Regulatory Impact Analysis for the Proposed Federal Transport Rule 

Docket ID No. EPA-HQ-OAR-2009-0491 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

June 2010 
ii 

 PROPOSED TRANSPORT RULE RIA – TABLE OF CONTENTS 

Section Page

1. EXECUTIVE SUMMARY .................................................................................................1 

1.1 Key Findings 

1.1.1 Health Benefits ............................................................................................3 

1.1.2 Welfare Benefits ..........................................................................................3 

1.1.3 Assessment of More and Less Stringent Scenarios .....................................9 

 1.1.3.1 Assessment of Other Alternatives...................................................9 

 1.1.3.2 Alternatives that are More or Less Stringent................................10 

1.2 Not All Benefits Quantified...................................................................................11 

1.3 Costs and Economic Impacts.................................................................................13 

1.4 Small Entity and Unfunded Mandates Impacts .....................................................15 

1.5 Limitations and Uncertainties................................................................................16 

1.6 References..............................................................................................................19 

2. INTRODUCTION AND BACKGROUND.......................................................................20 

2.1 Introduction............................................................................................................20 

2.2 Background............................................................................................................20 

 2.2.1 Methodology for Identifying Needed Reductions .....................................21 

 2.2.2 How Reductions Will Be Achieved, and Different Options 

 To Do So....................................................................................................22 

 2.2.3 States Covered by the Proposed Rule ........................................................22 

2.3 Regulated Entities ..................................................................................................28 

2.4 Baseline and Years of Analysis .............................................................................28 

2.5 Control Scenarios...................................................................................................30 

2.6 Benefits of Emission Controls ...............................................................................31 

2.7 Cost of Emission Controls................................................... ...............31 

2.8 Organization of the Regulatory Impact Analysis........................ ...............32 
iii 

3. EMISSIONS IMPACTS .............................................................................................34 

3.1 Overview of Modeling Platform and Emissions Processing Performed .........34 

3.2 Development of 2005 Base Year Emissions....................................................35 

3.3 Development of Future Year Base Case Emissions ........................................43 

3.4 Development of Future Year Control Case Emissions....................................50 

4. AIR QUALITY MODELING AND IMPACTS .........................................................55 

4.1 Air Quality Impacts .........................................................................................55 

4.1.1 Air Quality Modeling Platform ...........................................................55 

4.1.1.1 Simulation Periods...............................................................56 

4.1.1.2 Air Quality Modeling Domain.............................................56 

4.1.1.3 Air Quality Model Inputs.....................................................59 

4.1.1.4 Air Quality Model Evaluation .............................................59 

 4.2 Results for PM2.5 and Ozone ...........................................................................61 

 4.2.1 Converting CAMx PM2.5 Outputs to Benefits Inputs .........................61 

4.2.2 PM2.5 Air Quality Results.....................................................................62 

4.2.3 Converting CAMx Outputs to Full-Season Profiles for 

Benefits Analysis ................................................................................65 

4.2.4 Ozone Air Quality Results...................................................................66 

4.3 Visibility Degradation Estimates.....................................................................67 

4.4 References........................................................................................................69 

5. BENEFITS ANALYSIS AND RESULTS .................................................................70 

 5.1 Overview..........................................................................................................70 

5.2 Benefits Analysis Methods ..............................................................................76 

 5.2.1 Health Impact Assessment...................................................................78 

 5.2.2 Economic Valuation of Health Impacts...............................................79 

 5.2.3 Benefit per Ton Estimates....................................................................81 

 

 5.3 Uncertainty Characterization ...........................................................................83 

 5.4 Benefits Analysis Data Inputs..........................................................................87 

 5.4.1 Demographic Data ...............................................................................87 

 5.4.2 Effect Coefficients ...............................................................................88 

 5.4.2.1 PM2.5 Premature Mortality Effect Coefficients ...................94 

 5.4.2.2 Ozone Premature Mortality Effect Coefficients ..................97 
iv 

5.4.2.3 Chronic Bronchitis...............................................................99 

5.4.2.4 Nonfatal Myocardial Infarctions (Heart Attacks)..............100 

5.4.2.5 Hospital and Emergency Room Admissions .....................100 

5.4.2.6 Acute Health Effects and School/Work Loss Days ...........103 

5.4.2.7 School Absences ................................................................106 

5.4.2.8 Outdoor Worker Productivity ...........................................106 

 5.4.3 Baseline Incidence Estimates.............................................................107 

 5.4.4 Economic Valuation Estimates..........................................................110 

5.4.4.1 Mortality Valuation............................................................111 

5.4.4.2 Chronic Bronchitis Valuation ............................................117 

5.4.4.3 Nonfatal Myocardial Infarctions Valuation.......................117 

5.4.4.4 Hospital Admissions Valuation .........................................120 

5.4.4.5 Asthma-Related Emergency Room Visits Valuation ........128 

5.4.4.6 Minor Restricted Activity Days Valuation ........................128 

5.4.4.7 School Absences Valuation ...............................................128 

5.4.4.8 Visibility Valuation............................................................129 

5.4.4.9 Growth in WTP Reflecting National Income Growth 

 Over Time ..........................................................................137 

