effects of ozone on the productivity of natural vegetation in a high meadow of the

Shenandoah National Park of Virginia. J of

Env. Manage. 17:299-308.

Duchelle, S. F., J. M. Skelly, and B. I. Chevone. 1982.

Oxidant effects on forest tree seedling growth

in the Appalachian Mountains. J Water, Air,

Soil Pollut. 18:363-373.

Ferguson, P., R.N. Robinson, M.C. Press, and J.A.

Lee. 1984. Element concentrations in five

Sphagnum species in relation to atmospheric

pollution. Journal of Bryology 13: 107-114.
Appendix E: Ecological Effects of Criteria Pollutants

E-19

Fisher, D.C. and M. Oppenheimer. 1991. Atmospheric nitrogen deposition and the Chesapeake Bay estuary. Ambio 20:102-108.

Fitzgerald, W.F., R.P. Mason, and G.M. Vandal. 1991.

Atmospheric Cycling and Air-Water Exchange of Mercury Over Mid-Continental

Lacustrine Regions. Water, Soil, Air & Soil

Poll. 56:745-767.

Glass, G. E., J.A. Sorenson, K.W. Schmidt, and G.R.

Rapp, Jr. 1990. New Source Identification of

Mercury Contamination in the Great Lakes.

Environ. Sci. Technol. 24: 1059-1069.

Gorham, E., S.E. Bayley, and D.W. Schindler. 1984.

Ecological effects of acid deposition upon

peatlands: A neglected field in “acid-rain”

research. Can. J. Fish. Aquat. Sci. 41: 1256-

1268.

Grieb, T.M., C.T. Driscoll, S.T. Gloss, C.L. Schofield,

G.L. Bowie, and D.B. Porcella. 1990. Factors affecting mercury accumulation in fish

in the upper Michigan peninsula. Environ.

Toxicol. Chem. 9:9191-930.

Hansson, S. and L.G. Rudstam. 1990. Eutrophication and Baltic fish communities. Ambio

19:123-125.

Hayes, E. M., and J. M. Skelly. 1977. Transport of

ozone from the northeast U.S. into Virginia

and its effect on eastern white pines. Plant

Dis. Rep. 61: 778-782.

Haynes, R.W. and H.F. Kaiser. 1990. Forests: Methods for Valuing Acidic Deposition/Air Pollution Effects. NAPAP SOS/T Report 27,

Section B2, In: Acidic Deposition: State of

Science and Technology, Volume IV, National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington,

D.C. 20503.

Hecky, R.E. and P. Kilham. 1988. Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment. Limnology and Oceanography 33:796-822.

Hinga, K.R., A.A. Keller, and C.A. Oviatt. 1991.

Atmospheric deposition and nitrogen inputs

to coastal waters. Ambio 20:256-260.

Hogsett, W. E., A. A. Herstom, J. A. Laurence, E. H.

Lee, J. E. Weber, and D. T. Tingey. 1995.

Risk characterization of tropospheric ozone

to forests. In: Comparative Risk Analysis and

Priority Setting for Air Pollution Issues. Proceedings of the 4th U.S.-Dutch International

Symposium. Pittsburgh, PA. Air and Waste

Management Association. 119-145.

Lee, J.A. and S.J. Woodin. 1988. Vegetation structure and the interception of acidic deposition

by ombrotrophic mires. In: Vegetation Structure in Relation to Carbon and Nutrient

Economy, J.T.A. Verhoeven, G.W. Heil, and

M.J.A. Werger, Eds. SPB Academic Publishing bv, The Hague, pp. 137-148.

Lee, J.A., M.C. Press, and S.J. Woodin. 1986. Effects of NO2

 on aquatic ecosystems. In: Environment and Quality of Life: Study on the

Need for an NO2

 Long-term Limit Value for

the Protection of Terrestrial and Aquatic Ecosystems. Commission of the European Communities, Luxembourg, pp. 99-116.

Locke, A. 1993. Factors influencing community

structure along stress gradients: zooplankton

responses to acidification. Ecology 73: 903-

909.

Logofet, D.O. and G.A. Alexandrov. 1984. Modelling of matter cycle in a mesotropic bog ecosystem. II. Dynamic model and ecological

succession. Ecol. Modell. 21:259-276

McCool, P. M., and R. C. Musselman. 1990. Impact

of ozone on growth of peach, apricot, and almond. Hortscience 25: 1384-1385.

Miller, P.R., J.R. McBride, S.L. Schilling, and A.P.

Gomez. 1989. Trend of ozone damage to

conifer forests between 1974 and 1988 in the

San Bernardino mountains of southern California. In: Effects of Air Pollution on Western Forests. R.K. Olson and A.S. Lefohn, Eds.

Pittsburgh, PA: Air and Waste Management

Association, Pittsburgh, PA, pp. 309-324

(Transaction series no. 16).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-20

Miller, P.R., J.R. McBride, and S.L. Schilling. 1991.

Chronic ozone injury and associated stresses

affect relative competitive capacity of species

comprising the California mixed conifer forest type. In: Memorias del Primer Simposial

Nacional; Agricultura Sostenible: Una Opcion

para Desarollo sin Deterioro Ambiental.

Comision de Estudios Ambientales, Colegio

de Postgraduados, Montecillo, Edo. Mexico,

Mexico, pp. 161-172.

Mills, K.H., S.M. Chalanchuk, L.C. Mohr, and I.J.

Davies. 1987. Responses of fish populations

in Lake 223 to 8 years of experimental acidification. Can. J. Fish. Aquat. Sci. 44(Suppl.

1):114-125.

Miskimmin, B.M., J.W.M. Rudd, and C.A. Kelly.

1992. Influence of dissolved organic carbon,

pH, and microbial respiration rates on mercury methylation and demethylation in lake

water. Can. J. Fish. Aquat. Sci. 49:17-22

Mitsch, W.J. and J.G. Gosselink. 1986. Wetlands.

Van Nostrand reinhold. New York.

Moore, D.R.J., P.A. Keddy, C.L. Gaudet, and I.C.

Wisheu. 1989. Conservation of wetlands:

do infertile wetlands deserve a higher priority? Biological Conservation 47: 203-217.

National Acid Precipitation Assessment Program

(NAPAP). 1991. 1990 Integrated assessment

report. National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington, D.C. 20503.

National Economics Research Associates (NERA),

Inc. 1994. The Benefits of Reducing Emissions of Nitrogen Oxides under Phase I of

Title IV of the 1990 Clean Air Act Amendments.

Neufeld, H. S., J. R. Renfro, W. D. Hacker, and D.

Silsbee. 1992. Ozone in Great Smoky Mountains National Park: dynamics and effects on

plants. In: Tropospheric ozone and the environment II - effects, modeling and control:

papers from an international specialty conference; November; Atlanta, GA, Pittsburgh,

PA: Air & Waste Management Association;

pp. 594-617. (A&WMA transactions series:

TR-20).

Ontario Ministry of the Environment and Ministry of

Natural Resources. 1990. Guide to eating

Ontario sport fish. Public Information Centre, Environment Ontario, Toronto.

Owens, N.J.P., J.N. Galloway, and R.A. Duce. 1992.

Episodic atmospheric nitrogen deposition to

oligotrophic oceans. Nature 357:397-399.

Paerl, H.W. 1988. Nuisance phytoplankton blooms

in coastal, estuarine, and inland waters.

Limnol. Oceanogr. 33:823-847.

Paerl, H.W. 1993. Emerging role of atmospheric nitrogen deposition in coastal eutrophication:

biogeochemical and trophic perspectives.

Can. J. Fish. Aquat. Sci. 50:2254-2269.

Peterson D.C. et al. 1987. Improving accuracy and

reducing costs of environmental benefit assessments. Office of Policy, Planning and

Evaluation, U.S. Environmental Protection

Agency, Washington D.C.

Peterson, D. L., and M. J. Arbaugh. 1988. An evaluation of the effects of ozone injury on radial

growth of ponderosa pine (Pinus ponderosa)

in the southern Sierra Nevada. JAPCA 38:

921-927.

Peterson, D. L., and M. J. Arbaugh. 1992. Coniferous forests of the Colorado front range. Part

B: ponderosa pine second-growth stands. In:

Olson, R. K.; Binkley, D.; Boehm, M., eds.

The response of western forests to air pollution. New York, NY: Springer-Verlag; pp.

365 and 385-401. (Billings, W. D.; Golley,

F.; Lange, O. L.; Olson, J. S.; Remmert, H.

Ecological studies; analysis and synthesis v.

97).

Peterson, D.L., M. J. Arbaugh, and J. R. Linday. 1991.

Regional growth changes in ozone-stressed

ponderosa pine (Pinus ponderosa) in the Sierra Nevada, California, USA. Holocene 1:50-

61.

Peterson, D. L., M. J. Arbaugh, V. A. Wakefield, and

P. R. Miller. 1987. Evidence of growth reduction in ozone-injured Jeffrey pine (Pinus

jeffreyi Grev. and Balf.) in Sequoia and Kings

Canyon National Parks. JAPCA 37: 906-912.
Appendix E: Ecological Effects of Criteria Pollutants

E-21

Powell, D. S., et al. 1993. Forest Resources of the

United States, 1992. USDA-Forest Service,

Fort Collins, CO. General Technical Report

RM-234.

Retzlaff, W. A., T. M. DeJong, and L. E. Williams.

1992a. Photosynthesis and growth response

of almond to increased atmospheric ozone

partial pressures. J. Environ. Qual. 21:

208-216.

Retzlaff, W. A., L. E. Williams, and T. M. Dejong.

1992b. Photosynthesis, growth, and yield

response of ‘Casselman’ plum to various

ozone partial pressures during orchard establishment. J. Am. Soc. Hortic. Sci. 117:

703-710.

Retzlaff, W. A., L. E. Williams, and T. M. DeJong.

1991. The effect of different atmospheric

ozone partial pressures on photosynthesis and

growth of nine fruit and nut tree species. Tree

Physiol. 8: 93-105.

Rochefort, L., D. Vitt, and S. Bayley. 1990. Growth,

production, and decomposition dynamics of

Sphagnum under natural and experimentally

acidified conditions. Ecology 71(5): 1986-

2000.

Roelofs, J.G.M. 1986. The effect of airborne sulphur

and nitrogen deposition on aquatic and terrestrial heathland vegetation. Experientia

42:372-377.

Rosenthal, D. 1990. Forest Recreation. NAPAP SOS/

T Report 27, Section B2.4, In: Acidic Deposition: State of Science and Technology, Volume IV, National Acid Precipitation Assessment

Rosenberg, R., R. Elmgren, S. Fleischer, P. Jonsson,

G. Persson, and H. Dahlin. 1990. Marine

eutrophication case studies in Sweden.

Ambio 19:102-108.

Rosseland, B.O. 1986. Ecological effects of acidification on tertiary consumers: fish population

responses. Water Air Soil Pollution 30:451-

460.

Schofield, C.L., C.T. Driscoll, R. K. Munson, C. Yan,

and J.G. Holsapple. 1994. The Mercury Cycle

and fish in the Adirondack Lakes. Environ.

Science & Tech. 28:3:136-143.

Shaver, C. L., K. A. Tonnessen, and T. G. Maniero.

1994. Clearing the air at Great Smoky Mountains National Park. Ecol. App. 4: 690-701.

Shriner D.S., W.W. Heck, S.B. McLaughlin, D.W.

Johnson, P.M. Irving, J.D. Joslin, and C.E.

Peterson. 1990. Response of vegetation to

atmospheric deposition and air pollution.

NAPAP SOS/T Report 18, In: Acidic Deposition: State of Science and Technology, Volume III, National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington, D.C. 20503.

Shuyler L.R. 1992. “Cost Analysis for Nonpoint

Source Control Strategies in the Chesapeake

Basin.” Annapolis MD: U.S. Environmental

Protection Agency Chesapeake Bay Program,

March.

Skelly, J. M., Y. S. Yang, B. I. Chevone, S. J. Long, J.

E. Nellessen, and W. E. Winner. 1984. Ozone

concentrations and their influence on forest

species in the Blue Ridge Mountains of Virginia. In: Davis, D.D.; Millen, A.A.;

Dochinger, L., eds. Air pollution and the productivity of the forest: proceedings of the

symposium; October 1983; Washington, DC.

Arlington, VA; Izaak Walton League of

America Endowment; pp. 143-159.

Sorenson, J.A., G. E. Glass, K. W. Schmidt, and G.R.

Rapp, Jr. 1990. Airborne Mercury Deposition

and Watershed Characteristics in Relation to

Mercury Concentrations in Water, Sediment,

Plankton and Fish of Eighty Northern Minnesota Lakes. Environ. Sci. Technol. 24:

1716-1727.

Spry, D.J. and J.G. Wiener. 1991. Metal

bioavailability and toxicity to fish in low-alkalinity lakes: a critical review. Environmental Pollution 71:243-304.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

E-22

Turner, R.S., R.B. Cook, H. Van Miegroet, D.W.

Johnson, J.W. Elwood, O.P. Bricker, S.E.

Lindberg, and G. M. Hornberger. 1990.

Watershed and Lake Processes Affecting Surface Water Acit-Base Chemistry. NAPAP

SOS/T Report 10, In: Acidic Deposition:

State of Science and Technology, Volume II,

National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington,

D.C. 20503.

U.S. DOI, 1993. Fish and Wildlife Service and U.S.

Department of Commerce, 1991 National

Survey of Fishing, Hunting, and WildlifeAssociated Recreation, U.S. Government

Printing Office, Washington D.C.

U.S. EPA. 1993. Air Quality Criteria for Oxides of

Nitrogen. Office of Health and Environmental Assessment, Environmental Criteria and

Assessment Office, Research Triangle Park,

NC; EPA report no. EPA600/8-91/049bF. 3v.

U.S. EPA. 1994. Deposition of Air Pollutants to the

Great Waters: First Report to Congress. Office of Air Quality Planning and Standards.

Research Triangle Park, NC: EPA Report

Number EPA-453/R-93-055.

U.S. EPA. 1995. The Benefits and Costs of the Clean

Air Act 1970 to 1990 — Report to Congress.

U.S. EPA. 1996a. External Draft, Air Quality Criteria for Ozone and Related Photochemical

Oxidants. Volume II. Office of Health and

Environmental Assessment, Environmental

Criteria and Assessment Office, Research

Triangle Park, NC; EPA report no. EPA/600/

AP-93/004af-cf.

U.S. EPA. 1996b. Review of National Ambient Air

Quality Standards for Ozone: Assessment of

Scientific and Technical Information: OAQPS

Staff Paper. Office of Air Quality Planning

and Standards. Research Triangle Park, NC;

EPA report no. EPA-452\R-96-007. June.

Vitousek, P.M. and R.W. Howarth. 1991. Nitrogen

limitation on land and in the sea: How can it

occur? Biogeochemistry 13:87-115.

Walsh, R.G., F.A. Ward, and J.P. Olienyk. 1989. Recreational demand for trees in national forests.

J. Environ. Manage. 28:255-268.

Watras, C.J. and N.S. Bloom. 1992. Mercury and

methylmercury in individual zooplankton:

implications for bioaccumulation. Limnology and Oceanography 37:1313-1318.

Watras, C.J., N.S. Bloom, R.J.M. Hudson, S. Gherini,

R. Munson, S.A. Claas, K.A. Morrison, J.

Hurley, J.G. Wiener, W.F. Fitzgerald, R.

Mason, G. Vandal, D. Powell, R. Rada, L.

Rislov, M. Winfrey, J. Elder, D. Krabbenhoft,

A.W. Andren, C. Babiarz, D.B. Porcella, and

J.W. Huckabee. 1994. Sources and fates of

mercury and methylmercury in Wisconsin

lakes. In: Mercury Pollution: Integration and

Synthesis, C.J. Watras and J.W. Huckabee,

Eds. Lewis Publishers, Boca Raton, Florida,

pp. 153-180.

Wiener, J.G., R.E. Martini, T.B. Sheffy, and G.E.

Glass. 1990. Factors influencing mercury

concentrations in walleyes in northern Wisconsin lakes. Trans. Am. Fish. Soc. 119:862-

870.

Wisheu, I.C. and P.A. Keddy. 1989. The conservation and management of a threatened coastal

plain plant community in eastern North

America (Nova Scotia, Canada). Biological

Conservation 48: 229-238.
Appendix F: Effects of Criteria Pollutants on


Agriculture 

Introduction 

One potential impact of air pollutants on economic 

welfare is their effect on agricultural crops, including 

annual and perennial species. Pollutants may affect 

processes within individual plants that affect growth 

and reproduction, thereby affecting yields of agricultural crops. Possible physiological effects of pollutants include the following: decreased photosynthesis; 

changes in carbohydrate allocation; increased foliar 

leaching; decreased nutrient uptake; increased sensitivity to climatic stress, pests, and pathogens; decreased competitive ability; and decreased reproductive efficiency. These physiological effects, in conjunction with environmental factors and intraspecies 

differences in susceptibility, may affect crop yields. 

Primary air pollutants that might damage plants 

include SO2, NOx, and volatile organic compounds 

(VOCs). These pollutants may have direct effects on 

crops, or they may damage crops indirectly by contributing to tropospheric (ground-level) ozone, 

peroxyacetyl nitrate (PAN), and/or acid deposition, 

all of which damage plants. Tropospheric ozone is 

formed by photochemical reactions involving VOCs 

and NOx, while SO2 and NOx

 cause acidic deposition. 

While all of these air pollutants may inflict incremental stresses on crop plants, in most cases air pollutants other than ozone are not a significant danger 

to crops. Based primarily on EPA’s National Acid 

Precipitation Assessment Program (NAPAP) conclusions,1 this analysis considers ozone to be the primary 

pollutant affecting agricultural production. 

This analysis estimates the economic value of the 

difference in agricultural production that has resulted 

due to the existence of the CAA since 1970. The analysis is restricted to a subset of agricultural commodities, and excludes those commodity crops for which 

ozone response data are not available. Fruits, vegetables, ornamentals, and specialty crops are also excluded from this analysis. To estimate the economic 

value of ozone reductions under the CAA, agricultural production levels expected from control scenario 

ozone conditions are first compared with those expected to be associated with ozone levels predicted 

under the no-control scenario. Estimated changes in 

economic welfare are then calculated based on a comparison of estimated economic benefits associated with 

each level of production. 

Ozone Concentration Data 

To estimate the nationwide crop damages as a 

result of ozone exposure, the first step is to estimate 

the nationwide ozone concentrations under the control and no-control scenarios. This section describes 

the methodology used to estimate ozone concentrations for each county in each of these two scenarios. 

First, historical ozone concentration data at the 

monitor level were compiled from EPA’s AIRS system. Differences between the modeled control and nocontrol scenario ozone concentrations were then used 

to scale historical data to derive no-control scenario 

ozone air quality profiles.2 Next, the ozone index used 

in the exposure response evaluation was calculated 

and applied at the monitor level. For this analysis, the 

W126 index, a peak-weighted average of cumulative 

ozone concentrations, was selected to conform with 

the index currently being used by EPA in ozone 

NAAQS benefits analysis. The W126 index is one of 

several cumulative statistics, and may correlate more 

closely to crop damage than do unweighted indices.3 

EPA has not yet made a final determination of the 

appropriate index to use in agricultural benefits analy1

 Shriner et al., 1990; NAPAP, 1991. 

2

 Derivation of these ozone air quality profiles for the control and no-control scenario is summarized in the following section and 

described in detail in Appendix C. 

3

 Lefohn et al., 1988. 

F-1

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

sis; thus this analysis should be viewed only as an 

indicator of the magnitude of potential benefits. 

The third step in ozone concentration estimation 

involved the use of triangulation and planar interpolation to arrive at a W126 statistic at the county, rather 

than at the monitor, level. For each county centroid, 

the closest surrounding triangle of monitors is located 

and the W126 is calculated for that county using a 

distance-weighted average of the ozone concentration 

at each of these monitors. 

Control and No-control Scenario Ozone 

Concentration Data 

The initial estimation of ozone concentrations in 

the control and no-control scenarios was performed 

by Systems Applications International (SAI). To create the control scenario, SAI compiled ozone data from 

the EPA’s Aerometric Information and Retrieval System (AIRS).4 SAI summarized these data by fitting 

gamma distributions to them and providing the alpha 

and the beta parameters to these distributions. Each 

of these distributions describes a set of ozone concentration levels, and the distributions are categorized 

by year, season, and averaging time. SAI defines six 

distinct “seasons,” each composed of a two month 

period in the year. This analysis uses those distributions which describe 1-hour average ozone concentrations taken from 7 AM to 7 PM and separated into 

seasons. The analysis utilizes only those monitor 

records that were modeled in both the control and nocontrol scenarios. 

To determine the ozone concentrations for the nocontrol scenario, SAI utilized the Ozone Isopleth Plotting with Optional Mechanisms-IV (OZIPM4) model. 

The input data required for OZIPM4 includes air quality data, surface and upper-air meteorological data, 

and estimates of anthropogenic and biogenic emissions of volatile organic compounds, NOx and CO.5 

To create these inputs, SAI used (among other sources) 

outputs from the Regional Acid Deposition Model 

(RADM) and the SJVAQS/AUSPEX Regional Modeling Adaptation project (SARMAP). Additional detail concerning the development of ozone concentration data is available in Appendix C and in the SAI 

report to EPA.6 

Calculation of the W126 Statistic 

Using the SAI ozone concentration distributions, 

we calculated a sigmoidally weighted ozone index for 

each monitor. The generalized sigmoidal weighting 

function used in calculating such indices is presented 

in Lefohn and Runeckles (1987) as: 

where: 

wi = 1 / [1 + M • exp( –A • i)] (1) 

wi = weighting factor for concentrationi 

(unitless) 

ci = concentrationi

 (ppm) 

M= an arbitrary constant 

A = an arbitrary constant 

The constants M and A are chosen to give different 

weights to higher or lower concentrations. The index 

used in this analysis is the W126 statistic, which is 

calculated as follows:7 

wi = 1 / [1 + 4403 • exp( –126 • ci)] (2) 

and 

W126 = Σwi (3) 

Missing values are accounted for by multiplying the 

resulting W126 statistic by the ratio of the number of 

potential observations to the number of actual observations (i.e., total hours in period/hours of data in period). 

To calculate W126 indices from the monitor level 

gamma distributions, we used an inverse cumulative 

density function to calculate a separate representative 

air concentration for each hour in the two month season. These values are then used in the above equation 

to obtain a monitor-level W126 statistic. 

To ensure that the interpretation of the gamma 

distributions in this manner does not generate errors, 

we tested our gamma-derived control-scenario W126s 

4

 SAI, ICF Kaiser, 1995. 

5

 SAI, ICF Kaiser, 1995. 

6

 SAI, ICF Kaiser, 1995. 

7

 Lefohn et al., 1988. 

F-2

Appendix F: Effects of Criteria Pollutants on Agriculture 

against W126s calculated directly from the AIRS database. We found that insignificant error resulted from 

the utilization of the gamma distributions to create 

W126 statistics. 

Aggregating Ozone Data to the County 

Level 

Because crop production data are available at the 

county level, the lowest level of aggregation that could 

be used for ozone indices is also the county level. 

Therefore, monitor level data needed to be aggregated 

to a county level. For each county, we first located 

the monitors from which we would be interpolating 

data. To identify these monitors, we searched for the 

three monitors which formed the closest triangle 

around the centroid of the county.8 The closest triangle was defined as that triangle in which the sum of 

the distances from the three monitors to the county 

centroid was the least. The algorithm stopped searching for closest triangles of monitors when it had 

searched all monitors within 500 km of a given county 

centroid (an arbitrary distance, selected to reduce computational requirements). 

For coastal counties and some rural counties in 

some years, monitor triangles around the county centroid do not exist. We assigned the W126 value from 

the monitor closest to the centroid to these counties. 

Approximately 15 percent of all county-years (36,973 

of 248,880 records) were assigned W126s in this manner. 

For the remaining 85 percent, after the closest triangle of monitors was found, a “planar interpolation” 

was used to calculate the W126 at that county for that 

year. One way to picture this process is to plot each of 

the three monitors as a point in space. For each monitor, the x axis represents longitude, the y axis represents latitude and the z axis represents the W126 statistic. A plane can then be drawn through these three 

points in space. Furthermore, using the equation for 

the plane, and given the x and y values (latitude and 

longitude) for the county centroid, the county 

centroid’s z value (W126 statistic) can be calculated. 

In essence, this procedure calculates a distanceweighted average of three monitors’ W126 index values and assigns this value to the county centroid. 

The result of this data manipulation is a monthly 

W126 statistic for each county in the continental 

United States for the years 1971-1990. From these 

data, yield change estimates were generated, and economic impacts were estimated. 

Yield Change Estimates 

There are several steps involved in generating 

yield change estimates. The first is the selection of 

relevant ozone exposure-response functions (minimum and maximum) for each crop in the analysis. 

Ozone data, triangulated to the county level, are transformed into an index suitable for use in the selected 

function(s) to estimate county level predicted yield 

losses for both the control and no-control scenarios. 

In the next step, the proportion of each county to the 

national production of each crop is calculated to permit national aggregation of estimated yield losses. 

Finally, the control scenario percentage relative yield 

loss (PRYL) is compared to the minimum and maximum PRYL for the no-control scenario. Each step is 

discussed in more detail below. 

Exposure-Response Functions 

To estimate yield impacts from ozone, exposureresponse functions are required for each crop to be 

analyzed. This analysis was restricted to estimating 

changes in yields for those commodity crops for which 

consistent exposure-response functions are available 

and that are included in national agricultural sector 

models. To maintain consistency with the current 

ozone NAAQS benefits analysis being conducted by 

OAQPS, NCLAN-based exposure-response functions 

using a Weibull functional form and a 12-hour W126 

ozone index were used. 

Several crops included in the NCLAN research 

program were not evaluated in this analysis. Non-commodity crops that are not modeled in national agricultural sector models were not included in this analysis: lettuce, tomatoes, potatoes, alfalfa, tobacco, turnips, and kidney beans. In addition, one commodity 

crop, spring wheat, was excluded because the NCLAN 

exposure-response function was only developed for 

winter wheat. 

8

 The vast majority of monitors had latitude and longitude data available through AIRS. 1,528 of 1,536 monitors were located in 

this manner. For the remaining 8 monitors, if in a given year of monitor data another monitor exists in the county of the unfound 

monitor, we discarded the unlocated monitor’s data. Otherwise, we located that monitor at the county’s centroid. We located 5 of the 

remaining 8 monitors in this fashion. 

F-3

Source: EPA/CERL (unpublished) for all functions. 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Minimum/Maximum Exposure-Response 

Functions 

Estimated responsiveness of a given crop to ozone 

varies within the NCLAN data. This range of response 

is partially explained by the program’s evaluation of 

several cultivars for some crops; ozone sensitivity 

varies across cultivars. In addition, the conditions for 

different experiments varied due to variations in location, year, and additional treatments included in 

some experiments. No one exposure-response function can be assumed to be representative of all cultivars in use, or of all environmental conditions for crop 

production. To develop a range of benefits estimates 

that reflects this variation in responsiveness, a minimum responsiveness and a maximum responsiveness 

function were selected for each crop. In actuality, a 

number of different cultivars are planted by producers, and so ozone response will be a weighted average 

of the responsiveness of each cultivar to its ozone condition and its proportion of total acreage. It is important to note that these values do not necessarily bound 

the analysis, since the number of cultivars evaluated 

by NCLAN is small relative to the number grown for 

many crops. 

for each crop. Two crops, peanuts and sorghum, did 

not have multiple NCLAN experiments on which to 

base a comparison of the responsiveness of different 

cultivars or the variation in response with different 

experimental conditions. 

Calculation of Ozone Indices 

Each NCLAN ozone exposure-response experiment exposed each studied crop over a portion of the 

crop’s growing season. The duration of the NCLAN 

experiments was provided by CERL and was rounded 

to the nearest month. The W126 index is cumulative, 

and so is sensitive both to the duration over which it 

is calculated and to the specific month(s) within a 

growing season that are included in it. Because cropping seasons vary across the U.S., the ozone index 

used to calculate county-level changes in yield due to 

ozone must reflect the local season for each crop. To 

determine which portion of the growing season a particular exposure period pertains to (in order to calculate an exposure index), we developed state-specific 

growing seasons based on planting and harvesting data 

developed by USDA.9 The W126 index was calcuFor the crops used 

in this study, CERL Table F-1. Agriculture Exposure-Response Functions. conducted an analysis 

to identify the ozone 

concentration required 

to reduce yields by 10 

percent for each crop 

cultivar using its 12-

hour W126 exposureresponse function. For 

each crop, the function 

demonstrating the lowest ozone concentration 

at a 10 percent yield 

loss represents the 

maximum response, 

and the function with 

the highest concentration at 10 percent yield 

Crop Cultivar Equation 

Type 

Yield Function 

(PRYL, ppm) 

Duration 

(da ys) 

Barley CM-72 Both 1-exp (-(W126 /6 998.5)

1.388 95 

Corn -Field PAG 39 7 Min 1-exp (-(W126 /9 4.2 )

4.176 83 

Corn -Field Pion eer 378 0 Max 1-exp (-(W126 /9 2.7 )

2.585 83 

Cotton McNair 235 Min 1-exp (-(W126 /1 13.3) 1.397 125 

Cotton Acala SJ2 Max 1-exp (-(W126 /7 4.6 )

1.066 98 

Gr ain 

So rghum DeKalb A28+ Both 1-exp (-(W126 /2 05.3) 1.957 85 

Peanuts NC-6 Both 1-exp (-(W126 /9 6.8 )

1.890 112 

Soybeans Corsoy-7 9 Min 1-exp (-(W126 /4 76.7) 1.113 93 

Soybeans Davis Max 1-exp (-(W126 /1 30.1) 1.000 93 

Wheat AR T Min 1-exp (-(W126 /7 6.8 )

2.031 54 

Wheat VONA Max 1-exp (-(W126 /2 4.7 )

1.00) 61 

Equation 

Type 

Yield Function 

(PRYL, ppm) 

Duration 

(da ys) 

Cotton 

Gr ain 

So rghum 

Wheat VONA Max 1-exp (-(W126 /2 4.7 )

1.00) 61 loss represents the mini-


mum response. Table F-


1 reports the minimum Source: EPA/CERL (unpublished) for all functions.

and maximum expo-


sure-response functions


9

 USDA, 1984. Some states did not have explicit growing seasons reported for certain crops due to the low production in these 

states. In these cases a proxy state growing season was used. In most of these cases the proxy growing season was taken from a state 

with an adjoining boundary within the same geographic region. 

F-4

Appendix F: Effects of Criteria Pollutants on Agriculture 

lated using the county level ozone data developed in 

the prior section, summed for the number of months 

of NCLAN experimental duration, with the exposure 

period anchored on the usual harvest month for each 

crop.10 

Calculations of County Weights 

Because the benefits analysis did not require a 

regional level of disaggregation and to minimize computational burdens the economic analysis was conducted at a national level. Ozone data and estimated 

yield responses, however, were developed at a county 

level. To conduct a national analysis, the county level 

yield change estimates were weighted to develop a 

single national percent relative yield loss for each crop 

relative to the control scenario, for both the minimum 

and the maximum yield responses. As a part of calculating a percent change in yield at the national level, 

weights for each county and crop were created for 

1975, 1980, 1985, and 1990. The weights for these 

four years were used to represent the year itself and 

the four years immediately following it (e.g., 1975 

weights were also used for 1976, 1977, 1978, and 

1979). Although weather and other conditions may 

change the proportion of counties’ production to the 

total national production in each year, five year 

weights should reflect stable periods of agricultural 

policy between each Farm Bill, and are sufficient for 

the level of precision needed for this analysis. The 

weights were calculated by dividing the production 

level of a crop in a county11 by the sum of all states’ 

reported production for that crop.12 These county 

weights were applied to the percent relative yield loss 

results for each county, as discussed below. 

Calculation of Percent Change in Yield 

Ozone exposure-response functions are expressed 

in terms of percent relative yield loss (PRYL); the 

ozone level being analyzed is compared with “clean” 

(charcoal filtered/zero ozone) air. To calculate a percent change in yield between the control and no-control scenarios, we first calculate a PRYL based on the 

county-level control scenario W126 ozone index, and 

a PRYL based on the no-control scenario W126 index. Next, the county weights are applied to the 

PRYLs. The change in yield, measured relative to the 

hypothetical zero-ozone crop production, is then: 

(PRYLC – PRYLNC) (4) 

To obtain the change in terms of our (non-zero) 

baseline yield, we divide by that yield, and get: 

(PRYLC – PRYLNC) / (100 – PRYLC) (5) 

To create the national percent change in yield for 

each crop, the results of this equation are summed for 

each scenario (maximum and minimum) and for each 

year. Tables F-2 and F-3 present the resulting percent 

yield changes that were used as inputs to the economic 

model. 

Economic Impact Estimates 

To estimate the economic benefits of controls on 

ozone precursor pollutants under the Clean Air Act, 

changes in yields due to those controls need to be 

evaluated in terms of their effect on agricultural markets. To do this, yield changes can be incorporated 

into an economic model capable of estimating the associated changes in economic surpluses within the 

agricultural economy, preferably one that reflects 

changes in producers’ production decisions and demand substitution between crops. This type of dynamic analysis is needed because even small changes 

in yield or price expectations can cause large shifts in 

the acreage allocated to specific crops, and the degree 

to which alternative crops will be substituted (particularly for feed uses). 

Agricultural Simulation Model (AGSIM) 

The modeling approach used in this analysis is an 

econometric model of the agricultural sector, which 

estimates demand and supply under different production technologies and policy conditions. The 

AGricultural SImulation Model (AGSIM) has been 

10 This analysis required “rounding” some months: if a harvest date was specified to be from the 15th to the end of a month, the 

W126 index was calculated using that month’s data; if the harvest date was specified to be from the first to the 14th of a month, the 

W126 index was calculated using the prior month’s data as the final month in the exposure period. 

11 USDA, 1995. 

12 The national total does not include USDA areas designated “other counties”. These areas are groups of counties that for one 

reason or another (disclosure rules, low amount of production, etc.) are not individually listed. Because we did not have ozone values 

for these groups, we did not use their production levels in the calculation of the total national production. 

F-5

The Benefits and Costs of the Clean Air Act, 1970 to 1990

F-6

Crop

Year Barley Corn Cotton Peanuts Soybeans Sorghum Winter Wheat

1975 -0.000020 -0.000171 -0.011936 -0.006635 -0.001166 -0.000717 -0.005631 

1976 -0.000013 -0.000329 -0.017505 -0.024048 -0.002171 -0.001841 -0.004841 

1977 -0.000013 -0.000169 -0.013114 -0.015150 -0.001562 -0.001118 -0.005464 

1978 -0.000019 -0.000291 -0.018692 -0.017606 -0.002480 -0.001844 -0.005894 

1979 -0.000027 -0.000100 -0.017217 -0.013067 -0.001898 -0.001389 -0.004998 

1980 -0.000019 -0.000200 -0.021315 -0.022761 -0.002397 -0.002222 -0.005385 

1981 -0.000016 -0.000071 -0.018552 -0.014269 -0.001951 -0.000802 -0.003964 

1982 -0.000020 -0.000070 -0.017295 -0.014200 -0.002107 -0.001050 -0.004773 

1983 -0.000023 -0.000617 -0.020842 -0.028601 -0.003901 -0.002366 -0.005904 

1984 -0.000027 -0.000111 -0.023552 -0.019225 -0.002919 -0.002881 -0.006121 

1985 -0.000025 -0.000132 -0.020947 -0.017965 -0.002645 -0.001726 -0.007316 

1986 -0.000029 -0.000158 -0.027968 -0.031605 -0.002899 -0.001564 -0.007597 

1987 -0.000033 -0.000358 -0.034584 -0.043854 -0.003776 -0.001812 -0.009669 

1988 -0.000027 -0.000662 -0.035069 -0.038085 -0.004563 -0.002922 -0.019873 

1989 -0.000024 -0.000150 -0.031245 -0.022094 -0.003769 -0.001359 -0.007605 

1990 -0.000024 -0.000210 -0.037988 -0.047395 -0.003819 -0.001567 -0.006449 

Note: There is only one scenario for barley, peanuts, and sorghum, because there was only one exposure-response function..

Barley Corn Cotton Peanuts Soybeans Sorghum Winter Wheat

-0.000020 -0.000171 -0.011936 -0.006635 -0.001166 -0.000717 -0.005631 

-0.000013 -0.000329 -0.017505 -0.024048 -0.002171 -0.001841 -0.004841 

-0.000013 -0.000169 -0.013114 -0.015150 -0.001562 -0.001118 -0.005464 

-0.000019 -0.000291 -0.018692 -0.017606 -0.002480 -0.001844 -0.005894 

-0.000027 -0.000100 -0.017217 -0.013067 -0.001898 -0.001389 -0.004998 

-0.000019 -0.000200 -0.021315 -0.022761 -0.002397 -0.002222 -0.005385 

-0.000016 -0.000071 -0.018552 -0.014269 -0.001951 -0.000802 -0.003964 

-0.000020 -0.000070 -0.017295 -0.014200 -0.002107 -0.001050 -0.004773 

-0.000023 -0.000617 -0.020842 -0.028601 -0.003901 -0.002366 -0.005904 

-0.000027 -0.000111 -0.023552 -0.019225 -0.002919 -0.002881 -0.006121 

-0.000025 -0.000132 -0.020947 -0.017965 -0.002645 -0.001726 -0.007316 

-0.000029 -0.000158 -0.027968 -0.031605 -0.002899 -0.001564 -0.007597 

-0.000033 -0.000358 -0.034584 -0.043854 -0.003776 -0.001812 -0.009669 

-0.000027 -0.000662 -0.035069 -0.038085 -0.004563 -0.002922 -0.019873 

-0.000024 -0.000150 -0.031245 -0.022094 -0.003769 -0.001359 -0.007605 

-0.000024 -0.000210 -0.037988 -0.047395 -0.003819 -0.001567 -0.006449 

Note: There is only one scenario for barley, peanuts, and sorghum, because there was only one exposure-response function..

Table F-2. Relative No-control to Control Percent Yield Change (harvested acres) for the Minimum

Scenario.

Crop

Year Barley Corn Cotton Peanuts Soybeans Sorghum Winter Wheat

1975 -0.000020 -0.001139 -0.021059 -0.006635 -0.005808 -0.000717 -0.034803 

1976 -0.000013 -0.002281 -0.032063 -0.024048 -0.010298 -0.001841 -0.040303 

1977 -0.000013 -0.001232 -0.025773 -0.015150 -0.007764 -0.001118 -0.049636 

1978 -0.000019 -0.002015 -0.033075 -0.017606 -0.011803 -0.001844 -0.050308 

1979 -0.000027 -0.001052 -0.031433 -0.013067 -0.009592 -0.001389 -0.052211 

1980 -0.000019 -0.001537 -0.037278 -0.022761 -0.011845 -0.002222 -0.054128 

1981 -0.000016 -0.000923 -0.035058 -0.014269 -0.009902 -0.000802 -0.053470 

1982 -0.000020 -0.000974 -0.034101 -0.014200 -0.010815 -0.001050 -0.058409 

1983 -0.000023 -0.003888 -0.040405 -0.028601 -0.018597 -0.002366 -0.063556 

1984 -0.000027 -0.001443 -0.043890 -0.019225 -0.014502 -0.002881 -0.067612 

1985 -0.000025 -0.001377 -0.040845 -0.017965 -0.013384 -0.001726 -0.072177 

1986 -0.000029 -0.001451 -0.052426 -0.031605 -0.014754 -0.001564 -0.081225 

1987 -0.000033 -0.002565 -0.061295 -0.043854 -0.018578 -0.001812 -0.089042 

1988 -0.000027 -0.004318 -0.061660 -0.038085 -0.021767 -0.002922 -0.120703 

1989 -0.000024 -0.001987 -0.059573 -0.022094 -0.018739 -0.001359 -0.086958 

1990 -0.000024 -0.002056 -0.071659 -0.047395 -0.018670 -0.001567 -0.082309 

Note: There is only one scenario for barley, peanuts, and sorghum, because there was only one exposure-response function.