 5.5 Unquantified Health and Welfare Benefits....................................................140 

 5.5.1 Ecosystem Services............................................................................141 

 5.5.2 Ecosystem Benefits of Reduced Nitrogen and 

 Sulfur Deposition...............................................................................143 

5.5.2.1 Science of Deposition ........................................................143 

5.5.2.2 Ecological Effects of Acidification....................................145 

5.5.2.3 Aquatic Ecosystems...........................................................145 

5.5.2.4 Terrestrial Ecosystems.......................................................150 

5.5.3 Ecological Effects Associated with the Role of Sulfate in Mercury 

 Methylation........................................................................................154 

 5.5.4 Ecological Effects Associated with Gaseous Sulfur Dioxide........... 160 

 5.5.5 Nitrogen Enrichment......................................................................... 161 

5.5.5.1 Aquatic Enrichment ...........................................................161 

5.5.5.2 Terrestrial Enrichment .......................................................163 

 5.5.6 Benefits of Reducing Ozone Effects on Vegetation 

 and Ecosystems................................................................................. 164 

5.5.6.1 Ozone Effects on Forests ..................................................166 

5.5.6.2 Ozone Effects on Crops and Urban 

Ornamentals ......................................................................171 

5.5.7 Unquantified SO2 and NO2-Related Human Health Benefits........... 172 

 

5.6 Social Cost of Carbon and Greenhouse Gas Benefits....................................173 

 5.7 Benefits Results ............................................................................................176 

 5.8 Discussion......................................................................................................193 
v 

 5.9 References......................................................................................................195 

 

6. ELECTRIC POWER SECTOR PROFILE................................................................219 

 6.1 Power-Sector Overview.................................................................................219 

 6.1.1 Generation..........................................................................................219 

 6.1.2 Transmission......................................................................................222 

 6.1.3 Distribution ........................................................................................222 

 6.2 Deregulation and Restructuring.....................................................................223 

 6.3 Pollution and EPA Regulation of Emissions .................................................224 

 6.4 Pollution Control Technologies.....................................................................226 

 6.5 Regulation of the Power Sector .....................................................................227

 6.6 Price Elasticity of Electricity .........................................................................231 

 6.7 Reference .......................................................................................................231 

7. COST, ECONOMIC, AND ENERGY IMPACTS....................................................232 

 7.1 Background....................................................................................................232 

 7.2 Projected SO2 and NOx Emissions and Reductions.......................................243 

 7.3 Overview of Costs and Other Impacts...........................................................253 

 7.4 Projected Compliance Costs ..........................................................................255 

 7.5 Projected Approaches to Emissions Reductions............................................256 

 7.6 Projected Generation Mix..............................................................................259 

 7.7 Projected Capacity Additions ........................................................................262 

 7.8 Projected Coal Production for the Electric Power Sector..............................262 

 7.9 Projected Retail Electricity Prices .................................................................263 

 7.10 Projected Fuel Price Impacts .........................................................................265 

 7.11 Key Differences in EPA Model Runs for Transport Rule Modeling ...........266 
vi 

 7.12 Projected Primary PM and Carbon Dioxide Emissions from 

 Power Plants ..................................................................................................267 

 

 7.13 Limitations of Analysis..................................................................................269 

 7.14 Significant Energy Impact ............................................................................273 

 7.15 References......................................................................................................275 

8. MACROECONOMIC IMPACTS AND SOCIAL COSTS.......................................277 

8.1 EMPAX Computable General Equilbrium (CGE) Model: Overview ..........278 

8.1.1 Data Sources .....................................................................................280 

 8.1.2 Production Functions .........................................................................282 

 8.1.3 Utility Functions ................................................................................284 

8.1.3.1 Welfare Measures ..............................................................285 

 8.1.4 Treatment of Trade ............................................................................286 

 8.1.5 Tax Rates and Distortions..................................................................286 

 8.1.6 Intertemporal Dynamics and Economic Growth ...............................287 

 8.1.7 Linkage with the Integrated Planning Model ....................................288 

 8.1.8 Qualifications.....................................................................................290 

8.2 EMPAX-CGE Model Results .......................................................................291 

8.2.1 Macroeconomic Variables and Social Costs .....................................291 

8.2.2 Industry Effects..................................................................................293 

8.2.2.1 Energy Sectors ...................................................................294 

8.2.2.2 Energy-Intensive Sectors ...................................................295 

8.2.2.3 Nonenergy Sectors.............................................................295 

8.3 References......................................................................................................299 

9. STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES .....................302 

9.1 Small Entity Impacts .....................................................................................302 

9.1.1 Identification of Small Entities ..........................................................304 

9.1.2 Overview of Analysis and Results.....................................................304 

9.1.2.1 Methodology for Estimating Impacts of the 

Transport Rule on Small Entities.......................................305 

9.1.2.2 Results................................................................................307 

9.1.3 Summary of Small Entity Impacts.....................................................312 

9.2 Unfunded Mandates Reform Act (UMRA) Analysis ...................................312 

9.2.1 Identification of Government-Owned Entities ..................................313 

9.2.2 Overview of Analysis and Results.....................................................314 

9.2.2.1 Methodology for Estimating Impacts of the 

Transport Rule on Government Entities ............................314 
vii 

9.2.2.2 Results................................................................................316 

9.2.3 Summary of Government Entity Impacts ..........................................320 

9.3 Paperwork Reduction Act..............................................................................321 

9.4 Environmental Justice....................................................................................322 

9.4.1 Consideration of Environmental Justice Issues in the 

 Rule Development Process ................................................................322 

9.4.2 Potential Environmental and Public Health Impacts to 

 Vulnerable Populations......................................................................324 

9.4.3 Meaningful Public Participation .......................................................330 

9.4.4 Summary ............................................................................................331 

10. COMPARISON OF BENEFITS AND COSTS ........................................................332 

10.1 Comparison of Benefits and Costs.................................................................332 

10.2 References......................................................................................................339 

Appendix A: Human Health Benefits of Direct Control and Intrastate Trading Remedies 

 and Presentation of State-Level Benefits of Proposed Remedy.....................340 

Appendix B: Economic Impact Analyses Outside of Electric Power Sector and 

 Social Costs – Alternative Remedies...................................................355 

Appendix C: Comparison of State Level Electrical Generating Unit Emissions 

Under Various Regulatory Alternatives To Reduce SO2 and NOx 

Emissions Under The Transport Rule...................................................370 

Appendix D: Integrated Planning Model Runs .........................................................................377 

Appendix E: Allowance Values for Emissions Trading Programs...........................................381 
1

CHAPTER 1 

EXECUTIVE SUMMARY 

This Regulatory Impact Analysis (RIA) presents the health and welfare benefits, costs, and 

other impacts of the proposed Transport Rule focusing primarily on 2014. 

1.1 Key Findings 

EPA plans to lower the sulfur dioxide (SO2) and nitrogen oxide (NOx) emissions of 

the electric power industry in 32 eastern states through the proposed Transport Rule. EPA 

estimates in 2014 this proposed rule will have annual net benefits (in 2006$) between $120 

to 290 billion using a 3% discount rate and $110 and $260 billion using a 7% discount rate. 

At these respective rates, the annual social costs are $2.0 billion and $2.2 billion and the 

annual quantified benefits are $120 to $290 billion or $110 to $270 billion. The capital costs 

spent for pollution controls installed for CAIR were not included in the annual social costs 

since the Transport Rule did not lead to their installation. Those CAIR-related capital 

investments are roughly estimated to have an annual social cost less than $1.15 to $ 1.29 

billion (under the two discount rates.) The benefits outweigh social costs by 60 to145 to 1, 

or 55 to 130 to 1. The benefits are primarily from 14,000 to 36,000 fewer PM2.5 and ozonerelated premature mortalities. There are some costs and important benefits that EPA could 

not monetize. Upon considering these limitations and uncertainties, it remains clear that the 

benefits of the proposed Transport Rule are substantial and far outweigh the costs. The 

annualized private compliance costs to the power industry in 2014 are $2.8 billion, higher 

than the social costs. Consideration of the above benefit cost ratios and analysis of a greater 

SO2 control suggests that, if EPA could require additional emission reductions, there could 

be greater net benefits. Notably, since the proposed rule expedites installation of pollution 

controls in 2012 that were formerly happening by 2014, the benefits of the Transport Rule in 

2012 are actually even greater at the outset of the program. 

 The benefits and costs in 2014 of the preferred remedy (State Budgets/Limited 

Trading) in the proposed rule are in Table 1-1. This preferred remedy covers the electric 
2

power industry and allows intrastate emissions trading of sulfur dioxide (SO2) and nitrogen 

oxides (NOx) and limited interstate trading of these are pollutants in 32 eastern states.1

 

 

1 The states are AL, AR, CT, DE, DC, FL, GA, IL, IN, IA, KS, KY, LA, MD, MA, MI, MN, MS, MR, NE, NJ, NY, NC, OH, OK, PA, SC, 

TN, TX, VA, WV, and WI. 

Table 1-1. Summary of EPA’s Estimates of Benefits, Costs, and Net Benefits of the 

Preferred Remedy in the Proposed Transport Rule in 2014a

 (billions of 2006$) 

Description Estimate 

(3% Discount Rate) 

Estimate 

(7% Discount Rate) 

Social costsb $2.03 $2.23 

Social benefitsc,d $120 to $290 + B $110 to $270 + B 

Health-related benefits: $120 to $290 + B $110 to $260 + B 

Visibility benefitse

 $3.6 $3.6 

Net benefits (benefits-costs) $120 to $290 $110 to $260 

a

 All estimates are rounded to two significant digits and represent annualized benefits and costs anticipated for 

the year 2014. For notational purposes, unquantified benefits are indicated with a “B” to represent the sum 

of additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these 

endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to 

quantify. A listing of health and welfare effects is provided in Table 1-6. Estimates here are subject to 

uncertainties discussed further in the body of the document. 

b

 The social costs are the loss of household utility as measured in Hicksian equivalent variation. 

c The reduction in premature mortalities account for over 90% of total monetized benefits. Benefit estimates 

are national except for visibility that covers Class I areas. Valuation assumes discounting over the SABrecommended 20-year segmented lag structure described in Chapter 5. Results reflect 3 percent and 7 

percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (U.S. EPA, 

2000; OMB, 2003). The estimate of social benefits also includes CO2-related benefits calculated using the 

social cost of carbon, discussed further in chapter 5. 

d

 Potential benefit categories that have not been quantified and monetized are listed in Table 1-6. 

e

 Over 99% of visibility-related benefits occur within Class-1 areas located in the Eastern U.S. 
3

1.1.1 Health Benefits 

 The proposed Transport Rule is expected to yield significant health benefits by 

reducing emissions of two key contributors to fine particle and ozone formation. Sulfur 

dioxide contributes to the formation of fine particle pollution (PM2.5), and nitrogen oxide 

contributes to the formation of both PM2.5 and ground-level ozone. 