Barley Corn Cotton Peanuts Soybeans Sorghum Winter Wheat

-0.000020 -0.001139 -0.021059 -0.006635 -0.005808 -0.000717 -0.034803 

-0.000013 -0.002281 -0.032063 -0.024048 -0.010298 -0.001841 -0.040303 

-0.000013 -0.001232 -0.025773 -0.015150 -0.007764 -0.001118 -0.049636 

-0.000019 -0.002015 -0.033075 -0.017606 -0.011803 -0.001844 -0.050308 

-0.000027 -0.001052 -0.031433 -0.013067 -0.009592 -0.001389 -0.052211 

-0.000019 -0.001537 -0.037278 -0.022761 -0.011845 -0.002222 -0.054128 

-0.000016 -0.000923 -0.035058 -0.014269 -0.009902 -0.000802 -0.053470 

-0.000020 -0.000974 -0.034101 -0.014200 -0.010815 -0.001050 -0.058409 

-0.000023 -0.003888 -0.040405 -0.028601 -0.018597 -0.002366 -0.063556 

-0.000027 -0.001443 -0.043890 -0.019225 -0.014502 -0.002881 -0.067612 

-0.000025 -0.001377 -0.040845 -0.017965 -0.013384 -0.001726 -0.072177 

-0.000029 -0.001451 -0.052426 -0.031605 -0.014754 -0.001564 -0.081225 

-0.000033 -0.002565 -0.061295 -0.043854 -0.018578 -0.001812 -0.089042 

-0.000027 -0.004318 -0.061660 -0.038085 -0.021767 -0.002922 -0.120703 

-0.000024 -0.001987 -0.059573 -0.022094 -0.018739 -0.001359 -0.086958 

-0.000024 -0.002056 -0.071659 -0.047395 -0.018670 -0.001567 -0.082309 

Table F-3. Relative No-control to Control Percent Yield Change (harvested acres) for the Maximum

Scenario.
Appendix F: Effects of Criteria Pollutants on Agriculture 

used extensively to evaluate air pollution impacts, as 

well as a number of other environmental policy analyses. AGSIM is an econometric-simulation model that 

is based on a large set of statistically estimated demand and supply equations for agricultural commodities produced in the United States. The model is capable of estimating how farmers will adjust their crop 

acreages between commodities when relative profitability changes as a result of crop yield and production cost changes. Acreage and yield changes from 

various scenarios will affect total production of crops, 

which then affects commodity prices and consumption. The commodity price changes, in turn, affect 

profitability and cropping patterns in subsequent years. 

Federal farm program and conservation reserve effects are also incorporated into the model. 

The initial version of AGSIM (which went 

through various acronym revisions) was developed 

in 1977.13 The model was developed to permit estimation of aggregate impacts associated with relatively 

small changes in crop yields or production costs, which 

might result from various policy conditions such as 

changes in pesticide input availability, or in this case, 

changes in crop exposure to ozone. Subsequent revisions to the model as well as the current specification 

are described in Taylor (1993a).14 Several policy applications of AGSIM were tested and reported in Taylor (1993b)15 to provide a comparison to the results 

of several alternative agricultural sector models. These 

tests included an expansion of Conservation Reserve 

acreage, reduced target prices, elimination of agricultural programs in the U.S. other than the Conservation Reserve Program (CRP), and a tax on nitrogenous 

fertilizer use in the U.S. The model has been used to 

evaluate the effects of changes to the CRP,16 changes 

in agricultural price support programs,17 and evaluations of policies concerning pesticide availability.18 

AGSIM is designed to estimate changes in the 

agricultural sector resulting from policies that affect 

either the yields or the costs of crop production. 

Changes in economic variables are computed by comparing a policy simulation of the model with a baseline 

simulation of the model. For this retrospective evaluation, the baseline reflects actual farm programs, 

prices, and other parameters since 1970. The model’s 

author, Dr. C. Robert Taylor, modified AGSIM for 

this analysis to reflect production conditions and policies as they changed through the 20-year span of the 

Clean Air Act, from 1970 to 1990. During this period, U.S. farm policy parameters changed every five 

years with the enactment of each Farm Bill, and producer participation varied significantly over the period. Over this time, due to policy, weather, technological development, and other variations, production 

levels and prices have varied, as have production technologies, costs of production, and relevant cultivars. 

To reflect these changes, Dr. Taylor re-estimated demand relationships for three periods (1975-79; 1980-

84; and 1985-89) based on the farm policies in effect 

in each period, and structured the model to run on a 

national level rather than a regional level. The period 

from 1970-1975 was not modeled because of data limitations and because there was limited impact from the 

CAA on ozone levels during that period. 

The AGSIM baseline production and price data 

serve as the control scenario baseline. Percent relative yield losses (PRYLs) between the control and nocontrol scenarios are the relevant input parameter for 

this analysis, from which AGSIM calculates new yield 

per planted acre values. Based on these values (as well 

as on lagged price data, ending stocks from the previ13 Taylor, C.R., R.D. Lacewell, and H. Talpaz. 1979. Use of Extraneous Information with the Econometric Model to Evaluate 

Impacts of Pesticide Withdrawals. Western J. of Ag. Econ. 4:1-8. 

14 Taylor, C.R. 1993a. AGSIM: An Econometric-Simulation Model of Regional Crop and National Livestock Production in the 

United States. In: C.R. Taylor, K.H. Reichelderfer, and S.R. Johnson (Eds) Agricultural Sector Models for the United States: 

Descriptions and Selected Policy Applications. Ames Iowa: Iowa State University Press. 

15 Taylor, C.R. 1993b. Policy Evaluation Exercises with AGSIM. In: C.R. Taylor, K.H. Reichelderfer, and S.R. Johnson (Eds) 

Agricultural Sector Models for the United States: Descriptions and Selected Policy Applications. Ames Iowa: Iowa State University 

Press. 

16 Taylor, C.R. 1990. Supply Control Aspects of the Conservation Reserve. In: T.L. Napier (Ed) Implementing the Conservation 

Title of the Food Security Act of 1985. Ankeny, Iowa: Soil and Water Conservation Society; Taylor, C.R., H.A. Smith, J.B. Johnson, 

and T.R. Clark. 1994. Aggregate Economic Effects of CRP Land Returning to Production. J. of Soil and Water Conservation 49:325-

328. 

17 Talyor, C.R. 1994. Deterministic vs. Stochastic Evaluation of the Aggregate Effects of Price Support Programs. Agricultural 

Systems 44:461-474. 

18 Taylor, C.R., G.A. Carlson, F.T. Cooke, K.H. Reichelderfer, and I.R. Starbird. Aggregate Economic Effects of Alternative Boll 

Weevil Management Strategies. Agricultural Econ. Res. 35:19-19;Taylor, C.R., J.B. Penson Jr., E.G. Smith, and R.D. Knutson. 1991. 

Impacts of Chemical Use Reduction in the South. S. J. Of Ag. Econ. 23:15-23. 

F-7

Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum 

0 0 243 486 236 993 477 1,479 

0 0 -97 -259 349 1,557 253 1,297 

43 345 30 298 392 1,646 379 1,597 

0 0 -140 -406 449 2,000 309 1,594 

0 0 8 -178 392 2,049 400 1,870 

112 518 -99 -406 440 2,594 231 1,670 

168 981 64 107 377 2,730 273 1,856 

153 1,009 231 958 316 1,969 395 1,917 

-182 808 82 560 -279 1,686 -14 1,437 

289 1,291 181 879 616 2,054 509 1,644 

270 1,356 230 966 462 2,265 422 1,875 

469 2,033 320 1,405 708 2,990 558 2,361 

557 2,023 316 1,508 796 2,943 556 2,428 

329 1,401 161 614 527 2,572 358 1,785 

1990/91 414 1,927 180 473 618 3,047 384 1,593 

Cumulative Present Value of Net Surplus at 5 percent ($ 1990) 7,763 37,225 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

ous year, and other variables), AGSIM predicts acreage, production, supply, and price parameters for each 

crop for each year, as well as calculating yield per 

harvested acre. From these results and the demand 

relationships embedded in the model, AGSIM calculates the utilization of each crop (i.e., exports, feed 

use, other domestic use, etc.), as well as the change in 

consumer surplus, net crop income, deficiency payments and other government support payments. Net 

surplus is calculated as net crop income plus consumer 

surplus, less government payments. The first year of 

results is 1976 because AGSIM must have one year 

(1975) of lagged data. 

Table F-4 presents the net changes in economic 

surpluses (in 1990 dollars) annually and as a cumulative present value (discounted at 5 percent) over the 

period 1976-1990 due to the Clean Air Act. The positive surpluses exhibited in almost all years are a result of the increase in yields associated with lower 

ozone levels than those predicted to occur under the 

no-control scenario. The present value of the estimated 

agricultural benefits of the CAA ranges between $7.8 

billion in the minimum response case to approximately 

$37 billion in the maximum response case. This range 

represents the impacts that would occur if all of the 

acreage planted to a given crop had an ozone response 

function similar to either the minimum available response function or the maximum available response 

function. The available response functions do not necessarily bracket the true range of potential crop responses, and it is unrealistic to anticipate that all acreage will be planted in cultivars with a uniform response 

to ozone exposure. These considerations notwithstanding, these values do indicate the likely magnitude of 

agricultural benefits associated with control of ozone 

precursors under the CAA, but not the precise value 

of those benefits. In addition to estimating the present 

value of net surplus at a discount rate of five percent, 

two alternative discount rates were used. At a three 

percent discount rate, the range of net surplus is between $6.7 billion and $32 billion; at seven percent 

discount rate, the range is between $9 billion and $43 

billion. For more detail on AGSIM intermediate model 

outputs, see Abt Associates (1996). 

Table F-4. Change in Farm Program Payments, Net Crop Income, Consumer Surplus, and Net 

Surplus Due to the CAA (millions 1990 $). 

Change in 

Farm Program Payments 

Change in 

Net Crop Income 

Change in 

Consumer Surplus 

Change in 

Net Surplus 

Year Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum 

1976/77 0 0 243 486 236 993 477 1,479 

1977/78 0 0 -97 -259 349 1,557 253 1,297 

1978/79 43 345 30 298 392 1,646 379 1,597 

1979/80 0 0 -140 -406 449 2,000 309 1,594 

1980/81 0 0 8 -178 392 2,049 400 1,870 

1981/82 112 518 -99 -406 440 2,594 231 1,670 

1982/83 168 981 64 107 377 2,730 273 1,856 

1983/84 153 1,009 231 958 316 1,969 395 1,917 

1984/85 -182 808 82 560 -279 1,686 -14 1,437 

1985/86 289 1,291 181 879 616 2,054 509 1,644 

1986/87 270 1,356 230 966 462 2,265 422 1,875 

1987/88 469 2,033 320 1,405 708 2,990 558 2,361 

1988/89 557 2,023 316 1,508 796 2,943 556 2,428 

1989/90 329 1,401 161 614 527 2,572 358 1,785 

1990/91 414 1,927 180 473 618 3,047 384 1,593 

Cumulative Present Value of Net Surplus at 5 percent ($ 1990) 7,763 37,225 

F-8

Appendix F: Effects of Criteria Pollutants on Agriculture 

Conclusions 

Agricultural benefits associated with control of 

ozone precursors under the Clean Air Act are likely 

to be fairly large. Because it is possible that over time 

producers have adopted more ozone-resistant cultivars, it may be appropriate to consider the lower end 

of the range of predicted benefits to be more indicative of the likely total benefits. The estimates developed in this analysis, however, do not represent all of 

the likely benefits accruing to agriculture, in that many 

high-value and/or ozone sensitive crops could not be 

included in the analysis due to either exposure-response data limitations or agricultural sector modeling limitations. The second consideration implies that 

benefits will likely be larger than estimated. The minimum case may be the most appropriate starting point, 

however, due to the first consideration: the current 

crop mix is probably biased toward lower ozone responsiveness. Therefore, we anticipate that cumulative total agricultural benefits from the Clean Air Act 

are on the order of ten billion dollars (real terms). 

F-9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Agricultural Effects References 

Abt Associates. 1996. Section 812 Retrospective 

Analysis: Quantifying Health and Welfare 

Benefits (Draft). Prepared by Abt Associates 

under Contract No. 68-W4-0029. U.S. EPA, 

Office of Policy, Planning, and Evaluation. 

Lefohn, Allen S. et. al. 1988. A comparison of indices that describe the relationship between 

exposure to ozone and reduction in the yield 

of agricultural crops. Atmospheric Environment 22: 1229-1240. 

Lee, E. Henry et. al. 1994. Attainment and effects issues regarding alternative secondary ozone air 

quality standards. J. Environ. Qual. 23:1129-

1140. 

National Acid Precipitation Assessment Program 

(NAPAP). 1991. 1990 Integrated assessment 

report. National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington, D.C. 20503. 

SAI, ICF Kaiser. 1995. Retrospective Analysis of 

ozone air quality in the United States: final 

report. Prepared by Systems Applications International under contract 68-D4-0103. U.S. 

EPA, Office of Policy Analysis and Review. 

Shriner, D.S., W.W. Heck, S.B. McLaughlin, D.W. 

Johnson, P.M. Irving, J.D. Joslin, and C.E. 

Peterson. 1990. Response of vegetation to atmospheric deposition and air pollution. 

NAPAP SOS/T Report 18, In: Acidic Deposition: State of Science and Technology, Volume III, National Acid Precipitation Assessment Program, 722 Jackson Place NW, Washington, D.C. 20503. 

Taylor, C.R. 1990. Supply Control Aspects of the 

Conservation Reserve. In: T.L. Napier (Ed) 

Implementing the Conservation Title of the 

Food Security Act of 1985. Ankeny, Iowa: 

Soil and Water Conservation Society. 

Taylor, C.R. 1993a. AGSIM: An Econometric-Simulation Model of Regional Crop and National 

Livestock Production in the United States. In: 

C.R. Taylor, K.H. Reichelderfer, and S.R. 

Johnson (Eds) Agricultural Sector Models for 

the United States: Descriptions and Selected 

Policy Applications. Ames Iowa: Iowa State 

University Press. 

Taylor, C.R. 1993b. Policy Evaluation Exercises with 

AGSIM. In: C.R. Taylor, K.H. Reichelderfer, 

and S.R. Johnson (Eds) Agricultural Sector 

Models for the United States: Descriptions 

and Selected Policy Applications. Ames Iowa: 

Iowa State University Press. 

Taylor, C.R. 1994. Deterministic vs. Stochastic Evaluation of the Aggregate Effects of Price Support Programs. Agricultural Systems 44:461-

474. 

Taylor, C.R., G.A. Carlson, F.T. Cooke, K.H. 

Reichelderfer, and I.R. Starbird. Aggregate 

Economic Effects of Alternative Boll Weevil 

Management Strategies. Agricultural Econ. 

Res. 35:19-19. 

Taylor, C.R., R.D. Lacewell, and H. Talpaz. 1979. Use 

of Extraneous Information with the Econometric Model to Evaluate Impacts of Pesticide Withdrawals. Western J. of Ag. Econ. 

4:1-8. 

Taylor, C.R., J.B. Penson Jr., E.G. Smith, and R.D. 

Knutson. 1991. Impacts of Chemical Use 

Reduction in the South. S.J. Of Ag. Econ. 

23:15-23. 

1994. Aggregate Economic Effects of CRP Land Returning to Production. J. of Soil and Water 

Conservation 49:325-328. 

USDA. 1984. Usual Planting and Harvesting Dates 

for U.S. Field Crops. Statistical Reporting 

Service Agricultural Handbook No. 628. 

USDA. 1995. Crops County Data. National Agricultural Statistics Service Dataset (Electronic 

File) 93100A and 93100B. 

F-10

Appendix G: Lead Benefits Analysis

G-1

Appendix G: Lead Benefits Analysis

Introduction

The scientific understanding of the relationship

between lead and human health is rapidly expanding.

This expansion is documented in numerous EPA studies on the health effects associated with lead exposure. In a pioneering study, Schwartz et al. (U.S. EPA,

1985) quantified a number of health benefits that

would result from reductions in the lead content of

gasoline. The work was extended by EPA’s analysis

of lead in drinking water (U.S. EPA, 1986a) and by

an EPA-funded study of alternative lead National

Ambient Air Quality Standards (U.S. EPA, 1987).

Despite this substantial research, much uncertainty

remains. While the health effects of very high levels

of blood lead (PbB) are quite severe (including convulsions, coma and death from lead toxicity) and have

been known for many years, the effects of lower lead

doses continue to be the subject of intensive scientific investigation. Dose-response functions are available for only a handful of health endpoints associated

with elevated blood lead levels. Other known or

strongly suspected health endpoints cannot be quantified due to a lack of information on the relationship

between dose and effect. Table G-1 presents the health

effects that are quantified in this analysis, as well as

important known health effects that are not quantified.

Some of the health effects that are quantified in

this analysis have not been estimated in previous EPA

analyses. This is largely due to more recent information about the dose-response functions that makes it

possible to expand the health effect coverage beyond

what was done previously. Recent information is available for previously unquantified health effects, and

new information on previously estimated dose-response functions is also available.

Population Grou p Quantified Health Effect Unquantified Health Effect

Adult Male For men in specified age ranges:

Hypertension

Non-fatal coronary heart disease

Non-fatal Strokes

Mortality

Quantified health effects for men in other age

ranges

Other cardiovascular diseases

Neurobehavioral function

Adult Female For women in specified age ranges:

Non-fatal coronary heart disease

Non-fatal stroke

Mortality

Quantified health effects for women in other age

ranges

Other cardiovascular diseases

Reproductive effects

Neurobehavioral function

Children IQ loss effect on lifetime earnings

IQ loss effects on special educational needs

Neonatal mortality due to low birth weight

caused by maternal exposure to lead

Fetal effects from maternal exposure (including

diminished IQ)

Other neurobehavioral and physiological effects

Delinquent and anti-social behavior

Quantified Health Effect Unquantified Health Effect

For men in specified age ranges:

Hypertension

Non-fatal coronary heart disease

Non-fatal Strokes

Mortality

Quantified health effects for men in other age

ranges

Other cardiovascular diseases

Neurobehavioral function

For women in specified age ranges:

Non-fatal coronary heart disease

Non-fatal stroke

Mortality

Quantified health effects for women in other age

ranges

Other cardiovascular diseases

Reproductive effects

Neurobehavioral function

IQ loss effect on lifetime earnings

IQ loss effects on special educational needs

Neonatal mortality due to low birth weight

caused by maternal exposure to lead

Fetal effects from maternal exposure (including

diminished IQ)

Other neurobehavioral and physiological effects

Delinquent and anti-social behavior

Table G-1. Quantified and Unquantified Health Effects of Lead.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-2

Methods Used to Measure and

Value Health Effects

The following sections present relevant dose-response relationships for three population groups: children, men, and women. These sections also discuss

data sources used for the dose-response relationships,

although an extensive review of the literature is not

given.1 In addition, each section includes the methods used to value the changes in health effects determined using these dose-response relationships.

Health Benefits to Children

Changes in IQ

Elevated Pb levels may induce a number of effects on the human nervous system. Generally, these

neurobehavioral effects are more serious for children

than for adults because of children’s rapid rate of development. It is believed that neurobehavioral deficits in children may result from both pre-natal and

post-natal exposure. These nervous system effects may

include hyperactivity, behavioral and attentional difficulties, delayed mental development, and motor and

perceptual skill deficits. Quantification of certain

manifestations of these effects is possible because

sufficient data exist to estimate a dose-response relationship and IQ loss. The relationship used in the

analysis is discussed below.

Quantifying the Relationship Between Blood

Lead Levels and IQ

A dose-response relationship for IQ decrements

has been estimated by a meta-analysis of seven research studies.2 Regression coefficients for each study

were used to determine a weighted average linear regression coefficient for the relationship between lead

and IQ. Each regression coefficient was weighted by

the inverse of the variance of each estimate. To determine an overall coefficient, the regression coefficients

for studies that used natural logarithms of lead as the

exposure index were linearized. In general, the coefficient was linearized in the blood lead range of 10 to

20 μg/dL. However, in one study (Bellinger et al.,

1991), 70 percent of the data were below 10 μg/dL;

thus, the Bellinger data were linearized in the 5 to 15

μg/dL range. For the studies that did not transform

lead concentrations, the regression coefficients were

used directly. Given the typical uncertainty within

individual studies, the variation in the regression coefficients among studies was not more than would be

expected. The relationship determined by Schwartz

(1993) suggests that for a 1 μg/dL increase in lead, a

decrease of 0.25 IQ points can be expected. The pvalue (< 0.0001) indicates that this relationship is

highly significant.

To obtain the total change in number of IQ points

for a population of children, the 0.25 points lost per

μg/dL change in blood lead is multiplied by the average blood lead level for that population. The average

blood lead level modeled in this analysis is a geometric mean, not an arithmetic mean. To adjust for this,

we use a relationship between the expected value and

the geometric mean of a lognormally distributed random variable:

where E(X) is the expected value (mean) of the distribution, GM is the geometric mean, and GSD is the

geometric standard deviation. Taking the natural logarithm of Equation 1 and rearranging gives the ratio

between the expected value and the GM:

For a GSD of 1.6 (the assumed GSD of children’s

blood lead levels3 ), the resulting ratio between E(X)

and GM is 1.117. This ratio is used in equation 5.

1

 For a detailed review of this literature see U.S. Environmental Protection Agency, (1986b) Air Quality Criteria Document for

Lead, and 1989 Addendum. Environmental Criteria and Assessment Office, Office of Research and Development, March.

2

 Schwartz, 1993.

3

 Suggested value for sub-populations provided by IEUBK guidance manual (U.S. EPA, 1994).

 E[x] = exp 1n (GM) + ––––––––– (1) (1n (GSD))2

2













 1n (E(X)) – 1n (GM) = ––––––––– (2) (1n (GSD))2

2

 1n ––––– = ––––––––– (3) (1n (GSD))2

2













E(X)

GM

 ––––– = exp ––––––––– (4) (1n (GSD))2

2













E(X)

GM
Appendix G: Lead Benefits Analysis

G-3

The total lost IQ points for each group was estimated

as:

where (Pop)k

 represents the number of children (up to

age six) around a given industrial source (in the case

of estimating benefits from reduced industrial emissions) or the total U.S. population of children (in the

case of estimating benefits from reductions in gasoline lead emissions).

As shown in equation 5, the population of children up to age six is divided by seven to avoid double

counting. If we assume that children are evenly distributed by age, this division applies this equation to

only children age 0-1. If we did not divide, this equation would count a child who is age zero in the first

year of the analysis and count that same child 6 more

times in successive years. Dividing by seven does create some undercounting because in the first year of

the analysis children from age 1 to 6 are not accounted

for, while presumably they are affected by the lead

exposure.

The analysis assumes a permanent loss of IQ based

on blood lead levels estimated for children six years

and younger. Recent studies4 provide concrete evidence of long-term effects from childhood lead exposure.

Valuing Changes in Children’s Intelligence

Available economic research provides little empirical data for society’s willingness to pay (WTP) to

avoid a decrease in an infant’s IQ. Some research,

however, has addressed monetization of a subset of

the effects of decreased IQ. These effects would represent components of society’s WTP to avoid IQ decreases. Employed alone, these monetized effects

should underestimate society’s WTP. Nevertheless,

for the purpose of this analysis, these effects are used

to approximate the WTP to avoid IQ decrements.

IQ deficits incurred through lead exposure are

assumed to persist throughout the exposed infant’s

lifetime. Two consequences of this IQ decrement are

then considered: the decreased present value of expected lifetime earnings for the infant, and the increased educational resources expended for a infant

who becomes mentally handicapped or is in need of

compensatory education as a consequence of lead

exposure. The value of foregone earnings is addressed

in this section.

The reduction in IQ has a direct and indirect effect on earnings. The direct effect is straightforward:

lower IQs decrease job attainment and performance.

Reduced IQ also results in reduced educational attainment, which, in turn, affects earnings and labor force

participation. These effects on earnings are additive

since the studies used for this analysis have controlled

for these effects separately.5 If personal decisions

about the total amount of education and labor force

participation were based entirely on each individual

maximizing the expected present value of lifetime

income, the magnitude of the indirect effect on income of a small change in educational attainment

would be close to zero,6 and certainly less than the

magnitude of the direct effect. However, individuals

make educational decisions based on a number of

considerations in addition to the effect on the present

value of lifetime earnings, such as satisfaction (utility) derived from formal education, non-compensation aspects of alternative career opportunities, the

ability to pay educational costs, etc. Such considerations could lead to either a positive or negative marginal return to education. Studies7 of educational attainment and lifetime earnings have generally identified a positive marginal return to education, suggesting that the educational attainment decision may not

be based simply on expected earnings.

This analysis uses two sets of estimates of the effects of IQ on earnings. The first estimate, used by

Abt Associates in a previous analysis, is based on several older studies. The second estimate is based on

Salkever (1995).

Older Estimate of the Effect of IQ on Earnings:

The Direct Effect of IQ on Wage Rate

Henry Aaron, Zvi Griliches, and Paul Taubman

have reviewed the literature examining the relation-

(TOTAL LOST IQ)k = ∆GMk × 1.117 × 0.25 × (Pop)k / 7 (5)

4

 For example, Bellinger (1992).

5

 IQ is also correlated with other socio-economic factors which have not been quantified in this analysis.

6

 This is a straightforward result of the “envelope theorem” in economics. In this context, the envelope theorem shows that if

individuals select the level of education that maximizes expected income, then the marginal benefit of additional education (i.e., the

partial derivative of income with respect to education) will be zero at that optimal education level.

7

 Including Chamberlain and Griliches (1977), Ashenfelter and Ham (1979), and Salkever (1995)
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-4

ship between IQ and lifetime earnings.8 They found

that the direct effect, (schooling held constant) of IQ

on wage rates ranged from 0.2 percent to 0.75 percent

per IQ point. Perhaps the best of these studies is

Griliches (1977).9 He reported the direct effect of IQ

on wage rates to be slightly more than 0.5 percent per

IQ point. Because this is roughly the median estimate

of the U.S. EPA review of the literature, this estimate

is used.

Older Estimate of the Effect of IQ on Earnings:

The Indirect Effect of IQ on Earnings

From Needleman et al. (1990) it is possible to

estimate the change in years of schooling attained per

one IQ point change. The study’s regression coefficients for the effect of tooth lead on achieved grade

provide an estimate of current grade achieved. However, many of these children were in college at the

time and are expected to achieve a higher grade level.

Following Schwartz (1990), after adjusting the published results for the fact that a higher percentage of

children with low tooth lead were attending college, a

0.59 year difference in expected maximum grade

achieved between the high and low exposure groups

was estimated. It is assumed that educational attainment relates with blood lead levels in proportion to

IQ. The difference in IQ score between the high and

low exposure group was 4.5 points (from Needleman

et al. (1990)). Dividing 0.59/4.5 = 0.131 suggests that

the increase in lead exposure which reduces IQ by

one point may also reduce years of schooling by 0.131

years.

Studies that estimate the relationship between

educational attainment and wage rates (while controlling for IQ and other factors) are less common. Chamberlain and Griliches (1977) estimate that a one year

increase in schooling would increase wages by 6.4

percent. In a longitudinal study of 799 subjects over 8

years, Ashenfelter and Ham (1979) reported that an

extra year of education increased the average wage

rate over the period by 8.8 percent. We use the average of these two estimates (7.6 percent) to calculate

the indirect effect of increased schooling on the present

value of lifetime income. Increased wages per IQ point

are calculated using: (7.6 percent wage increase/school

year) x (0.131 school years/IQ) = 1.0 percent increase

in earnings per IQ point.

There is one final indirect effect on earnings.

Changes in IQ affect labor force participation. Failure to graduate high school, for example, correlates

with participation in the labor force, principally

through higher unemployment rates and earlier retirement ages. Lead is also a strong correlate with attention span deficits, which likely reduce labor force participation. The results of Needleman et al. (1990) relating lead to failure to graduate high school can be

used to estimate changes in earnings due to labor force

participation. Using the odds ratio from Needleman

et al., it was estimated that a one IQ point deficit would

also result in a 4.5 percent increase in the risk of failing to graduate. Krupnick and Cropper (1989) provide estimates of labor force participation between

high school graduates and non-graduates, controlling

for age, marital status, children, race, region, and other

socioeconomic status factors. Based on their data,

average participation in the labor force is reduced by

10.6 percent for persons failing to graduate from high

school. Because labor force participation is only one

component of lifetime earnings (i.e., earnings = wage

rate X years of work), this indirect effect of schooling

is additive to the effect on wage rates. Combining this

estimate with the Needleman result of 4.5 percent increase in the risk of failing to graduate high school

per IQ point, indicates that the mean impact of one IQ

point loss is a (10.6 percent x 4.5 percent) = 0.477

percent decrease in expected earnings from reduced

labor force participation.

Combining the direct effect of 0.5 percent with

the two indirect effects (1.0 percent for less schooling

and 0.477 percent for reduced labor force participation) yields a total of 1.97 percent decrease in earnings for every loss of one IQ point.

Newer Estimate of the Effect of IQ on Earnings:

Salkever (1995)

One of the most recent studies of the effects of IQ

on earnings is Salkever (1995). Such an analysis with

more recent data is valuable because the labor market

has undergone many changes over the quarter century in which earlier studies have appeared. Like the

analysis of the effect of IQ on earnings presented

above, Salkever (1995) estimates this as the sum of

direct and indirect effects. The direct effect is the sum

of effects of IQ test scores on employment and earn8

 U.S. EPA, 1984.

9

 Griliches used a structural equations model to estimate the impact of multiple variables on an outcome of interest. This method

has conceptual advantages over other empirical estimates used in the literature because it successfully controls for the many confounding variables that can affect earnings.
Appendix G: Lead Benefits Analysis

G-5

ings for employed persons, holding years of schooling constant. The indirect effect works through the

effect of IQ test scores on years of schooling attained,

and the subsequent effect of years of schooling on the

probability of employment, and on earnings for employed persons.

Salkever (1995) provides updated estimates all of

the necessary parameters using the most recent available data set, the National Longitudinal Survey of

Youth (NLSY). Three regression equations provide

these parameters. The years of schooling regression

shows the association between IQ scores and highest

grade achieved, holding background variables constant. The employment regression shows the association between IQ test scores, highest grade, and background variables on the probability of receiving earned

income. This regression thus provides an estimate of

the effect of IQ score on employment, holding schooling constant, and the effect of years of schooling on

employment, holding IQ constant. The earnings regression shows the association between IQ test scores,

highest grade, and background variables on earnings,

for those with earned income.

These regressions provide parameters needed to

estimate the total effect of a loss of an IQ point on

earnings. The direct effects of IQ on employment and

earnings for employed persons, holding schooling

constant, come from the employment and earnings

regressions. The indirect effect of IQ on employment

through schooling is the product of the effect of IQ on

years of schooling, from the years of schooling regression, and the effect of highest grade on employment, from the employment regression. The indirect

effects of IQ on earnings for employed persons through

schooling is the product of the effect of IQ on years

of schooling, from the years of schooling regression,

and the effect of highest grade on earnings for employed persons, from the earnings regression.

The total estimated effect of a loss of an IQ point

on earnings is larger than the previous estimate of 1.97

percent. Based on the Salkever study, the most recent

estimate of the effect of an IQ point loss is now a

reduction in earnings of 1.93 percent for men and 3.22

percent for women, which is a participation-weighted

average of 2.39 percent.

Value of Foregone Earnings

In the next step to monetize intelligence effects,

the percent earnings loss estimate must be combined

with an estimate of the present value of expected lifetime earnings. Data on earnings for employed persons and employment rates as a function of educational attainment, age, and gender were reported for

the U.S. population in 1992 by the Bureau of the Census.10 Assuming this distribution of earnings for employed persons and labor force participation rates remains constant over time, and further assuming a trend

rate of real wage growth (productivity effect), an annual discount factor, and year-to-year survival probabilities, the current Census data on earnings can be

used to calculate the mean present value of lifetime

earnings of a person born today. This analysis assumed

a person received earned income from age 18 to age

64, and assumed a real wage growth rate of one percent and an annual discount rate of five percent. Men

tend to earn more than women because of higher wage

rates and higher labor force participation. However,

for both men and women, expected lifetime earnings

increase greatly with education.

While the Census data are most likely the best

available basis for projecting lifetime earnings, a number of uncertainties deserve mention. Labor force participation rates of women, the elderly, and other groups

will most likely continue to change over the next decades. Real earnings of women will probably continue

to rise relative to real earnings of men. Unpredictable

fluctuations in the economy’s growth rate will probably affect labor force participation rates and real wage

growth of all groups. Medical advances will probably

raise survival probabilities.

One problem that was addressed was the fact that

the current educational distribution for older persons

today is an especially poor predictor of educational

attainment for those born today, since educational attainment has risen over time. In fact, if one simply

projected educational attainment for a person born

today using this method, this person would lose years

of schooling with age (starting between ages 40 and

50), since average years of schooling declines with

age in a one-time snapshot of the current population.

To address this issue, the analysis assumes education

levels cannot fall as a person ages.

Note that use of earnings is an incomplete measure of an individual’s value to society. Those individuals who choose not to participate in the labor force

for all of their working years must be accounted for,

since the lost value of their productive services may

not be accurately measured by wage rates. The largest group are those who remain at home doing housework and child rearing. Also, volunteer work contrib10 U.S. Department of Commerce, 1993
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-6

utes significantly to social welfare and rates of

volunteerism tend to increase with educational attainment and income.11 If the opportunity cost of nonwage compensated work is assumed to be the average

wage earned by persons of the same sex, age, and education, the average lifetime earnings estimates would

be significantly higher and could be approximated by

recalculating the tables using full employment rates

for all age/sex/education groups. To be conservative,

only the value of lost wages is considered in this analysis.

The adjusted value of expected lifetime earnings

obtained above is a present value for an individual

entering the labor force at age 18 and working until

age 64. Given a five percent discount rate, the other

assumptions mentioned, and current survival probabilities,12 the present value of lifetime earnings of a

person born today would be $170,169.

Costs of Additional Education

The increase in lifetime earnings from additional

education is the gross return to education. The gross

return to education, however, does not reflect the cost

of the additional education. The cost of the marginal

education must be subtracted from the gross return in

order to obtain the net increase per IQ point from additional education. There are two components of the

cost of marginal education; the direct cost of the education, and the opportunity cost of lost income during

the education. An estimate of the educational cost

component is obtained from the U.S. Department of

Education.13 The marginal cost of education used in

this analysis is assumed to be $5,500 per year. This

figure is derived from the Department of Education’s

reported ($5,532) average per-student annual expenditure (current plus capital expenditures) in public

primary and secondary schools in 1989-’90. For comparison, the reported annual cost of college education

(tuition, room and board) in 4 year public institutions

is $4,975, and $12,284 for private institutions.

The estimated cost of an additional 0.131 years

of education per IQ point (from the older estimate of

IQ effects) is $721 (i.e., 0.131 x $5,500). Because this

marginal cost occurs at the end of formal education,

it must be discounted to the time the exposure and

damage is modeled to occur (age zero). The average

level of educational attainment in the population over

age 25 is 12.9 years.14 Therefore, the marginal educational cost is assumed to occur at age 19, resulting in

a discounted present value cost of $285.

The other component of the marginal cost of education is the opportunity cost of lost income while in

school. Income loss is frequently cited as a major factor in the decision to terminate education, and must

be subtracted from the gross returns to education. An

estimate of the loss of income is derived assuming

that people in school are employed part time, but

people out of school are employed full time. The opportunity cost of lost income is the difference between

full-time and part-time earnings. The median annual

income of people ages 18-24 employed full-time is

$16,501, and $5,576 for part-time employment.15 The

lost income associated with being in school an additional 0.131 years is $1,431, which has a present discounted value at age zero of $566.

Salkever found a smaller effect of IQ on educational attainment (0.1007 years per IQ point, versus

0.131 years), which results in smaller estimated costs.

Using the same method and data described above, the

estimated present value of educational cost per IQ

point is $219, and the income opportunity cost is $435.

Final Estimate of the Effect of IQ on Earnings.

Combining the value of lifetime earnings with the

two estimates of percent wage loss per IQ point yields

a low estimate of $170,169 x 1.97 percent = $3,000

per lost IQ point, and a higher estimate of $4,064 based

on Salkever (1995). Subtracting the education and

opportunity costs reduces these values to $2,505 and

$3,410 per IQ point, respectively. This analysis uses

the midpoint of these two estimates, which is $2,957.

Of course, changing the discount rate would change

this estimate. With an assumed discount rate of seven

percent, the final estimate is only $1,311. With an

assumed discount rate of three percent, the final estimate rises to $6,879.

11 U.S. Department of Commerce, 1986. Table No. 651, p. 383.

12 Special education costs for children who do not survive to age 18 are not counted, which results in some underestimation of

benefits. However, most child mortality occurs before the age of 7, when the special education begins, so this under-counting is not

substantive.

13 “Digest of Education Statistics”. U.S. Dept. of Education, 1993.

14 “Digest of Education Statistics”. U.S. Dept. of Education, 1993.

15 “Money Income of Households, Families, and Persons in the United States: 1992”. U.S. Department of Commerce, 1993.
Appendix G: Lead Benefits Analysis

G-7

Children with IQs Less Than 70

Quantifying the Number of Children with IQs

Less than 70

In addition to the total IQ point decrements that

can be predicted to occur in a population of children

having a specified blood lead distribution, increases

are also expected to occur in the incidence of children

having very low IQ scores as the mean blood lead

level for that population increases. IQ scores are

normalized to have a mean of 100 and a standard deviation of fifteen. An IQ score of 70, which is two

standard deviations below the mean, is generally regarded as the point below which children require special compensatory education tailored to the mentally

handicapped.

The relationship presented here for estimating

changes in the incidence of IQ < 70 was developed to

make use of the most current IQ point decrement function provided by Schwartz (1993). It is assumed that

for a baseline set of conditions where a population of

children has a blood lead distribution defined by some

geometric mean and geometric standard deviation, that

population also has a normalized IQ point distribution with a mean of 100 and a standard deviation of

15. For this baseline condition, the proportion of the

population expected to have IQ < 70 is determined

from the standard normal distribution function:

where:

P(IQ <70) = Probability of IQ scores less than

70

z = standard normal variate; computed for an IQ score of 70, with

mean IQ score of 100 and standard deviation of 15 as:

Φ(z) = Standard normal distribution

function:

The integral in the standard normal distribution

function does not have a closed form solution. Therefore, values for Φ(z) are usually obtained readily from

software with basic statistical functions or from tables

typically provided in statistics texts. The solution for

Φ(z) where z = -2 is 0.02275. That is, for the normalized IQ score distribution with mean of 100 and standard deviation of 15, it is expected that approximately

2.3 percent of children will have IQ scores below 70.

To estimate changes in the proportion of children

with IQ scores below 70 associated with changes in

mean blood lead levels for a population of children,

the following two key assumptions are made:

1. The mean IQ score will change as a result of

changes in the mean blood lead level as:

where

are the changes in the mean IQ score and in

the mean blood lead levels, respectively, between the no-control and control scenarios.

This relationship relies on Schwartz’ estimate

(1993) of a decrease of 0.25 IQ points for each

μg/dL increase in blood lead. Note that the

mean blood lead level referred to here is the

arithmetic mean (or expected value) for the

distribution obtained as described previously

from the GM and GSD.

2. The standard deviation for the IQ distribution

remains at 15.