Our analyses suggest this would yield benefits in 2014 of $120 to $290 billion (based 

on a 3 percent discount rate) and $110 to $270 billion (based on a 7 percent discount rate) 

that includes the value of avoiding approximately 14,000 to 36,000 premature deaths, 22,000 

nonfatal heart attacks, 11,000 hospitalizations for respiratory and cardiovascular diseases, 1.8 

million lost work days, 100,000 school absences, and 10 million days when adults restrict 

normal activities because of respiratory symptoms exacerbated by PM2.5 and ozone pollution. 

We also estimate substantial additional health improvements for children from 

reductions in upper and lower respiratory illnesses, acute bronchitis, and asthma attacks. See 

Table 1-2 for a list of the annual reduction in health effects expected in 2014 and Table 1-3 

for the estimated value of those reductions. In these tables we summarize the benefits 

according to whether they accrue within or beyond the Transport region (Eastern part of the 

US covered by the proposed rule). While not analyzed here, we expect the benefits in 2012 

to be significantly larger than those modeled for 2014 because of the much greater 

incremental SO2 reductions in 2012 compared to 2014 from the base case. This occurs 

because the proposed rule expedites the start of SO2 emissions controls that are planned in 

the base case to occur after 2012 and be underway by 2014. 

1.1.2 Welfare Benefits 

 The term welfare benefits covers both environmental and societal benefits of reducing 

pollution, such as reductions in damage to ecosystems, improved visibility and improvements 

in recreational and commercial fishing, agricultural yields, and forest 
4

Table 1-2. Proposed Transport Rule: Estimated Reduction in Incidence of Adverse 

Health Effects in 2014 for the Proposed Remedya,b

Health Effect

Within transport 

region

Beyond transport 

region Total

PM-Related endpoints 

Premature Mortality 

Pope et al. (2002) 

(age >30)

14,000 

(4,000—24,000) 

130 

(35—220) 

14,000 

(4,000—

25,000) 

Laden et al. (2006) 

(age >25)

36,000 

(17,000—55,000) 

320 

(150—500) 

36,000 

(17,000—

56,000) 

Infant 

(< 1 year)

59 

(-66—180)b 

0.3 

(-0.3—0.8) 

59 

(-66—180) 

Chronic Bronchitis 9,200 

(310—18,000) 

89 

(3—160) 

9,200 

(320—

18,000) 

Non-fatal heart attacks (age > 18) 22,000 

(5,700—39,000) 

250 

(64—440) 

22,000 

(5,800—

39,000) 

Hospital admissions—respiratory 

(all ages)

3,500 

(1,400—5,500) 

35 

(14—56) 

3,500 

(1,400—

5,500) 

Hospital admissions—cardiovascular 

(age > 18)

7,500 

(5,200—8,800) 

76 

(51—93) 

7,500 

(5,200—

8,900) 

Emergency room visits for asthma 

(age < 18)

14,000 

(7,100—21,000) 

71 

(36—110) 

14,000 

(7,200—

21,000) 

Acute bronchitis (age 8-12) 21,000 

(-4,800—46,000) 

150 

(33—320) 

21,000 

(-4,800—

46,000) 

Lower respiratory symptoms 

(age 7-14)

250,000 

(98,000—400,000) 

1,700 

(670—2,800) 

250,000 

(98,000—

400,000) 

Upper respiratory symptoms 

(asthmatics age 9-18)

190,000 

(36,000—350,000) 

1,300 

(250—2,400) 

190,000 

(36,000—

350,000) 

Asthma exacerbation 

(asthmatics 6-18)

230,000 

(8,300—800,000) 

1,700 

(11—5,700) 

240,000 

(8,300—

800,000) 

Lost work days (ages 18-65) 1,800,000 

(1,500,000—2,000,000) 

14,000 

(12,000—17,000) 

1,800,000 

(1,500,000—

2,000,000) 

Minor restricted-activity days 

(ages 18-65)

10,000,000 

(8,600,000—12,000,000) 

86,000 

(71,000—100,000) 

10,000,000 

(8,600,000—

12,000,000) 
5

Ozone-related endpoints 

Premature mortality 

Bell et al. (2004) (all ages) 50 

(16—83) 

0.6 

(0.2—1) 

50 

(17—84) 

Schwartz et al. (2005) 

(all ages)

76 

(23—130) 

1 

(0.2—2) 

77 

(24—130) 

Multi-city 

and 

NMMAPSHuang et al. (2005) 

(all ages)

83 

(31—130) 

1 

(0.3—2) 

84 

(31—140) 

Ito et al. (2005) 

(all ages)

220 

(130—310) 

3 

(2—4) 

230 

(140—320) 

Bell et al. (2005) (all ages) 160 

(76—250) 

2 

(1—3) 

160 

(77—250) 

Meta-analyses

Levy et al. (2005) (all ages) 230 

(160—300) 

3 

(2—4) 

230 

(160—300) 

Hospital admissions—respiratory 

causes 

(ages > 65)

380 

(-18—730) 

4 

(-0.4—9) 

390 

(-18—740) 

Hospital admissions—respiratory 

causes (ages <2)

290 

(130—460) 

4 

(1—6) 

300 

(130—460) 

Emergency room visits for asthma (all 

ages)

230 

(-30—730) 

2 

(-0.4—8) 

230 

(-30—730) 

Minor restricted-activity days (ages 

18-65)

300,000 

(120,000—480,000) 

3,700 

(1,300—6,100) 

300,000 

(130,000—

480,000) 

School absence days 110,000 

(38,000—160,000) 

1,300 

(380—2,100) 

110,000 

(38,000—

160,000) 

A Estimates rounded to two significant figures; column values will not sum to total value. B

 The negative 5th percentile estimates for certain endpoints are the result of the weak statistical power of the 

study used to calculate these health impacts and do not suggest imply that increases in air pollution exposure 

result in decreased health impacts.
6

Table 1-3. Estimated Monetary Value of Reductions in Incidence of Health and 

Welfare Effects Effects for the Proposed Remedy (in billions of 2006$)a,b,c 

Health Effect Pollutant 

Within transport 

region 

Beyond transport 

regionB Total 

Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality 

estimates) 

 3% discount rate PM2.5 & O3

$110 

($8.8—$330) 

$0.1 

($0.08—$3) 

$110 

($8.8—$340) 

 7% discount rate PM2.5 & O3

$100 

($7.9—$300) 

$0.09 

($0.07—$2.7) 

$100 

($7.9—$300) 

Premature Mortality (Laden et al. 2006 PM mortality and Levy et al. 2005 ozone 

mortality estimates) 

 3% discount rate PM2.5 & O3

$280 

($25—$810) 

$2.5 

($0.2—$7.3) 

$280 

($25—$820) 

 7% discount rate PM2.5 & O3

$250 

($22—$300) 

$2.3 

($0.2—$6.6) 

$260 

($22—$310) 

Chronic Bronchitis PM2.5

$4.3 

($0.2—$20) 

$0.04 

($0.002--$0.2) 

$4.3 

($0.2—$20) 

Non-fatal heart attacks 

 3% discount rate PM2.5

$2.5 

($0.4—$6) 

$0.03 

($0.005—$0.07) 

$2.5 

($0.4—$6) 

 7% discount rate PM2.5

$2.4 

($0.4—$5.9) 

$0.03 

($0.005—$0.07) 

$2.4 

($0.4—$5.9) 

Hospital admissions—

respiratory PM2.5 & O3

$0.06 

($0.03—$0.1) 

$0.00006 

($0.00003—$0.001) 

$0.06 

($0.03—$0.1) 

Hospital admissions—

cardiovascular PM2.5

$0.2 

($0.1—$0.3) 

$0.002 

($0.001—$0.003) 

$0.2 

($0.1—$0.3) 

Emergency room visits for 

asthma PM2.5 & O3

$0.005 

($0.002—$0.008) --- $0.005 

($0.002—$0.008) 

Acute bronchitis PM2.5

$0.009 

(-$0.0004—$0.03) --- $0.009 

(-$0.0004—$0.03) 

Lower respiratory 

symptoms PM2.5

$0.005 

($0.002—$0.009) --- $0.005 

($0.002—$0.009) 

Upper respiratory 

symptoms PM2.5

$0.006 

($0.001—$0.014) --- $0.006 

($0.001—$0.014) 

Asthma exacerbation PM2.5

$0.012 

($0.001--$0.046) --- $0.012 

($0.001--$0.046) 

Lost work days PM2.5

$0.2 

($0.19—$0.24) 

$0.002 

($0.0016--$0.0002) 

$0.2 

($0.19—$0.24) 

School loss days O3

$0.01 

($0.004—$0.013) --- $0.01 

($0.004—$0.013) 

Minor restricted-activity 

days PM2.5 & O3

$0.64 

($0.34—$0.97) 

$0.005 

($0.003—$0.008) 

$0.64 

($0.34—$0.97) 

Recreational visibility, 

Class I areas PM2.5 $3.5 $0.03 $3.6 
7

Monetized total Benefits 

(Pope et al. 2002 PM2.5 mortality and Bell et al. 2004 ozone mortality estimates) 

3% discount rate PM2.5, O3 

$120 

($10—$360) 

$1.1 

($0.09—$3.3) 

$120 

($10—$360) 

7% discount rate PM2.5, O3 

$110 

($9—$330) 

$0.9 

($0.08—$2.9) 

$110 

($9—$330) 

Monetized total Benefits 

(Laden et al. 2006 PM2.5 mortality and Levy et al. 2005 ozone mortality estimates) 

3% discount rate PM2.5, O3 

$290 

($26—$840) 

$2.6 

($0.2—$7.5) 

$290 

($26—$840) 

7% discount rate PM2.5, O3 

$260 

($23—$760) 

$2.4 

($0.2—$6.8) 

$270 

($24—$760) A Estimates rounded to two significant figures. 