Using these assumptions, the change in the proportion of children having IQ <70 can then be determined for a given change in mean blood lead from:

where,

For a given change in PbB between the control

and no-control scenarios a response in terms of IQ is

calculated. The procedure above yields an estimate

of the percent of the population with IQs less than 70.

This percentile is multiplied by the exposed population of children to estimate the increased incidence of

 P(IQ<70) = Φ(z) (6)

 z = –––––––– = –2 (7) 70 – 100

15

 ––– e du (8)

– —2

z

⌠



⌡

−∞

u2

1

√

2π

∆IQ = -0.25 × ∆PbB

∆IQ and ∆PbB

∆P(IQ<70) = Φ(zNo-control) - Φ(zcontrol) = Φ(z

No-control) - 0.02275 (9)

 z

No-control = ––––––––––––––––––––– (10) 70 – (100 + 0.25 × ∆PbB)

15
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-8

children with low IQs. As in the IQ point loss equation, the results of this function are applied to children age 0-6 and divided by seven to avoid double

counting. (See discussion under equation 5).

This procedure quantifies only the change in the

number of children who pass below the IQ=70 threshold. Any other changes in children’s IQ are quantified using the IQ point loss function described previously. Treating these two endpoints additively does

not result in double counting, because the value associated with the IQ point loss function is the change in

worker productivity while the value associated with

IQs less than 70 is the increased educational costs for

the individual, as discussed below.

Valuing the Reduction in Number of Children

with IQs less than 70

To value the reduction in the number of children

with IQs less than 70, the reduction in education costs

were measured - a clear underestimate of the total

benefits.16 Kakalik et al. (1981), using data from a

study prepared for the Department of Education’s

Office of Special Education Programs, estimated that

part-time special education costs for children who remained in regular classrooms cost $3,064 extra per

child per year in 1978. Adjusting for inflation and real

income growth using the GNP price deflator yields

an estimate of $6,318 per child in 1990 dollars. For

the calculations, this incremental estimate of the cost

of part-time special education was used to estimate

the cost per year per child needing special education

as a result of impacts of lead on mental development.

Costs would be incurred from grades one through

twelve. Discounting future expenses at a rate of three

percent yields an expected present value cost of approximately $52,700 per infant (assuming compensatory education begins at age 7 and continues through

age 18). Note that this underestimates the cost, since

Kakalik et al. measured the increased cost to educate

children attending regular school — not a special education program.

Changes in Neonatal Mortality

Quantifying the relationship between PbB

levels and neonatal mortality

U.S. EPA (1990c) cites a number of studies linking fetal exposure to lead (via in utero exposure from

maternal intake of lead) to several adverse health effects. These effects include decreased gestational age,

reduced birth weight, late fetal death, and increases

in infant mortality.17 The Centers for Disease Control

(CDC, 1991a) presents a method to estimate changes

in infant mortality due to changes in maternal blood

lead levels during pregnancy.18 The analysis links two

relationships. The first relationship, between maternal blood lead level and gestational age of the newborn, was estimated by Dietrich et al. (1987). CDC

then estimated infant mortality as a function of gestational age, using data from the Linked Birth and Infant Death Record Project from the National Center

for Health Statistics. The resulting association is a decreased risk of infant mortality of 10-4 (or 0.0001) for

each 1 μg/dL decrease in maternal blood lead level

during pregnancy. This is the relationship used in the

current analysis.

Valuing changes in neonatal mortality

The central estimate of the monetary benefit associated with reducing risks of neonatal mortality is

$4.8 million per avoided mortality. This analysis attempts to capture the credible range of uncertainty

associated with this estimate by describing the monetary benefit as a distribution of values: a Weibull

distribution with a mean value of $4.8 million and a

standard deviation of $3.24 million. Appendix I documents the derivation of this distribution and the sources

of uncertainty in valuing reduced mortality risks.

Health Benefits to Men

In addition to adversely affecting children’s

health, lead exposure has also been shown to adversely

affect adults. The health effects in adults that are quantified and included in the benefits analysis are all re16 The largest part of this benefit is the parents’ willingness to pay to avoid having their child become mentally handicapped,

above and beyond the increased educational costs.

17 Due to unavailability of suitable data, non-fatal health impacts due to decreased gestational age or reduced birth weight have

not been included in this analysis. For example, the benefits from avoided developmental disabilities such as sensory and motor

dysfunction associated with decreased gestational age have not been included.

18 The estimated change in infant mortality due to change in birth weight was not modeled because the data relating prenatal lead

exposure to birth weight are not as strong as data relating lead exposure and gestational age.
Appendix G: Lead Benefits Analysis

G-9

lated to the effects of lead on blood pressure.19 The

estimated relationships between these health effects

and lead exposure differ between men and women.

The quantified health effects include increased incidence of hypertension (estimated for males only), initial coronary heart disease (CHD), strokes (initial cerebrovascular accidents and atherothrombotic brain

infarctions), and premature mortality. Other health

effects associated with elevated blood pressure, and

other adult health effects of lead including

neurobehavioral effects, are not included in this analysis. This section describes the quantified health effects for men; the next section describes the health

effects for women.

Hypertension

Quantifying the relationship between PbB

levels and hypertension

Elevated blood lead has been linked to elevated

blood pressure (BP) in adult males, especially men

aged 40-59 years.20 Further studies have demonstrated

a dose-response relationship for hypertension (defined

as diastolic blood pressure above 90 mm Hg for this

model) in males aged 20-74 years.21 This relationship is:

where:

∆Pr(HYP) = the change in the probability of

hypertension;

PbB1 = blood lead level in the control

scenario; and

PbB2 = blood lead level in the no-control

scenario.

Valuing reductions in hypertension

The best measure of the social costs of hypertension, society’s willingness to pay to avoid the condition, cannot be quantified without basic research well

beyond the scope of this project. Ideally, the measure

would include all the medical costs associated with

treating hypertension, the individual’s willingness to

pay to avoid the worry that hypertension could lead

to a stroke or CHD, and the individual’s willingness

to pay to avoid changes in behavior that may be required to reduce the probability that hypertension leads

to a stroke or CHD. Medical costs of hypertension

can be divided into four categories: physician charges,

medication costs, hospitalization costs and lost work

time.

This analysis uses recent research results to quantify two components of this benefit category. Krupnick

and Cropper (1989), using data from the National

Medical Care Expenditure Survey, have estimated the

medical costs of hypertension. These costs include

physician care, drugs and hospitalization costs. In

addition, hypertensives have more bed disability days

and work loss days than others of their age and sex.

Krupnick and Cropper estimated the increase in work

loss days at 0.8 per year, and these were valued at the

mean daily wage rate. Adjusting the above costs to

1990 dollars gives an estimate of the annual cost of

each case of hypertension of $681. The estimate is

likely to be an underestimate of the true social benefit

of avoiding a case of hypertension for several reasons. First, a measure of the value of pain, suffering

and stress associated with hypertension is not included.

Second, the direct costs (out-of-pocket expenses) of

diet and behavior modification (e.g., salt-free diets,

etc.) are not valued. These costs are likely to be significant, since modifications are typically severe.

Third, the loss of satisfaction associated with the diet

and behavior modifications are ignored. Finally, the

medication for hypertension may produce side effects

including drowsiness, nausea, vomiting, anemia, impotence, cancer, and depression. The benefits of avoiding these side effects are not included in this estimate.

Quantifying the relationship between blood lead

and blood pressure

Because blood lead has been identified as a risk

factor in a number of cardiovascular illnesses,22 it is

useful to quantify the effect of changes in blood lead

levels on changes in blood pressure for reasons other

than predicting the probability of hypertension. Based

on results of a meta-analysis of several studies,

Schwartz (1992a) estimated a relationship between a

19 Citing laboratory studies with rodents, U.S. EPA (1990c) also presents evidence of the genotoxicity and/or carcinogenicity of

lead compounds. While such animal toxicological evidence suggests that human cancer effects are possible, dose-response relationships are not currently available.

20 Pirkle et al., 1985.

21 Schwartz, 1988.

22 Shurtleff, 1974; McGee and Gordon, 1976; Pooling Project Research Group, 1978.

∆Pr(HYP)= –––––––––––––– - –––––––––––––– (11) 1

1 + e2.744 - .793*(1n PbB1) 1 + e2.744 - .793*(1n PbB2)

1
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-10

change in blood pressure associated with a decrease

in blood lead from 10 μg/dL to 5 μg/dL.23 The coefficient reported by Schwartz leads to the following function relating blood pressure to blood lead for men:

where:

∆DBPmen = the change in men’s diastolic blood

pressure expected from a change in

PbB;

PbB1 = blood lead level in the control scenario (in μg/dL); and

PbB2 = blood lead level in the no-control

scenario (in μg/dL).

This blood lead to blood pressure relationship is

used to estimate the incidence of initial coronary heart

disease, strokes (atherothrombotic brain infarctions

and initial cerebrovascular accidents) and premature

mortality in men.

Changes In Coronary Heart Disease

Quantifying the relationship between blood

pressure and coronary heart disease

Estimated blood pressure changes can be used to

predict the increased probability of the initial occurrence of CHD and stroke.24 Increased blood pressure

would also increase the probability of reoccurrence

of CHD and stroke, but these quantified relationships

are not available. First-time coronary heart disease

events in men can be predicted using an equation with

different coefficients for each of three age groups. For

men between 40 and 59 years old, information from a

1978 study by the Pooling Project Research Group

(PPRG) is used. PPRG (1978) presents a multivariate

model (controlling for smoking and serum cholesterol)

that relates the probability of coronary heart disease

(CHD) to blood pressure. The model used data from

five different epidemiological studies. From this study,

the equation for the change in 10-year probability of

occurrence of CHD is:

where:

∆Pr(CHD40-59) = change in 10-year probability of occurrence of CHD event

for men between 40-59 years old,

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

The relationship between BP and first-time CHD

in older men was determined from information presented in Shurtleff (1974). This study also uses data

from the Framingham Study (McGee and Gordon,

1976) to estimate univariate relationships between BP

and a variety of health effects by sex and for each of

the following age ranges: 45-54, 55-64, and 65-74

years. Single composite analyses for ages 45-74 were

also performed for each sex. For every equation, tstatistics on the variable blood pressure are significant at the 99th percent confidence interval. For men

aged 60 to 64 years old, first-time CHD can be predicted from the following equation:

where:

∆Pr(CHD60-64) = change in 2 year probability

of occurrence of CHD event for

men from 60 to 64 years old;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

For men aged 65 to 74 years old, the following

equation uses data from Shurtleff (1974) to predict

the probability of first-time CHD:

where:

∆Pr(CHD65-74) = change in 2 year probability

of occurrence of CHD event for

men from 65 to 74 years old;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

 ∆DBPmen = 1.4 × 1n ––––––– (12) PbB1

PbB2













23 Schwartz, 1992a.

24 U.S. EPA, 1987.

∆Pr(CHD40-59)= –––––––––––––– - –––––––––––––– (13) 1

1 + e4.996 - 0.030365*DBP1)

1

1 + e4.996 - 0.030365*DBP2)

∆Pr(CHD60-64)= –––––––––––––– - –––––––––––––– (14) 1

1 + e5.19676 - 0.02351*DBP1)

1

1 + e5.19676 - 0.02351*DBP2)

∆Pr(CHD65-74)= –––––––––––––– - –––––––––––––– (15) 1

1 + e4.90723 - 0.02031*DBP1)

1

1 + e4.90723 - 0.02031*DBP2)
Appendix G: Lead Benefits Analysis

G-11

The probability changes calculated using the functions above are used to estimate the number of CHD

events avoided in a given year due to air quality improvements attributable to the Clean Air Act. The resulting CHD incidence estimates include both fatal

and non-fatal events. However, because mortality

benefits are independently estimated in this analysis,

it is important to capture only the non-fatal CHD

events. Shurtleff (1974) reported that two-thirds of

all CHD events were non-fatal. This factor was therefore applied to the estimate of avoided CHD events

for each age category.

Valuing reductions in CHD events

General methodology

Because of the lack of information on WTP to

avoid an initial CHD event, WTP was estimated by

estimating the associated cost of illness (COI). This

will underestimate WTP, as explained in Appendix I.

Full COI consists of the present discounted value of

all costs associated with the event, including both direct and indirect costs incurred during the hospital stay,

as well as the present discounted values of the streams

of medical expenditures (direct costs) and lost earnings (indirect costs) incurred once the individual leaves

the hospital.

Wittels et al. (1990) estimate the total medical

costs within 5 years of diagnosis of each of several

types of CHD events (including acute myocardial infarction, angina pectoris, unstable angina pectoris,

sudden death and nonsudden death) examined in the

Framingham Study. Costs were estimated by multiplying the probability of a medical test or treatment

within five years of the initial CHD event (and associated with that event) by the estimated price of the

test or treatment. All prices were in 1986 dollars. (It

does not appear that any discounting was used.) The

probabilities of tests or treatments were based on

events examined in the Framingham Study. The authors estimate a total expected cost over a five year

period (in 1986 dollars) of $51,211 for acute myocardial infarction, $24,980 for angina pectoris, and

$40,581 for unstable angina pectoris. Converted to

1990 dollars (using the consumer price index for medical care, U.S. Bureau of the Census, 1992), this is

$68,337 for acute myocardial infarction, $33,334 for

angina pectoris, and $54,152 for unstable angina pectoris. (The figures for sudden death and nonsudden

death are not included because the CHD events in this

analysis exclude those that resulted in death, to avoid

double counting.)

Cropper and Krupnick (1990) suggest, in an unpublished study, that CHD-related lost earnings could

be a significant component of total COI, although the

value of earnings lost may vary substantially with the

age of onset of CHD. They estimate, for example, that

an individual whose first heart attack occurs between

ages 55 and 65 will have an expected annual earnings

loss of $12,388 (in 1990 dollars), and a present discounted value of lost earnings over a five-year period

of about $53,600, using a five percent discount rate.

This is almost as much as the total medical costs over

5 years estimated by Wittels et al. (1990) for unstable

angina pectoris, and substantially more than the corresponding estimate of medical costs for angina pectoris. For an individual whose first heart attack occurs between ages 45 and 54, on the other hand, Cropper and Krupnick estimate annual average lost earnings of $2,143 (in 1990 dollars), and a present discounted value of lost earnings over a five-year period

of about $9,300, again using a five percent discount

rate. Cropper and Krupnick do not estimate medical

costs for exactly the same disease categories as Wittels

et al., but their research suggests that whether the fiveyear COI of a CHD event, including both medical costs

and lost earnings, is lower or higher than the average

of the three estimates reported by Wittels et al. depends on an individual’s age at the onset of CHD.

Combining Cropper and Krupnick’s five-year lost

earnings estimates with their estimates for average

annual medical expenditures for ischemic heart disease summed over five years, for example, yields a

total COI of about $47,000 for a 50 year old and

$72,000 for a 60 year old, compared to the $52,000

average of the three estimates reported by Wittels et

al.

In addition to the variability in estimates of medical costs and lost earnings arising from CHD, there is

uncertainty regarding the proportion of pollution-related CHD events associated with the various classes

of CHD. To characterize this uncertainty it was assumed, in the absence of further information, that all

pollution-related CHD events are either acute myocardial infarctions, angina pectoris, or unstable angina pectoris. A distribution of estimates of COI for

pollution-related CHD was generated by Monte Carlo

methods. On each iteration, a value was randomly

selected from each of three continuous uniform distributions. Each value selected was normalized by
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-12

dividing by the sum of the three values, so that the

three normalized values summed to 1.0. The resulting triplet of proportions represents a possible set of

proportions of pollution-related CHD events that are

acute myocardial infarction, angina pectoris, and unstable angina pectoris. The corresponding dollar value

for the iteration is a weighted average of the estimated

dollar values for the three types of CHD event (from

Wittels et al.), where the weights are the three randomly selected proportions. The central tendency estimate of the COI associated with a case of pollutionrelated CHD is the mean of this distribution, about

$52,000.

This estimate is likely to understate full COI because it does not include lost earnings. It is likely to

underestimate total WTP to an even greater extent

because it does not include WTP to avoid the pain

and suffering associated with the CHD event. It is,

however, substantially greater than an estimate based

only on the direct and indirect costs incurred during

the hospital stay.

The valuation for CHD is additive with the valuation for hypertension despite the fact that the conditions often occur together, because the two values represent different costs associated with the conditions.

The valuation for hypertension is based on loss of work

days as a result of hypertension and some of the medical costs associated with treating hypertension. The

valuation for CHD is based on the willingness to pay

to avoid the pain and suffering of the CHD itself.

Therefore, these two valuations can be separated and

added together.

Changes in Initial Cerebrovascular Accidents and

Initial Atherothrombotic Brain Infarctions

Quantifying the relationship between blood

pressure and first-time stroke

Two types of health events are categorized as

strokes: initial cerebrovascular accidents (CA) and

initial atherothrombotic brain infarctions (BI). The risk

has been quantified for the male population between

45 and 74 years old.25 For initial cerebrovascular accidents, the logistic equation is:

where:

∆Pr(CAmen) = change in 2 year probability of

cerebrovascular accident in men;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

For initial atherothrombotic brain infarctions, the

logistic equation is:

where:

∆Pr(BImen) = change in 2 year probability of

brain infarction in men;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

Similar to CHD events, this analysis estimates

only non-fatal strokes (to avoid double-counting with

premature mortality). Shurtleff (1974) reported that

70 percent of strokes were non-fatal. This factor was

applied to the estimates of both CA and BI.

Valuing reductions in strokes

Taylor et al. (1996) estimate the lifetime cost of

stroke, including the present discounted value (in 1990

dollars) of the stream of medical expenditures and the

present discounted value of the stream of lost earnings, using a five percent discount rate. Estimates are

given for each of three separate categories of stroke,

separately for males and females at ages 25, 45, 65,

and 85. For all three types of stroke, the indirect costs

(lost earnings) substantially exceed the direct costs at

the two younger ages, and are about the same as or

smaller than direct costs at the older ages.

Both types of stroke considered in this analysis

fall within the third category, ischemic stroke, considered by Taylor et al. To derive a dollar value of

avoiding an initial ischemic stroke for males, a dollar

value for avoiding ischemic stroke among males age

55 was interpolated from the values for males ages 45

and 65; similarly, a dollar value for avoiding ischemic

stroke among males age 75 was interpolated from the

values for males ages 65 and 85. Of males in the United

25 Shurtleff, 1974.

∆Pr(CAmen)= –––––––––––––– - –––––––––––––– (16) 1

1 + e8.58889 - 0.04066*DBP1)

1

1 + e8.58889 - 0.04066*DBP2)

∆Pr(BImen)= –––––––––––––– - –––––––––––––– (17) 1

1 + e9.9516 - 0.04840*DBP1)

1

1 + e9.9516 - 0.04840*DBP2)
Appendix G: Lead Benefits Analysis

G-13

States between the ages of 45 and 74 (the age group

for which lead-related stroke is predicted), 41.2 percent are ages 45-54 and the remaining 58.8 percent

are ages 55-74. The value of an avoided stroke among

males was calculated as the weighted average of the

values for males in age group 45-54 and males in age

group 55-74 , where the weights are the above percents. The value for age group 45-54 is the average of

the values for ages 45 and 55; the value for age group

55-74 is the average of the values for ages 55, 65 and

75. The resulting average value of an avoided stroke

among males aged 45-74 is about $200,000.

Changes in Premature Mortality

Quantifying the relationship between blood

pressure and premature mortality

Information also exists to predict the increased

probability of premature death from all causes as a

function of elevated blood pressure. U.S. EPA (1987)

used population mean values for serum cholesterol

and smoking to reduce results from a 12 year followup of men aged 40-54 in the Framingham Study

(McGee and Gordon, 1976) to an equation in one explanatory variable:

where:

∆Pr(MORT40-54) = the change in 12 year probability of death for men aged 40-

54;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

Information from Shurtleff (1974) can be used to

estimate the probability of premature death in men

older than 54 years old. This study has a 2 year follow

up period, so a 2 year probability is estimated. For

men aged 55 to 64 years old, mortality can be predicted by the following equation:

where:

∆Pr(MORT55-64)= the change in 2 year probability of death in men aged 55-

64;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

For men aged 65 to 74 years old, premature mortality can be predicted by the following equation:

where:

∆Pr(MORT65-74) = the change in 2 year probability of death in men aged 55-

64;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

Valuing reductions in premature mortality

As discussed above, premature mortality is valued at $4.8 million per case (discussed further in Appendix I). Because this valuation is based on the willingness to pay to the risk of death, and the CHD valuation is based on the willingness to pay to avoid the

pain and suffering of a CHD event (defined as a CHD

event that does not end in death, to avoid double counting), these two endpoints are additive as well.

Health Benefits to Women

Available evidence suggests the possibility of

health benefits from reducing women’s exposure to

lead. Recent expanded analysis of data from the second National Health and Nutrition Examination Survey26 (NHANES II) by Schwartz (1990) indicates a

significant association between blood pressure and

blood lead in women. Another study, by Rabinowitz

et al. (1987), found a small but demonstrable association between maternal blood lead and pregnancy hypertension and blood pressure at time of delivery.

∆Pr(MORT40-54)= –––––––––––––– - –––––––––––––– (18) 1

1 + e5.3158 - 0.03516*DBP1)

1

1 + e5.3158 - 0.03516*DBP2)

∆Pr(MORT55-64)= –––––––––––––– - –––––––––––––– (19) 1

1 + e4.89528 - 0.01866*DBP1)

1

1 + e4.89528 - 0.01866*DBP2)

∆Pr(MORT65-74)= –––––––––––––– - –––––––––––––– (20) 1

1 + e3.05723 - 0.00547*DBP1)

1

1 + e3.05723 - 0.00547*DBP2)

26 The Second National Health and Nutrition Examination Survey (NHANES II) was conducted by the U.S. Department of

Health and Human Services from 1976 to 1980 and provides researchers with a comprehensive set of nutritional, demographic and

health data for the U.S. population.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-14

The effect of lead exposure on the blood pressure

of women, relative to the effect on men, is examined

in a review of ten published studies.27 All of the reviewed studies included data for men, and some included data for women. A concordance procedure was

used to combine data from each study to predict the

decrease in diastolic BP associated with a decrease

from 10 μg/dL to 5 μg/dL PbB. The results suggest

that the effect on blood pressure for women of this

decrease in blood lead is 60 percent of the effect of

the same change observed in men. Thus, for women,

Equation can be rewritten as:

where:

∆DBPwomen = the change in women’s diastolic

blood pressure expected from a

change in PbB;

PbB1 = blood lead level in the control

scenario; and

PbB2 = blood lead level in the no-control

scenario.

Although women are at risk of having lead-induced hypertension, there is not a dose-response function for hypertension in women available at this time.

Omitting the hypertension benefits for women creates an underestimate of the total benefits, but the

impact on the total benefits estimation will likely be

small. Lead raises blood pressure in women less than

in men, so the probability of causing hypertension is

likely to be less than in men, and the total value of

hypertension in men is a small portion of the overall

estimated benefits.

Changes in Coronary Heart Disease

Quantifying the relationship between blood

pressure and coronary heart disease

Elevated blood pressure in women results in the

same effects as for men (the occurrence of CHD, two

types of stroke, and premature death). However, the

general relationships between BP and these health

effects are not identical to the dose-response functions

estimated for men. All relationships presented here

have been estimated for women aged 45 to 74 years

old using information from Shurtleff (1974). First-time

CHD in women can be estimated from the following

equation:

where:

∆Pr(CHDwomen) = change in 2 year probability

of occurrence of CHD event for

women aged 45-74;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

Again, non-fatal CHD events were estimated by

assuming that two-thirds of the estimated events were

not fatal (Shurtleff, 1974).

Valuing reductions in CHD events

Values of reducing CHD events for women are

assumed to be equal to those calculated for men

(above): $52,000 per CHD event.

Changes in Atherothrombotic Brain Infarctions

and Initial Cerebrovascular Accidents

Quantifying the relationship between blood

pressure and first-time stroke

For initial atherothrombotic brain infarctions in

women, the logistic equation is:

where:

∆Pr(BIwomen)= change in 2 year probability of

brain infarction in women aged

45-74;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

The relationship between BP and initial cerebrovascular accidents can be predicted by the following logistic equation:

 ∆DBPwomen = (0.6 × 1.4) × 1n ––––––– (21) PbB1

PbB2













27 Schwartz, 1992b.

∆Pr(CHDwomen)= –––––––––––––– - –––––––––––––– (22) 1

1 + e6.9401 - 0.03072*DBP1)

1

1 + e6.9401 - 0.03072*DBP2)

∆Pr(BIwomen)= –––––––––––––– - –––––––––––––– (23) 1

1 + e10.6716 - 0.0544*DBP1)

1

1 + e10.6716 - 0.0544*DBP2)

∆Pr(CAwomen)= –––––––––––––– - –––––––––––––– (24) 1

1 + e9.07737 - 0.04287*DBP1)

1

1 + e9.07737 - 0.04287*DBP2)
Appendix G: Lead Benefits Analysis

G-15

where:

∆Pr(CAwomen) = change in 2 year probability

of cerebrovascular accident in

women aged 45-74;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

The predicted incidences of avoided BI and CA

were multiplied by 70 percent to estimate only nonfatal strokes (Shurtleff, 1974).

Valuing reductions in strokes

The value of avoiding an initial cerebrovascular

accident or an initial atherothrombotic brain infarction for women was calculated in the same way as for

men (see above). Of women in the United States between the ages of 45 and 74 (the age group for which

lead-related stroke was predicted), 38.2 percent are

ages 45-54 and the remaining 61.8 percent are ages

55-74. Using these percentages, and the gender- and

age-specific values in Taylor et al. (1996) the average

value among women ages 45-74 of avoiding either

type of stroke was estimated to be about $150,000.

Changes in Premature Mortality

Quantifying the relationship between blood

pressure and premature mortality

The risk of premature mortality in women can be

estimated by the following equation:

where:

∆Pr(MORTwomen) = the change in 2 year probability of death for women aged

45-74;

DBP1 = mean diastolic blood pressure in

the control scenario; and

DBP2 = mean diastolic blood pressure in

the no-control scenario.

Valuing reductions in premature mortality

The value of reducing premature mortality for

women is assumed to be equal to that estimated for

all premature mortality, $4.8 million per incident (see

Appendix I).

Quantifying Uncertainty

Characterizing Uncertainty Surrounding the DoseResponse Relationships

The dose-response functions described for each

health endpoint considered above generally quantify

the adverse health effects expected due to increased

exposure to lead. For children, these relationships are

described directly in terms of changes in blood lead.

For adults, effects are estimated in terms of changes

in blood pressure (which are related to changes in

blood lead levels). As with any estimate, uncertainty

is associated with the dose-response relationships.

Consistent with the approach outlined in Appendix D for the non-lead criteria air pollutants, this analysis attempts to capture the uncertainty associated with

these relationships. This is accomplished by estimating a distribution associated with each dose-response

coefficient using the information reported in the literature. This analysis assumes these distributions to

be normal. For each of the coefficients used to relate

adverse health effects to lead exposure, Table G-2

summarizes the means and standard deviations of the

normal distributions used in this analysis.

Characterizing Uncertainty Surrounding the

Valuation Estimates

The procedure for quantifying uncertainty associated with the valuation estimates is similar to that

used to characterize the dose-response coefficient estimates. The valuation distributions for health effects

considered in the lead analysis are documented in

Appendix I. ∆Pr(MORTwomen)= –––––––––––––– - –––––––––––––– (25) 1

1 + e5.40374 - 0.01511*DBP1)

1

1 + e5.40374 - 0.01511*DBP2)
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-16

Industrial Processes and Boilers

and Electric Utilities

This section describes the methods and data

sources used to estimate changes in blood lead levels

due to changes in lead emissions from industrial processes and boilers between 1970 and 1990 and from

electric utilities between 1975 and 1990. The estimates

of the changes in health effects resulting from changes

in lead emissions due to the CAA are also presented.

Methods Used to

Determine Changes in

Lead Emissions from

Industrial Processes

from 1970 to 1990

This analysis used several

sources to determine the

changes in facility-specific

emissions of lead from industrial processes. To summarize,

the analysis extracted 1990 facility-specific lead emissions

data from the Toxics Release

Inventory (TRI), which provides recent emissions data for

over 20,000 U.S. manufacturing facilities. This study then

adjusted these data by the relative changes in lead emissions

between 1970 and 1990; these

relative changes were derived

from several data sources described below. This method

yielded facility-specific emissions for five year intervals between 1970 and 1990 for both

the controlled and uncontrolled

scenarios. The five-year values

were interpolated to derive annual changes for each year between 1970 and 1990. Specific

details on this approach are

given below.

TRI Data

The Toxics Release Inventory (TRI) is mandated by the

Superfund Amendment Reauthorization Act (SARA) Title

III Section 313 and requires that U.S. manufacturing

facilities with more than 10 employees file annual reports documenting multimedia environmental releases

and off-site transfers for over 300 chemicals. Facilities report both stack and fugitive releases to air. Reported releases are generally estimates rather than

precise quantifications. Emissions data can be presented as numerical point estimates, or, if releases are

below 1,000 pounds, as an estimated range of emissions.

Parameters of Normal

distributions describing DoseResponse Coefficients

Health Effect Mean Standard

Deviation

Blood Lead-Blood Pressure Coefficient

(Adults)

1.44 0.85

Adult Males

Mortality (ages 40-54) 0.03516 0.16596

Mortality (ages 55-64) 0.01866 0.00533

Mortality (ages 65-74) 0.00547 0.00667

Chronic Heart Disease (ages 40-59) 0.030365 0.003586

Chronic Heart Disease (ages 60-64) 0.02351 0.028

Chronic Heart Disease (ages 65-74) 0.02031 0.00901

Cerebrovascular Accidents 0.04066 0.00711

Atherothrombotic Brain Infarctions 0.0484 0.00938

Hypertension 0.793 not available

Adult Females

Mortality (ages 45-74) 0.01511 0.00419

Chronic Heart Disease 0.03072 0.00385

Cerebrovascular Accidents 0.04287 0.00637

Atherothrombotic Brain Infarctions 0.0544 0.00754

Children

Infant Mortality 0.0001 not available

Lost IQ Points 0.245 0.039

IQ<70 (cases) relies on Lost IQ Point distribution 

Parameters of Normal

distributions describing DoseResponse Coefficients

Health Effect Mean Standard

Deviation

Table G-2. Uncertainty Analysis: Distributions Associated With DoseResponse Coefficients Used to Estimate Lead Health Effects.
Appendix G: Lead Benefits Analysis

G-17

From the TRI data base, this analysis extracted

data from the reporting year 1990 for all facilities reporting emissions of lead to air, as either stack or fugitive emissions. Data were reported as annual emissions (in pounds per year). Where emissions are reported as a range, this analysis used the upper bound

of the range to represent the emissions.28 TRI facilities also report their location by latitude and longitude. In order to later match facilities emitting lead

with Census data on surrounding exposed populations,

this analysis uses the latitudes and longitudes of leademitting facilities.

Derivation of Industrial Process Emissions

Differentials 1970-1990

The TRI database is the Agency’s single best

source of consistently reported release data; however,

the database does not include information for most of

the years modeled in this analysis. Furthermore, this

analysis required estimates of hypothetical emissions

in the absence of the CAA. Therefore, estimates were

created for the emissions of lead from industrial

sources under the CAA, and in the absence of the CAA,

for the years 1970, 1975, 1980, 1985, and 1990. The

percent changes, or differentials, reflected by these

estimates were then applied to the 1990 TRI data to

obtain facility-level release estimates for the years of

interest for the control and no-control scenarios.

The method for creating these differentials captured the two potential causes of the differences between emissions from industrial sources regulated by

the CAA and emissions from those same sources in

the absence of the CAA. The first cause of the difference in emissions is a change in overall industrial

output, resulting from the macroeconomic impact of

the CAA. The second element is a change in emissions per unit of output, which results from the adoption of cleaner processes and the application of emissions control technology mandated by the CAA. The

methods used to project the effects of these two causes,

described below, were designed to be as consistent as

possible with other emissions projection methods for

other segments of the CAA retrospective analysis.

Data sources

Data for the differentials estimates were taken

from the following sources:

• the Jorgenson/Wilcoxen (J/W) model projections, conducted as part of the section 812

analysis. This data source addresses the first

cause of changes in emissions: the macroeconomic changes that resulted from the implementation of the 1970 CAA. The J/W model

calculated the change in economic output for

each of thirty-five industrial sectors, roughly

analogous to two-digit standard industrial

classification (SIC) codes, that resulted from

the CAA’s implementation. The specific output used from the J/W model in this analysis

was the percentage change in economic output for the various industrial sectors, rather

than any absolute measure of economic activity.

• the 1991 OAQPS Trends database. This data

base is an emissions projection system that

was used to produce the report, “The National

Air Pollutant Emission Estimates, 1940-

1990.” It contains information on economic

activity, national level emissions and emission controls, by industrial process, from 1970

through 1990. Three different elements were

extracted from the Trends database: the emissions of lead per unit economic output for

various industrial processes for the years

1970-1990; annual economic output data for

these industrial processes; and the emission

calculation formula.

• the National Energy Accounts (NEA), compiled by the Bureau of Economic Analysis.

This database records the historical levels of

industrial energy consumption, disaggregated

by fuel type at the approximately three-digit

SIC code level.

The manner in which these data were combined

to derive lead emissions estimates is described below.

Estimates of industrial process emissions in the

control scenario

Emissions data for industrial processes were estimated for the years 1970, 1975, 1980, 1985, and 1990.

For each of these years, this analysis extracted an

emission factor and a control efficiency for each lead28 Ranges are infrequently reported and are either reported as 0-500 lbs. or 500-1000 lbs. The infrequency of the incidence of a

facility reporting a range and the relatively small quantities of lead released by those facilities means any overestimation of benefits

that results from using the upper limit of the range is extremely minor.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-18

emitting industrial process in the Trends database.

Emissions factors are expressed as amount of lead

emitted per unit of economic activity, and control efficiencies are reported as the percent that emissions

are reduced through the application of pollution control technology to the process. The year-specific emission factors and control efficiencies were multiplied

by the economic activity data for that year, for that

process, as reported in the Trends database, using the

following equation found in the Trends report:

This calculation yielded the estimated control scenario emissions, by industrial process. Industrial processes were then assigned to an NEA code. Finally,

all processes assigned to a given NEA code were

summed to give a total emissions estimate for that

NEA code.

Estimates of industrial process emissions in the

no-control scenario

The results from the J/W model were used to estimate process emissions in the no-control scenario.

As stated above, the J/W model provides percent

changes in economic outputs by industrial sector. To

use these values, lead-emitting industrial processes

(in the Trends database) were assigned to a J/W sector. The percent change for that sector from the J/W

model was then used to adjust the economic activity

data for that process from the Trends database. These

adjusted economic output figures were used together

with 1970 emission factors and control efficiencies to

derive the estimated lead emissions for each industrial process in the no-control scenario. The 1970

emission factors and control efficiencies were used

for all years in the analysis (1970, 1975, 1980, 1985

and 1990) in the no-control scenario; this assumes that

emissions per unit economic output and control efficiencies would have been constant over time in the

absence of the CAA. This is the same approach that

was used to project the changes in emissions from

industrial processes for other criteria pollutants in other

portions of the CAA retrospective analysis. The process-level emissions were then aggregated to the NEAcode level, as in the controlled scenario.

Matching TRI Data to Industrial Process

Emissions Differentials

The methods described in the preceding section

yielded emissions estimates from industrial processes

in the control and no-control scenarios, by NEA code.

We used these estimates to derive percent changes in

emissions between control and no-control scenarios,

by NEA code, for application to the TRI emissions

data. However, since TRI data are reported by SIC

code, we first mapped” NEA codes to the appropriate

SIC codes, and used the percent change for each NEA

code to represent the percent change for all SIC codes

covered by that NEA code.

It should be noted that the Trends data base covers only the most important sources of lead in air, not

all sources; as a result, not all SIC codes reporting

lead emissions in TRI correspond to an NEA code for

which emission differentials have been estimated.

However, we assume that the TRI emissions sources

that have a match are the most important sources of

lead air emissions. In fact, although only 48 out of

519 legitimate SIC codes reporting lead emissions in

TRI have matching differentials, these SIC codes account for over 69 percent of the lead emissions reported in TRI. The remaining 31 percent of the emissions are distributed relatively evenly among the remaining 471 SIC codes, each of which contributes a

small amount to total emissions.

For the 31 percent of the emissions without differentials, this analysis has no information regarding

the change in the lead emissions over time or between

the control and no-control scenarios; therefore, we are

unable to predict benefits attributable to the CAA for

these emission sources. Although excluding these

sources may lead us to underestimate total benefits,

we believe these sources are unlikely to contribute

significantly to the difference between control and nocontrol scenarios. The Trends data focus on the point

sources of lead emissions of greatest concern to the

Project Team and of greatest regulatory activity. If a

process within an SIC code does not appear in the

Trends, it is unlikely to have had specific CAA controls instituted over the past 20 years. A lack of control efficiencies for smaller sources prevents them

from being included.

It should also be noted that the total industrial

process emissions of lead estimated in the 1990 Trends

report actually exceeds the reported lead emissions in

 Emissions = (Economic Activity) ×

 (Emission Factor) × (1 - Control Efficiency) (26)
Appendix G: Lead Benefits Analysis

G-19

TRI, despite the fact that TRI covers more SIC codes.

This is probably attributable in part to the fact that

TRI covers only a subset of the facilities contributing

to economic output in an SIC code. TRI reporting rules

only require facilities with greater than 10 employees

and who use certain amounts of lead in their processes

to submit information to TRI, while the Trends report

attempted to estimate emissions from all sources contributing to the economic output for the industrial sector, regardless of size. However, the components of

the Trends data base used in this analysis (i.e., emissions factors, economic output data) represent typical

conditions at average facilities; they do not allow for

the representation of the distribution of emissions

across particular facilities. In contrast, a major strength

of the TRI is its match of emissions data with geographical information. Because the distribution of

emissions geographically determines the size of exposed populations, this analysis used the TRI data,

rather than Trends data, to characterize lead release

quantities, and used the Trends figures only to characterize relative emissions and changes over time,

rather than to estimate total quantities.

Because the Trends data are intended only as an

estimate of emissions using typical conditions at average facilities, and do not capture the differences in

facility-level emissions, the data do not provide sufficient information to make specific quantitative adjustments to the TRI-based benefits estimates to account

for the overall higher emissions estimates in Trends.

However, since Trends does generally suggest that

there are many more sources than are accounted for

by TRI, it is possible that our benefits calculations

may be underestimated.

Some additional assumptions were necessary

when matching the TRI lead release data and the differentials from the Trends data. Ideally, we would like

to know whether the facilities present at a given location, as reported in the 1990 TRI, were present and

operating in earlier years; whether facilities operating in 1970 have ceased to operate; and whether new

facilities would have been constructed in the no-control situation. Unfortunately, data do not exist in an

accessible form at this level of detail for the years

1970 through 1990. Therefore, for the purposes of this

exercise, we have assumed that the locations and numbers of the 1990 sources are the same as they were in

1970.

Methods Used to Determine Changes in

Lead Emissions from Industrial Boilers

from 1970 to 1990

Several sources were used to determine the change

in lead emissions from industrial boilers. TRI

locational data, Trends database national fuel consumption levels and emissions factors, and NEA and

SIC codes were used to derive the emissions for the

control and no-control scenarios.