B Monetary value of endpoints marked with dashes are < $100,000. States included in transport region may be 

found in Chapter 2. 

C The negative 5th percentile estimates for certain endpoints are the result of the weak statistical power of the 

study used to calculate these health impacts and do not suggest that increases in air pollution exposure result in 

decreased health impacts. 

productivity. Although we are unable to monetize all welfare benefits, EPA estimates the 

proposed Transport Rule will yield welfare benefits of $3.5 billion in 2014 (2006$) for 

visibility improvements in southeastern Class I (national park) areas for a total of $3.6 billion 

in benefits across southeastern, southwestern and California Class I areas. 

Figure 1-1 summarizes an array of PM2.5-related monetized benefits estimates based 

on alternative epidemiology and expert-derived PM-mortality estimate as well as the sum of 

ozone-related benefits using the Bell et al. (2004) mortality estimate. 

 Figure 1-2 summarizes the estimated net benefits for the proposed remedy by 

displaying all possible combinations of PM and ozone-related monetized benefits and costs. 

The graphic includes one estimate of ozone-related mortality and fourteen different PM2.5

related mortality and a single 3% or 7% discounted cost estimate.2 Each of the 14 bars in 

each graph represents a separate point estimate of net benefits under a certain combination of 

 

2 Versions of this figure found in previous EPA RIA’s have included the full suite of ozone mortality 

estimates. Because total benefits are relatively insensitive to the specification of ozone mortality estimate, for 

simplicity of presentation we have not included this full suite. 
8

cost and benefit estimation methods. Because it is not a distribution, it is not possible to infer 

the likelihood of any single net benefit estimate. 

Figure 1-1: Estimated Monetized Value of Estimated PM2.5- Related Premature 

Mortalities Avoided According to Epidemiology or Expert-derived Derived PM 

Mortality Risk EstimateA

A Column total equals sum of PM2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate. 
9

Figure 1-2: Net Benefits of the Transport Rule According to PM2.5 Epidemiology or 

Expert-derived Mortality Risk EstimateA

A Column total equals sum of PM2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate. 

1.1.3 Assessment of More and Less Stringent Scenarios 

1.1.3.1 Assessment of Other Alternatives 

EPA also analyzed the costs and benefits of the two alternative proposed remedies - 

direct control and intrastate trading programs. Finally, the Agency also considered options 

that were more and less stringent for the control of SO2 emissions. 

Air quality modeling was not conducted for these alternatives; thus we estimated the 

benefits of these alternatives by applying the same benefit per-ton approach as done for the 

alternative remedy options. The costs of these alternatives are estimated using IPM. Table 

1-4 below presents the health-related benefits and social costs, including net social benefit, of 

the two scenarios alongside that of the proposed Transport Rule remedy

Table 1-4 provides the benefits of the direct control and intrastate trading remedies. 
10

Table 1-4. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the 

Proposed Remedy Option in 2014a (billions of 2006$) 

Description Proposed Remedy- State 

Budgets/Limited Trading Direct Control Intrastate Trading 

Social costsb 

3 % discount rate $2.03 $2.68 $2.49 

7 % discount rate $2.23 $2.91 $2.70 

Health-related benefitsc,d 

3 % discount rate 

7 % discount rate 

$118 to $288 + B 

$108 to $260 + B 

$117 to $286 + B 

$108 to $262 + B 

$113 to $276 + B 

$104 to $252 + B 

Net benefits (benefits-costs)

e 

3 % discount rate 

7 % discount rate 

$116 to $286 

$105 to $258 

$115 to $283 

$105 to $259 

$110 to $273 

$101 to $249 a When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In this case we have rounded 

to three significant digits to facilitate comparison of the benefits and costs among the proposed remedy and alternatives. b

 The social costs are the loss of household utility as measured in Hicksian equivalent variation. More information on the social costs can be 

found in Chapter 8 of this RIA. c Due to methodological limitations, the health benefits of the direct control and intrastate trading remedies include PM2.5 –related benefits 

but omit visibility, ozone, and CO2-related benefits. We present the PM2.5 –related benefits of the proposed remedy, omitting these other 

important benefits, so that readers may compare directly the benefits of the proposed and alternate remedies. Total benefits are 

comprised of the value of PM-related avoided premature mortalities. The reduction in these premature mortalities in each year account 

for over 90 percent of total PM2.5 –related monetized benefits. Benefits in this table are nationwide and are associated with NOx and SO2

reductions. d

 Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories that have not been 

quantified and monetized are listed in Table 1-6. e

 Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Results reflect the use of 3 percent and 7 

percent discount rates consistent with EPA and OMB guidelines. 

1.1.3.2 Alternatives that Are More or Less Stringent 

In accordance with Circular A-4 guidance, EPA also analyzed the costs and benefits 

of two options that differed in their stringency from the preferred State Budgets/Limited 

Trading option – one less stringent, the other more stringent. Both options have the same 

2012 requirements and varied in the requirements for SO2 emissions reductions in 2014. 

Unlike the preferred TR option which requires greater SO2 reductions in 15 states 

(Group 1) beginning in 2014 from 2012 emissions levels, the less stringent option maintains 

the 2012 requirements in all subsequent years. This option allows about 1.4 million tons 

more SO2 to be emitted annually than the preferred approach after 2013. 