TRI Data

The TRI does not appear generally to contain combustion emissions data. In general, the emissions data

are from process sources. We reached this conclusion

based on two pieces of information:

(1) TRI reporting requirements: TRI has three

reporting requirements: (a) the facility must fall in

SIC codes 20-39; (b) the facility must employ more

than 10 persons; and (c) the facility must manufacture or process more than 25,000 pounds of a TRI

chemical, or otherwise use more than 10,000 pounds

Firms must submit reports only for the chemical that

exceeds the thresholds given in item (c), but they must

report all releases of that chemical, including releases

from uses that would not qualify alone. If the TRI

chemical is part of a blended substance and the quantity of the TRI chemical in the blend exceeds the

threshold, it must be reported. For industrial boilers,

if the amount of lead in the fuel were to exceed the

10,000 pounds threshold, then the firm would be required to report all emissions of lead from combustion of fuel. There is an exemption, however, for ingredients present in small proportions. If the amount

of lead in the oil were less than 0.1 percent (1,000

ppm), then the firm would not be required to report

the emissions.

The conclusion from the above information is that

most firms burning used oil are probably not reporting lead combustion emissions to TRI because these

releases fall outside the TRI reporting requirements.

The concentration at which lead is typically found is

used oil (100 ppm) (NRDC, 1991) is much less than

the minimum concentration required for reporting

(1,000 ppm).

(2) Use data from the TRI data base: The hypothesis that firms do not report lead combustion was confirmed by an analysis of the data submitted by the
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-20

firms reporting lead use to TRI. On the TRI submission forms, firms must indicate how the chemical is

used. Our analysis of category codes submitted by

firms reporting lead emissions showed the following

four use category reports: as a formulation component; as a reactant; as an article component; and repackaging only. None of these category codes suggest that the source of the reported lead release is combustion. Therefore, we may conclude that all of the

lead emissions reported in TRI are process emissions.

Based on these analyses, the Project Team could

not use the TRI release data to evaluate releases of

lead from industrial combustion. However, the TRI

geographical information was used to locate industrial facilities by longitude and latitude in order to

combine combustion data with population information. For combustion emissions, the calculations included all TRI reporting facilities, not just those who

reported lead emissions. The assignment of combustion emissions to these facilities is described below.

Derivation of Industrial Combustion Emissions

1970-1990

As with industrial process emissions, estimates

were created for the emissions of lead from industrial

combustion under the CAA, and in the absence of the

CAA, for the years 1970, 1975, 1980, 1985, and 1990.

These emissions estimates were used, in combination

with the TRI data base geographic information, to

obtain facility-level release estimates for the years of

interest for the control and no-control scenarios. The

method for deriving these emissions estimates included both the macroeconomic impact of the CAA

and the change in emissions per unit of output that

resulted from specific pollution control mandates of

the CAA. The same data sources were used to derive

combustion differentials as were used to derive process differentials. The particular data elements and

the methods by which these data were combined to

derive lead emissions estimates from industrial combustion are described below.

Estimates of combustion emissions under the

control scenario

The Trends database contains a national aggregate industrial fuel consumption estimate, by fuel type

(coal, natural gas, oil). For each fuel type, the fuel

consumption estimate was disaggregated by the share

of that fuel used by each NEA industrial category,

using the NEA data base. It should be noted that the

NEA includes data only for the years 1970 through

1985. For 1990, the 1985 figures were used to disaggregate the national-level consumption figure into

NEA industrial categories.

The Trends database also contains emissions factors for industrial fuel use, by fuel type, as well as

control efficiencies. The lead emissions from industrial combustion for each NEA category was derived

by multiplying the fuel-specific combustion estimate

for each NEA category by the emission factor and

control efficiency for that fuel type. The result was

emissions of lead by NEA code and by fuel type.

Emissions from all fuel types were then summed by

NEA code. By using the NEA data to disaggregate

the industrial fuel consumption figures, the analysis

assumes that the industrial combustion emissions are

the same among all industries covered by a given NEA

code, an assumption which may bias the analysis.

Estimates of combustion emissions under the

no-control scenario

As in the control scenario, the national aggregate

industrial fuel consumption estimate, by fuel type

(coal, natural gas, oil), was disaggregated by the share

of that fuel used by each NEA industrial category.

The fuel use was then adjusted by one of two factors:

(1) seven of the NEA codes were specifically modeled by the Industrial Combustion Emissions (ICE)

model — for these sectors, the ICE modeled percent

changes were used instead of J/W percent changes; or

(2) the remaining NEA codes were matched to J/W

sectors — the J/W percent changes were then applied

to those matched NEA codes. These fuel use estimates

were then combined with the 1970 emission factors

and control efficiencies for industrial combustion by

fuel type from the Trends database to obtain combustion-related lead emissions from industrial boilers in

the no-control scenario, by NEA code.

The process-specific data in the Trends database,

and the energy use data in the NEA, are much more

disaggregated than the J/W sectoral projections. For

the purpose of the analysis, it was assumed that all of

the specific industrial processes in the Trends database and industrial categories in the NEA data set assigned to a given J/W sector changed at the same rate

as the entire J/W sector. For example, if the economic

activity in the J/W Sector 20, “Primary Metals,”

changed by one percent between the control and nocontrol scenarios, then the analysis assumed that economic activity in each industrial process assigned to
Appendix G: Lead Benefits Analysis

G-21

the Primary Metals sector also increased by one percent. This approach assumes that the economic activities of specific industries within a sector are equally

affected by the imposition of the CAA. This assumption is consistent with the projection of the change in

emissions from industrial processes for the other criteria air pollutants, which were calculated using a similar process.

Matching TRI Data to Industrial Combustion

Emissions Data

Because of the structure of the TRI reporting requirements, it does not appear that TRI generally contains releases from combustion sources. Although TRI

may incidentally contain lead combustion emissions,

TRI would contain data on such releases only if the

reporting facility also used more than 10,000 pounds

of lead per year for manufacturing or processing. As

a result, the combustion releases, estimated using the

methods described above, do not have corresponding

data in the TRI data base. Therefore, we devised a

different method for estimating benefits from changes

in combustion releases.

The first step in the method was to divide the estimates of total releases of lead from industrial combustion, by NEA code, by an estimate of the number

of facilities in each NEA code. The number of facilities in each NEA category was estimated using the

1987 Census of Manufactures. This Census, conducted

by the U.S. Department of Commerce, tallies the number of facilities by four-digit SIC code; these SIC codes

were matched to the NEA codes.

Dividing total lead emissions emitted by number

of facilities yielded the average yearly lead emissions

from industrial combustion for each SIC code. We

then assigned this average value to all reporting TRI

facilities in the SIC code. The consequence of this

approach is that the modeling of combustion from

industrial facilities includes substantially more sources

than the modeling of industrial process emissions;

combustion emissions are assigned to essentially all

facilities reporting to TRI, while the process emissions are only evaluated for facilities actually reporting lead air emissions from processes.

One unavoidable drawback to this approach is that

it cannot capture differences in release quantities

among facilities within an SIC code. Furthermore, this

approach does not capture all combustion emissions

because we assign average emissions only to facilities that report to TRI. TRI facilities account for between two percent and 50 percent of all facilities listed

in the Census of Manufacturers, depending on the SIC

code. Because of the inability to place the remaining

facilities geographically, this analysis excludes the

consideration of emissions from non-TRI facilities.

Methods Used to Determine Changes in

Lead Emissions from Electric Utilities

from 1975 to 1990

The estimation of lead emissions from electric

utilities required data from three different sources.

Energy use data for the control and no-control scenarios were obtained from the national coal use estimates prepared for the section 812 analysis by ICF

Incorporated. The OAQPS Trends Database provided

emissions factors and control efficiencies. Individual

plant latitudes, longitudes, and stack information were

collected from the EPA Interim Emissions Inventory.

This analysis combines these three sets of data and

estimates annual lead emissions at the plant level for

coal burning electric utilities in the control and nocontrol scenarios. This section describes the sources

and the methods used to create the final data set.

Coal-Use Data

The energy use data obtained from national coaluse estimates provide plant level energy consumption

information for 822 electric utilities. The data set were

separated into four distinct sets for the years 1975,

1980, 1985, and 1990. Each set of data provided the

state where the plants are located, the plant names,

and the amount of coal consumed, for both the control and no-control scenarios. The four data sets were

combined into one comprehensive set by matching

the plants’ names and states.

The EPA Interim Emissions Inventory

The EPA Office of Air Quality Planning and Standards Technical Support Division provided the 1991

EPA Interim Emissions Inventory. The Interim Inventory contains data for all electric utility and industrial

plants in the United States including latitude, longitude, stack height, stack diameter, stack velocity, and

stack temperature. The additional stack parameter data

allowed the use of plant-specific parameters in the air

modeling for electric utilities rather than average parameters for all facilities as was done for industrial

emissions.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-22

Matching the Coal-Use Data to the Interim

Emissions Inventory

The combination of the Interim Emissions Inventory and the coal-use data required two steps. First,

the Interim Emissions Inventory had to be pared down

to include only electric utility data, and to narrow the

information provided for each utility. Second, the two

databases had to be combined. One difficulty in combining them was the lack of a common data field that

would allow a quick and complete matching process.

Electric utility plants were identified in the Interim Emissions Inventory by SIC code (code 4911).

The associated stack information file, which lists the

size of every stack on every plant, was reduced to include only the tallest stack for each plant. This provides a reasonable estimate of the stack height at which

most emissions occur. The air modeling assumes that

each electric utility releases its emissions from the

largest stack that exists at that plant.

Next, the procedure matched the abridged Interim

Emissions Inventory file with the coal use data. Due

to the lack of a common data field between the two

sets, this process required several phases. Both data

sets had name fields, but these fields utilized different naming conventions for the plants. Therefore the

name fields were matched directly, with individual

words in the names, and then with abridged words

from the names. Abridged word matches were double

checked by ensuring that the names were indeed similar and by verifying that the state fields matched. Finally some matches were made by hand.

Only 27 unmatched plants with positive coal use

remained. There were 493 matched plants with positive coal usage and these were included in the final

data set.29 To eliminate under-counting of emissions,

the emissions from the 27 unmatched plants were allocated to matched plants within the states where the

unmatched plants were located. Allocations were

weighted according to the emission level for each

matched plant within that state in the year in which

the allocation was being made.

Emissions Factors and Control Efficiencies

At this stage, the electric utilities data set contained coal consumption by plant by year in the control and no-control cases as well as air modeling parameters. Using emission factors for lead and control

efficiencies for electric utilities, estimates of lead emissions per plant per year could now be calculated. As

in the industrial source analysis, the emission factors

and control efficiencies come from the 1991 OAQPS

Trends database.

Control efficiencies are available for coal-fired

electric utilities in each year between 1975 and 1990.

As in the industrial source analysis, it is assumed that

pollution control on coal-burning power plants without the CAA would be the same as the pollution control level in 1970. Therefore, the control efficiency

from 1970 is used in the no-control analysis.

The emission factor obtained from the Trends

database is expressed in terms of lead emitted per ton

of coal burned (6,050 grams per 1,000 tons).30 The

combined data set, though, contains quantity of coal

burned per plant per year in energy units (trillions of

BTUs). To reconcile this difference, a conversion factor was obtained from a 1992 DOE report titled Cost

and Quality of Fuels for Electric Utility Plants 1991.

The conversion factor used (20.93 million BTUs per

ton of coal) is the average BTU per pound of coal

burned for all domestic electric utility plants in 1990.

Data for a small subset of other years were also provided in the DOE report, but they did not differ significantly from the 1990 number. Therefore, the 1990

conversion factor (637.3 pounds of lead per trillion

BTU) is assumed valid over the entire study period.

The final equation for lead emissions looks quite similar to the equation used in the industrial source analysis.31 The only change is that “Economic Activity”

has been replaced by “Coal Consumed” for this particular analysis:

This equation produces estimates of the emissions

per plant per year in both the control and the no-control scenarios.

29 Plants with zero coal usage were not immediately excluded from the analysis due to the possibility of analyzing lead emissions

from oil combustion at these plants. However, OAQPS has suggested that oil combustion comprises under two percent of the total

lead emitted from electric utilities. For this reason, the electric utility analysis focused entirely on coal.

30 The actual figure cited is 12.1 metric pounds per 1,000 tons. A metric pound is one two-thousandth of a metric ton.

31 U.S. EPA, 1991a

 Emissions = (Coal Consumed) ×

 (Emission Factor) × (1 - Control Efficiency) (26)
Appendix G: Lead Benefits Analysis

G-23

Use of Air Dispersion Modeling to

Estimate Ambient Air Lead Levels

To link estimates of lead emissions to blood lead

levels of populations living in the vicinity of a facility, the lead benefits model first uses air dispersion

modeling to estimate air lead concentrations surrounding facilities that emit lead into the air. The air concentrations are then linked to blood lead levels.

This analysis uses the Industrial Source Complex

Long Term (ISCLT) air dispersion model, a steadystate Gaussian plume model, to estimate long-term

lead concentrations downwind of a source. The concentration is modeled as a function of site parameters

(stack height, stack velocity).32 The general form of

the concentration equation from a point source at a

distance r greater than one meter away is as follows:33

where,

Cair = concentration at distance r (μg/m3

),

Q = pollutant emission rate (g/sec),

f = frequency of occurrence of wind speed

and direction,

Θ = sector width (radians),

S = smoothing function used to smooth

discontinuities at sector boundaries,

u = mean wind speed (m/sec),

σz = standard deviation of vertical concentration distribution (m),

V = vertical term (m),

K = scaling coefficient for unit agreement.

For each facility modeled in the lead benefits

model, a 21 by 21 kilometer grid around the source is

specified. The model stores data in 1 km by 1 km cells

and calculates the air lead concentrations for each of

the 441 cells surrounding a given facility. Fugitive

sources are modeled similarly, the only difference

being a modified form of Equation 28.

For facility-specific weather data, the model used

Stability Array (STAR) data. The STAR data contain

information on typical wind speed and direction for

thousands of weather stations in the U.S. For each

facility, the model accesses the STAR data for the

weather station nearest the source. Standard default

parameters are used for the other parameters because

facility-specific data are not available for them (except for utilities). Table G-3 lists default parameters

for the ISCLT, and summarizes sources for other parameters.

Industrial process emissions were modeled as either point or fugitive sources, depending on how they

were reported in TRI. All industrial combustion emissions were modeled as “fugitive” emissions. This is a

more appropriate model scenario for boiler emissions

than a 10 meter stack scenario. All electric utility

sources were modeled as point sources.

The model tracks all lead emissions to a given

grid cell. That is, if the plumes of two or more sources

overlap in a given cell, the air concentration in the

given cell is determined from the sum of all of the

contributing sources.

Determination of Blood Lead Levels

from Air Lead Concentrations

Once the air lead concentrations surrounding a

given plant are estimated, the model estimates blood

lead levels for children and adults living in those areas. This section describes the methods and data

sources used to derive blood lead levels from estimated air lead concentrations.

Relationship Between Air Lead Concentrations

and Blood Lead Levels

The rates at which lead is absorbed from air depend on the age of the exposed individual, distance

from the facility, the initial concentration of blood

lead, and other factors. In addition, rates determined

from empirical data may differ depending on whether

or not the analyses from which rates are derived have

controlled for factors such as lead exposure through

deposition on dust and soil (i.e., “indirect exposure”).

Especially when children constitute the exposed group,

the inclusion of indirect exposure results in higher air

lead to blood lead slopes. In both cases, the slope reCair,r = ––––––––– - ––––––––––– (28) 2K Q f S V D

uσz ijk √

2π rΘ

32 Ideally, reported stack and fugitive air releases would be modeled using site-specific data (such as source area or stack height).

However, since TRI does not contain such facility-specific information, default values are used to model TRI facilities.

33 This equation is from U.S. EPA (1992). The equation is for a specific wind speed, direction, and category (ijk). Each facility

has several combinations of these that must be added to arrive at a total concentration at that point. The equation for area sources is

similar.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-24

lationship is expressed as the change in blood lead

(μg/dL) per change in air concentration (μg/m3

).

In performing this analysis, a choice had to be

made between the use of air lead:blood lead relationships that account for inhalation exposure (“direct”

slopes) and those that account for exposure to lead

deposited from air onto soil and dust (“indirect”

slopes). The choice of which slopes to use considered

both the effects on the estimate of benefits over time

(from 1970 to 1990) and the estimate of the difference in benefits between the control and no-control

scenarios. The indirect slope is more comprehensive

in its coverage of the types of exposures that will result from air releases, and thus captures more of the

health effects predicted to occur from lead exposures,

especially to children. For this reason, indirect slopes

are preferred to direct slopes, especially when comparing the control and no-control scenarios: using only

the direct slope would underestimate the benefits of

avoiding deposition that controls confer. However,

indirect slopes may capture effects from exposure to

soil and dust lead deposited from both current air releases and historic air releases. Since lead’s dissipation from soil is slow relative to its removal from air,

the reservoir of lead in soil and dust is unlikely to

change at the same rate as the reductions in air lead

concentrations. Therefore, using indirect slopes to

represent a change in blood lead over time due to reduced air emissions may overestimate the change in

blood lead, and thus overestimate the benefits of reductions over time, to the extent that the indirect slope

captures exposure to the total reservoir of soil and dust

lead, rather than only recently deposited lead.

Given that the focus of this analysis is the difference between the control and no-control scenarios, it

is important to capture both the benefits from reduced

lead deposition that result from the CAA, and the direct benefits from reduced air concentrations. Therefore, this analysis modeled changes in blood lead levels using indirect slopes. It should be kept in mind

Parameter

Industrial

Source

Value

Electric

Utility

Value

Source/

Comment

Stack height 10 m site-specific or 115.0

m*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

Exit velocity 0.01 m/s site-specific or 22.5

m/s*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

Stack diameter 1 m site-specific or 5.15

m*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

Exit gas temperature 293o

 K site-specific or

427.5*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

Area source size 10 m2 10 m2 U.S. EPA (1992)

Area source height 3 m 3 m U.S. EPA (1992)

Lead emission rate site-specific site-specific Industrial -- TRIS (lbs/yr)

Utilities -- SAI & OA QPS (lbs/yr) 

Frequency of win d speed

and direction

site-specific site-specific ST AR data

Sector width 22.5o 22.5o 360o

 divided by 16 wind directions

Wind speed site-specific site-specific ST AR data (m/sec)

Smoothing function calculated calculated

Vertical term calculated calculated

* average value for electric utilities, utilized for utilities without this information

Source

Value Value

Comment

10 m site-specific or 115.0

m*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

0.01 m/s site-specific or 22.5

m/s*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

1 m site-specific or 5.15

m*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

293o K site-specific or

427.5*

Industrial -- U.S. EPA (1992) Utilities -- U.S.

EPA (1991b)

10 m2 10 m2 U.S. EPA (1992)

3 m 3 m U.S. EPA (1992)

site-specific site-specific Industrial -- TRIS (lbs/yr)

Utilities -- SAI & OA QPS (lbs/yr) 

site-specific site-specific ST AR data

22.5o 22.5o 360o divided by 16 wind directions

site-specific site-specific ST AR data (m/sec)

calculated calculated

Vertical term calculated calculated

* average value for electric utilities, utilized for utilities without this information

Table G-3. Air Modeling Parameters.
Appendix G: Lead Benefits Analysis

G-25

that this choice may overestimate blood lead changes

over time for both the control and no-control scenarios.

The relationship between concentrations of lead

in ambient air and blood lead concentrations has been

evaluated by a variety of methods. These include experimental studies of adult volunteers, as well as epidemiological studies of different populations of children and adults. The discussion below describes the

slopes used in this analysis for children and adults,

and for individuals with blood lead values greater than

30 μg/dL.

Children

U.S. EPA (1986b) reports that slopes which include both direct (inhalation) and indirect (via soil,

dust, etc.) air lead contributions vary widely, but typically range from three to five μg/dL increment in

children’s blood lead per μg/m3

 increment in air lead

concentration (roughly double the slope due to inhaled

air lead alone). Since hand dust levels can play a significant role in blood lead levels (U.S. EPA, 1986b),

this higher slope may be due to mouthing behavior of

children that brings them into contact with dust and

soil.

Specific values for estimating contribution of air

lead to blood lead, including indirect pathways, are

cited in U.S. EPA (1986b); slope values (ranging from

-2.63 to 31.2) and data sources for these values are

presented in Table 11-36 of U.S. EPA (1986b). The

median of these values is 4.0 μg/dL per μg/m3

, which

matches the midpoint of the range of typical slope

values. This analysis used this value to represent the

relationship between air lead concentrations and blood

lead concentrations for children living in the vicinity

of point sources of lead emissions.

The use of this slope assumes that indirect exposure” principally measures indirect effects of lead

emissions to air (through deposition to dust and soil).

However, it is possible that these slopes include other

exposures not related to air lead. In many cases researchers have measured other possible exposures,

such as water and food, and have confirmed that the

most significant contribution comes from soil and dust

lead, which is assumed to result from air deposition

of lead. Those studies that measured lead in tap water

showed that mean levels were generally low or not

significantly related to blood lead. Landrigan et al.

(1975) measured lead in pottery and food; lead in pottery was found in only 2.8 percent of homes, and food

and water made no more than a negligible contribution to lead uptake. Lead in paint was measured in

some studies.34 Landrigan and Baker (1981) measured

lead in paint at levels greater than one percent in about

one fourth to one third of the houses in each area studied. Brunekreef et al. (1981) measured high levels of

paint in some houses, but excluded these data points

from the analysis.

Despite the possibility of confounding factors, this

analysis uses the median value determined above (4.0

μg/dL per μg/m3

) as the appropriate slope for children living within five kilometers of the point source.

Five kilometers is chosen as the cut off point because

the data from most of the studies cited collected the

majority of their data points near lead smelters.35 Furthermore, these slopes, although measured primarily

in the vicinity of smelters, are assumed applicable to

all point sources that emit lead into the air.

Adults

For adult males and females, the air lead/ blood

lead slopes that include indirect effects due to soil and

dust differ very little from slopes that include only

direct effects. This result is expected since the higher

indirect slope values estimated for children are assumed to be as a result of mouthing behavior typical

of young children.

U.S. EPA (1986b) describes several population

studies that estimate indirect slopes for men; these

slopes range from -0.1 to 3.1 μg/dL per μg/m3

.

36 Snee

(1981) determined a weighted average of these studies and one other study.37 The average slope, weighted

by the inverse of each study’s variance, is 1.0 μg/dL

per μg/m3

. However, the Azar study measured the direct relationship between air lead and blood lead. Excluding the Azar study from the weighted average,

the average slope is 1.1 μg/m3

. Excluding the highest

and lowest slopes from this group (from Goldsmith,

34 Landrigan and Baker, 1981; Brunekreef et al., 1981.

35 U.S. EPA, 1986b, Table 11-36.

36 Johnson et al., 1976; Nordman, 1975; Goldsmith, 1974; Tsuchiya et al., 1975; Fugas et al., 1973.

37 Azar et al., 1975.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-26

1974 and Tsuchiya et al., 1975), both of which had

difficulties,38 the resulting slope is 1.4 μg/dL per μg/

m3

.

Slopes for females range from 0.6 to 2.4 for general atmospheric conditions.39 Snee determined an

average slope for women of 0.9 μg/dL per μg/m3,

weighted by the inverse of the variances of the studies. Excluding the slope for women from Goldsmith

(1974), the resulting slope for women is 1.0 μg/dL

per μg/m3

.

These values are adjusted by a factor of 1.3 to

account for the resorption of lead from bone tissue

(according to Chamberlain, 1983), thus deriving an

adjusted slope estimate of 1.8 μg/dL blood lead per

μg/m3

 increment in air lead concentration for men and

1.3 for women. These are the slope estimates used in

this analysis.

Individuals with initial blood lead levels of 30

μg/dL and greater

For individuals with high blood lead levels, the

air lead to blood lead uptake slopes have been shown

to be much shallower, as described by U.S. EPA

(1986b). An appropriate change in blood lead per

change in air lead is 0.5 μg/dL per μg/m3

 for individuals that have initial blood lead levels in the range

of 30 to 40 μg/dL. This value is based on cross-sectional and experimental studies.40 For individuals with

initial blood lead levels greater than 40 μg/dL, an appropriate range of slopes is 0.03 to 0.2, as determined

by occupational studies listed in Table 11-37 of U.S.

EPA (1986b). The median value of these studies is

0.07. These two slopes (0.5 for the population with

blood lead levels between 30 and 40 μg/dL and 0.07

for blood lead levels greater than 40 μg/dL) are used

for both children and adults in this analysis. These

relationships are summarized in Table G-4.

Estimates of Initial Blood Lead Concentrations

The benefits model requires an initial distribution

of blood lead levels in the exposed populations to

model health benefits of reducing lead air emissions.

The model estimates the new distribution of blood lead

levels that would exist after a given change in air concentrations using the slopes described above. Finally,

the model estimates the difference between the two

distributions. This analysis begins with an initial 1970,

no-control scenario blood lead distribution from which

all subsequent changes are modeled. This approach

requires an estimate of the blood lead distributions in

the U.S. population in 1970. Unfortunately, there are

no actual national blood lead distribution estimates

for 1970. Although the first NHANES study covered

1970, blood lead data were not collected in this study.41

Nonetheless, a 1970 distribution of blood lead was

estimated using NHANES II data (from 1976-1980),

combined with estimates of typical changes in blood

lead levels from 1970-1976 observed in localized

screening studies.

Individuals with blood lead

levels < 30 μg/d L

Individuals with blood lead

levels 30-40 μg/d L

Individuals with blood

lead levels > 40 μg/dL

Adult Males 1.8 0.5 0.07

Adult Females 1.3 0.5 0.07

Children 4.0 0.5 0.07

levels < 30 μg/d L levels 30-40 μg/d L lead levels > 40 μg/dL

1.8 0.5 0.07

1.3 0.5 0.07

4.0 0.5 0.07

Table G-4. Estimated Indirect Intake Slopes: Increment of Blood Lead Concentration (in μg/dL) per

Unit of Air Lead Concentration (μg/m3

).

38 Goldsmith (1974) refrigerated (rather than froze) the blood samples, and did not analyze the samples until 8 or 9 months after

they were taken, and restricted the analysis to one determination for each blood sample. Tsuchiya et al. (1975) measured air lead

concentrations after blood samples were taken; blood was drawn in August and September of 1971, whereas air samples were taken

during the 13 month period from September 1971 to September 1972.

39 Tepper and Levin, 1975; Johnson et al., 1976; Nordman, 1975; Goldsmith, 1974; Daines et al., 1972.

40 U.S. EPA, 1986b.

41 NCHS, 1993a.
Appendix G: Lead Benefits Analysis

G-27

A major drawback to this approach is the uncertainty in deriving the 1970 estimates. Another drawback to beginning with the 1970 level and modeling

changes from that point is the analysis only represents

changes in lead exposure from air; reductions from

other sources of lead exposure are not accounted for.

The purpose of this analysis is to identify changes attributable to the CAA mandates; changes from other

sources of lead exposure should not be considered.

However, due to nonlinear nature of the lead concentration-response functions (see above), the overall

exposure context in which the air lead exposure reductions take place will influence the estimate of benefits from those reductions. Specifically, at higher

blood lead levels, the slope of the concentration-response curve is shallower than at lower levels. As a

result, a given change in the mean blood lead level

may result in a smaller change in the health effect if

the change occurs from a relatively high starting level.

On the other hand, if one accounts for the fact that

other sources of lead exposure are reduced at the same

time that the given air reductions occur, then those air

emissions reductions may result in greater changes in

health risk.

This issue is of concern even though the analysis

focuses on the difference between the control and nocontrol scenarios, since the health benefit implications

of the emissions differentials between the scenarios

will depend on the point on the blood lead distribution curve at which the differences are considered.

That is, a difference between a mean blood lead of 25

μg/dL and one of 20 μg/dL may have different health

implications than a difference between 15 μg/dL and

10 μg/dL, even though the absolute value of the difference is the same (5 μg/dL).

An alternative method is to “start” with a 1990

blood lead level and to “back-calculate” benefits by

representing the differentials as increases over the

1990 levels, rather than decreases from 1970 levels.

The advantage of this approach is that it accounts for

reductions in lead exposure from other sources, as represented by current blood lead levels. Its disadvantage is that it holds other sources constant to (lower)

1990 levels, and thus the modeling may underestimate actual blood lead distributions in earlier years,

and thereby overestimate benefits from controls during those years. This analysis presents the results of

both approaches, indicated as “forward-looking” and

“backward-looking”.

Combination of Air Concentration

Estimates with Population Data

The modeled air lead concentrations at various

distances from the sources were combined with population data from the Census Bureau to arrive at an

estimate of the number of cases of health effects for

each of the years from 1970 to 1990 in both the control and no-control scenarios. The primary census information was accessed from the Graphical Exposure

Modeling System Database (GEMS), an EPA mainframe database system. The following data were obtained from GEMS for the years 1970, 1980, and 1990:

total population for each Block Group/Enumeration

District (BG/ED); state and county FIPS codes associated with each BG/ED; latitude and longitude of each

BG/ED; and population of males under 5 and females

under 5 for each BG/ED. The intervening five year

intervals (1975 and 1985) were estimated using the

Intercensal County Estimates from the Census, which

estimate annual populations on a county by county

basis. The decennial Census data and the Intercensal

County Estimates data sets were related by county

FIPS codes; the population in each BG/ED was assumed to grow or shrink at the same rate as the county

population as a whole.

Since the concentration-response data are particular to specific sex and adult age groups, additional

population data were also required to determine the

sizes of affected subpopulations. For 1990 age and

sex, the U.S. Census, 1992 was used, with age groups

tallied as necessary. For 1980 age and sex, the U.S.

Census, 1982 was used, with age groups also tallied

as necessary. The 1970 age and sex breakdowns were

obtained through personal communication with the

Census Bureau.42 The age and sex percentages were

interpolated for intervening years.

Pregnant women are often a subpopulation of interest for lead effects. Although pregnant women

themselves may be harmed by exposure to lead, this

analysis was concerned with pregnant women because

of possible effects on their fetuses who will be born

42 Personal communication, Karl Kuellmer, Abt Associates and the Bureau of Census, Population, Age and Sex telephone staff,

March, 1994.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-28

and evince effects as young children. To estimate the number of exposed fetuses who were born during the years

of interest,43 birth rates for 1970, 1980 and 1990 were obtained from the Census Bureau.44 These birth rates

were used to interpolate for years between 1970 and 1980, and for the years between 1980 and 1990.

Results

For both the control and no-control scenarios, Table G-5 shows estimated lead emissions from electric

utilities, industrial processes, and industrial combustion. Tables G-6 and G-7 show the differences in health

impacts between the two scenarios (for industrial processes, industrial combustion and electric utilities only) for

the “forward-looking” and “backward-looking” analyses. The modeled population for each year is also presented.

43 Note that we do not record the number of pregnancies, since the valuation only applies if the child is born and lives to exhibit

the effect. Neither are we concerned with whether the births are single or multiple births, since each fetus is at risk, whether a

pregnant women carries one or more fetuses.

44 Personal communication, Karl Kuellmer, Abt Associates and the Bureau of Census, Population, Fertility/Births telephone

staff.

1970 1975 1980 1985 1990

Electric Utilitiesaa

Control Scenario

1,351 636 175 190

Electric Utilitiesaa

No-control Scenario

2,309 3,143 3,670 3,864

Industrial Processes Control

Scenario

7,789 3,317 1,032 670 658

Industrial Processes

No-control Scenario

7,789 7,124 6,550 5,696 5,305

Industrial Combustion

Control Scenario

4,329 4,354 1,880 190 187

Industrial Combustion

No-control Scenario

4,329 4,457 4,653 4,584 4,596

a

 Appropriate data on electric utilities do not exist for years prior to 1975.

1970 1975 1980 1985 1990

1,351 636 175 190

2,309 3,143 3,670 3,864

7,789 3,317 1,032 670 658

7,789 7,124 6,550 5,696 5,305

4,329 4,354 1,880 190 187

No-control Scenario

4,329 4,457 4,653 4,584 4,596

a

 Appropriate data on electric utilities do not exist for years prior to 1975.

Table G-5. Estimated Lead Emissions from Electric Utilities, Industrial Processes, and

Industrial Combustion (in Tons).
Appendix G: Lead Benefits Analysis

G-29

Health Effect 1975 1980 1985 1990

Mortality

 Men (40-54) 0.1 1.5 2.5 2.7

 Men (55-64) 0.0 1.1 1.8 1.8

 Men (65-74) 0.0 0.4 0.7 0.8

 Women (45-74) 0 0.8 1.3 1.4

 Infants 0 0.001 0.002 0.002

 Total 0.1 3.9 6.3 6.7

Coronary Heart Disease

 Men (40-54) 0.1 1.8 3.0 3.3

 Men (55-64) 0.0 0.7 1.2 1.2

 Men (65-74) 0.0 1.0 1.6 1.7

 Women (45-74) 0.1 1.3 2.1 2.1

 Total 0.2 4.8 8.0 8.3

Strokes

 Cerebrovascular Accident (men 45-74) 0.1 1.1 1.8 1.8

 Cerebrovascular Accident (women 45-74) 0 0.5 0.9 0.9

 Brain Infarction (men 45-74) 0 0.7 1.1 1.1

 Brain Infarction (women 45-74) 0 0.4 0.6 0.6

 Total 0.1 2.7 4.4 4.4

Hypertension (men 20-74) 149 3,790 6,350 6,670

IQ Decrement

 Lost IQ Points 630 14,300 22,700 23,900

 IQ<70 (cases) 3 60 120 125

Population Exposed (millions) 188 197 207 217

Table G-6. Yearly Differences in Number of Health Effects Between the Control and Nocontrol Scenarios: Industrial Processes, Boilers, and Electric Utilities (Holding Other Lead

Sources at Constant 1970 Levels).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-30

Health Effect 1975 1980 1985 1990

Mortality

 Men (40-54) 0.3 6.9 11.5 12.5

 Men (55-64) 0.2 5.1 8.3 8.2

 Men (65-74) 0.1 2.0 3.5 3.9

 Women (45-74) 0.2 3.9 6.4 6.4

 Infants 0 0.001 0.002 0.002

 Total 0.8 17.9 29.7 31.0

Coronary Heart Disease

 Men (40-54) 0.4 8.3 13.8 15.0

 Men (55-64) 0.1 3.4 5.6 5.6

 Men (65-74) 0.2 4.4 7.6 8.0

 Women (45-74) 0.2 5.9 9.6 9.7

 Total 0.9 22.1 36.6 38.3

Strokes

 Cerebrovascular Accident (men 45-74) 0.2 5.0 8.1 8.2

 Cerebrovascular Accident

 (women 45-74)

0.1 2.6 4.1 4.2

 Brain Infarction (men 45-74) 0.1 2.8 4.6 4.7

 Brain Infarction (women 45-74) 0.1 1.6 2.7 2.7

 Total 0.5 12.0 19.5 19.8

Hypertension (men 20-74) 422 10,800 18,100 19,000

IQ Decrement

 Lost IQ Points 630 14,300 22,700 23,900

 IQ<70 (cases) 0 31 50 61

Population Exposed (millions) 188 197 207 217

Health Effect 1975 1980 1985 1990

Table G-7. Yearly Differences in Number of Health Effects Between the Control and No-control

Scenarios: Industrial Processes, Boilers, and Electric Utilities (Holding Other Lead Sources at

Constant 1990 Levels).
Appendix G: Lead Benefits Analysis

G-31

Reduction in Health Effects

Attributable to Gasoline Lead

Reductions

Estimating Changes in Amount of Lead

in Gasoline from 1970 to 1990

The relationship between the national mean blood

lead level and lead in gasoline is calculated as a function of the amount of lead in gasoline consumed. Thus,

to calculate the health benefits from gasoline lead reductions, necessary inputs are estimates of lead in

gasoline consumed over the period 1970 to 1990 and

the amount of lead in gasoline that would have been

consumed in the absence of the Clean Air Act. These

values are calculated using the quantity of both leaded

and unleaded gasoline sold each year and the concentration of lead in leaded and unleaded gasoline for

each year in the period of interest. For each year, the

relationship is expressed as:

where:

LEAD = average lead per day in gasoline

sold in a given year (metric tons/

day),

SOLD = total quantity of gasoline sold

(million gal/yr),

FRACPb = fraction of total gasoline sales

represented by leaded gasoline

(dimensionless),

Pbleaded = lead content of leaded gasoline

(g/gal), and

Pbunleaded = lead content of unleaded gasoline

(g/gal).

Gasoline Sales (SOLD): Data on annual gasoline

sales were taken from a report by Argonne National

Laboratories (1993) which presented gasoline sales

for each state in five year intervals over the period

1970-1990. This analysis used linear interpolation to

estimate the gasoline sales for years between the reported years. These data were summed to obtain national sales figures.

Fraction of Total Sales Comprised of Leaded Gasoline (FRACPb): For the control scenario, this analysis

used information reported by Kolb and Longo (1991)

for the fraction of the gasoline sales represented by

leaded gasoline for the years 1970 through 1988. For

1989 and 1990, data were taken from DOE (1990 and

1991, respectively). For the no-control scenario, all of

the gasoline sold was assumed to be leaded for all years.

Lead Content of Gasoline (Pbleaded and Pbunleaded):

Argonne National Laboratory in Argonne, Illinois was

the source for the data on the lead content of leaded

and unleaded gasoline for the period 1974-1990.

Argonne compiled these data from historical sales data

submitted to EPA, from Clean Air Act regulations on

lead content, and from recent Motor Vehicle Manufacturers Association (MVMA) surveys. For 1970

through 1973, this analysis assumed the lead content

of gasoline to be at the 1974 level. For the no-control

scenario, this analysis used the 1974 lead content in

leaded gasoline as the lead content in all gasoline for

each year.

Estimating the Change in Blood Lead

Levels from the Change in the Amount of

Lead in Gasoline

Several studies have found positive correlations

between gasoline lead content and blood lead levels.45

Data from the National Health and Nutrition Examination Survey (NHANES II) have been used by other researchers who determined similar positive correlations

between gasoline lead and blood lead levels.46

The current analysis used a direct relationship between consumption of lead in gasoline and blood lead

levels to estimate changes in blood lead levels resulting from Clean Air Act regulation of the lead content

of gasoline. This relationship was based on regression

analyses of the reduction of leaded gasoline presented

in the 1985 Regulatory Impact Analysis (RIA).47 Several multiple regressions were performed in the RIA to

relate gasoline usage with individuals’ blood lead levLEAD = –––––––– × [FRACPb × PBleaded + (1 - FRACPb) × PBunleaded] (29) SOLD

365 days













45 U.S. EPA, 1985; Billick et al., 1979; Billick et al., 1982.

46 Janney, 1982; Annest et al., 1983; Centers for Disease Control, 1993; National Center for Health Statistics, 1993b.

47 U.S. EPA, 1985.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-32

els, which were taken from NHANES II. These regressions of blood lead on gasoline usage controlled

for such variables as age, sex, degree of urbanization,

alcohol consumption, smoking, occupational exposure, dietary factors, region of the country, educational

attainment, and income. The regressions suggested

that a decrease of 100 metric tons per day (MTD) of

lead used in gasoline is associated with a decrease in

mean blood lead concentration of 2.14 μg/dL for

whites and 2.04 μg/dL for blacks. In both of these

regressions, gasoline use was found to be a highly significant predictor of blood lead (p < 0.0001).48

To determine a single gasoline usage-blood lead

slope for the entire population of the U.S., this analysis used the average of the slopes for blacks and for

whites, weighted by the percentage of blacks and

whites in the U.S. during the time period of the analysis.49 The resulting relationship is 2.13 μg/dL blood

lead per 100 metric tons of lead in gasoline consumed

per day. The same relationship was used to model

changes in both children’s and adults’ blood lead levels. The U.S. EPA (1985) analyzed data from a study

of black children in Chicago during the time period

1976 to 1980 and determined a slope of 2.08 μg/dL

per 100 MTD. This slope for children is very similar

to the one used in this analysis.

1970-Forward and 1990-Backward Approaches

As with the industrial processes and boilers analysis, this analysis used two different approaches to determine mean blood lead levels based on changes in

lead concentrations in gasoline. In the 1970-forward

approach, the calculations began with the estimated

blood lead level for 1970. The change in blood lead

level from one year to the next was based upon the

change in the amount of lead in gasoline sold, as discussed above, for both the control and no-control scenarios. For example, to calculate the blood lead level

for 1971, the calculated change in blood lead from

1970 to 1971 was added to the 1970 value. This process was repeated for each succeeding year up to 1990.

The 1990-backward approach began with a mean

blood lead level in 1990 for the control scenario. For

the no-control scenario, the starting blood lead was

estimated from the 1990 level used in the control scenario, plus an additional blood lead increment resulting from the difference between the 1990 consumption of lead in gasoline under the two scenarios. Again,

the difference in mean blood lead levels from one year

to the next was based on the change in gasoline lead

for the corresponding years. For example, the difference in blood lead levels between 1990 and 1989 was

subtracted from the 1990 level to determine the 1989

level. The process was continued for each year back

to 1970.

Relating Blood Lead Levels to Population Health

Effects

The mean blood lead levels calculated using the

methods described above were used in the dose-response functions for various health effects (e.g., hypertension, chronic heart disease, mortality). This information was then combined with data on the resident population of the 48 conterminous states in each

year to determine the total incidence of these health

effects attributable to lead in gasoline. A Department

of Commerce Publication (1991) was used to obtain

the total population in 1970, 1980, and 1983-1990,

while a different publication was the source of the

1975 population values.50 Linear interpolation was

used to estimate the populations in years for which

specific data were not available.

For certain health effects, it was necessary to know

the size of various age groups within the population.

Two different sources were used to estimate the proportions of the population in the age groups of interest. A U.S. Census summary (U.S. Dept. of Commerce, 1990) was used for information for 1990 for

children and adults and for 1980 for adults, and Census Telephone Staff (U.S. Dept. of Commerce, 1994)

provided information for 1980 for children and 1970

for children and adults. The populations for the intervening years were estimated by linear interpolation.

Changes in Leaded Gasoline Emissions and

Resulting Decreased Blood Lead Levels and Health

Effects

Table G-8 shows the estimated quantity of lead

burned in gasoline in the five year intervals from 1970

to 1990. Tables G-9 and G-10 show the difference in

48 U.S. EPA, 1985.

49 U.S. Department of Commerce, 1992. Although the percentages of blacks and whites changed slightly over this time period

(1970-1990), the change did not affect the value of the weighted slope.

50 U.S. Dept. of Commerce, 1976.
Appendix G: Lead Benefits Analysis

G-33

health impacts between the two scenarios (for lead in

gasoline only) for the “forward-looking” and “backward-looking” analyses. In general, health effect benefits resulting from gasoline lead reductions exceed

those predicted from lead reductions at the point

sources examined (i.e., industrial processes and boilers and electric utilities) by three orders of magnitude.

1970 1975 1980 1985 1990

Control Scenario 176,100 179,200 86,400 22,000 2,300

No-control Scenario No-control Scenario 176,100 176,100 202,600 202,600 206,900 206,900 214,400 214,400 222,900 222,900

Table G-8. Lead Burned in Gasoline (in tons).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-34

Health Effect 1975 1980 1985 1990

Mortality

 Men (40-54) 309 1,820 3,340 4,150

 Men (55-64) 220 1,340 2,380 2,700

 Men (65-74) 81 520 999 1,260

 Women (45-74) 155 939 1,710 2,060

 Infants 456 2,340 3,930 4,940

 Total 1,220 6,960 12,400 15,100

Coronary Heart Disease

 Men (40-54) 230 1,360 2,540 3,280

 Men (55-64) 92 563 1,030 1,220

 Men (65-74) 113 723 1,380 1,750

 Women (45-74) 73 442 805 965

 Total 508 3,090 5,760 7,210

Strokes

 Cerebrovascular Accident (men 45-74) 147 884 1,610 1,960

 Cerebrovascular Accident 

 (women 45-74)

73 442 805 965

 Brain Infarction (men 45-74) 85 508 927 1,130

 Brain Infarction (women 45-74) 47 286 521 624

 Total 352 2,120 3,862 4,679

Hypertension (men 20-74) 677,000 4,200,000 7,840,000 9,740,000

IQ Decrement

 Lost IQ Points 1,030,000 5,020,000 8,580,000 10,400,000

 IQ<70 (cases) 3,780 20,100 36,500 45,300

Population Exposed (millions) 214 225 237 247

Health Effect 1975 1980 1985 1990

Table G-9. Yearly Differences in Number of Health Effects Between the Control and No-control

Scenarios: Lead in Gasoline only (Holding Other Lead Sources at Constant 1970 Levels).
Appendix G: Lead Benefits Analysis

G-35

Health Effect 1975 1980 1985 1990

Mortality

 Men (40-54) 476 3,040 6,140 7,950

 Men (55-64) 342 2,250 4,430 5,240

 Men (65-74) 128 886 1,880 2,480

 Women (45-74) 242 1,590 3,210 4,030

 Infants 456 2,340 3,930 4,940

 Total 1,640 10,100 19,600 24,600

Coronary Heart Disease

 Men (40-54) 356 2,280 4,690 6,310

 Men (55-64) 142 945 1,910 2,370

 Men (65-74) 176 1,220 2,570 3,380

 Women (45-74) 113 740 1,490 1,860

 Total 787 5,180 10,700 13,900

Strokes

 Cerebrovascular Accident (men 45-74) 225 1,460 2,940 3,720

 Cerebrovascular Accident 

 (women 45-74)

113 740 1,490 1,860

 Brain Infarction (men 45-74) 129 837 1,680 2,120

 Brain Infarction (women 45-74) 73 477 955 1,190

 Total 540 3,514 7,065 8,890

Hypertension (men 20-74) 984,000 6,350,000 12,300,000 15,600,000

IQ Decrement

 Lost IQ Points 1,030,000 5,030,000 8,580,000 10,400,000

 IQ<70 (cases) 3,790 20,100 36,500 45,300

Population Exposed (millions) 214 225 237 247

Health Effect 1975 1980 1985 1990

Table G-10. Yearly Differences in Number of Health Effects Between the Control and Nocontrol Scenarios: Lead in Gasoline only (Holding Other Lead Sources at Constant 1990

Levels).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-36

Lead Benefits Analysis

References

Abt Associates, Inc. 1992. The Medical Costs of Five

Illnesses Related to Exposure to Pollutants.

Prepared for: Nicholas Bouwes, Regulatory

Impacts Branch, Economics and Technology

Division, Office of Pollution Prevention and

Toxics, U.S. Environmental Protection

Agency, Washington, D.C.

Abt Associates, Inc. 1995. The Impact of the Clean

Air Act on Lead Pollution: Emissions Reductions, Health Effects, and Economic Benefits

From 1970 to 1990, Draft. Prepared for Economic Analysis and Innovations Division,

Office of Policy Planning and Evaluation,

U.S. EPA. January 19.

Annest, J.L., J.L. Pirkle, D. Makuc, J.W. Neese, D.D.

Bayse, and M.G. Kovar. 1983. “Chronological Trend in Blood Lead Levels Between 1976

and 1980.” New England Journal of Medicine 308: 1373-1377.

Argonne National Laboratories (Argonne). 1993.

National Gasoline Sales Data, 1970-1990.

Ashenfelter, O. and J. Ham. 1979. “Education, Unemployment and Earnings.” J. Political

Economy 87(5): S99-S131.

Azar, R.D., et al. 1975. An Epidemiologic Approach

to Community Air Lead Exposure Using Personal Air Samplers. In: Griffin, T.B. and

Knelson, J.H., eds. Lead. Stuttgart, West Germany: Georg Thieme Publishers; pp.254-290.

(Coulston, F. and Korte, F., eds. Environmental Quality and Safety: Supplement V. 2).

Bellinger, D., J. Sloman, A. Leviton, M. Rabinowitz,

H.L. Needleman, and C. Waternaux. 1991.

“Low-level Lead Exposure and Children’s

Cognitive Function in the Preschool Years.”

Pediatrics 87(2): 219-227.

Bellinger, D.C. 1992. “Lead Exposure, Intelligence

and Academic Achievement. Pediatrics

90(6): 855.

Billick, I.H., A.S. Curran, and D.R. Shier. 1979.

“Analysis of Pediatric Blood Lead Levels in

New York City for 1970-1976.” Environmental Health Perspectives 31: 183-190.

Billick, I.H., et al. 1982. Predictions of Pediatric Blood

Lead Levels from Gasoline Consumption. U.S.

Department of Housing and Urban Development. [Cited in U.S. EPA, 1985.]

Brunekreef, B.D., et al. 1981. “The Arnhem Lead

Study: 1. Lead Uptake by 1- to 3-year-old

Children Living in the Vicinity of a Secondary Lead Smelter in Arnhem, the Netherlands.” Environ. Res. 25: 441-448.

Centers for Disease Control (CDC). 1985. Preventing Lead Poisoning in Young Children. U.S.

Department of Health and Human Services,

Public Health Service, Centers for Disease

Control, Atlanta, GA.

Centers for Disease Control (CDC). 1991a. Strategic

Plan for Elimination of Childhood Lead Poisoning. U.S. Department of Health and Human Services, Public Health Service, Centers

for Disease Control, Atlanta, GA. February.

Centers for Disease Control (CDC). 1991b. Preventing Lead Poisoning in Young Children. U.S.

Department of Health and Human Services,

Public Health Service, Centers for Disease

Control, Atlanta, GA. October.

Centers for Disease Control (CDC). 1993. Personal

communication between Abt Associates and

Jim Pirkle. November 16.

Chamberlain, A.C. 1983. “Effect of Airborne Lead

on Blood Lead.” Atmos. Environ. 17: 677-692.

Daines, R.H., et al. 1972. “Air Levels of Lead Inside

and Outside of Homes.” Ind. Med. Surg. 41:

26-28.

Dietrich, K.N., K.M. Krafft, R. Shukla, R.L.

Bornschein, and P.A. Succop. 1987. The

Neurobehavioral Effects of Prenatal and Early

Postnatal Lead Exposure. In: Toxic Substances and Mental Retardation:

Neurobehavioral Toxicology and Teratology,
Appendix G: Lead Benefits Analysis

G-37

S.R. Schroeder, Ed. American Association of

Mental Deficiency, Washington, DC, pp. 71-

95 (Monograph No. 8).

Elixhauser, A., R. M. Andrews, and S. Fox. 1993.

Clinical Classifications for Health Policy

Research: Discharge Statistics by Principal

Diagnosis and Procedure. Agency for Health

Care Policy and Research, Center for General Health Services Intramural Research,

U.S. Department of Health and Human Services.

Environmental Law Institute (ELI). 1992. Projecting

With and Without Clean Air Act Emissions

for the Section 812 Retrospective Analysis:

A Methodology Based upon the Projection

System Used in the OAQPS “National Air

Pollutant Emission Estimates: Reports.”

[Jorgenson/Wilcoxen Model Projections], Jim

Lockhart.

Fugas, M., et al. 1973. “Concentration Levels and

Particle Size Distribution of Lead in the Air

of an Urban and an Industrial Area as a Basis

for the Calculation of Population Exposure.”

In: Barth, D., et al. eds. Environmental Health

Aspects of Lead: Proceedings, International

Symposium; October 1972; Amsterdam, The

Netherlands. Luxembourg: Commission of

the European Communities, pp. 961-968.

Goldsmith, J.R. 1974. Food Chain and Health Implications of Airborne Lead. Sacramento, CA:

State of California, Air Resources Board;

Report No. ARB-R-102-74-36.

Griliches, Zvi. 1977. “Estimating the Returns to

Schooling: Some Econometric Problems.”

Econometrica 45:1-22.

Hasselblad, V. 1995. Personal Communication between V. Hasselblad and Abt Associates, February 28, 1995.

Janney, A. 1982. The Relationship Between Gasoline

Lead Emissions and Blood Poisoning in

Americans. Prepared for U.S. EPA, Office of

Policy Analysis. [Cited in U.S. EPA, 1985.]

Johnson, D.E., et al. 1976. Base Line Levels of Platinum and Palladium in Human Tissue. U.S.

EPA, Health Effects Research Laboratory,

Research Triangle Park, N.C. EPA-600/1-76-

019.

Kakalik, J., et al. 1981. The Cost of Special Education. Rand Corporation Report N-1791-ED.

Kolb, J. and K. Longo. 1991. Memorandum to Joel

Schwartz, U.S. EPA, Washington, DC, November 5.

Krupnick, A.J. and M.L. Cropper. 1989. Valuing

Chronic Morbidity Damages: Medical Costs

and Labor Market Effects. Draft Final Report

to U.S. Environmental Protection Agency,

Office of Policy Planning and Evaluation.

June 26.

Landrigan, P.J., et al. 1975. “Epidemic Lead Absorption near an Ore Smelter: the Role of Particulate Lead.” N. Engl. J. Med. 292: 123-129.

Landrigan, P.J. and E.L. Baker. 1981. “Exposure of

Children to Heavy Metals from Smelter: Epidemiology and Toxic Consequences.”

Environ. Res. 25: 204-224.

McGee and Gordon. 1976. The Results of the

Framingham Study Applied to Four Other

U.S.-based Epidemiologic Studies of Coronary Heart Disease. The Framingham Study:

An Epidemiological Investigation of Cardiovascular Disease. Section 31, April.

National Center for Health Statistics (NCHS). 1993a.

Facsimile received by Abt Associates from

Margaret McDowell regarding the types of

laboratory tests conducted during NHANES

I. December 14.

National Center for Health Statistics (NCHS). 1993b.

Personal communication between Abt Associates and NCHS Public Information Specialist. November 3.

National Energy Accounts, Bureau of Economic

Analysis.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-38

Needleman, H.L., et al. 1990. “The Long Term Effects of Exposure to Low Doses of Lead in

Children.” New England Journal of Medicine

322(2): 83-88.

NHANES, National Health and Nutrition Examination Survey.

NHANES II, National Health and Nutrition Examination Survey, 1976-1980.

Nordman, C.H. 1975. Environmental Lead Exposure

in Finland: a Study on Selected Population

Groups [dissertation]. Helsinki, Finland: University of Helsinki.

Oliver, T. 1911. “Lead Poisoning and the Race.” British Medical Journal 1(2628): 1096-1098.

[Cited in USEPA (1990).]

Piomelli et al. 1984. “Management of Childhood Lead

Poisoning.” Pediatrics 4: 105.

Pirkle, J.L., J. Schwartz, J.R. Landis, and W.R. Harlan.

1985. “The Relationship Between Blood Lead

Levels and Blood Pressure and its Cardiovascular Risk Implications.” American Journal

of Epidemiology 121: 246-258.

Pirkle, J. L., et al. 1994. “Decline in Blood Lead Levels in the United States, the National Health

and Nutrition Examination Survey

(NHANES).” JAMA 272(4): 284.

Pooling Project Research Group. 1978. “Relationship

of Blood Pressure, Serum Cholesterol, Smoking Habit, Relative Weight and ECG Abnormalities to Incidence of Major Coronary

Events: Final Report of the Pooling Project.”

Journal of Chronic Disease. Vol. 31.

Rabinowitz, M., D. Bellinger, A. Leviton, H.

Needleman, and S. Schoenbaum. 1987. “Pregnancy Hypertension, Blood Pressure During

Labor, and Blood Lead Levels.” Hypertension 10(4): October.

Salkever, D.S. 1995. “Updated Estimates of Earnings

Benefits from Reduced Exposure of Children

to Environmental Lead.” Environmental Research 70: 1-6.

Schwartz, J. 1988. “The Relationship Between Blood

Lead and Blood Pressure in the Nhanes II

Survey.” Environmental Health Perspectives.

78: 15-22.

Schwartz, J. 1990. “Lead, Blood Pressure, and Cardiovascular Disease in Men and Women.”

Environmental Health Perspectives, in press.

Schwartz, J. 1992a. “Blood Lead and Blood Pressure:

a Meta-analysis.” Presented at the Annual

Meeting of Collegium Ramazzini. November.

Schwartz, J. 1992b. “Chapter 13: Lead, Blood Pressure and Cardiovascular Disease.” In: Human

Lead Exposure, H. L. Needleman, Ed. CRC

Press.

Schwartz, J. 1993. “Beyond LOEL’s, p Values, and

Vote Counting: Methods for Looking at the

Shapes and Strengths of Associations.”

Neurotoxicology 14(2/3): October.

Shurtleff, D. 1974. Some Characteristics Related to

the Incidence of Cardiovascular Disease and

Death. The Framingham Study: An Epidemiological Investigation of Cardiovascular Disease. Section 30, February.

Silbergeld, E.K., J. Schwartz, and K. Mahaffey. 1988.

“Lead and Osteoporosis: Mobilization of Lead

from Bone in Postmenopausal Women.” Environmental Research 47: 79-94.

Snee, R.D. 1981. “Evaluation of Studies of the Relationship Between Blood Lead and Air Lead.”

Int. Arch. Occup. Environ. Health 48: 219-

242.

Taylor, T.N., P.H. Davis, J.C. Torner, J. Holmes, J.W.

Meyer, and M. F. Jacobson. 1996. “Lifetime

Cost of Stroke in the United States.” Stroke

27(9): 1459-1466.

Tepper, L.B. and L.S. Levin. 1975. “A Survey of Air

and Population Lead Levels in Selected

American Communities.” In: Griffin, T.B.;

Knelson, J.H., eds. Lead. Stuttgart, West Germany: Georg Thieme Publishers; pp. 152-196.

(Coulston, F.; Korte, f., eds. Environmental

Quality and Safety: Supplement v. 2).
Appendix G: Lead Benefits Analysis

G-39

Tsuchiya, K., et al. 1975. “Study of Lead Concentrations in Atmosphere and Population in Japan.”

In: Griffin, T.B. and Knelson, J.H.. eds. Lead.

Stuttgart, West Germany: Georg Thieme Publishers; pp.95-145. (Coulston, F.; Korte, F.,

eds/ Environmental Quality and Safety:

Supplement v. 2)

U.S. Census. 1982. United States Summary, General

Population Characteristics, Table 41: Single

Years of Age by Race, Spanish Origin, and

Sex: 1980.

U.S. Census. 1992. United States Summary, General

Population Characteristics, Table 13: Single

Years by Sex, Race, and Hispanic Origin:

1990.

U.S. Department of Commerce. 1976. Statistical Abstract of the United States: 95th Edition. Bureau of the Census. Washington, DC.

U.S. Department of Commerce. 1980. U.S. Census,

United States Summary, General Population

Characteristics.

U.S. Department of Commerce. 1986. Statistical Abstract of the United States: 105th Edition.

Bureau of the Census. Washington, DC.

U.S. Department of Commerce. 1987. Census of

Manufacturers.

U.S. Department of Commerce. 1990. Earnings by

Occupation and Education: 1990. Bureau of

the Census. Washington, DC.

U.S. Department of Commerce. 1990. U.S. Census,

United States Summary, General Population

Characteristics.

U.S. Department of Commerce. 1991. Statistical Abstract of the United States: 111th Edition.

Bureau of the Census. Washington, DC.

U.S. Department of Commerce. 1992. Statistical Abstract of the United States: 112th Edition.

Bureau of the Census. Washington, DC.

U.S. Department of Commerce. 1993. Money Income

of Households, Families, and Persons in the

United States: 1992. Bureau of the Census,

Series P60-184.

U.S. Department of Commerce. 1993. Personal Communication between Bureau of Census, Population, Age and Sex Telephone Staff and Karl

Kuellmer of Abt Associates on December 8,

1993.

U.S. Department of Commerce. 1994. City and County

Databook: 1994. Bureau of the Census. Washington, DC.

U.S. Department of Commerce. 1994. Personal Communication between Bureau of Census, Population, Age and Sex Telephone Staff and Karl

Kuellmer of Abt Associates on February 7,

1994.

U.S. Department of Education. 1993. Digest of Educational Statistics, 1993. National Center for

Educaton Statistics, Office of Educational

Research and Improvement. D.Ed. publication number NCES 93-292.

U.S. Department of Energy (DOE). 1990. Petroleum

Supply Annual, 1989, Volume 1. DOE publication number EIA-0340(89)/1

U.S. Department of Energy (DOE). 1991. Petroleum

Supply Annual, 1990, Volume 1. DOE publication number EIA-0340(90)/1

U.S. Department of Energy (DOE). 1992. Cost and

Quality of Fuels for Electric Utility Plants

1991. DOE/EIA-0191(91) Energy Information Administration, August.

U.S. Environmental Protection Agency (U.S. EPA).

1984. A Survey of the Literature Regarding

the Relationship Between Measures of IQ and

Income. Prepared by ICF, Inc. Report to U.S.

Environmental Protection Agency, Office of

Policy Analysis, June.

U.S. Environmental Protection Agency (U.S. EPA).

1985. Costs and Benefits of Reducing Lead

in Gasoline: Final Regulatory Impact Analysis. Prepared by U.S. Environmental Protection Agency, Office of Policy Analysis, Economic Analysis Division. February.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

G-40

U.S. Environmental Protection Agency (U.S. EPA).

1986a. Reducing Lead in Drinking Water: A

Benefit Analysis. Prepared by U.S. Environmental Protection Agency, Office of Policy

Planning and Evaluation, Draft Final Report.

December.

U.S. Environmental Protection Agency (U.S. EPA).

1986b. Air Quality Criteria for Lead: Volume

III. Environmental Criteria and Assessment

Office, Research Triangle Park, NC. EPA600/8-83/028cF. June.

U.S. Environmental Protection Agency (U.S. EPA).

1987. Methodology for Valuing Health Risks

of Ambient Lead Exposure. Prepared by

Mathtech, Inc. for U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Ambient Standards

Branch, Contract No. 68-02-4323.

U.S. Environmental Protection Agency (U.S. EPA).

1990a. AIRS Facility Subsystem Source Classification Codes and Emission Factor Listing for Criteria Air Pollutants. Office of Air

Quality Planning and Standards, Research

Triangle Park, NC. EPA-450/4-90-003.

March.

U.S. Environmental Protection Agency (U.S. EPA).

1990b. National Air Pollutant Emission Estimates 1940-1988. Office of Air Quality Planning and Standards, Technical Support Division, National Air Data Branch. Research

Triangle Park, NC. EPA-450/4-90-001.

U.S. Environmental Protection Agency (U.S. EPA).

1990c. Review of the National Ambient Air

Quality Standards for Lead: Assessment of

Scientific and Technical Information. OAQPS

Staff Paper, Air Quality Management Division, Research Triangle Park, NC. December.

U.S. Environmental Protection Agency (U.S. EPA).

1991a. National Air Quality and Emissions

Trends Report, 1989. Office of Air Quality

Planning and Standards. Research Triangle

Park, NC. EPA-450/4-91-003.

U.S. Environmental Protection Agency (U.S. EPA).

1991b. The Interim Emissions Inventory. Office of Air Quality Planning and Standards,

Technical Support Division, Source Receptor Analysis Branch. Research Triangle Park,

NC.

U.S. Environmental Protection Agency (U.S. EPA).

1992. 1990 Toxics Release Inventory. Office

of Pollution Prevention and Toxics, Washington, DC. EPA-700-S-92-002.

U.S. Environmental Protection Agency (U.S. EPA).

1994. Guidance Manual for the Integrated Exposure Uptake Biokinetic Model for Lead in

Children. February. EPA 540-R-93-081.

U.S. Environmental Protection Agency (U.S. EPA)

database. Graphical Exposure Modeling System Database (GEMS).

Wallsten and Whitfield. 1986. Assessing the Risks to

Young Children of Three Effects Associated

with Elevated Blood Lead Levels. Argonne

National Laboratory. December.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990.

“Medical Costs of Coronary Artery Disease

in the United States,” The American Journal

of Cardiology 65: 432-440.
Appendix H: Air Toxics 

Introduction 

Air toxics are defined as air pollutants other than 

those six criteria pollutants for which EPA sets acceptable concentrations in ambient air. The SARA 313 

Toxic Release Inventory (TRI), covering 328 of the 

approximately 3000 potentially hazardous compounds 

detected in air, estimated that approximately 1.2 million tons of air toxics were released to the atmosphere 

in 1987 from U.S. stationary sources alone. While the 

TRI estimate tends to understate emissions of toxics 

for a number of reasons, it does show that large quantities of toxics are emitted into the atmosphere annually. 

Effects of air toxics emissions are divided into 

three categories for study and assessment: cancer; 

“noncancer” effects, e.g. a wide variety of serious 

health effects such as abnormal development, birth 

defects, neurological impairment, or reproductive 

impairment, etc.; and ecological effects. Each year, 

these air toxics emissions contribute to significant 

adverse effects on human health, human welfare, and 

ecosystems. In EPA’s 1987 Unfinished Business Report1 cancer and noncancer air toxics risk estimates 

were considered sufficiently high, relative to risks 

addressed by other EPA programs, that the air toxics 

program area was among the few rated “high risk”. 

Limited Scope of this 

Assessment 

The effects of air toxics emissions are difficult to 

quantify. The adverse health effects of toxics are often irreversible, not mitigated or eliminated by reduction in ongoing exposure, and involve particularly 

painful and/or protracted disease. Therefore these effects are not readily studied and quantified in human 

clinical studies, in contrast to, for example, ambient 

ozone. In addition, epidemiological studies of effects 

in exposed populations are often confounded by simultaneous exposure of subjects to a variety of pollutants. Therefore, the effects of air toxics are often 

quantified by extrapolating data from animal studies 

to human exposure and expressed as risk per unit of 

exposure. Incidence of noncancer effects, for example, 

often are difficult to translate into monetized benefits. 

Similarly, the quantification of ecological effects 

due to emissions of air toxics is hampered by lack of 

sufficient information regarding contribution of 

sources to exposure, associations between exposure 

to mixtures of toxics and various ecological endpoints, 

and economic valuation for ecological endpoints. 

The air toxics portion of this study is, of necessity, separate and more qualitative in nature than the 

benefit analysis conducted for the criteria air pollutants. Limitations in the quantitative analyses of air 

toxics effects led the Project Team to decide to exclude the available quantitative results from the primary analysis of CAA costs and benefits. Table H-1 

presents the range of potential human health and ecological effects that can occur due to air toxics exposure. As indicated, this appendix presents quantitative estimates of benefits of CAA air toxics control 

for the cancer mortality endpoint for only nonutility 

stationary source and mobile source categories. 

Noncancer effects and ecological effects are described 

qualitatively. 

1

 U.S. EPA. Office of Policy Planning and Evaluation. Unfinished Business: A Comparative Assessment of Environmental 

Problems. February 1987. 

H-1

Effects on global climate 

Other Welfare 

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Table H-1. Health and Welfare Effects of Hazardous Air Pollutants. 

Effect Ca tegory Qua ntified Effects Unquantified Effects Other Possible Effects 

Hum an Health Cancer M ortality 

- nonutility stationary 

source 

- mobile source 

Cancer M ortality 

- utility sour ce 

- ar ea so urce 

No ncancer effects 

- neurological 

- respiratory 

- rep rodu ctive 

- hematop oetic 

- develop mental 

- immu nological 

- organ toxicity 

Hum an Welfare Decreased income an d 

recreation 

opp ortunities d ue to 

fish advisories 

Od ors 

Decreased income resulting 

from decreased phy sical 

perfor man ce 

Ecological Effects on wildlife 

Effects on p lants 

Ecosystem effects 

Loss of biological 

diversity 

Effects on global climate 

Other Welfare Visib ility 

M aterials Dama ge 

History of Air Toxics Standards 

under the Clean Air Act of 1970 

The 1970 Clean Air Act required the EPA to list a 

chemical as a hazardous air pollutant if it met the legislative definition provided: 

“The term ‘hazardous air pollutant’ means 

an air pollutant to which no ambient air 

quality standard is applicable and which in 

the judgment of the Administrator may cause, 

or contribute to, an increase in mortality or 

an increase in serious irreversible, or 

incapacitating reversible, illness.”2 

Once a HAP was listed, the EPA Administrator 

was required to: 

“establish any such standard at the level 

which in his judgment provides an ample 

margin of safety to protect the public health 

from such hazardous air pollutant.”3 

In other words the EPA had to first determine that 

a chemical was a HAP, and then regulate the emissions of each HAP based solely on human health effects and with an ample margin of safety. This regulatory mandate proved extremely difficult for EPA to 

fulfill, for reasons discussed below, and the result was 

that only seven HAPs were regulated over a period of 

20 years. 

Listing chemicals became a difficult task because 

of debates within and outside of the EPA surrounding 

issues of how much data are needed and which meth2

 42 U.S.C. §1857(a)(1). 

3

 42 U.S.C. §1857(b). 

H-2

Appendix H: Air Toxics 

odologies should be used to list a chemical as a HAP. tionary source HAP-related direct inhalation cancer


An even more difficult issue was how to define the incidence reductions. Results from each of these stud-


Congressional mandate to provide an “ample margin ies are presented below.


of safety.” For carcinogens, there is generally no


threshold of exposure considered to be without risk. EPA Analyses of Cancer Risks from


What level of risk, then, is acceptable, and how should Selected Air Toxic Pollutants


it be calculated? The EPA struggled to provide an­


swers to these questions, and was challenged in court. The Agency conducted two efforts to broadly as-
 The end result was a 1987 ruling by the D.C. Circuit sess the magnitude and nature of the air toxics prob-
 Court that provided the EPA with a legal framework lem. The 1985 report entitled, “The Air Toxics Prob­ with which to determine an “ample margin of safety.” lem in the United States: An Analysis of Cancer Risks This framework was interpreted and used by the EPA for Selected Pollutants”4 otherwise known as the “Six 

in its 1989 benzene regulations. Month Study,” was intended to serve as a “scoping” 

study to provide a quick assessment of the air toxics 

Quantifiable Stationary Source problem utilizing only readily available data on comAir Toxics Benefits pound potencies, emissions, and ambient pollutant 

concentrations. The Agency updated this analysis of 

cancer risks in the 1990 report entitled “Cancer Risk 

One might be tempted to presume that the few from Outdoor Exposure to Air Toxics” referred to here 

federal HAP standards set would have achieved rela- as the “1990 Cancer Risk study.”5 

tively substantial reductions in quantifiable risk. While 

some standards set under section 112 of the Clean Air For the pollutant and source categories examined, 

Act appear to have achieved significant reductions in the 1990 Cancer Risk study estimated the total nacancer incidence, the coverage, quantification, and tionwide cancer incidence due to outdoor concentramonetization of the full range of potential adverse tions of air toxics to range from 1,700 to as many as 

effects remains severely limited. This fact serves to 2,700 excess cancer cases per year, with 14 compounds 

highlight the inadequacy of current methods of evalu- accounting for approximately 95 percent of the anating HAP control benefits. This limited ability to es- nual cancer cases. Additionally, point sources contimate the total human health and ecological benefits tribute 25 percent of annual cases and area sources 

of HAP reductions is an important area for future re- contribute 75 percent of annual cases. Mobile sources 

search. Thus the quantifiable benefits for CAA air account for 56 percent of the nationwide total.6 

toxics control presented here are limited in scope. 

The Six Month study indicates that the criteria air 

There are three sources of information that pro- pollutant programs appear to have done more to revide a picture of potential stationary source air toxics duce air toxics levels during the 1970 to 1990 period 

benefits of the CAA. EPA’s Cancer Risk studies at- than have regulatory actions aimed at specific toxic 

tempted to broadly assess the magnitude and nature compounds promulgated during the same period. 

of the air toxics problem by developing quantitative Metals and polynuclear compounds usually are emitestimates of cancer risks posed by selected air toxics ted as particulate matter and most of the volatile orand their sources. Secondly, risk assessments con- ganic compounds are ozone precursors. As such, they 

ducted in conjunction with the promulgation of Na- are regulated under State Implementation Plan (SIP) 

tional Emissions Standards for Hazardous Air Pollut- and New Source Performance Standard (NSPS) proants (NESHAPs) offer a snapshot of potential mon- grams and Title II motor vehicle regulations. A numetized cancer mortality benefits. Finally, the Project ber of reports cited indicate significant reductions in 

Team attempted to estimate historical non-utility sta- air toxics emissions attributable to actions taken un4

 U.S. EPA. Office of Air Quality Planning and Standards. The Air Toxics Problem in the United States: An Analysis of Cancer 

Risks for Selected Pollutants. May 1985. EPA-450/1-85-001. 

5

 U.S. EPA. Office of Air Quality Planning and Standards. Cancer Risk from Outdoor Exposure to Air Toxics. September 1990. 

EPA-450/1-90-004a. 

6

 The 1990 Cancer Risk study reported approximately 500 - 900 more cancer cases per year than the Six Month Study due 

primarily to the inclusion of more pollutants, better accounting of emissions sources, and, in some cases, increases in unit risk 

estimates. 

H-3

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

der SIP, NSPS and mobile source programs. Additionally, EPA conducted a comparison of air quality 

and emissions data for 1970 with the estimates of cancer incidence for 1980.7 Methods, assumptions and 

pollutants included were held constant over the period. The analysis showed a significant decrease in 

incidence during the decade due to improvements in 

air quality, presumably related to general regulatory 

programs. For the 16 pollutants studied, estimated 

nationwide cancer incidence decreased from 3600 in 

1970 to 1600 in 1980. The 1990 Cancer Risk Study 

did not attempt to update this analysis. 

Although it is difficult to draw quantitative conclusions from these two studies regarding the benefits of CAA air toxics control, it is apparent that the 

pollutant-specific and source category-specific 

NESHAPs were not structured to reduce significant 

air toxic emissions from area and mobile sources. In 

fact, the 1990 Cancer Risk Study indicates that considerable cancer risk remained prior to passage of the 

1990 CAA Amendments: as many as 2,700 excess 

cancer cases annually. Some studies indicate that the 

criteria air pollutant program played a critical role 

during the 1970 to 1990 period in achieving air toxic 

emission reductions and therefore decreasing cancer 

risk. 

Cancer Risk Estimates from NESHAP 

Risk Assessments 

In looking back at the estimated effects of the HAP 

standards, EPA found that the effects of the NESHAPs 

were not quantified completely. These estimates occurred at a time when emission estimation and risk 

assessment methodologies for HAPs were first being 

developed. One consequence is that because emissions 

were not fully characterized, air toxics exposures could 

not be completely assessed. Additionally, most assessments only focused on the specific HAP being listed 

under the CAA and did not assess the reduction of 

other pollutants, which are currently considered HAPs. 

For example, while the vinyl chloride standard reduces 

emissions of ethylene dichloride, these emission reductions were not assessed in the risk assessment. In 

a different context, reductions of HAP may also 

achieve reductions of VOC and PM. The benefits of 

such reductions generally were also not evaluated. In 

addition, EPA generally did not assess the potential 

exposure to high, short-term concentrations of HAP 

and therefore did not know whether toxic effects from 

acute exposures would have been predicted and possibly addressed by the HAP standards. 

In addition, people living near emission sources 

of concern are often exposed to a mix of pollutants at 

once. Some pollutants have been shown to act synergistically together to create a health risk greater than 

the risk that would be expected by simply adding the 

two exposure levels together. More research is needed 

to understand the effects of multiple-pollutant exposures. Finally, HAP risks tend to be distributed unevenly across exposed populations, with particularly 

high exposures occurring closest to emission sources. 

It should be noted that HAP exposure to specific populations may tend to fall disproportionately among the 

poor and minorities, who are more likely to live in 

close proximity to emitting facilities. 

With the above caveats in mind, Table H-2 provides information about maximum individual risk 

taken from the Federal Register notices for the 

NESHAPs promulgated before the 1990 amendments 

to the Clean Air Act. The benefits are calculated by 

multiplying the estimated annual incidence reduction 

by the $4.8 million valuation per statistical life (1990 

dollars). These benefit estimates provide a snapshot 

of potential monetized benefits for the year in which 

each NESHAP was promulgated. Of course these estimates do not include air toxics benefits for other 

health and ecological benefit categories, or air toxics 

benefits from co-control of criteria air pollutants. All 

uncertainties associated with the original estimates 

remain. 

Non-utility Stationary Source 

Cancer Incidence Reductions 

The Project Team commissioned two studies to 

estimate reductions in cancer incidence due to pre1990 NESHAPs: the PES Study and the ICF Re-analysis. The methodology used for most air pollutant evaluations involved a “back calculation” for the estimation of incidence reductions. However, the EPA has 

elected not to rely on the results of this analysis given 

critical methodological flaws. Despite the Project 

Team’s concerns, the methodology and results of the 

two studies are presented below in the interest of full 

disclosure and to guide efforts to develop a more valid 

7

 Hunt, W.F., Faoro, R.B. and Curran, T.C., “Estimation of Cancer Incidence Cases and Rates for Selected Toxic Air Pollutants 

Using Ambient Air Pollution Data, 1970 vs. 1980”. U.S. EPA. April 1985. 

H-4

Appendix H: Air Toxics 

Table H-2. Cancer Incidence Reductions and Monetized Benefits for NESHAPs. 

Pollutan t 

S ource 

Ca tego ry 

Year 

P rom ulgated 

Pre-Reg 

M axim u m 

Individual 

Risk 

Post -Re g 

M axim um 

Individual 

Risk 

Red uction 

in Ca ncer 

Incide nce 

(per year) 

Benefits in 

$m illion per 

yea r 

(1 99 0$) 

benzene 1985 1. 5x10 -3 4. 5x10 -4 .3 1 1. 5 

benzene coke byp rod uct 1984 7x10 -3 2 x10 -4 1. 95 9. 4 

benzene storage 

vessels 

1982 4. 5x10 -4 3 x10 -5 0. 01 to 0 .06 0. 05 to 

0. 3 

benzene waste 

operations 

1986 2x10 -3 5 x10 -5 0. 55 2. 6 

benzene transfer 

operations 

1987 6x10 -3 4 x10 -5 0. 98 4. 7 

arsenic prim ary 

copp er 

1986 1. 3x10 -3 to 

5x10 -6 

1. 2x10 -3 

to 3 x10-6 

0. 09 0. 4 

arsenic glass m an uf . 1986 7x10 -4 to 

3x10 -5 

1. 7x10 -4 

to 6 x10-6 

0. 117 to 

0. 0034 

0. 02 to 0 .6 

asbesto s dem olition 1973 100 480 

vinyl 

chloride 

PV C 

p rod uction 

1975 10.5 50.4 

and reliable analysis of the health-related benefits of 

HAP reductions in the upcoming section 812 Prospective studies. 

PES Study 

Methodology 

The first attempt to estimate, for this study, historical non-utility stationary source HAP-related direct inhalation cancer incidence reductions was conducted by Pacific Environmental Services (PES). The 

basic approach used in the PES study was to adjust 

the cancer incidence estimates developed for EPA’s 

1990 Cancer Risk study to reflect the changes in emissions of, and exposures to, 14 key HAPs: arsenic, asbestos, benzene, 1,3-butadiene, carbon tetrachloride, 

chloroform, hexavalent chromium, dioxin, ethylene 

dichloride, ethylene dibromide, formaldehyde, gasoline vapors, products of incomplete combustion 

(PICs), and vinyl chloride. 

The first step was to compile baseline incidence 

levels, defined as cancer cases per million population, 

for each of the 14 pollutants. The point estimates of 

incidence from the 1990 Cancer Risk study were used 

for this purpose. For some source categories, the “best 

point estimate” from the 1990 Cancer Risk study was 

used, for others a mid-point was selected.8 These 

baseline incidence levels were based on measured 

ambient concentrations of the pollutant, modeled concentrations, or both. 