The more stringent options changes the requirement for Group 1 state SO2 emissions 

reductions beginning in 2014 and moves 8 additional states to Group 1 from Group 2. 

Connecticut, Florida, Kansas, Maryland, Massachusetts, Minnesota, Nebraska, and New 
11

Jersey join the 15 Group 1 states of the proposed rule, making 23 states in all and leaving 4 

states and the District of Columbia in Group 2. Also, an additional 200,000 tons of SO2

reduction is required in the 23 Group 1 states. 

Table 1-5. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the 

Proposed Remedy Option in 2014a (billions of 2006 dollars) 

Description Preferred Remedy-State 

Budgets/Limited Trading 

Less Stringent 

Scenario 

More Stringent 

Scenario 

Social costsb 

3 % discount rate $2.03 $1.12* $2.21* 

7 % discount rate $2.23 $1.23* $2.43* 

Health-related benefitsc,d 

3 % discount rate 

7 % discount rate 

$118 to $288 

$108 to $262 

$82 to 200 

$76 to 184 

$120 to 292 

$110 to 267 

Net benefits (benefits-costs)

e 

3 % discount rate 

7 % discount rate 

$116 to $288 

$105 to $260 

$81 to 200 

$74 to 182 

$117 to 290 

$107 to 264 a When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In this case we have rounded 

to three significant digits to facilitate comparison of the benefits and costs among the proposed remedy and the less and more stringent 

scenarios. b

 The social costs are the loss of household utility as measured in Hicksian equivalent variation. More information on the social costs can 

be found in Chapter 8 of this RIA. c Due to methodological limitations, the health benefits of the direct control and intrastate trading remedies include PM2.5 –related benefits 

but omit visibility, ozone, and CO2-related benefits. We present the PM2.5 –related benefits of the proposed remedy, omitting these other 

important benefits, so that readers may compare directly the benefits of the proposed and alternate remedies. Total benefits are primarily 

of the value of PM-related avoided premature mortalities. The reduction in these premature mortalities in each year account for over 90 

percent of total PM2.5 –related monetized benefits. Benefits in this table are nationwide and are associated with NOx and SO2 reductions. 

Visibility and ozone-related benefits not calculated for the more and less stringent scenarios because these impacts were estimated using 

PM2.5-related benefit per ton estimates. d

 Not all possible benefits or disbenefits are monetized in this analysis. These are listed in Table 1-6. e

 Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Results reflect the use of 3 % and 7 % 

discount rates consistent with EPA and OMB guidelines. 

1.2 Not All Benefits Quantified 

 EPA was unable to quantify or monetize all of the health and environmental benefits 

associated with the proposed Transport Rule. EPA believes these unquantified benefits are 

substantial, including the value of increased agricultural crop and commercial forest yields, 

visibility improvements, reductions in nitrogen and acid deposition and the resulting changes 

in ecosystem functions, and health and welfare benefits associated with reduced mercury 

emissions. Table 1-6 provides a list of these benefits. 
12

Table 1-6: Human Health and Welfare Effects of Pollutants Affected by the Transport Rule 

Pollutant/ Effect Quantified and monetized in base estimate Unquantified 

Premature mortality based on cohort study 

estimatesb Low birth weight 

Premature mortality based on expert 

elicitation estimates Pulmonary function 

Hospital admissions: respiratory and 

cardiovascular 

Chronic respiratory diseases other than chronic 

bronchitis 

Emergency room visits for asthma Non-asthma respiratory emergency room visits 

Nonfatal heart attacks (myocardial 

infarctions) UVb exposure (+/-)c

Lower and upper respiratory illness 

Minor restricted activity days 

Work loss days 

Asthma exacerbations (among asthmatic 

populations 

Respiratory symptoms (among asthmatic 

populations) 

PM: healtha

Infant mortality 

Household soiling Visibility in Class I areas Visibility in residential and non-class I areas PM: welfare UVb exposure (+/-)c

Global climate impactsc

Premature mortality based on short-term 

study estimates Chronic respiratory damage 

Hospital admissions: respiratory Premature aging of the lungs 

Emergency room visits for asthma Non-asthma respiratory emergency room visits 

Minor restricted activity days UVb exposure (+/-)c

Ozone: health 

School loss days 

Decreased outdoor worker productivity 

Yields for: 

--Commercial forests 

--Fruits and vegetables, and 

--Other commercial and noncommercial crops 

Damage to urban ornamental plants 

Recreational demand from damaged forest 

aesthetics 

Ozone: welfare 

 Ecosystem functions 

UVb exposure (+/-)c

Respiratory hospital admissions 

Respiratory emergency department visits 

 Asthma exacerbation 

Acute respiratory symptoms 

 Premature mortality 

NO2: health 

 Pulmonary function 

Commercial fishing and forestry from acidic 

deposition 

Commercial fishing, agriculture and forestry 

from nutrient deposition 

Recreation in terrestrial and estuarine 

ecosystems from nutrient deposition 

NO2: welfare 

Other ecosystem services and existence values 
13

for currently healthy ecosystems 

Coastal eutrophication from nitrogen deposition 

 Respiratory hospital admissions 

Asthma emergency room visits 

 Asthma exacerbation 

Acute respiratory symptoms 

 Premature mortality 

SO2: health 

 Pulmonary function 

Commercial fishing and forestry from acidic 

deposition 

Recreation in terrestrial and aquatic ecosystems 

from acid deposition 

SO2: welfare 

 Increased mercury methylation 

Incidence of neurological disorders 

Incidence of learning disabilities 

Incidences in developmental delays 

Mercury: 

health 

Potential cardiovascular effects including: 