The second step involved allocating baseline incidence levels to the individual source categories 

known to emit the relevant pollutant. In some cases, 

8

 For some of the source categories, the original NESHAP/Air Toxic Exposure and Risk Information System (NESHAP/ 

ATERIS) estimates of incidence were not available, in which case the baseline incidence was obtained from the 1989 National Air 

Toxics Information Clearinghouse( NATICH) Database Report. (See PES, “Draft Summary of Methodology Used for Cancer from 

Stationary Sources,” memorandum from Ken Meardon, PES to Vasu Kilaru, US EPA, March 22, 1993, p. 2.) 

H-5

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

adjustments were made to reflect differences among 

the vintages of source category-specific data.9 All 

baseline incidence estimates were ultimately expressed relative to a 1985 base year.10 The assumption was then made that source-category incidence 

rates were proportional to the level of emissions from 

that source category. 

Next, levels of control for each source categoryspecific incidence rate were estimated for each of the 

target years of the present analysis (i.e., 1970, 1975, 

1980, 1985, and 1990).11 Source category-specific 

activity level indicators were then established and 

linked to changes in corresponding activity indicators provided by the J/W macroeconomic modeling 

results. Activity levels were estimated for each source 

category, for each of the target years, and for each of 

the two scenarios. 

Finally, source category/pollutant combination 

incidence levels for both the control and no-control 

scenarios were developed. These incidence levels were 

developed based on the baseline incidence levels, the 

activity indicators, and the control levels for each year. 

Both of these latter two factors varied between the 

control and no-control scenarios. The activity levels 

differed based on the specific levels of related sector 

economic activity predicted by the J/W model for the 

control and no-control scenario. The control levels 

prevailing in each of the target years were used for 

the control scenario, and the 1970 control level was 

applied throughout the 1970 to 1990 period for the 

no-control scenario.12 The formula used for these calculations was as follows:13 

A

ty 

P

ty (1 - Cty)
 I

ty = Iby × [–––] × [–––] × [–––––––] (1)
 Aby Pby (1 - Cby)


where: 

I = cancer incidence for a source categorypollutant combination 

A = activity level for a source category 

P = population 

C = control level for a source category-pollutant combination 

ty = target year (1970 ... 1990) 

by = base year 

Findings 

The PES analysis concluded that substantial reductions in HAP-related cancer cases were achieved 

during the reference period of the present study. The 

vast majority of these estimated reductions were attributable to reduced exposures to asbestos, particularly from manufacturing and fabricating sources.14 

In fact, roughly 75 percent of the total reduction in 

cancer cases averaged over the 1970 to 1990 period 

were attributed to asbestos control.15 Figure H-1 summarizes the PES study overall cancer incidence reductions and the relative contribution of asbestos-related reductions over the study period. 

The Project Team had several concerns about the 

PES results. First and foremost, the reductions in asbestos-related cancer cases appeared to be substantially higher than expected, particularly in the earlier 

target years. Second, the control scenario activity level 

indicators for several sources with which Project Team 

members were familiar did not appear to be even remotely consistent with actual historical activity patterns.16 Finally, the level of documentation of the analytical methodologies, assumptions, and results was 

insufficient to ascertain the validity and reliability of 

9

 For example, six discrete sources for vinyl chloride were identified in the Six-Month Study Update. Point estimate incidences 

for each of these source categories came from separate references with databases corresponding to different years. (See PES, “retrospective analysis for section 812(a) Benefits Study,” September 30, 1992, p. 8.) 

10 See PES, March 22, 1993 memorandum, p. 3. 

11 Control level estimates were based on one of the following: control efficiencies for related criteria pollutants defined in the 

criteria pollutant analysis, reference documents such as Control Technology Guidelines (CTGs) or Background Information Documents (BIDs), preambles for related regulations, or EPA experts. (See PES, March 22, 1993 memorandum, p. 3.) 

12 More detailed descriptions of the methodology and associated uncertainties are provided in “Retrospective Analysis for section 

812(a) Benefits Study,” a September 30, 1992 memorandum from Ken Meardon, PES to Vasu Kilaru, US EPA. 

13 See PES, March 22, 1993 memorandum, p. 4. 

14 PES, “Cancer Risk Estimates from Stationary Sources,” memorandum from Ken Meardon, PES to Vasu Kilaru, US EPA, 

March 5, 1993. 

15 ICF, “Direct Inhalation Incidence Benefits,” Draft Report, November 11, 1994, p. 10. 

16 For example, the activity indicators for Municipal Waste Combustors (MWCs) incorporated in the PES analysis decline 

dramatically throughout the 1975 to 1990 period. (See PES, March 5, 1993 memorandum to Vasu Kilaru, p. 10). In reality, overall 

MWC capacity and throughput increased significantly over this period. 

H-6

Appendix H: Air Toxics 

Figure H-1. PES Estimated Reductions in HAP-Related 

Cancer Cases. Cancer Cases Reduced Thousands 

potential sources of uncertainty in the PES results and provided revised cancer incidence reduction estimates for several HAPs. 

A key uncertainty in the PES results was 

associated with the use of a “back-calculation” 

technique to estimate incidence reductions for 

some HAPs. The back-calculation technique 

estimates uncontrolled incidence by dividing 

residual incidence by the assumed control efficiency. This approach means uncontrolled incidence, and therefore incidence reductions, are 

highly sensitive to small changes in assumed 

control efficiency.18 In some cases, the PES 

analysis may have used control efficiencies 

which were too high, resulting in overestimation of uncontrolled incidence and therefore inthe results. Ultimately, the Project Team determined 

that it was necessary to conduct a formal review and 

re-analysis of the cancer incidence reductions associated with non-utility stationary source HAP controls. 

The results of the PES analysis remain a relevant part 

of the record of the present study, however, since they 

provided a substantial basis for the subsequent reanalysis by ICF Incorporated. 

ICF Re-analysis 

Methodology 

The purposes of the ICF Re-analysis were to examine the methodology and results of the PES study, 

particularly to address the aforementioned concerns 

of the Project Team, and to develop a revised set of 

estimates. Due to significant constraints on the resources remaining for HAP analysis in the section 812 

study, however, only a few key HAPs could be investigated in depth and many important issues could not 

be addressed.17 Furthermore, the effects of two early 

and potentially important HAP standards –the Beryllium and Mercury NESHAPs– could not be evaluated. Nevertheless, the ICF Re-analysis clarified some 

cidence reductions attributable to the CAA.19 

The vinyl chloride incidence reduction estimates appear to be significantly influenced by the use of this 

back-calculation technique. Another important source 

of uncertainty identified by ICF involved the potential overestimation of incidence totals when source 

apportionment is based on measured ambient concentrations.20 ICF was unable, however, to perform an 

extensive evaluation of the activity level indicators 

used in the PES study.21 

The first step undertaken in the re-analysis was to 

conduct a screening test to identify the HAPs which 

accounted for the most significant estimated incidence 

reductions. Based on this screening analysis, ICF 

eliminated 1,3-butadiene, carbon tetrachloride, chloroform, gasoline vapors, chromium, formaldehyde, 

and PICs from the detailed re-analysis effort. 

Detailed reviews were then conducted for the remaining HAPs: vinyl chloride, dioxins, ethylene 

dibromide (EDB), ethylene dichloride (EDC), benzene, asbestos, and arsenic. In the re-analysis of these 

HAPs, ICF determined whether a forward- or backcalculation technique was used for the relevant source 

categories of a given HAP, reviewed the regulatory 

17 For example, the Project Team sought to develop and apply a methodology for estimating a central tendency estimate for the 

total carcinogenic risk imposed by all the HAPs examined. The intent was to address concerns about potential overestimation of 

aggregate risk measures when combining upper bound risk estimates of multiple HAPs. Unfortunately, resources were insufficient to 

continue development of this methodology. 

18 An example of this back-calculation technique illustrating the sensitivity to the assumed control efficiency is presented on page 

12 of the draft ICF report. 

19 See ICF Draft Report, p. 12. 

20 See ICF Draft Report, p. 9. 

21 See ICF Draft Report, p. 13. 

H-7

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

history of the relevant source categories to re-evaluate the assumed control efficiencies, and reviewed 

the upper-bound unit risk factor for each HAP. 

Revised total incidence reduction estimates for each 

HAP and for each target year were then calculated 

using the same basic calculation procedure used 

by PES. Finally, ICF identified a number of residual 

deficiencies in the analysis which could only be 

addressed through additional research and analysis.22 

Findings 

The ICF Re-analysis largely affirmed the original results obtained by PES; primarily because the 

PES analysis itself served as the basis for the reanalysis and only minor adjustments were adopted 

for many critical variables. In particular, most 

Project Team concerns regarding the PES methodology could not be resolved, including uncertainties associated with activity levels, assumed control efficiencies, and the unexpectedly high estimated incidence reductions associated with asbestos. In fact, the ICF Re-analysis produced a revised 

upper bound estimate for vinyl chloride-related incidence reductions which were even higher than 

the asbestos benefits. 

Several sets of results were developed by ICF 

and presented in either the November 1994 draft 

report or in briefing materials prepared for the Science Advisory Board Clean Air Act Compliance 

Analysis Council Physical Effects Subcommittee 

(SAB/ACCACAPERS) in May 1995. The first set 

of results is based on the assumption of 100 perFigure H-2. ICF Estimated Reductions in Total HAPRelated Cancer Cases Using Upper Bound Asbestos 

Incidence and Lower Bound Non-Asbestos HAP Incidence. Cancer Cases Reduced Thousands 

Figure H-3. 

Related Cancer Cases Using Upper Bound Incidence for 

All HAPs. Cancer Cases Reduced Thousands 

ICF Estimated Reduction in Total HAPcent source compliance with HAP control requirements. An alternative set of results was developed assuming an 80 percent compliance rate with applicable 

standards. Given the linear effect of changes in compliance rates, these results were precisely 20 percent 

lower than the first set of estimates. At the May 1995 

ACCACAPERS briefing, estimates based on the 100 

percent compliance estimates were presented. For asbestos, the revised incidence reductions were presented and characterized as upper bound. The asbestos estimates were then combined with upper and 

lower bound estimates for vinyl chloride and for “all 

other compounds.” Figure H-2 presents the total cancer incidence reductions derived from the ICF Reanalysis, using the asbestos estimates combined with 

the lower bound estimates for non-asbestos HAPs. 

Figure H-3 presents a comparable compilation reflecting the upper bound estimates for all HAPs. 

The Project Team remains concerned about these 

incidence reduction estimates, particularly given the 

doubts raised by the SAB/ACCACAPERS at the May 

1995 presentation of these results. For instance, several critical assumptions are needed to make this analysis valid when applied to EPA’s NESHAPs. The flaws 

in these assumptions are described below. 

(1) The risk estimates described in the 1990 Cancer Risk study, which served as the baseline for determining risk reductions, were accepted without question. There are myriad uncertainties in these estimates 

22 Additional details of the ICF Re-analysis methodology can be found in ICF, “Direct Inhalation Incidence Benefits,” Draft 

Report, November 11, 1994. 

H-8

Appendix H: Air Toxics 

that must be recognized, as the study was designed 

only to generate rough order-of-magnitude estimates 

of the extent of the air toxics cancer problem. 

(2) The percent control efficiency for emission 

reductions, which is calculated in each standard, would 

have to apply across every facility. Typically, the 

emissions reductions vary between facilities; using a 

single average reduction could skew the results. 

(3) There is a direct correlation between the number of tons of emissions reduced and incidence reduced by a specific regulation. Given the assumption 

of a linear, non-threshold dose-response curve (as is 

typically done for cancer), this is theoretically correct. 

(4) Finally, the back calculation approach assumes 

that there is 100 percent compliance with the regulation. 

EPA staff reviewed the “back calculation” approach for one of the more controversial aspects of 

the vinyl chloride (VC) NESHAP. The PES study estimates benefits at 426 cases reduced in 1990. The 

ICF Re-analysis resulted in an even higher estimate, 

between 1,000 and 7,000 cases annually. An analysis 

by EPA staff indicated that these vinyl chloride risk 

estimates are highly suspect given historical cancer 

incidence data for hepatic angiocarcinoma, a specific 

cancer that has been linked to vinyl chloride (Koppikar 

and Fegley, 1995). The following analysis demonstrates the inadequacies of the assumptions in the 1993 

study. 

(1) In the actual standard, no control technology 

was required for emissions from oxychlorination vents 

at ethylene dichloride (EDC)/VC plants. Applying 

“back calculation” for these emissions is inappropriate. 

(2) In 1985, there were an estimated 8,000 fabrication plants which processed resins produced by PVC 

plants, thus resulting in VC emissions, which were 

exempt from the VC NESHAP. They emit very small 

quantities of VC and back calculation is not appropriate. 

(3) The 1993 study uses a baseline estimate of 18 

residual cases from the NESHAP/ATERIS data base. 

There is no evidence that these cases resulted only 

from emissions from PVC and EDC/VC plants. 

(4) The risk analysis performed for the October 

21, 1976 final VC regulation projected an incidence 

reduction of 11 cases per year. 

In contrast, the PES study, using the “back calculation” method derived the following annual incidence 

reductions: 

1980 - 250 cases 

1985 - 360 cases 

1990 - 430 cases 

The subsequent back calculation conducted in the 

ICF Re-analysis resulted in incidence reductions as 

much as an order of magnitude higher than these. 

Even considering the slightly different industrial 

output assumptions imposed by macroeconomic modeling, such a stark contrast is difficult to explain except for a critically flawed approach. Growth in activity and population nor other factors explain the difference in these two estimates. Given that the same 

general methodology was used for all of the air toxic 

pollutant assessments as was used for the VC 

NESHAP evaluation, there is reason to believe that 

cancer incidence results for the other air toxic pollutants are also flawed. 

Mobile Source HAP Exposure 

Reductions 

EPA’s Cancer Risk report estimated that approximately 60 percent of the total carcinogenic risk posed 

by HAPs was attributed to mobile sources, with stationary sources contributing 15 percent and area 

sources contributing the remaining 25 percent.23 The 

relative importance of mobile sources to total HAP 

exposure was a significant motivation behind EPA’s 

subsequent effort to examine exposures and risks from 

mobile source HAPs.24 Although available analytical 

resources were severely limited, the Project Team 

nevertheless decided it was necessary to perform at 

least an initial screening analysis to estimate the differences in mobile source HAP exposures between 

the control and no-control scenarios configured for 

the present study. 

23 Cancer Risk report, Page ES-12.


24 See US EPA/OAR/OMS, “Motor Vehicle-Related Air Toxics Study,” EPA 420-R-93-005. April 1993.


H-9

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Methodology 

The approach used by ICF/SAI in conducting the 

mobile source HAP analysis closely followed the approach used in the EPA Motor Vehicle-Related Air 

Toxics Study (MVATS).25 Recognizing the dearth of 

HAP ambient concentration and exposure data, both 

studies use carbon monoxide (CO) concentrations as 

the basis for estimating mobile source HAP concentrations and exposures. An important difference between the two studies, however, is that the ICF/SAI 

study adjusted the estimated change in ambient CO 

concentrations to take account of background26 and 

non-mobile source27 CO emissions. The HAP exposure function used in the ICF/SAI analysis is summarized by the following equation: 

(VOC × HAP) E = ((C × A) – B) × S × M × –––––––––––– (2) CO 

where : 

E =	 exposure to motor vehicle-emitted 

HAP 

C =	 annual ambient CO concentration to 

annual CO exposure concentration 

conversion factor 

A = county-level annual average ambient 

CO concentration 

B = background CO concentration 

S = no-control to control scenario CO 

concentration adjustment factor 

(equals 1 for the control scenario) 

M = total CO exposure to mobile source 

CO exposure conversion factor 

VOC = VOC emissions by year, county, and 

scenario 

HAP = VOC speciation factor by mobile 

source HAP 

CO = CO emissions by year, county, and 

scenario 

Details of the derivation of each of the variables 

applied in the above equation are provided in the ICF/ 

SAI report. However, in essence, the calculation involves the following basic steps. 

First, annual average county-level CO ambient 

monitoring data are compiled from the EPA 

Aerometric Information Retrieval System (AIRS) 

database. After adjusting for background and nonmobile source contributions, these annual average 

ambient CO concentrations are converted to annual 

average CO exposure concentrations. As in the EPA 

MVATS, this conversion is made based on the Hazardous Air Pollutant Exposure Model - Mobile Sources 

(HAPEM-MS) population exposure model, which 

takes account of time spent in five indoor and outdoor microenvironments: indoors at home, other indoor, in-vehicle, outdoors near roadway, and other 

outdoor.28 After adjusting for CO exposures attributable to non-mobile sources of CO, the CO exposures 

are converted to exposures for each of the mobile 

source HAPs based on available VOC speciation data 

and the ratio of co-located VOC and CO emissions.29 

These calculations are repeated for the no-control scenario after adjusting for differences in CO ambient 

concentrations for each target year and for differences 

in fuel composition. 

Results 

By 1990, CAA controls resulted in significant 

reductions in exposure to motor vehicle HAPs. Figure H-4 summarizes the nationwide annual average 

exposure levels, in micrograms per cubic meter, for 

each of the five HAPs analyzed under the control and 

no-control scenarios. Additional detailed results, including breakdown by urban versus rural environments and comparisons with the EPA MVATS estimates, are provided in the ICF/SAI report. 

Analytical resources to carry forward these exposure estimates to derive estimates of the changes in 

motor vehicle HAP-related adverse effects attributable to historical CAA programs were not available. 

25 ICF/SAI, “Retrospective Analysis of Inhalation Exposure to Hazardous Air Pollutants from Motor Vehicles,” October 1995, p. 4. 

26 Background CO is produced by the oxidation of biogenic hydrocarbons. See ICF/SAI, p. 7. 

27 The EPA MVATS attributed all measured CO to motor vehicles, resulting in an overestimation of motor-vehicle contributions to 

CO concentration changes. See ICF/SAI, p. 8. The MVATS assumption would also lead to a subsequent overestimation of changes in 

HAP exposures. 

28 See ICF/SAI, p. 3. 

29 The same HAP emission fractions used in the EPA MVATS were used herein, except for diesel PM which is not proportional to 

VOC emissions. Instead, diesel PM emission factors were developed using year-specific PART5 diesel PM emission factors and VMT 

estimates for diesel-powered vehicles. 

H-10

Appendix H: Air Toxics 

Figure H-4. National Annual Average Motor Vehicle 

HAP Exposures (μg/m3). Exposures in micrograms per cubic meter 

borne sources of pollution. Toxaphene, a pesticide used primarily in the southeastern U.S. cotton belt, has been found as far away as the Arctic, with a decreasing air concentration gradient 

from the southeast toward the Great Lakes and 

the north Atlantic regions. 

Similarly, a growing body of evidence 

showed that pollutants that were persistent (do 

not easily break down) and bioaccumulating (not 

significantly eliminated from the body) were 

magnifying up the food chain, such that top 

predator fish contained levels up to millions of 

times greater than the harmless levels in the 

water. As such, those who ate those large fish, 

such as humans, eagles, mink, and beluga whales 

could receive very high exposures to the pollutNon-Cancer Health Effects 

Broad gaps exist in the current state of knowledge about the quantifiable effects of air toxics exposure. This is particularly true for a wide range of health 

effects such as tumors, abnormal development, birth 

defects, neurological impairment, or reproductive 

impairment, etc. For example, the EPA’s Non-Cancer Study30 found that ambient concentrations for a 

substantial number of monitored and modeled HAPs 

exceeded one or more health benchmarks.31 However 

no accepted methodology exists to quantify the effects of such exceedences. More data on health effects is needed for a broad range of chemicals. 

Ecological Effects 

Through the 1970s and 1980s, the adverse effects 

of toxic pollution on the Great Lakes became clear 

and undeniable. Over the same time period, scientists 

began collecting a convincing body of evidence that 

toxic chemicals released to the air can travel long distances and be deposited on land or water far from the 

original sources. An example of this evidence is the 

presence of such contaminants as PCBs, toxaphene, 

and other pesticides in fish in Lake Siskiwit, a lake on 

an island on upper Lake Superior, which has no waterants. Wildlife were beginning to show adverse 

effects in the wild, that could be duplicated in the lab. 

In the Great Lakes, such chemicals as PCBs, mercury, 

dieldrin, hexachlorobenzene, Lindane, lead compounds, cadmium compounds, DDT/DDE, and others are of significant concern. In other places in the 

country, similar effects are being experienced, especially with mercury, which is transported primarily 

by air, but exposure to which is primarily through contaminated fish. It was this kind of information about 

DDT and toxaphene that led to their being banned in 

the U.S. under FIFRA. 

While ecological and economical sciences are not 

yet sufficiently advanced to support the kind of comprehensive, quantitative evaluation of benefits needed 

for the present study, selected local and regional scale 

adverse ecological effects of HAPs, and their adverse 

consequences for human health and welfare, can and 

have been surveyed. In May 1994, the EPA issued its 

first “Report to Congress on Deposition of Air Pollutants to the Great Waters.”32 The Great Waters Report 

examined the pollutants contributing to adverse ecological effects, the potential significance of the contribution to pollutant loadings from deposition of airborne pollutants, and the potential adverse effects associated with these pollutant loadings. Key HAPs identified in the Great Waters Report include PCBs, mercury, dioxins, and other heavy metals and toxic organics. 

30 U.S. Environmental Protection Agency, “Toxic Air Pollutants and Noncancer Risks: Screening Studies,” External Review 

Draft, September, 1990. 

31 Relevant benchmarks include Acceptable Daily Intake (ADI), the estimate of daily exposure at which adverse health effects are 

unlikely; and Lowest Observed Actual Effect Level (LOAEL), which is the lowest exposure level at which significant adverse health 

effects are observed. 

32 USEPA/OAR/OAQPS, “Deposition of Air Pollutants to the Great Waters, First Report to Congress,” EPA-453/R-93-055, May 

1994. 

H-11

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Of particular relevance to the present assessment, 

the Great Waters Report demonstrated the significance 

of transport and transformation of HAPs through food 

webs, leading to increased toxicity and 

biomagnification. A prime example of adverse transport and transformation is mercury. Transformation 

from inorganic to methylated forms significantly increases the toxic effects of mercury in ecosystems. A 

prime example of biomagnification is PCBs. As noted 

in the Great Waters Report: 

“Pollutants of concern [such as PCBs] 

accumulate in body tissues and magnify up 

the food web, with each level accumulating 

the toxics from its diet and passing the burden 

along to the animal in the next level of the 

food web. Top consumers in the food web, 

usually consumers of large fish, may 

accumulate chemical concentrations many 

millions of times greater than the 

concentrations present in the water...High risk 

groups...include breast-feeding mothers 

because breast-fed babies continue to 

accumulate [pollutants] from their mothers 

after birth. For example, they can have PCB 

levels four times higher than their mothers 

after six to nine months of breast feeding.”33 

Because of the risk of significant exposure to infants and other high-risk groups, such as “sport anglers, Native Americans, and the urban poor,”34 a substantial number of fish consumption advisories have 

been issued in recent years. Current fish advisories 

for the Great Lakes alone include widespread advisories for PCB’s, chlordane, mercury and others, cautioning that nursing mothers, pregnant women, women 

who anticipate bearing children, female children of 

any age and male children age 15 and under not eat 

certain high-food chain fish species. It should be noted 

as well that 40 states have issued mercury advisories 

in some freshwater bodies, and nine states have issued mercury advisories for every freshwater 

waterbody in the state (these states are Maine, New 

Hampshire, Vermont, Massachusetts, New York, New 

Jersey, Missouri, Michigan, and Florida). 

There is little evidence indicating that the CAA 

had much beneficial effect on air toxic deposition to 

water bodies. Since the early NESHAPs were based 

on direct inhalation, primarily cancer effects close to 

a plant, they did not address the issue of cumulative 

effects of persistent pollutants far from the source. It 

was for this reason that section 112(m) was included 

in the 1990 CAA Amendments, with requirements to 

study and document the atmospheric contribution of 

water pollutants, the adverse human health and environmental effects resulting and the sources that should 

be controlled to prevent adverse effects, and additionally, to promulgate regulations to prevent adverse effects. 

Conclusions — Research Needs 

As has been demonstrated, there are broad gaps 

in the current state of knowledge about the quantifiable effects of air toxics exposure for a wide range of 

both human health and environmental effects. The 

following discussion outlines areas in which further 

research is needed in order to adequately quantify the 

benefits of air toxics control. 

Health Effects 

•	 Develop health effects data on pollutants for 

which limited or no data currently exists. Such 

studies should be focused on pollutants with 

a relatively high probability of exposure and/ 

or potential adverse health effects. 

•	 Understand mechanism of action of pollutants, for example through pharmacokinetic 

modeling. This will allow for a more accurate assessment of the effects of these pollutants on humans. 

•	 Conduct research on factors that affect variations in susceptibility of human populations 

and determine the distribution of these factors in the U.S. 

•	 Conduct research to better understand interactive effects of multiple pollutant exposures. 

•	 Develop methodologies to derive alternative 

estimates of human cancer risk from existing 

upper-bound methods. 

•	 Acquire data and develop dose-response relationships for critical noncancer effects such 

as developmental, neurotoxic, mutagenic, res33 EPA-453/R-93-055, May 1994, p. ix. 

34 EPA-453/R-93-055, May 1994, p. x. 

H-12

Appendix H: Air Toxics 

piratory and other effects. In particular, design methodology to quantify effects of exposures above health benchmarks. 

•	 Acquire data and develop methods to estimate 

effects from acute exposure. 

Exposure Assessment 

•	 Expand data collection efforts: pre- and postcontrol emissions; HAP speciation; facilities 

location; facility parameters (stack heights, 

distances from stacks to fencelines, etc.). 

•	 Develop more comprehensive exposure models which incorporate activity patterns, indirect exposures, total body burden, ratios of 

time spent indoors to outdoors. 

•	 Continue to refine uncertainty analysis methods. 

Ecosystem Effects 

•	 Reliable estimates/measures of the levels of 

persistent bioaccumulating toxics in different 

media (air, water column, soils and sediments) 

•	 Work to correlate levels of persistent 

bioaccumulating toxics with exposures, biota 

concentrations/accumulation, and adverse 

effects, especially subtle effects such as wasting, behavioral effects, and developmental 

effects. 

•	 Criteria for effects, such as a wildlife correlate to a RfD or dose-response curve. This 

work should be done to complement the mass 

balance efforts now being completed, which 

will model source emissions to water column 

concentrations, then design research to predict effects on living resources given those 

predicted levels. 

•	 Work to determine the effects of mixtures of 

persistent bioaccumulating toxic pollutants, 

and to determine cause-effect relationships of 

exposures over long periods of time. 

•	 Studies to evaluate toxic effects in less well 

understood terrestrial systems such as: soil 

organisms/invertebrates, food web effects, 

amphibian effects, effects on endangered species and phytotoxic effects. 

•	 Work to improve understanding of effects of 

toxic air pollutants on wetland species and 

wetland functions. 

Economic Valuation 

•	 Develop valuation estimates for endpoints for 

which inadequate estimates currently exist. 

These valuation estimates must be consistent 

with the kinds of damages expected. 

•	 Initiate broad-scope economic valuation of air 

toxics program using survey techniques. 

H-13

The Benefits and Costs of the Clean Air Act, 1970 to 1990 

Air Toxics References 

Hunt, W.F., R.B. Faoro, and T.C. Curran, “Estimation of Cancer Incidence Cases and Rates for 

Selected Toxic Air pollutants Using Ambient Air Pollution Data, 1970 vs. 1980,” U.S. 

Environmental Protection Agency, April 

1985. 

ICF Kaiser, “Direct Inhalation Incidence Benefits,” 

Draft Report, November 11, 1994. 

ICF Kaiser and Systems Applications International, 

“Retrospective Analysis of Inhalation Exposure to Hazardous Air Pollutants From Motor Vehicles,” October 1995. 

Koppikar, Aparna and Robert Fegley. 1995. “Analysis of ‘Reasonableness’ of Cancer Risk Assessment Findings for Asbestos and Vinyl 

Chloride in section 812 Retrospective CostBenefit Analysis,” Memorandum to Jim 

DeMocker, Office of Policy Analysis and 

Review, Office of Air and Radiation, U.S. 

Environmental Protection Agency. November 2, 1995. 

Pacific Environmental Services, “Cancer Risk Estimates From Stationary Services,” Memorandum to Vasu Kilaru, U.S. EPA, March 5, 

1993. 

Pacific Environmental Services, “Draft Summary of 

Methodology Used For Cancer From Stationary Services,” Memorandum to Vasu Kilaru, 

U.S. EPA, March 22, 1993. 

Pacific Environmental Services, “Retrospective 

Analysis for Section 812 (a) Benefits Study,” 

September 30, 1992. 

U.S. Environmental Protection Agency, The Air Toxics 

Problem in the United States: An Analysis of 

Cancer Risks for Selected Pollutants, Office 

of Air Quality Planning and Standards, EPA450/1-85-001, May 1985. 

U.S. Environmental Protection Agency, Cancer Risk 

From Outdoor Exposure to Air Toxics, Office of Air Quality Planning and Standards, 

EPA-450/1-90-004a, September 1990. 

U.S. Environmental Protection Agency, Deposition 

of Air Pollutants to the Great Waters, First 

Report to Congress, Office of Air Quality 

Planning and Standards, EPA-453/R-93-055, 

May 1994. 

U.S. Environmental Protection Agency, Motor Vehicle-Related Air Toxics Study, Office of 

Mobile Sources, EPA-420/12-93-005, April 

1993. 

U.S. Environmental protection Agency, “Toxic Air 

Pollutants and Noncancer Risks: Screening 

Studies,” External Review Draft, September 

1990. 

U.S. Environmental protection Agency, Unfinished 

Business: A Comparative Assessment of Environmental Problems, Office of Policy, Planning, and Evaluation, February 1987. 

H-14

Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-1

Appendix I: Valuation of Human Health and

Welfare Effects of Criteria Pollutants

This appendix describes the derivations of the

economic valuations for health and welfare endpoints

considered in the benefits analysis. Valuation estimates were obtained from the literature and reported

in dollars per case avoided for health effects, and dollars per unit of avoided damage for welfare effects.

This appendix first introduces the method for monetizing improvements in health and welfare, followed

by a summary of dollar estimates used to value benefits and detailed descriptions of the derivation of each

estimate. These economic valuations are given both

in terms of a central (point) estimate as well as a probability distribution which characterizes the uncertainty

about the central estimate. All dollar values are

rounded and are in 1990 dollars. Next, results of the

economic benefits analysis are presented. Finally, uncertainties in valuing the benefits attributable to the

CAA are explored.

Methods Used to Value Health

and Welfare Effects

Willingness to pay (WTP) and willingness to accept (WTA) are the two measures commonly used to

quantify the value an individual places on something,

whether it is something that can be purchased in a

market or not. Both WTP and WTA are measures of

the amount of money such that the individual would

be indifferent between having the good (or service)

and having the money. Whether WTP or WTA is the

appropriate measure depends largely on whether an

increase or a decrease of the good is at issue. WTP is

the amount of money an individual would be willing

to pay to have a good (or a specific increase in the

amount of the good) — i.e., the amount such that the

individual would be indifferent between having the

money and having the good (or having the specific

increase in the good). WTA is the amount of money

the individual would have to be compensated in order

to be indifferent to the loss of the good (or a specific

decrease in the amount of the good). WTP is the appropriate measure if the baseline case is that the individual does not have the good or when an increase in

the amount of the good is at issue; WTA is the appropriate measure if the baseline case is that the individual has the good or when a decrease in the amount

of the good is at issue. An important difference between WTP and WTA is that, in theory, WTP is limited by the individual’s budget, whereas WTA is not.

Nevertheless, while the underlying economic valuation literature is based on studies which elicited expressions of WTP and/or WTA, the remainder of this

report refers to all valuation coefficients as WTP estimates. In some cases (e.g., stroke-related hospital

admissions), neither WTA nor WTP estimates are

available and WTP is approximated by cost of illness

(COI) estimates, a clear underestimate of the true

welfare change since important value components

(e.g., pain and suffering associated with the stroke)

are not reflected in the out-of-pocket costs for the

hospital stay.

For both market and non-market goods, WTP reflects individuals’ preferences. Because preferences

are likely to vary from one individual to another, WTP

for both market and non-market goods (e.g., healthrelated improvements in environmental quality) is

likely to vary from one individual to another. In contrast to market goods, however, non-market goods

such as environmental quality improvements are public goods, whose benefits are shared by many individuals. The individuals who benefit from the environmental quality improvement may have different

WTPs for this non-market good. The total social value

of the good is the sum of the WTPs of all individuals

who “consume” (i.e., benefit from) the good.

In the case of health improvements related to pollution reduction, it is not certain specifically who will

receive particular benefits of reduced pollution. For

example, the analysis may predict 100 days of cough

avoided in a given year resulting from CAA reductions of pollutant concentrations, but the analysis does

not estimate which individuals will be spared those

days of coughing. The health benefits conferred on

individuals by a reduction in pollution concentrations

are, then, actually reductions in the probabilities of

having to endure certain health problems. These benefits (reductions in probabilities) may not be the same

for all individuals (and could be zero for some individuals). Likewise, the WTP for a given benefit is

likely to vary from one individual to another. In theory,

the total social value associated with the decrease in

risk of a given health problem resulting from a given
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-2

reduction in pollution concentrations is

where Bi

 is the benefit (i.e., the reduction in probability of having to endure the health problem) conferred on the ith individual (out of a total of N) by the

reduction in pollution concentrations, and WTPi

(Bi)

is the ith individual’s WTP for that benefit. If a reduction in pollution concentrations affects the risks

of several health endpoints, the total health-related

social value of the reduction in pollution concentrations is

where Bij is the benefit related to the jth health

endpoint (i.e., the reduction in probability of having

to endure the jth health problem) conferred on the ith

individual by the reduction in pollution concentrations,

and WTPi

(Bij) is the ith individual’s WTP for that

benefit.

The reduction in probability of each health problem for each individual is not known, nor is each

individual’s WTP for each possible benefit he or she

might receive known. Therefore, in practice, benefits

analysis estimates the value of a statistical health problem avoided. For example, although a reduction in

pollutant concentrations may save actual lives (i.e.,

avoid premature mortality), whose lives will be saved

cannot be known ex ante. What is known is that the

reduction in air pollutant concentrations results in a

reduction in mortality risk. It is this reduction in mortality risk that is valued in a monetized benefit analysis. Individual WTPs for small reductions in mortality risk are summed over enough individuals to infer

the value of a statistical life saved. This is different

from the value of a particular, identified life saved.

Rather than “WTP to avoid a death,” then, it is more

accurate to use the term “WTP to avoid a statistical

death,” or, equivalently, “the value of a statistical life.”

Suppose, for example, that a given reduction in

PM concentrations results in a decrease in mortality

risk of 1/10,000. Then for every 10,000 individuals,

one individual would be expected to die in the absence of the reduction in PM concentrations (who

would not die in the presence of the reduction in PM

concentrations). If WTP for this 1/10,000 decrease in

mortality risk is $500 (assuming, for now, that all individuals’ WTPs are the same), then the value of a

statistical life is 10,000 x $500, or $5 million.

A given reduction in PM concentrations is unlikely, however, to confer the same risk reduction (e.g.,

mortality risk reduction) on all exposed individuals

in the population. (In terms of the expressions above,

Bi

 is not necessarily equal to Bj , for i j). In addition,

different individuals may not be willing to pay the

same amount for the same risk reduction. The above

expression for the total social value associated with

the decrease in risk of a given health problem resulting from a given reduction in pollution concentrations

may be rewritten to more accurately convey this. Using mortality risk as an example, for a given unit risk

reduction (e.g., 1/1,000,000), the total mortality-related benefit of a given pollution reduction can be

written as

where (number of units of risk reduction)i

 is the

number of units of risk reduction conferred on the ith

exposed individual as a result of the pollution reduction, (WTP per unit risk reduction)i

 is the ith

individual’s willingness to pay for a unit risk reduction, and N is the number of exposed individuals.

If different subgroups of the population have substantially different WTPs for a unit risk reduction and

substantially different numbers of units of risk reduction conferred on them, then estimating the total social benefit by multiplying the population mean WTP

to save a statistical life times the predicted number of

statistical lives saved could yield a biased result. Suppose, for example, that older individuals’ WTP per

unit risk reduction is less than that of younger individuals (e.g., because they have fewer years of expected life to lose). Then the total benefit will be less

than it would be if everyone’s WTP were the same. In

addition, if each older individual has a larger number

of units of risk reduction conferred on him (because a

given pollution reduction results in a greater absolute

reduction in risk for older individuals than for younger

individuals), this, in combination with smaller WTPs

of older individuals, would further reduce the total

benefit.

While the estimation of WTP for a market good

(i.e., the estimation of a demand schedule) is not a

simple matter, the estimation of WTP for a non-market good, such as a decrease in the risk of having a

particular health problem, is substantially more difficult. Estimation of WTP for decreases in very specific health risks (e.g., WTP to decrease the risk of a

day of coughing or WTP to decrease the risk of admission to the hospital for respiratory illness) is further limited by a paucity of information. Derivation

of the dollar value estimates discussed below was often limited by available information.

 WTPi(Bi Σ ) (1)

N

i=1

 WTPi(Bij Σ ) (2)

J

Σ

N

i=1 j=1 (number of units of risk reduction)i

 × (WTP per unit risk reduction)i

Σ (3)

N

i=1
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-3

Valuation of Specific Health Endpoints

Valuation of Premature Mortality Avoided

As noted above, it is actually reductions in mortality risk that are valued in a monetized benefit

analysis. Individual WTPs for small reductions in

mortality risk are summed over enough individuals

to infer the value of a statistical life saved. This is

different from the value of a particular, identified

life saved. The “value of a premature death

avoided,” then, should be understood as shorthand

for “the value of a statistical premature death

avoided.”

The value of a premature death avoided is based

on an analysis of 26 policy-relevant value-of-life

studies (see Table I-1). Five of the 26 studies are

contingent valuation (CV) studies, which directly

solicit WTP information from subjects; the rest are

wage-risk studies, which base WTP estimates on

estimates of the additional compensation demanded

in the labor market for riskier jobs. Each of the 26

studies provided an estimate of the mean WTP to

avoid a statistical premature death. Several plausible standard distributions were fit to the 26 estimates of mean WTP. A Weibull distribution, with

a mean of $4.8 million and standard deviation of

$3.24 million, provided the best fit to the 26 estimates. The central tendency estimate of the WTP

to avoid a statistical premature death is the mean of

this distribution, $4.8 million. The considerable uncertainty associated with this approach is discussed

in detail below, in the subsection titled “The Economic Benefits Associated with Mortality,” within

the section titled “Uncertainties.”

Life-years lost is a possible alternative measure

of the mortality-related effect of pollution, as discussed in Appendix D. If life-years lost is the measure used, then the value of a statistical life-year lost,

rather than the value of a statistical life lost would be

needed. Moore and Viscusi (1988) suggest one approach for determining the value of a statistical lifeyear lost. They assume that the willingness to pay to

save a statistical life is the value of a single year of

life times the expected number of years of life remaining for an individual. They suggest that a typical respondent in a mortal risk study may have a life expectancy of an additional 35 years. Using a mean estimate of $4.8 million to save a statistical life, their

approach would yield an estimate of $137,000 per lifeyear lost or saved. If an individual discounts future

additional years using a standard discounting procedure, the value of each life-year lost must be greater

than the value assuming no discounting. Using a 35

year life expectancy, a $4.8 million value of a statistical life, and a 5 percent discount rate, the implied value

of each life-year lost is $293,000. The Moore and

Viscusi procedure is identical to this approach, but

uses a zero discount rate.

Using the value of a life-year lost and the expected

number of years remaining (obtained from life expectancy tables), and assuming a given discount rate,

values of age-specific premature mortality can be derived. Examples of valuations of pollution-related

mortality using the life-years lost approach are given

below, in the subsection titled “The Economic Benefits Associated with Mortality,” within the section

titled “Uncertainties.”

Valuation of Hospital Admissions Avoided

In the case of hospital admissions, cost-of-illness

(COI), or “costs avoided,” estimates were used in lieu

of WTP because of the lack of other information reStudy Study Type of Type of

Estim ate Estim ate

Valuation Valuation

(m illions (m illions

1990$) 1990$)

Kneisner and Leeth (1991) (US) Labor Market 0.6

Smith and Gilbert (1984) Labor Market 0.7

Dillingham (1985) Labor Market 0.9

Butler (1983) Labor Market 1.1

Miller and Guria (1991) Cont. Value 1.2

Moore and Viscusi (1988a) Labor Market 2.5

Viscusi, Magat, and Huber (1991b) Cont. Value 2.7

Gegax et al. (1985) Cont. Value 3.3

Marin and Psacharopoulos (1982) Labor Market 2.8

Kneisner and Leeth (1991)

(Australia)

Labor Market 3.3

Gerking, de Haan, and Schulze

(1988)

Cont. Value 3.4

Cousineau, Lacroix, and Girard

(1988)

Labor Market 3.6

Jones-Lee (1989) Cont. Value 3.8

Dillingham (1985) Labor Market 3.9

Viscusi (1978, 1979) Labor Market 4.1

R.S. Smith (1976) Labor Market 4.6

V.K. Smith (1976) Labor Market 4.7

Olson (1981) Labor Market 5.2

Viscusi (1981) Labor Market 6.5

R.S. Smith (1974) Labor Market 7.2

Moore and Viscusi (1988a) Labor Market 7.3

Kneisner and Leeth (1991) (Japan) Labor Market 7.6

Herzog and Schlottman (1987) Labor Market 9.1

Leigh and Folson (1984) Labor Market 9.7

Leigh (1987) Labor Market 10.4

Gaten (1988) Labor Market 13.5

SOURCE: Viscusi, 1992

Table I-1. Summary of Mortality Valuation Estimates

(millions of 1990 dollars).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-4

garding willingness to pay to avoid illnesses that necessitate hospital admissions. For those hospital admissions which were specified to be the initial hospital admission (in particular, hospital admissions for

coronary heart disease (CHD) events and stroke), COI

estimates include, where possible, all costs of the illness, including the present discounted value of the

stream of medical expenditures related to the illness,

as well as the present discounted value of the stream

of lost earnings related to the illness. (While an estimate of present discounted value of both medical expenditures and lost earnings was available for stroke,

the best available estimate for CHD did not include

lost earnings. The derivations of the COI estimates

for CHD and stroke, both lead-induced effects, are

discussed in Appendix G.)

In those cases for which it is unspecified whether

the hospital admission is the initial one or not (that is,

for all hospital admissions endpoints other than CHD

and stroke), it is unclear what portion of medical expenditures and lost earnings after hospital discharge

can reasonably be attributed to pollution exposure and

what portion might have resulted from an individual’s

pre-existing condition even in the absence of a particular pollution-related hospital admission. In such

cases, the COI estimates include only those costs associated with the hospital stay, including the hospital

charge, the associated physician charge, and the lost

earnings while in the hospital. The derivations of these

costs are discussed in Abt Associates Inc., 1996.

These COI estimates are likely to substantially

understate total WTP to avoid an illness that began

with a hospital admission or to avoid a particular hospital admission itself. First, most of the COI estimates

fall short of being full COI estimates either because

of insufficient information or because of ambiguities

concerning what portion of post-hospital costs should

be attributed to pollution exposure. Even full COI estimates will understate total WTP, however, because

they do not include the value of avoiding the pain and

suffering associated with the illness for which the individual entered the hospital.

Valuation of Chronic Bronchitis Avoided

Although the severity of cases of chronic bronchitis valued in some studies approaches that of

chronic obstructive pulmonary disease, to maintain

consistency with the existing literature we do not treat

those cases separately for the purposes of this analysis. Chronic bronchitis is one of the only morbidity

endpoints that may be expected to last from the initial

onset of the illness throughout the rest of the

individual’s life. WTP to avoid chronic bronchitis

would therefore be expected to incorporate the present

discounted value of a potentially long stream of costs

(e.g., medical expenditures and lost earnings) associated with the illness. Two studies, Viscusi et al. (1991)

and Krupnick and Cropper (1992) provide estimates

of WTP to avoid a case of chronic bronchitis. The

study by Viscusi et al., however, uses a sample that is

larger and more representative of the general population than the study by Krupnick and Cropper (which

selects people who have a relative with the disease).

The valuation of chronic bronchitis in this analysis is

therefore based on the distribution of WTP responses

from Viscusi et al. (1991).

Both Viscusi et al. (1991) and Krupnick and Cropper (1992), however, defined a case of severe chronic

bronchitis. It is unclear what proportion of the cases

of chronic bronchitis predicted to be associated with

exposure to pollution would turn out to be severe cases.

The incidence of pollution-related chronic bronchitis

was based on Abbey et al. (1993), which considered

only new cases of the illness.1 While a new case may

not start out being severe, chronic bronchitis is a

chronic illness which may progress in severity from

onset throughout the rest of the individual’s life. It is

the chronic illness which is being valued, rather than

the illness at onset.

The WTP to avoid a case of pollution-related

chronic bronchitis (CB) is derived by starting with

the WTP to avoid a severe case of chronic bronchitis,

as described by Viscusi et al. (1991), and adjusting it

downward to reflect (1) the decrease in severity of a

case of pollution-related CB relative to the severe case

described in the Viscusi study, and (2) the elasticity

of WTP with respect to severity. Because elasticity is

a marginal concept and because it is a function of severity (as estimated from Krupnick and Cropper,

1992), WTP adjustments were made incrementally,

in one percent steps. At each step, given a WTP to

avoid a case of CB of severity level sev, the WTP to

avoid a case of severity level 0.99*sev was derived.

This procedure was iterated until the desired severity

level was reached and the corresponding WTP was

derived. Because the elasticity of WTP with respect

to severity is a function of severity, this elasticity

changes at each iteration. If, for example, it is believed

that a pollution-related case of CB is of average se1 It is important that only new chronic bronchitis be considered in this analysis because WTP estimates reflect lifetime expenditures and/or losses associated with this chronic illness, and incidences are predicted separately for each year during the period 1970-

1990. If the total prevalence of chronic bronchitis, rather than the incidence of only new chronic bronchitis were predicted each year,

valuation estimates reflecting lifetime expenditures could be repeatedly applied to the same individual for many years, resulting in a

severe overestimation of the value of avoiding pollution-related chronic bronchitis.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-5

verity, that is, a 50 percent reduction in severity from

the case described in the Viscusi study, then the iterative procedure would proceed until the severity level

was half of what it started out to be.

The derivation of the WTP to avoid a case of pollution-related chronic bronchitis is based on three components, each of which is uncertain: (1) the WTP to

avoid a case of severe CB, as described in the Viscusi

study, (2) the severity level of an average pollutionrelated case of CB (relative to that of the case described by Viscusi), and (3) the elasticity of WTP with

respect to severity of the illness. Because of these three

sources of uncertainty, the WTP is uncertain. Based

on assumptions about the distributions of each of the

three uncertain components, a distribution of WTP to

avoid a pollution-related case of CB was derived by

Monte Carlo methods. The mean of this distribution,

which was about $260,000, is taken as the central tendency estimate of WTP to avoid a pollution-related

case of CB. Each of the three underlying distributions

is described briefly below.

The distribution of WTP to avoid a severe case of

CB was based on the distribution of WTP responses

in the Viscusi study. Viscusi et al. derived respondents’ implicit WTP to avoid a statistical case of

chronic bronchitis from their WTP for a specified reduction in risk. The mean response implied a WTP of

about $1,000,000 (1990 dollars)2 ; the median response

implied a WTP of about $530,000 (1990 dollars).

However, the extreme tails of distributions of WTP

responses are usually considered unreliable. Because

the mean is much more sensitive to extreme values,

the median of WTP responses is often used rather than

the mean. Viscusi et al. report not only the mean and

median of their distribution of WTP responses, however, but the decile points as well. The distribution of

reliable WTP responses from the Viscusi study could

therefore be approximated by a discrete uniform distribution giving a probability of one ninth to each of

the first nine decile points. This omits the first five

and the last five percent of the responses (the extreme

tails, considered unreliable). This trimmed distribution of WTP responses from the Viscusi study was

assumed to be the distribution of WTPs to avoid a

severe case of CB. The mean of this distribution is

about $720,000 (1990 dollars).

The distribution of the severity level of an average case of pollution-related CB was modeled as a

triangular distribution centered at 6.5, with endpoints

at 1.0 and 12.0. These severity levels are based on the

severity levels used in Krupnick and Cropper, 1992,

which estimated with relationship between ln(WTP)

and severity level, from which the elasticity is derived.

The most severe case of CB in that study is assigned a

severity level of 13. The mean of the triangular distribution is 6.5. This represents a 50 percent reduction

in severity from a severe case.

The elasticity of WTP to avoid a case of CB with

respect to the severity of that case of CB is a constant

times the severity level. This constant was estimated

by Krupnick and Cropper, 1992, in the regression of

ln(WTP) on severity, discussed above. This estimated

constant (regression coefficient) is normally distributed with mean = 0.18 and standard deviation = 0.0669

(obtained from Krupnick and Cropper, 1992).

The distribution of WTP to avoid a case of pollution-related CB was generated by Monte Carlo methods, drawing from the three distributions described

above. On each of 16,000 iterations (1) a value was

selected from each distribution, and (2) a value for

WTP was generated by the iterative procedure described above, in which the severity level was decreased by one percent on each iteration, and the corresponding WTP was derived. The mean of the resulting distribution of WTP to avoid a case of pollution-related CB was $260,000.

This WTP estimate is reasonably consistent with

full COI estimates derived for chronic bronchitis, using average annual lost earnings and average annual

medical expenditures reported by Cropper and

Krupnick, 1990. Using a 5 percent discount rate and

assuming that (1) lost earnings continue until age 65,

(2) medical expenditures are incurred until death, and

(3) life expectancy is unchanged by chronic bronchitis, the present discounted value of the stream of medical expenditures and lost earnings associated with an

average case of chronic bronchitis is estimated to be

about $77,000 for a 30 year old, about $58,000 for a

40 year old, about $60,000 for a 50 year old, and about

$41,000 for a 60 year old. A WTP estimate would be

expected to be greater than a full COI estimate, reflecting the willingness to pay to avoid the pain and

suffering associated with the illness. The WTP estimate of $260,000 is from 3.4 times the full COI estimate (for 30 year olds) to 6.3 times the full COI estimate (for 60 year olds).

2 There is an indication in the Viscusi paper that the dollar values in the paper are in 1987 dollars. Under this assumption, the

dollar values were converted to 1990 dollars.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-6

Valuation of Other Morbidity Endpoints Avoided

WTP to avoid a day of specific morbidity endpoints, such as coughing or shortness of breath, has

been estimated by only a small number of studies (two

or three studies, for some endpoints; only one study

for other endpoints). The estimates for health endpoints involving these morbidity endpoints are therefore similarly based on only a few studies. However,

it is worth noting that the total benefit associated with

any reduction in pollutant concentrations is determined

largely by the benefit associated with the corresponding reduction in mortality risk because the dollar value

associated with mortality is significantly greater than

any other valuation estimate. More detailed explanations for valuation of specific morbidity endpoints is

given in Table I-2.

Estimates of WTP may be understated for a couple

of reasons. First, if exposure to pollution has any cumulative or lagged effects, then a given reduction in

pollution concentrations in one year may confer benefits not only in that year but in future years as well.

Benefits achieved in later years are not included. Second, the possible effects of altruism are not considered in any of the economic value derivations. Individuals’ WTP for reductions in health risks for others

are implicitly assumed to be zero.

Table I-2 summarizes the derivations of the economic values used in the analysis. More detailed descriptions of the derivations of lead-related endpoints

(hospital admissions for CHD and stroke, Lost IQ

points, IQ below 70, and hypertension) are discussed

in Appendix G.

Valuation of Welfare Effects

With the exception of agricultural benefits, economic valuations for welfare effects quantified in the

analysis (i.e., household soiling damage, visibility and

worker productivity) are documented in Table I-2. For

agricultural benefits, estimated changes in crop yields

were evaluated with an agricultural sector model,

AGSIM. This model incorporates agricultural price,

farm policy, and other data for each year. Based on

expected yields, the model estimates the production

levels for each crop, the economic benefits to consumers, and the economic benefits to producers associated with these production levels. To the extent that

alternative exposure-response relationships were

available, a range of potential benefits was calculated.

Appendix F documents the derivation of the monetary

benefits associated with improved agricultural production. The derivation of the residential visibility

valuation estimate is discussed further below.

Visibility Valuation

Residential visibility has historically been valued

through the use of contingent valuation studies, which

employ surveys and questionnaires to determine the

economic value respondents place on specified

changes in visibility. A number of such studies have

been published in the economics literature since the

late 1970s, and have reported a wide range of resulting values for visibility, expressed as household willingness to pay (WTP) for a hypothesized improvement in visibility. Those studies were carefully reviewed for their applicability to the retrospective

analysis.

One limitation of many existing contingent valuation studies of visibility is that they are local or regional in scope, soliciting values for visibility from

residents of only one or two cities in a single region

of the country. Studies of visibility values from western cities, the most recent of which was published in

1981, have reported somewhat lower values than those

from eastern cities, raising the question of whether

eastern and western visibility are different commodities and should be valued differently in this analysis.

While the different visibility values reported in

the literature may appear to imply that visibility is

not valued equally by survey respondents in the eastern and western U.S., other evidence suggests that

eastern and western visibility are not fundamentally

different commodities, and that the retrospective benefits calculations should not be based on separate eastern and western visibility values. For example,

NAPAP data indicate that California’s South Coast

Air Basin, which encompasses Los Angeles and extends northward to the vicinity of San Francisco, has

median baseline visibility more characteristic of the

eastern U.S. than of other areas of the west (NAPAP

1991; IEc 1992, 1993a), reflecting the influence of

the higher humidity typical of coastal areas. While

inland areas of the west will tend to have lower humidity, and hence greater baseline visibility than either the eastern region or the western coastal zone,

such baseline visibility differences are accounted for

in the conversion from the visual range metric to

DeciView.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-7

Perhaps the most compelling rationale for employing a single nationwide visibility valuation strategy

in the retrospective benefits analysis, however, relates

to the air quality modeling output used to calculate

the control and no-control scenario visibility profiles,

and its implications for the valuation of visibility as a

commodity. The RADM model and linear scaling

technique used for the retrospective analysis model

visibility improvements nationwide as changes in regional atmospheric haze. In other words, although the

magnitude of visibility effects may vary between regions, the model output does not distinguish between

a change in eastern visibility and a change in western

visibility as distinct phenomena. Thus, there is no clear

reason to value those same visibility changes differently in calculating the benefits of visibility improvements. Consequently, a single, consistent valuation

basis has been applied to residential visibility improvements nationwide for this analysis.

In light of advances in the state of the art of contingent valuation over the last decade, the age of many

of the existing studies raised questions regarding their

suitability to serve as the primary basis for the visibility benefits estimates. A review of the survey and

data analysis methods used in the available studies

indicated that a study conducted for EPA by

McClelland et al. (1991) addressed many of the methodological flaws of earlier studies, employing survey

methods and analytical techniques designed to minimize potential biases (IEc 1992). Although this study

is unpublished, given its methodological improvements over earlier studies it was chosen as the basis

for the central tendency of the visibility benefits estimate, yielding an estimated value of $14 per unit improvement in DeciView as the annual household WTP

for visibility improvements (IEc 1997), as specified

in Table I-2.

The difficulty of accurately defining the expected

statistical distribution of WTP values for visibility

improvements on the basis of published studies of

uneven reliability, along with the considerable variation in reported visibility values, led to the selection

of a hypothesized triangular distribution of values to

characterize the uncertainty in the visibility benefits

estimate. Reliance on any single study to estimate the

uncertainty range would be unlikely to adequately

characterize variations in visibility values that might

exist across cities, and in any case would fail to capture the full variability of visibility values reported in

the literature. Therefore, to ensure that the retrospective study characterizes the full range of uncertainty

in visibility values nationwide, the upper and lower

bounds of the triangular distribution were derived by

combining results from appropriate eastern and western residential visibility valuation studies.

Most of the existing residential visibility valuation studies were found to suffer from part-whole bias,

which results from the failure to differentiate values

for visibility from those for other air quality amenities, such as reductions in adverse health effects. Of

the studies reviewed for this analysis, only the

McClelland study and Brookshire et al. (1979) have

attempted to obtain bids explicitly for visibility improvements (IEc 1992). Since part-whole bias will tend

to produce overstated values for visibility, reported

values from all studies that do not correct for partwhole bias were adjusted prior to calculating the lower

bound of the uncertainty range. The upper bound of

the uncertainty range was calculated using the unadjusted values from all studies, which is equivalent to

assuming that the entire value of respondents’ stated

WTP for improved air quality can be attributed to increased visibility.

The uncertainty range specified in Table I-2 calculated using a consensus function derived from a

regression analysis, incorporates a 25 percent adjustment for part-whole bias (i.e., reported values were

multiplied by 0.25) in calculating the lower bound.

This represents an approximate midpoint of the range

defined by the McClelland study’s finding that respondents allocated, on average, 18.6 percent of their total

WTP to improvements in visibility, and Chestnut and

Rowe’s (1989) conclusion that visibility improvement

accounted for 34 percent of the total WTP reported in

the Brookshire et al. study. Similarly, the “Denver

Brown Cloud” study results indicate that respondents

allocated 27.2 percent of their total WTP to visibility

improvements (Irwin et al. 1990). Therefore, the application of a 25 percent adjustment for part-whole

bias to all but the McClelland and Brookshire values

would appear to be supported by the recent literature,

with the resulting consensus value representing a plausible lower bound for the uncertainty range of visibility values. The consensus function approach, incorporating the part-whole bias adjustment, yields estimated upper and lower bound values of $21 and $8,

respectively, for annual household WTP per unit improvement in DeciView.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-8

Table I-2. Unit Values for Economically Valuing Health and Welfare Endpoints.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-9
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-10
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-11
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-12
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-13
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-14
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-15
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-16

Results of Valuation of Health

and Welfare Effects

Table I-3 presents the results of combining the

economic valuations described in this Appendix with

the health and welfare effects results presented in

Appendix D. As noted in Appendix D, there are alternative estimates for some health and welfare impacts,

which form the basis of several alternative benefit

estimates. Each of the health effects estimates also

has quantified statistical uncertainty. The range of

estimated health and welfare effects, along with the

uncertain economic unit valuations, were combined

to estimate a range of possible results. The combining of the health and economic information used the

Monte Carlo method presented in Chapter 7. Table I3 shows the mean estimate results, as well as the measured credible range (upper and lower five percentiles of the results distribution), of economic benefits

for each of the quantified health and welfare categories.

The results for aggregate monetized benefits were

also calculated using a Monte Carlo method. The results of the Monte Carlo simulations for the economic

values for each of the major endpoint categories are

presented in Table I-4. Note that for the upper and

lower fifth percentiles the sum of the estimated benefits from the individual endpoints does not equal the

estimated total. The Monte Carlo method used in the

analysis assumes that each health and welfare endpoint is independent of the others. There is a very low

probability that the aggregate benefits will equal the

sum of the fifth percentile benefits from each of the

ten endpoints.

Table I-5 shows the estimated total benefits ranges

for the four modeled target years of this study: 1975,

1980, 1985, and 1990. The results of the Monte Carlo

simulations of the aggregate economic benefits for

these four target years are depicted in Figure I-1.

Table I-6 examines the impact of limiting the

scope of the analysis to locations with more certain

air quality estimates. The main analysis (as shown in

Tables I-3 through I-5) covers almost the entire population of the 48 States.3 However, the air quality information is less certain for locations far from a monitor. Table I-6 presents the results of limiting the analysis to people living within 50 km of an ozone, NO2,

SO2

, or CO monitor, or in counties with a PM monitor. The availability of monitors changes over time.

Hence the proportion of the population included in

this analysis changes over time as well. Table I-6 indicates that approximately a quarter of the total benefits estimated in the main analysis comes from areas

with less certain air quality information.

The results of the “all U.S. population” analysis

provides a more accurate depiction of the pattern of

economic benefits across years. The accuracy of the

scale of incidence is less certain. These results provide a better characterization of the total direct benefits of the Clean Air Act in the lower 48 states than

do the “monitored area only” results because the latter completely omits historical air quality improvements for about 25 percent of the population. However, the “all U.S. population” results rely on uncertain extrapolations of pollution concentrations, and

subsequent exposures, from distant monitoring sites

to provide coverage for the 25 percent or so of the

population living far from air quality monitors. Thus,

the main results presented in Tables I-3 through I-5

include important uncertainties.

Uncertainties

The uncertainty ranges for the results on the

present value of the aggregate measured monetary

benefits reported in Table I-3 reflect two important

sources of measured uncertainty:

• uncertainty about the avoided incidence of

health and welfare effects deriving from the

concentration-response functions, including

both selection of scientific studies and statistical uncertainty from the original studies; and

• uncertainty about the economic value of each

quantified health and welfare effect.

These aggregate uncertainty results incorporate many

decisions about analytical procedures and specific

assumptions discussed in the Appendices to this report.

In order to provide a more complete understanding of the economic benefit results in Table I-3, sensitivity analyses examine several additional important

aspects of the main analysis. First, this section ex3

 Except for lead, two to five percent (depending on pollutant) of the population who live in sparsely populated areas are

excluded from the main analysis to maximize computer efficiency. All of the population of the 48 states is included in the lead

analysis.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-17

 Present Value (billions of 1990$) Present Value (billions of 1990$)

Endpoint Endpoint Pollutant(s) Pollutant(s) 5th %ileile Mean Mean 95th 95th %ileile

Mortality

Mortality (long-term PM-10 exposure) PM $2,369 $16,632 $40,597

Mortality (Lead exposure) Lead $121 $1,339 $3,910 

Chronic Bronchitis PM $409 $3,313 $10,401 

Other Lead-induced Ailments

Lost IQ Points Lead $248 $377 $528 

IQ < 70 Lead $15 $22 $29 

Hypertension Lead $77 $98 $120 

Coronary Heart Disease Lead $0 $13 $40 

Atherothrombotic brain infarction Lead $1 $10 $30 

Initial cerebrovascular accident Lead $2 $16 $45 

Hospital Admissions

*All Respiratory PM & O3 $8 $9 $11 

*COPD + Pneumonia PM & O3 $8 $9 $10 

Ischemic Heart Disease PM $1 $4 $6 

Congestive Heart Failure PM & CO $3 $5 $7 

Other Respiratory-Related Ailments

Children

 Shortness of breath, days PM $0 $6 $17 

 **Acute Bronchitis PM $0 $7 $18 

 **Upper & Lower Respiratory Symptoms PM $1 $2 $4 

Adults

 Any of 19 Acute Symptoms PM & O3 $4 $46 $117 

All

 Asthma Attacks PM & O3 $0 $0 $1 

 Increase in Respiratory Illness NO2 $1 $2 $4 

 Any Symptom SO2 $0 $0 $0 

Restricted Activity and Work Loss Days

MRAD PM & O3 $50 $85 $123 

Work Loss Days (WLD) PM $30 $34 $39 

Human Welfare

Household Soiling Damage PM $6 $74 $192 

Visibility - Eastern U.S. particulates $38 $54 $71 

Decreased Worker Productivity O3 $3 $3 $3 

Agriculture (Net Surplus) O3 $11 $23 $35 

To avoid double-counting of benefits, the following endpoints were treated as alternatives:

 *Hospital admissions for COPD combined with those for pneumonia are treated as an equally-weighted alternative to hospital

 admissions for all respiratory illnesses. 

 **The definitions of acute bronchitis and upper and lower respiratory illness overlap; both studies count trouble breathing,

 dry cough, and wheezing in their estimates. These two studies are treated as alternatives, which reflects the variability of

 pollution-induced respiratory effects in children.

Table I-3. Crite ria Pollutan ts Health and We lfare Benefits -- E xtrapola ted to Entire 48 State

Population Present Value (in 199 0 using 5% discount ra te) of Benefits fro m 1970 - 1990 (in billions o f

1990 dollars).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-18

Present Value

Endpoint Pollutant(s) 5th %ile Mean 95th %ile

Mortality PM $2,369 $16,632 $40,597 

Mortality Pb $121 $1,339 $3,910 

Chronic Obstructive Pulmonary Disease PM $409 $3,313 $10,401 

IQ (Lost IQ Pts. + Children w/ IQ<70) Pb $271 $399 $551 

Hypertension Pb $77 $98 $120 

Hospital Admissions PM, O3, Pb, & CO $27 $57 $120 

Respiratory-Related Symptoms, Restricted

Activity, & Decreased Productivity

PM, O3, NO2, & SO2 $123 $182 $261 

Soiling Damage PM $6 $74 $192 

Visibility particulates $38 $54 $71 

Agriculture (Net Surplus) O3 $11 $23 $35 

Table I-4. Present Value of 197 0 to 1990 Monetized Benefits by Endpoint Category for 48 State

Population (billions of $1990, discounted to 1990 at 5 perce nt).

Total Benefits By Year ($Billions) 1975 1980 1985 1990 Present Value (5%)

 5th percentile $87 $235 $293 $329 $5,600 

 Mean $355 $930 $1,155 $1,248 $22,200 

 95th percentile $799 $2,063 $2,569 $2,762 $49,400 

Notes:

Present value reflects compounding of benefits from 1971 to 1990. 

"Uncertainty Estimates" are results of Monte Carlo analysis combining economic and physical effects uncertainty (i.e., using both 

between- and within-study variability). 

Full uncertainty analysis done only for years shown. Uncertainty estim ates for intermediate years computed based on ratios of 5th

to 50th percentile and 95th to 50th percentile for years shown. Ratios interpolated between years shown and applied to point

estimates for interm ediate years.

Table I-5. Monte Carlo Simulation Model Results for Target Years, Plus Present Value in 1990

Terms of Total Monetized Benefits for Entire 1970 to 1990 Period (in billions of 1990-value dollars).
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-19

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

1975 1980 1985 1990

Mean

95th%

5th%

Mean

95th%

5th%

Mean

95th%

5th%

Mean

95th%

5th%

Figure I-1. Monte Carlo Simulation Model Results for Target Years (in billions of 1990 dollars). Total Benefits ($Billions)

Mean Estimate of Monetized

Benefits

(millions o f 1 990-dollars)

Endpoint Pollutant(s) 48 Sta te Pop. Monito red

Areas*

 M ortality PM $892 ,39 0 $580 ,2 99 

 M ortality Pb $111 ,74 1 $111 ,7 41 

 C hronic Bron ch itis PM $179 ,75 5 $120 ,0 53 

 I Q (Lost IQ Points + Ch ildr en with IQ < 70) Pb $32 ,38 1 $32,381 

 Hyp ertension Pb $8,584 $8,584 

 H ospital Ad missions PM, O 3, Pb, & CO $4,281 $3,994 

 R espiratory-R elated Symp toms, R estricted PM, O 3, NO2, & SO 2 $10 ,24 9 $7,089 

 Activ ity, & Decr eased P rod uctivity

 Soilin g Damage PM $3,964 $2,709 

 Visibility particulates $3,382 $3,382 

 A griculture (Net Surp lus) O3 $986 $986 

TOTAL ($M illio ns) $1,247 ,71 3 $871 ,2 18 

* M onito red areas are those within 50 km of an O 3, N O2, SO2 , or CO mon itor or a PM -mon itored county . 

The "48 State P op ulation" modelin g estimate captures b en efits for pop ulations in un monitored areas. Air

pollution co ncentration s in these areas are assigned based on concentratio ns measured at the clo sest

monitor, f or O3, NO2 , SO2, and CO. PM con centr ations in un monitored co unties ar e derived by

extrap olatin g tho se in mon itored counties.

Table I-6. Co mparison o f 1990 (Single Year) M onetized Benefits by Endpoint for 48 Sta te

Population and M onitored Areas (in millions of 19 90 dolla rs).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-20

plores the effect of selecting alternative discount rates

on the aggregate present value benefits estimation.

Second, this section examines the sources of the measured aggregate uncertainty, identifying which of the

measured uncertainty components of incidence and

valuation for individual health effects categories drive

the overall uncertainty results. Third, this section examines several issues involving the estimated economic benefits of mortality.

The Effect of Discount Rates

The main analysis reflected in present value results shown in Table I-3 uses a five percent discount

rate. The discount rate primarily enters the calculations when compounding the economic benefits estimates from individual years between 1970 and 1990

to estimate the present value of the benefits in 1990.

The discount rate also directly enters in the calculations of the economic values of an IQ point and an

initial case of coronary heart disease.4 There is considerable controversy in the economics and policy literature about the appropriate discount rate to use in

different settings. Major alternatives recommended by

various authors include a discount rate based on the

social discount rate (typical estimates are in the 2 to 3

percent range), and a discount rate based on the riskfree rate of return on capital (typically in the 7 to 10

percent range). Table I-7 presents the aggregate uncertainty results using three different discount rates:

3 percent, 5 percent and 7 percent. While the aggregate benefits estimates are sensitive to the discount

rate, selecting one of these alternative discount rates

affects the aggregate benefits estimates by only about

15 percent.

The Relative Importance of Different

Components of Uncertainty

The estimated uncertainty ranges in Table I-3 reflect the measured uncertainty associated with both

avoided incidence and economic valuation. A better

understanding of the relative influence of individual

uncertain variables on the overall uncertainty in the

analysis can be gained by isolating the individual effects of important variables on the range of estimated

benefits. This can be accomplished by holding all the

inputs to the Monte Carlo uncertainty analysis constant (at their mean values), and allowing only one

variable -- for example, the economic valuation of

mortality -- to vary across the range of that variable’s

uncertainty. The sensitivity analysis then isolates how

this single source of variability contributes to the variation in estimated total benefits. The results are summarized in Figure I-2. The nine individual uncertainty

factors that contribute the most to the overall uncertainty are shown in Figure I-2, ordered by the relative

significance of their contribution to overall uncertainty. Each of the additional sources of quantified

uncertainty in the overall analysis not shown contribute a smaller amount of uncertainty to the estimates

of monetized benefits than the sources that are shown.

Present Value in 1990 of Total Benefits

(Trillions of 1990 Dollars) 3% 5% 7% 

 5th percentile $4.9 $5.6 $6.5 

 Mean $19.2 $22.2 $25.8 

 95th percentile $42.7 $49.4 $57.5 

Notes:

Present value reflects compounding of benefits from 1971 to 1990. 

Table I-7. Effect of Alternative Discount Rates on Present Value of Total Monetized Benefits for

1970 to 1990 (in trillions of 1990 dollars).

4

 The estimated economic value of lost IQ points due to lead exposure is based on the present value of the impact on lifetime

earnings. A discount rate is required to calculate that present value. The impact on income primarily occurs during adulthood, which

is 20 to 70 years after the initial lead exposure. This significant lag results in the discount rate having a significant impact on the

estimated economic benefits of the IQ loss. Similarly, the cost of illness estimate for an initial case of CHD includes the present value

of the annual stream of medical costs incurred after the event, the calculation of which requires an estimate of the discount rate.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-21

Because of the multiple uncertainties in the benefits estimation, the total estimated present value of

the monetary benefits of the 1970 to 1990 Clean Air

Act range from a low of about $5.6 trillion to a high

of about $49.4 trillion (in 1990 dollars, discounted at

five percent). Most of the uncertainty in the total estimated benefit levels comes from uncertainty in the

estimate of the economic valuation of mortality, followed by the uncertainty in the incidence of mortality

from PM (as a surrogate for all non-lead air pollution). The incidence of lead-induced mortality also

has a significant influence on the overall uncertainty.

The importance of mortality is not surprising, because

the benefits associated with reduced mortality are such

a large share of overall monetized benefits.

The uncertainty in both the incidence and valuation of chronic bronchitis are the two other significant factors driving the overall uncertainty range. The

modeled uncertainty in the other remaining health and

welfare endpoints in the analysis contribute relatively

small amounts to the overall uncertainty in the estimate of total monetary benefits of the Clean Air Act.

Most of these other endpoints account for a relatively

small proportion of the overall benefits estimates,

making it unlikely that they could contribute significantly to the overall uncertainty. Estimates of either

the mean values or standard errors of these variables

are generally very small relative to estimated total

monetary benefits.

Economic Benefits Associated with

Reducing Premature Mortality

Because the economic benefits associated with

premature mortality are the largest source of monetized benefits in the analysis, and because the uncertainties in both the incidence and value of premature

mortality are the most important sources of uncertainty

in the overall analysis, it is useful to examine the

mortality benefits estimation in greater detail.

The analytical procedure used in the main analysis to estimate the monetary benefits of avoided premature mortality assumes that the appropriate economic value for each incidence is a value from the

currently accepted range of the value of a statistical

life. As discussed above, the estimated value per predicted incidence of excess premature mortality is

modeled as a Weibull distribution, with a mean value

of $4.8 million and a standard deviation of $3.2 million. This estimate is based on 26 studies of the value

of mortal risks.

Figure I-2. Uncertainty Ranges Deriving From Individual Uncertainty Factors. Total Benefits Present Value ($Trillions)

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

All Components

Mortality

Valuation

PM-10 Mortality

Incidence

Chr. Bronchitis

Valuation

Pb Mortality

Incidence

Chr. Bronchitis

Incidence

Lost IQ

Incidence

Household

Soiling Valuation

Lost IQ Valuation

MRADs

Valuation

95th %ile

Mean

5th %ile

PM-2.5 Mortality
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-22

There is considerable uncertainty as to whether

the 26 studies on the value of a statistical life provide

adequate estimates of the value of a statistical life

saved by air pollution reduction. Although there is

considerable variation in the analytical designs and

data used in the 26 underlying studies, the majority of

the studies involve the value of risks to a middle-aged

working population. Most of the studies examine differences in wages of risky occupations, using a wagehedonic approach. Certain characteristics of both the

population affected and the mortality risk facing that

population are believed to affect the average willingness to pay (WTP) to reduce the risk. The appropriateness of a distribution of WTP estimates from the

26 studies for valuing the mortality-related benefits

of reductions in air pollution concentrations therefore

depends not only on the quality of the studies (i.e.,

how well they measure what they are trying to measure), but also on (1) the extent to which the subjects

in the studies are similar to the population affected by

changes in pollution concentrations, and (2) the extent to which the risks being valued are similar. As

discussed below, there are possible sources of both

upward and downward bias in the estimates provided

by the 26 studies when applied to the population and

risk being considered in this analysis.

If the individuals who die prematurely from air

pollution are consistently older than the population in

the valuation studies, the mortality valuations based

on middle-aged people may provide a biased estimate

of the willingness to pay of older individuals to reduce mortal risk. There is some evidence to suggest

that the people who die prematurely from exposure to

ambient particulate matter tend to be older than the

populations in the valuation studies. In the general

U.S. population far more older people die than younger

people; 88 percent of the deaths are among people

over 64 years old. It is difficult to establish the proportion of the pollution-related deaths that are among

the older population because it is impossible to isolate individual cases where one can say with even reasonable certainty that a specific individual died because of air pollution.

There is considerable uncertainty whether older

people will have a greater willingness to pay to avoid

risks than younger people. There is reason to believe

that those over 65 are, in general, more risk averse

than the general population, while workers in

wage-risk studies are likely to be less risk averse than

the general population. More risk averse people would

have a greater willingness to pay to avoid risk than

less risk averse people. Although the list of recommended studies excludes studies that consider only

much-higher-than- average occupational risks, there

is nevertheless likely to be some selection bias in the

remaining studies -- that is, these studies are likely to

be based on samples of workers who are, on average,

more risk-loving than the general population. In contrast, older people as a group exhibit more risk averse

behavior.

In addition, it might be argued that because the

elderly have greater average wealth than those

younger, the affected population is also wealthier, on

average, than wage-risk study subjects, who tend to

be blue collar workers. It is possible, however, that

among the elderly it is largely the poor elderly who

are most vulnerable to pollution-related mortality risk

(e.g., because of generally poorer health care). If this

is the case, the average wealth of those affected by a

pollution reduction relative to that of subjects in

wage-risk studies is uncertain. In addition, the workers in the wage-risk studies will have potentially more

years remaining in which to acquire streams of income from future earnings.

Although there may be several ways in which jobrelated mortality risks differ from air pollution-related

mortality risks, the most important difference may be

that job-related risks are incurred voluntarily whereas

air pollution-related risks are incurred involuntarily.

There is some evidence (see, for example, Violette

and Chestnut, 1983) that people will pay more to reduce involuntarily incurred risks than risks incurred

voluntarily. If this is the case, WTP estimates based

on wage-risk studies may be downward biased estimates of WTP to reduce involuntarily incurred air

pollution-related mortality risks.

Finally, another possible difference related to the

nature of the risk may be that some workplace mortality risks tend to involve sudden, catastrophic events

(e.g., workplace accidents), whereas air pollution-related risks tend to involve longer periods of disease

and suffering prior to death. Some evidence suggests

that WTP to avoid a risk of a protracted death involving prolonged suffering and loss of dignity and personal control is greater than the WTP to avoid a risk

(of identical magnitude) of sudden death. Some workplace risks, such as risks from exposure to toxic chemicals, may be more similar to pollution-related risks. It

is not clear, however, what proportion of the workplace risks in the wage-risk studies were related to

workplace accidents and what proportion were risks
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-23

from exposure to toxic chemicals. To the extent that

the mortality risks addressed in this assessment are

associated with longer periods of illness or greater pain

and suffering than are the risks addressed in the valuation literature, the WTP measurements employed in

the present analysis would reflect a downward bias.

The direction of bias resulting from the age difference is unclear, particularly because age is confounded by risk aversion (relative to the general population). It could be argued that, because an older person has fewer expected years left to lose, his WTP to

reduce mortality risk would be less than that of a

younger person. This hypothesis is supported by one

empirical study, Jones-Lee et al. (1985), that found

the value of a statistical life at age 65 to be about 90

percent of what it is at age 40. Citing the evidence

provided by Jones-Lee et al. (1985), a recent sulfaterelated health benefits study conducted for EPA (U.S.

EPA, 1995) assumes that the value of a statistical life

for those 65 and over is 75 percent of what it is for

those under 65.

There is substantial evidence that the income elasticity of WTP for health risk reductions is positive

(see, for example, Alberini et al., 1994; Mitchell and

Carson, 1986; Loehman and Vo Hu De, 1982; Gerking

et al., 1988; and Jones-Lee et al., 1985), although there

is uncertainty about the exact value of this elasticity.

Individuals with higher incomes (or greater wealth)

should, then, be willing to pay more to reduce risk, all

else equal, than individuals with lower incomes or

wealth. Whether the average income or level of wealth

of the population affected by PM reductions is likely

to be significantly different from that of subjects in

wage-risk studies, however, is unclear, as discussed

above.

The need to adjust wage-risk-based WTP estimates downward because of the likely upward bias

introduced by the age discrepancy has received significant attention (see Chestnut, 1995; IEc, 1992). If

the age difference were the only difference between

the population affected by pollution changes and the

subjects in the wage-risk studies, there might be some

justification for trying to adjust the point estimate of

$4.8 million downward. Even in this case, however,

the degree of the adjustment would be unclear. There

is good reason to suspect, however, that there are biases in both directions. Because in each case the extent of the bias is unknown, the overall direction of

bias in the mortality values is similarly unknown.

Adjusting the estimate upward or downward to compensate for any one source of bias could therefore increase the degree of bias. Therefore, the range of values from the 26 studies is used in the primary analysis without adjustment.

Examining the sensitivity of the overall results to

the mortality values can help illuminate the potential

impacts of alternative mortality valuations. As mentioned above, a contractor study performed for EPA

used one approach to evaluate the economic value of

sulfate-related human health improvements resulting

from 1990 Clean Air Act Amendments Title IV acid

rain controls. That study assumed that 85 percent of

the people dying from sulfates (an important component of particulate matter) were over 65, and that

people over 65 have a willingness to pay to avoid a

mortal risk that is 75 percent of the values that middleaged people have. Using this approach, the value of

an average statistical life (using a weighted average)

is reduced to 79 percent of the previous value.

If statistical life-years lost are used as the unit of

measure, rather than statistical lives lost, the benefit

attributed to avoiding a premature death depends directly on how premature it is. One way to estimate

the value of a statistical life-year assumes that the value

of a statistical life is directly related to remaining life

expectancy and a constant value for each life-year.

Such an approach results in smaller values of a statistical life for older people, who have shorter life expectancies, and larger values for younger people. For

example, if the $4.8 million mean value of avoiding

death for people with a 35 year life expectancy is assumed to be the discounted present value of 35 equalvalued statistical life-years, the implied value of each

statistical life-year is $293,000 (using a 5% discount

rate). The average number of life-years lost by individuals dying prematurely from exposure to PM is 14

years. This average is obtained by multiplying the

predicted number of PM-related premature deaths in

each age category by the life expectancy for that age

category and dividing by the total number of PM-related premature deaths.) Using $293,000 per life-year,

the discounted present value of a statistical life for a

person with 14 years of expected life remaining (e.g.,

a 70 year old) is $2.9 million). If statistical life-years

lost are used to value fatal risks, however, other

sources of uncertainty are introduced in the valuation

process.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-24

If statistical life-years lost is the unit of measure,

the value of a statistical life lost depends on (1) how

many years of expected life are lost, (2) the

individual’s discount rate, and (3) whether the value

of an undiscounted statistical life-year is the same no

matter which life-year it is (e.g., the undiscounted

value of the seventy-fifth year of life is the same as

the undiscounted value of the fortieth year of life).

Each of these is uncertain. The uncertainty surrounding the expected years of life lost by an individual

involves the uncertainty about whether individuals

who die from exposure to air pollution are average

individuals in the demographic (e.g., age-gender-race)

classification to which they belong. The uncertainty

surrounding individuals’ discount rates is well documented. Finally, even if it is assumed that all life-years

are valued the same (apart from differences due to

discounting), the value of a statistical life-year is derived from the value of a statistical life (of a 40 year

old) and the discount rate, each of which is uncertain.

Using life-years lost as the unit of measure means

that, rather than estimating a single value of a statistical life lost (applicable to all ages), the analysis would

instead estimate age-specific values of statistical lives

lost. It is unclear whether the variability of estimates

of age-specific values of statistical lives lost (in particular, for ages greater than the average age of workers in the wage-risk studies) would be less than or

greater than the variability of the original estimate of

the value of a statistical life lost from which they would

be derived. If there is an age-related upward bias in

the central tendency value of a statistical life that is

larger than any downward bias, then valuing life-years

rather than lives lost may decrease the bias. Even this,

however, is uncertain.

In spite of the substantial uncertainties and paucity of available information, this section presents an

example of a preliminary estimate of the present value

of avoided premature mortality using the life-years

lost approach. The basic approach is to (1) estimate

the number of pollution-related premature deaths in

each age category, (2) estimate the average number

of life-years lost by an individual in a given age category dying prematurely, and (3) using the value of a

statistical life-year of $293,000, described above (assuming that the undiscounted value of a life-year is

the same no matter when in an individual’s life it is)

and assuming a five percent discount rate, calculate

the value of a statistical life lost in each age category.

To obtain estimates of the number of air pollution-related deaths in each age cohort, it is preferable

to have age-specific relative risks. Many of the epidemiological studies, however, do not provide any estimate of such age-specific risks. In this case, the agespecific relative risks must be assumed to be identical.

Some epidemiology studies on PM do provide

some estimates of relative risks specific to certain age

categories. The limited information that is available

suggests that relative risks of mortality associated with

exposure to PM are greater for older people. Most of

the available information comes from short-term exposure studies. There is considerable uncertainty in

applying the evidence from short-term exposure studies to results from long-term (chronic exposure) studies. However, using the available information on the

relative magnitudes of the relative risks, it is possible

to form a preliminary assessment of the relative risks

by different age classes.

The analysis presented below uses two alternative assumptions about age-specific risks: (1) there is

a constant relative risk (obtained directly from the

health literature) that is applicable to all age cohorts,

and (2) the relative risks differ by age, as estimated

from the available literature. Estimates of age-specific PM coefficients (and, from these, age-specific

relative risks) were derived from the few age-specific

PM coefficients reported in the epidemiological literature. These estimates in the literature were used to

estimate the ratio of each age-specific coefficient to a

coefficient for “all ages” in such a way that consistency among the age-specific coefficients is preserved

— that is, that the sum of the health effects incidences

in the separate, non-overlapping age categories equals

the health effects incidence for “all ages.” These ratios were then applied to the coefficient from Pope et

al. (1995). Details of this approach are provided in

Post and Deck (1996). Because Pope et al. considered

only individuals age 30 and older (instead of all ages),

the resulting age-specific PM coefficients may be

slightly different from what they would have been if

the ratios had been applied to an “all ages” coefficient. The differences, however, are likely to be minimal and well within the error bounds of this exercise.

The age-specific relative risks used in the example

below assume that the relative risks for people under

65 are only 16 percent of the population-wide average relative risk, the risks for people from 65 to 74

are 83 percent of the population-wide risk, and people
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-25

75 and older have a relative risk 55 percent greater

than the population average. Details of this approach

are provided in Post and Deck (1996).

The life-years lost approach also requires an estimate of the number of life-years lost by a person dying prematurely at each given age. The average number of life-years lost will depend not only on whether

relative risks are age-specific or uniform across all

age groups, but also on the distribution of ages in the

population in a location. As noted above, using the

same relative risk for all age categories, the average

number of life-years lost in PM-related premature

deaths in the United States was estimated to be 14

years. Using the age-specific relative risk estimates

developed for this analysis, the average number of

life-years lost becomes 9.8 years. In a location with a

population that is younger than average in the United

States, the same age-specific relative risks will produce a larger estimated average number of life-years

lost. For example, using the same age-specific relative risks, the average number of life-years lost in PMrelated premature deaths in Los Angeles County,

which has a younger population, is estimated to be

15.6 years.

The present value benefits estimates for PM-related mortality using the alternative approaches discussed above are shown in Table I-8. Table I-8 is based

on a single health study: Pope et al., 1995. Alternative studies, or the uncertainty approach used in the

primary analysis, would result in a similar pattern of

the relationship between valuation approaches. The

pattern of monetized mortality benefits across the different valuation procedures shown in Table I-8 is essentially invariant to the particular relative risk and

the particular dollar value used.

As noted above, the life-years lost approach used

here assumes that people who die from air pollution

are typical of people in their age group. The estimated

value of the quantity of life lost assumes that the people

who die from exposure to air pollution had an average life expectancy. However, it is possible that the

people who die from air pollution are already in ill

health, and that their life expectancy is less than a

typical person of their age. If this is true, then the number of life years lost per PM-related death would be

lower than calculated here, and the economic value

would be smaller.

The extent to which adverse effects of particulate

matter exposure are differentially imposed on people

of advanced age and/or poor health is one of the most

important current uncertainties in air pollution-related

health studies. There is limited information, primarily from the short-term exposure studies, which suggests that at least some of the estimated premature

mortality is imposed disproportionately on people who

are elderly and/or of poor health. The Criteria Document for Particulate Matter (U.S. EPA, 1996) identifies only two studies which attempt to evaluate this

disproportionality. Spix et al. (1994) suggests that a

small portion of the PM-associated mortality occurs

in individuals who would have died in a short time

anyway. Cifuentes and Lave (1996) found that 37 to

87 percent of the deaths from short-term exposure

could have been premature by only a few days, although their evidence is inconclusive.

 Valuation Procedure

Present Value of PM

Mortality Benefits

 Primary Analysis Method ( $4.8 million per statistical life saved) $16.6

 Life Years Lost approaches

 Single relative risk, valuation using 5% discounting $9.1

 Approximate age-specific relative risk, valuation using 5% discounting $8.3

Notes:

Present value reflects compounding of benefits from 1971 to 1990, using a 5 percent discount rate. 

Table I-8. Alternative Estimates of the Present Value of Mortality Associated With PM 

(based on Pope et al., 1996, in trillions of 1990 dollars).
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-26

Prematurity of death on the order of only a few

days is likely to occur largely among individuals with

pre-existing illnesses. Such individuals might be particularly susceptible to a high PM day. To the extent

that the pre-existing illness is itself caused by or exacerbated by chronic exposure to elevated levels of

PM, however, it would be misleading to define the

prematurity of death as only a few days. In the absence of chronic exposure to elevated levels of PM,

the illness would either not exist (if it was caused by

the chronic exposure to elevated PM) or might be at a

less advanced stage of development (if it was not

caused by but exacerbated by elevated PM levels).

The prematurity of death should be calculated as the

difference between when the individual died in the

“elevated PM” scenario and when he would have died

in the “low PM” scenario. If the pre-existing illness

was entirely unconnected with chronic exposure to

PM in the “elevated PM” scenario, and if the individual who dies prematurely because of a peak PM

day would have lived only a few more days, then the

prematurity of that PM-related death is only those few

days. If, however, in the absence of chronic exposure

to elevated levels of PM, the individual’s illness would

have progressed more slowly, so that, in the absence

of a particular peak PM day the individual would have

lived several years longer, the prematurity of that PMrelated death would be those several years.

Long-term studies provide evidence that a portion of the loss of life associated with long-term exposure is independent of the death from short-term

exposures, and that the loss of life-years measured in

the long-term studies could be on the order of years.

If much of the premature mortality associated with

PM represents short term prematurity of death imposed on people who are elderly and/or of ill health,

the estimates of the monetary benefits of avoided

mortality may overestimate society’s total willingness

to pay to avoid particulate matter-related premature

mortality. On the other hand, if the premature mortality measured in the chronic exposure studies is detecting excess premature deaths which are largely independent of the deaths predicted from the short term

studies, and the disproportionate effect on the elderly

and/or sick is modest, the benefits measured in this

report could be underestimates of the total value. At

this time there is insufficient information from both

the medical and economic sciences to satisfactorily

resolve these issues from a theoretical/analytical standpoint. Until there is evidence from the physical and

social sciences which is sufficiently compelling to

encourage broad support of age-specific values for

reducing premature mortality, EPA will continue to

use for its primary analyses a range of values for mortality risk reduction which assumes society values reductions in pollution-related premature mortality

equally regardless of who receives the benefit of such

protection.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-27

Economic Valuation References

Abbey, D.E., F. Petersen, P. K. Mills, and W. L. Beeson.

1993. “Long-Term Ambient Concentrations of

Total Suspended Particulates, Ozone, and Sulfur Dioxide and Respiratory Symptoms in a

Nonsmoking Population.” Archives of Environmental Health 48(1): 33-46.

Abt Associates, Inc. 1992. The Medical Costs of Five

Illnesses Related to Exposure to Pollutants.

Prepared for U.S. EPA, Office of Pollution Prevention and Toxics, Washington, DC.

Abt Associates, Inc. 1996. Section 812 Retrospective

Analysis: Quantifying Health and Welfare Benefits. Draft. Prepared for U.S. EPA, Office of

Policy Planning and Evaluation, Washington

DC. May.

Alberini, A., A. Krupnick, M. Cropper, and W.

Harrington. 1994. “Air Quality and the Value

of Health in Taiwan.” Paper presented at the

annual meeting of the Eastern Economics Association, Boston, Massachusetts, March.

Brookshire, David S., Ralph C. d’Arge, William D.

Schulze and Mark A. Thayer. 1979. Methods

Development for Assessing Air Pollution Control Benefits, Vol. II: Experiments in Valuing

Non-Market Goods: A Case Study of Alternative Benefit Measures of Air Pollution Control

in the South Coast Air Basin of Southern California. Prepared for the U.S. Environmental

Protection Agency, Office of Research and Development.

Chestnut, Lauraine G. 1995. Dollars and Cents: The

Economic and Health Benefits of Potential

Particulate Matter Reductions in the United

States. Prepared for the American Lung Association.

Chestnut, Lauraine G. and Robert D. Rowe. 1989. “Economic Valuation of Changes in Visibility: A

State of the Science Assessment for NAPAP,”

as cited in National Acid Precipitation Assessment Program, Methods for Valuing Acidic

Deposition and Air Pollution Effects. NAPAP

State of Science and State of Technology Report No. 27, Part B. December.

Cifuentes, L. and L.B. Lave. 1996. “Association of

Daily Mortality and Air Pollution in Philadelphia, 1983-1988.” J. Air Waste Manage.

Assoc.: in press.

Crocker T. D. and R. L. Horst, Jr. 1981. “Hours of

Work, Labor Productivity, and Environmental Conditions: a Case Study.” The Review of

Economics and Statistics 63:361-368.

Cropper, M.L. and A.J. Krupnick. 1990. “The Social

Costs of Chronic Heart and Lung Disease,”

Resources for the Future Discussion Paper QE

89-16-REV.

Dickie, M. et al. 1991. Reconciling Averting Behavior and Contingent Valuation Benefit Estimates of Reducing Symptoms of Ozone Exposure (draft), as cited in Neumann, J.E.,

Dickie, M.T., and R.E. Unsworth. 1994. Industrial Economics, Incorporated. Memorandum to Jim DeMocker, U.S. EPA, Office of

Air and Radiation. March 31.

Elixhauser, A., R.M. Andrews, and S. Fox. 1993.

Clinical Classifications for Health Policy

Research: Discharge Statistics by Principal

Diagnosis and Procedure. Agency for Health

Care Policy and Research (AHCPR), Center

for General Health Services Intramural Research, U.S. Department of Health and Human Services.

Empire State Electric Energy Research Corporation

(ESEERCO). 1994. New York State Environmental Externalities Cost Study. Report 2:

Methodology. Prepared by: RCG/Hagler,

Bailly, Inc., November.

Gerking, S., M. DeHaan, and W. Schulze. 1988. “The

Marginal Value of Job Safety: A Contingent

Valuation Study.” Journal of Risk and Uncertainty 1: 185-199.

Industrial Economics, Incorporated (IEc). 1992. Approaches to Environmental Benefits Assessment to Support the Clean Air Act Section 812

Analysis. Prepared by Robert E. Unsworth,

James E. Neumann, and W. Eric Browne, for

Jim DeMocker, Office of Policy Analysis and

Review, Office of Air and Radiation, U.S.

Environmental Protection Agency. 6 November.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-28

Industrial Economics, Incorporated (IEc). 1993a.

“Analysis of Visibility Valuation Issues for

the Section 812 Study,” Memorandum to Jim

DeMocker, Office of Policy Analysis and

Review, Office of Air and Radiation, U.S.

Environmental Protection Agency, prepared

by Jim Neumann, Lisa Robinson, and Bob

Unsworth. September 30.

Industrial Economics, Incorporated (IEc). 1997. “Visibility Valuation for the CAA Section 812

Retrospective Analysis,” Memorandum to

Jim DeMocker, Office of Policy Analysis and

Review, Office of Air and Radiation, U.S.

Environmental Protection Agency, prepared

by Michael H. Hester and James E. Neumann.

18 February.

Irwin, Julie, William Schulze, Gary McClelland,

Donald Waldman, David Schenk, Thomas

Stewart, Leland Deck, Paul Slovic, Sarah

Lictenstein, and Mark Thayer. 1990. Valuing

Visibility: A Field Test of the Contingent Valuation Method. Prepared for Office of Policy,

Planning and Evaluation, U.S. Environmental Protection Agency. March.

Jones-Lee, M.W., et al. 1985. “The Value of Safety:

Result of a National Sample Survey.” Economic Journal 95(March): 49-72.

Krupnick, A.J. and M.L. Cropper. 1992. “The Effect

of Information on Health Risk Valuations,”

Journal of Risk and Uncertainty 5(2): 29-48.

Loehman, E.T., S.V. Berg, A.A. Arroyo, R.A.

Hedinger, J.M. Schwartz, M.E. Shaw, R.W.

Fahien, V.H. De, R.P. Fishe, D.E. Rio, W.F.

Rossley, and A.E.S. Green. 1979. “Distributional Analysis of Regional Benefits and Cost

of Air Quality Control.” Journal of Environmental Economics and Management 6: 222-

243.

Loehman, E.T. and Vo Hu De. 1982. “Application of

Stochastic Choice Modeling to Policy Analysis of Public Goods: A Case Study of Air

Quality Improvements.” The Review of Economics and Statistics 64(3): 474-480.

Manuel, E.H., R.L. Horst, K.M. Brennan, W.N. Lanen,

M.C. Duff, and J.K Tapiero. 1982. Benefits

Analysis of Alternative Secondary National

Ambient Air Quality Standards for Sulfur Dioxide and Total Suspended Particulates, Volumes I-IV. Prepared for U.S. Environmental

Protection Agency, Office of Air Quality

Planning and Standards, Research Triangle

Park, NC.

McClelland, Gary, William Schulze, Donald

Waldman, Julie Irwin, David Schenk, Thomas Stewart, Leland Deck and Mark Thayer.

1991. Valuing Eastern Visibility: A Field Test

of the Contingent Valuation Method. Prepared

for Office of Policy, Planning and Evaluation,

U.S. Environmental Protection Agency. June.

Mitchell, R.C. and R.T. Carson. 1986. “Valuing Drinking Water Risk Reductions Using the Contingent Valuation Methods: A Methodological Study of Risks from THM and Giardia.”

Paper prepared for Resources for the Future,

Washington, DC.

Moore, M.J. and W.K. Viscusi. 1988. “The QuantityAdjusted Value of Life”. Economic Inquiry

26(3): 369-388.

National Acid Precipitation Assessment Program

(NAPAP). 1991. Acidic Deposition: State of

Science and Technology (Summary Report).

(Washington, DC: NAPAP). September.

Neumann, J.E., M. T. Dickie, and R.E. Unsworth.

1994. Industrial Economics, Incorporated.

Memorandum to Jim DeMocker, U.S. EPA,

Office of Air and Radiation. Linkage Between

Health Effects Estimation and Morbidity

Valuation in the Section 812 Analysis —

Draft Valuation Document. March 31.

Ostro, B.D., M.J. Lipsett, J.K. Mann, H. BraxtonOwens, and M.C. White. 1995. “Air Pollution and Asthma Exacerbations Among African American Children in Los Angeles.” Inhalation Toxicology.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W.

Dockery, J.S. Evans, F.E. Speizer, and C.W.

Heath, Jr. 1995. “Particulate Air Pollution as

a Predictor of Mortality in a Prospective Study

of U.S. Adults.” Am. J. Respir. Crit. Care

Med. 151: 669-674.
Appendix I: Valuation of Human Health and Welfare Effects of Criteria Pollutants

I-29

Post, Ellen and L. Deck. 1996. Abt Associates Inc.

Memorandum to Tom Gillis, U.S. EPA, Office of Office of Policy Planning and Evaluation. September 20.

Rowe, R.D. and L.G. Chestnut. 1986. “Oxidants and

Asthmatics in Los Angeles: A Benefits Analysis—Executive Summary.” Prepared by Energy and Resource Consultants, Inc. Report

to the U.S. EPA, Office of Policy Analysis.

EPA-230-09-86-018. Washington, D.C.

March.

Salkever, D.S. 1995. “Updated Estimates of Earnings

Benefits from Reduced Exposure of Children

to Environmental Lead.” Environmental Research 70: 1-6.

Schwartz, J. 1994. “Societal Benefits of Reducing

Lead Exposure.” Environmental Research 66:

105-124.

Spix, C., J. Heinrich, D. Dockery, J. Schwartz, G.

Volksch, K. Schwinkowski, C. Collen, and

H.E. Wichmann. 1994. Summary of the

Analysis and Reanalysis Corresponding to the

Publication Air Pollution and Daily Mortality in Erfurt, East Germany 1980-1989. Summary report for: Critical Evaluation Workshop on Particulate Matter—Mortality Epidemiology Studies; November; Raleigh, NC.

Wuppertal, Germany: Bergische UniversitatGesamthochschule Wuppertal.

Taylor, T.N., P.H. Davis, J.C. Torner, J. Holmes, J.W.

Meyer, and M. F. Jacobson. 1996. “Lifetime

Cost of Stroke in the United States.” Stroke

27(9): 1459-1466.

Tolley, G.S. et al. 1986. Valuation of Reductions in

Human Health Symptoms and Risks. University of Chicago. Final Report for the U.S.

Environmental Protection Agency. January.

U.S. Department of Commerce, Economics and Statistics Administration. 1992. Statistical Abstract of the United States, 1992: The National

Data Book. 112th Edition, Washington, D.C.

U.S. Environmental Protection Agency (U.S. EPA).

1994. Documentation for Oz-One Computer

Model (Version 2.0). Office of Air Quality

Planning and Standards. Prepared by:

Mathtech, Inc., under EPA Contract No.

68D30030, WA 1-29. August.

U.S. Environmental Protection Agency (U.S. EPA).

1995. Human Health Benefits From Sulfate

Reductions Under Title IV of the 1990 Clean

Air Act Amendments. Prepared by Hagler

Bailly Consulting, Inc. for U.S. EPA, Office

of Air and Radiation, Office of Atmospheric

Programs. November 10.

U.S. Environmental Protection Agency (U.S. EPA).

1996. Air Quality Criteria for Particulate

Matter, Volume III of III. Office of Research

and Development, Washington DC. EPA/600/

P-95/001cF

Violette, D.M. and L.G. Chestnut. 1983. Valuing Reduction in Risks: A Review of the Empirical

Estimates. Report prepared for the U.S. Environmental Protection Agency, Washington,

D.C. EPA-230-05-83-002.

Viscusi, W.K. 1992. Fatal Tradeoffs: Public and Private Responsibilities for Risk. (New York:

Oxford University Press).

Viscusi, W.K., W. A. Magat, and J. Huber. 1991.

“Pricing Environmental Health Risks: Survey

Assessments of Risk-Risk and Risk-dollar

Tradeoffs.” Journal of Environmental Economics and Management 201: 32-57.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990.

“Medical Costs of Coronary Artery Disease

in the United States.” The American Journal

of Cardiology 65: 432-440.

World Health Organization (WHO). 1996. Final Consultation on Updating and Revision of the Air

Quality Guidelines for Europe. Bilthoven, The

Netherlands 28-31 October, 1996 ICP EHH

018 VD96 2.11.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

I-30
Appendix J: Future Directions

J-1

Appendix J: Future Directions

Research Implications

In virtually any benefit analysis of environmental issues, the state of scientific information limits the

degree of coverage possible and the confidence in

benefit estimation. For most benefit categories, further scientific research would allow for a better quantification of benefits. One of the major outcomes of the

retrospective analysis is a clear delineation of the

major limitations in the scientific and economics literature in carrying out an analysis of this scope. Often, a list of research needs is generated in studies

such as this, but there is no clear internal mechanism

to address these needs. With this study (and the ongoing section 812 program), a process has been initiated

where identified research needs are to be integrated

into EPA’s overall extramural research grants program, administered by the Office of Research and Development. It is hoped that the research projects that

flow from this process will enable future analyses to

be less uncertain and more comprehensive.

Certain of the limitations in the retrospective

analysis are directly related to the historical nature of

the analysis, such as sparse information about air quality in the early 1970’s in many areas in the country.

Other important limitations are related to the effects

of elevated airborne lead concentrations, which has

been virtually eliminated by the removal of lead from

gasoline. A better understanding of these relationships

would improve our understanding of the historical

impact of the Clean Air Act, but would only indirectly

contribute to developing future air pollution policy.

However, most of the research that will reduce the

major gaps and uncertainties needed to improve the

section 812 analyses will be directly relevant to EPA’s

primary ongoing mission of developing and implementing sound environmental policies to meet the

national goals established in the Clean Air Act and

other legislation.

There are a number of biological, physical and

economic research areas which the EPA Project Team

identified as particularly important for improving future section 812 analyses. These research topics can

be divided into two principal categories: (1) those

which might reduce uncertainties in cost and benefit

estimates with significant potential for influencing

estimated net benefits of the Clean Air Act, and (2)

those which might improve the comprehensiveness

of section 812 assessments by facilitating quantification and/or monetization of currently excluded cost

or benefit endpoints. The following subsections provide examples of research topics which, if pursued,

might improve the certainty and/or comprehensiveness of future section 812 studies.

Research Topics to Reduce Uncertainty

Scientific information about the effects of longterm exposure to air pollutants is just beginning to

emerge, but continues to be the subject of intense scientific inquiry. The relationship between chronic PM

exposure and excess premature mortality included in

the quantified results of the present analysis is one

example of such research. However, many other potential chronic effects that are both biologically plausible and suggested by existing research are not included. Research to identify the relationship linking

certain known or hypothesized physical effects (e.g.,

ozone’s effects on lung function or fibrosis) with the

development of serious health effects (e.g., cardiopulmonary diseases or premature mortality), and the

appropriate economic valuation of the willingness to

pay to avoid the risks of such diseases, would reduce

the uncertainty caused by a major category of excluded

health effects which could have a significant impact

on the aggregate benefits estimates.

As described in Chapter 7 and Appendix I, premature mortality is both the largest source of benefits

and the major source of quantified uncertainty in the
The Benefits and Costs of the Clean Air Act, 1970 to 1990

J-2

retrospective analysis. In addition to the quantified

uncertainty, there is considerable additional

unquantified uncertainty about premature mortality

associated with air pollution. Much of the information base about these relationships is relatively new,

more is coming out virtually daily, and there is substantial disagreement in the scientific community

about many of the key issues. EPA’s Research Strategy and Research Needs document for particulate

matter, currently under development, will address

many of these scientific issues as they relate to PM.

The following selection of highly uncertain issues

could have a significant impact on both the aggregate

mortality benefits estimates and the measured uncertainty range:

• the relationship of specific pollutants in the

overall premature mortality effect, including

the individual or interactive relationships between specific chemicals (e.g., ozone, sulfates, nitrates, and acid aerosols), and particle

sizes (i.e., coarse, fine and ultra-fine particles);

• the degree of overlap (if any) between the

measured relationships between effects associated with short term exposures and effects

from long term exposure;

• the confounding effect of changes in historic

air pollution, including changes over time in

both pollution levels and the composition of

the pollutant mix;

• the extent to which life spans are shortened

by exposure to the pollutants, and the distribution of ages at the time of death;

• the willingness to pay to avoid the risks of

shortened life spans; and

• the extent to which total PM2.5 exposure incrementally augments the variability of outdoor PM2.5 and increases the dose that would

cause excess morbidity or mortality.

After premature mortality, chronic bronchitis is

the next largest health effect benefit category included

in the retrospective analysis. There is considerable

measured uncertainty about both the incidence estimation and the economic valuation. Additional research could reduce uncertainties about the level of

the pollutants associated with the observed effects,

the baseline incidence used to model the changes in

the number of new cases, and the correspondence between the definition of chronic bronchitis used in the

health effects studies and the economic valuation studies.

Another area of potentially useful research would

be further examination of the effects of criteria pollutants on cardiovascular disease incidence and mortality. Considering available epidemiological evidence

and the potential economic cost of cardiovascular disease, the value of avoiding these outcomes may significantly influence the overall benefit estimates generated in future assessments.

Further research on the willingness to pay to avoid

the risk of hospital admissions for specific conditions

would reduce a potentially significant source of nonmeasured uncertainty. The Project Team used

“avoided costs” for the value of an avoided hospital

admission, based on the avoided direct medical cost

of hospitalization (including lost wages for the employed portion of the hospitalized population).

Avoided costs are likely to be a substantial underestimate of the appropriate willingness to pay, especially

for such serious health effects as hospitalization for

strokes and congestive heart failure, particularly because they omit the value of avoided pain, suffering,

and inconvenience. Furthermore, in addition to hospitalization, there is evidence that some people seek

medical assistance as outpatients. It is also likely that

there are additional people adversely affected by shortterm air pollution levels who seek physician services

(but stop short of hospital admissions). Revised estimates of the appropriate economic value of avoided

hospitalization and other primary care medical services could increase the total economic benefits of

this cluster of health effects sufficiently that it could

be a much larger portion of the aggregate benefit total.

Finally, one of the challenges in preparing the

retrospective analysis was modeling the integrated

relationships between emissions of many different

chemicals, the subsequent mixture of pollutants in the

ambient air, and the resulting health and welfare effects of simultaneous exposure to multiple pollutants.

One element of the uncertainty in the analysis derives

from the limited current understanding of any interactive (synergistic or antagonistic) effects of multiple

pollutants. The need to better understand these complex issues is not a limited scientific question to improve section 812 analyses, but is the primary focus

of EPA’s current activities, organized under the Fed-
Appendix J: Future Directions

J-3

eral Advisory Council Act (FACA) process, to develop an integrated set of attainment policies dealing

with ozone, particulate matter, sulfur and nitrogen

oxides, and visibility. Further research on multi-pollutant issues may both (a) reduce a source of unmeasured uncertainty in the section 812 analyses and (b)

allow for effective apportionment of endpoint reduction benefits to specific pollutants or pollutant mixes.

Research Topics to Improve

Comprehensiveness

Even though research efforts falling in this category may not result in significant changes in net monetary benefit estimates, one of the goals of the section

812 studies is to provide comprehensive information

about Clean Air Act programs. For example, programs

to control hazardous air pollutants (HAPs) tend to

impose costs and yield benefits which are relatively

small compared to programs of pervasive national

applicability such as those aimed at meeting National

Ambient Air Quality Standards. Nevertheless, there

are significant social, political, financial, individual

human health, and specific ecosystem effects associated with emissions of HAPs and the programs implemented to control them. Under these circumstances,

continued efforts to understand these consequences

and evaluate their significance in relation to other programmatic and research investment opportunities

might be considered reasonable, particularly in the

context of comprehensive program assessments such

as the present study.

Some cost and benefit effects could not be fully

assessed and incorporated in the net monetary benefit

estimate developed for the present study for a variety

of reasons. Various effects were excluded due to (a)

inadequate historical data (e.g., lack of data on historical ambient concentrations of HAPs), (b) inadequate scientific knowledge (e.g., lack of concentration-response information for ecological effects of

criteria and hazardous air pollutants), or (c) resourceintensity or limited availability of analytical tools

needed to assess specific endpoints (e.g., indirect effects resulting from deposition and subsequent exposure to HAPs). Other specific examples of presently

omitted or underrepresented effect categories include

health effects of hazardous air pollutants, ecosystem

effects, any long-term impact of displaced capital on

productivity slowdown, and redirected technological

innovation.

Although the primary focus of 1970 to 1990 CAA

programs was reduction of criteria pollutants to

achieve attainment of national ambient air quality standards, emissions of air toxics were also substantially

reduced. Some air toxics were deliberately controlled

because of their known or suspected carcinogenicity,

while other toxic emissions were reduced indirectly

due to control procedures aimed at other pollutants,

particularly ozone and particulate matter. The current

analysis was able to present only limited information

on the effects of changes in air toxic emissions. These

knowledge gaps may be more serious for future section 812 analyses, however, since the upcoming prospective study will include evaluation of the effects

of an expanded air toxic program under the CAA Title

III. Existing knowledge gaps that prevented a more

complete consideration of toxics in the present study

include (a) methods to estimate changes in acute and

chronic ambient exposure conditions nationwide, (b)

concentration-response relationships linking exposure

and health or ecological outcomes, (c) economic valuation methods for a broad array of potential serious

health effects such as renal damage, reproductive effects and non-fatal cancers, and (d) potential ecological effects of air toxics.

In addition to research to improve the understanding of the consequences of changes in air pollution on

human health and well-being, further research on nonhealth effects could further improve the comprehensiveness of future assessments. Improvements in air

quality have likely resulted in improvements in the

health of aquatic and terrestrial ecosystems and the

myriad of ecological services they provide, but knowledge gaps prevented them from being included in the

current analysis. Additional research in both scientific understanding and appropriate modeling procedures could facilitate inclusion of additional benefits

such as improvements in water quality stemming from

a reduction in acid deposition-related air pollutants.

Water quality improvements would benefit human

welfare through enhancements in certain consumptive services such as commercial and recreational fishing, in addition to non-consumptive services such as

wildlife viewing, maintenance of biodiversity, and

nutrient cycling. Similarly, increased growth, productivity and overall health of U.S. forests could occur

from reducing ozone, resulting in benefits from increased timber production, greater opportunities for

recreational services such as hunting, camping, wildlife observation, and nonuse benefits such as nutrient

cycling, temporary CO2

 sequestration, and existence
The Benefits and Costs of the Clean Air Act, 1970 to 1990

J-4

value. Finally, additional research using a watershed

approach to examine the potential for ecological service benefits which emerge only at the watershed scale

might be useful and appropriate given the broad geographic scale of the section 812 assessments.

While there are insufficient data and/or analytical resources to adequately model the short-run ecological and ecosystem effects of air pollution reduction, even less is known about the long-run effects of

prolonged exposure. Permanent species displacement

or altered forest composition are examples of potential ecosystem effects that are not reflected in the current monetized benefit analysis, and could be a source

of additional benefits. In addition to these ecological

research needs, an equally large, or larger, gap in the

benefit-cost analysis is the lack of adequate tools to

monetize the benefits of such ecosystem services.

Future Section 812 Analyses

This retrospective study of the benefits and costs

of the Clean Air Act was developed pursuant to section 812 of the 1990 Clean Air Act Amendments. Section 812 also requires EPA to generate an ongoing

series of prospective studies of the benefits and costs

of the Act, to be delivered as Reports to Congress every

two years.

Design of the first section 812 prospective study

commenced in 1993. The EPA Project Team developed a list of key analytical design issues and a

“strawman” analytical design reflecting notional decisions with respect to each of these design issues.1

The analytical issues list and strawman design were

presented to the Science Advisory Board Advisory

Council on Clean Air Compliance Analysis (Council), the same SAB review group which has provided

review of the retrospective study. Subsequently, the

EPA Project Team developed a preliminary design

for the first prospective study. Due to resource limitations, however, full-scale efforts to implement the first

prospective study did not begin until 1995 when expenditures for retrospective study work began to decline as major components of that study were completed.

As for the retrospective, the first prospective study

is designed to contrast two alternative scenarios; however, in the prospective study the comparison will be

between a scenario which reflects full implementation of the CAAA90 and a scenario which reflects

continued implementation only of those air pollution

control programs and standards which were in place

as of passage of the CAAA90. This means that the

first prospective study will provide an estimate of the

incremental benefits and costs of the CAAA90.

The first prospective study is being implemented

in two phases. The first phase involves development

of a screening study, and the second phase will involve a more detailed and refined analysis which will

culminate in the first prospective study Report to Congress. The screening study compiles currently available information on the costs and benefits of the implementation of CAAA90 programs, and is intended to

assist the Project Team in the design of the more detailed analysis by providing insights regarding the

quality of available data sources and analytical models, and the relative importance of specific program

areas; emitting sectors; pollutants; health, welfare, and

ecological endpoints; and other important factors and

variables.

In developing and implementing the retrospective

study, the Project Team developed a number of important modeling systems, analytical resources, and

techniques which will be directly applicable and useful for the ongoing series of section 812 Prospective

Studies. Principal among these are the Criteria Air

Pollutant Modeling System (CAPMS) model developed to translate air quality profile data into quantitative measures of physical outcomes; and the economic

valuation models, coefficients, and approaches developed to translate those physical outcomes to economic

terms.

The Project Team also learned valuable lessons

regarding analytical approaches or methods which

were not as productive or useful. In particular, the

Project Team plans not to perform macroeconomic

modeling as an integral part of the first prospective

analysis. In fact, there are currently no plans to conduct a macroeconomic analysis at all. Essentially, the

Project Team concluded, with confirmation by the

SAB Council, that the substantial investment of time

and resources necessary to perform macroeconomic

modeling would be better invested in developing high

quality data on the likely effects of the CAA on key

emitting sectors, such as utilities, on-highway vehicles,

refineries, etc. While the intended products of a mac1

 Copies of the prospective study planning briefing materials are available from EPA.
Appendix J: Future Directions

J-5

roeconomic modeling exercise – such as overall effects on productivity, aggregate employment effects,

indirect economic effects– are of theoretical interest,

the practical results of such exercises in the context

of evaluating environmental programs may be disappointing for several reasons.

First, the CAA has certainly had a significant effect on several industrial sectors. However, the coarse

structure of a model geared toward simulating effects

across the entire economy requires crude and potentially inaccurate matching of these polluting sectors

to macroeconomic model sectors. For example, the J/

W model used for the retrospective study has only 35

sectors, with electric utilities comprising a single sector. In reality, a well-structured analysis of the broader

economic effects of the CAA would provide for separate and distinct treatment of coal-fired utility plants,

oil-fired plants, and so on. Furthermore, the outputs

of the macroeconomic model are too aggregated to

provide useful and accurate input information for the

sector-specific emission models used to project the

emissions consequences of CAA programs. Again, the

critical flaw is the inability to project important details about differential effects on utilities burning alternative fuels.

The second critical problem with organizing a

comprehensive analysis of the CAA around a macroeconomic modeling approach is that the effect information produced by the macroeconomic model is relatively unimportant with respect to answering the fundamental, target variable: “How do the overall health,

welfare, ecological, and economic benefits of Clean

Air Act programs compare to the costs of these programs?” The Project Team believes that any adverse

effect, no matter how small in a global context, should

not be deemed “insignificant” if even one individual

is seriously harmed. However, the retrospective study

results themselves have shown that, when analytical

resources are limited, the resources invested in the

macroeconomic modeling would have been better

spent to provide a more complete and less uncertain

assessment of the benefit side of the equation. Even

on the cost side of the equation, it is far more important to invest in developing accurate and reliable estimates of sector-specific compliance strategies and the

direct cost implications of those strategies. This will

be even more true in the prospective study context

when the Project Team will be faced with forecasting

compliance strategies and costs rather than simply

compiling survey data on actual, historical compliance expenditures.

The third and most important limitation of macroeconomic modeling analysis of environmental programs is that, unlike the economic costs of protection

programs, the economic benefits are not allowed to

propagate through the economy. For example, while

productivity losses associated with reduced capital

investment due to environmental regulation are

counted, the productivity gains resulting from reduced

pollution-related illness and absenteeism of workers

are not counted. The resulting imbalance in the treatment of regulatory consequences raises serious concerns about the value of the macroeconomic modeling evaluation of environmental programs. In the future, macroeconomic models which address this and

other concerns may be developed; however, until such

time EPA is likely to have limited confidence in the

value of macroeconomic analysis of even broad-scale

environmental protection programs.

Based on these findings and other factors, the design of the first prospective study differs in important

ways from the retrospective study design. First, rather

than relying on broad-scale, hypothetical, macroeconomic model-based scenario development and analysis, the first prospective study will make greater use

of existing information from EPA and other analyses

which assess compliance strategies and costs, and the

emission and air quality effects of those strategies.

After developing as comprehensive a data set as possible of regulatory requirements, compliance strategies, compliance costs, and emissions consequences,

the data set will be reviewed, refined, and extended

as feasible and appropriate. In particular, a number of

in-depth sector studies will be conducted to develop

up-to-date, detailed projections of the effects of new

CAA requirements on key emitting sectors. Candidate sectors for in-depth review include, among others, utilities, refineries, and on-highway vehicles.

The first prospective study will also differ from

the retrospective study in that analytical resources will

be directed toward development of a more complete

assessment of benefits. Efforts will be made to address the deficiencies which prevailed in the retrospective study relating to assessment of the benefits

of air toxics control. In addition, the Project Team

will endeavor to provide a more complete and effective assessment of the ecological effects of air pollution control.
The Benefits and Costs of the Clean Air Act, 1970 to 1990

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