--Altered blood pressure regulation 

--Increased heart rate variability 

--Incidences of heart attack 

Potential reproductive effects 

Impact on birds and mammals (e.g. reproductive 

effects) 

Mercury: 

environment 

Mercury: 

welfare 

Impacts to commercial., subsistence and 

recreational fishing 

a

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

including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be 

partly represented by our quantified endpoints. b

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

incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of this issue). While some of the effects of 

short term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from short term PM 

exposure not captured in the cohort estimates included in the primary analysis. c

 May result in benefits or disbenefits. 

1.3 Costs and Economic Impacts 

 For the affected region, the projected annual incremental private costs of the 

proposed remedy option (intrastate trading with some interstate trading) to the power 

industry are $3.7 billion in 2012 and $2.8 billion in 2014. Costs are lower in 2014 than in 

2012 as the rule becomes more stringent because there are larger amounts of State and 

Federally enforceable controls that happen between 2012 and 2014 in the baseline. There are 

two other remedy options that EPA examined as part of our analyses. A remedy option that 

relies solely on intrastate trading has projected annual incremental private costs of $4.2 

billion in 2012 and $2.7 billion in 2014. Finally, a remedy option that applies controls 

directly to affected units with no trading (direct control remedy) yields projected annual 

incremental private costs of $4.3 billion in 2012 and $3.4 billion in 2014. These costs 
14

represent the total cost to the electricity-generating industry of reducing NOx and SO2

emissions to meet the emissions caps set out in the rule. Estimates are in 2006 dollars. 

These costs of the rule are estimated using the Integrated Planning Model (IPM). 

In estimating the net benefits of regulation above, the appropriate cost measure is 

“social costs.” Social costs represent the welfare costs of the rule measured as the loss of 

consumer utility in the macroeconomic analysis of this rule proposal. 

There are several national changes in energy prices that result from the Transport 

Rule. Retail electricity prices are projected to increase nationally by an average of 2.5 % in 

2012 and 1.5 % in 2014 with the proposed Transport Rule. The effects of the proposed rule 

on natural gas prices and the power-sector generation mix is also small, with a 1.7 percent or 

less increase in delivered gas prices projected in 2012 and 0.5 % in 2014. 

There are several other types of energy impacts from the Transport Rule. A relatively 

small amount of coal-fired capacity, about 1.2 GW (0.3 percent of all coal-fired capacity and 

0.1 % of all generating capacity), is projected to be uneconomic to maintain. In practice 

units projected to be uneconomic to maintain may be “mothballed,” retired, or kept in service 

to ensure transmission reliability in certain parts of the grid. For the most part, these units 

are small and infrequently used generating units that are dispersed throughout the proposed 

Transport Rule region. Coal production for use in the power sector is projected to decrease 

by 0.3 % in 2012 and by 0.8 % by 2014, and we expect greater coal production in 

Appalachia and the West and 15 % less production in the Interior coal regions of the country 

with the proposed Transport Rule. 

In 2014, EPA estimates that Gross Domestic Product (GDP) and consumption levels 

are approximately 0.01 % lower ($1.6 billion) with the proposed Transport Rule. There are 

declines of less than 0.05 % in GDP by region except for the Plains and West, where regional 

GDP increases as productive activities shift to these less regulated regions. Overall, the 

impacts of the proposed rule are modest, particularly in light of the large projected benefits 

mentioned earlier. 
15

1.4 Small Entity and Unfunded Mandates Impacts 

After preparing an analysis of small entity impacts, EPA has certified that this proposal 

will have no SISNOSE (significant economic impacts on a substantial number of small entities). 

First, of the 30 small entities (out of 81 affected) projected to have costs greater than 1 percent of 

revenues, around 75 percent of them operate in cost of service regions and would generally be 

able to pass any increased costs along to rate-payers. Furthermore, of the approximately 550 

units identified by EPA as being potentially owned by small entities, approximately two-thirds 

of the units that have higher costs are not expected to make operational changes as a result of 

this rule (e.g. install control equipment or switch fuels). Their increased costs are largely due to 

increased cost of the fuel they would be expected to use whether or not they had to comply with 

the proposed rule. Further, increased fuel costs are often passed through to rate-payers as 

common practice in many areas of the U.S. due to fuel adder arrangements instituted by state 

public utility commissions. Finally, EPA’s decision to exclude units smaller than 25 Megawatt 

capacity (MW) has already significantly reduced the burden on small entities. 

EPA examined the potential economic impacts on state and municipality-owned 

entities associated with this rulemaking based on assumptions of how the affected states will 

implement control measures to meet their emissions. These impacts have been calculated to 

provide additional understanding of the nature of potential impacts and additional 

information. 

According to EPA’s analysis, of the 84 government entities considered in this 

analysis and the 482 government entities in the Transport Rule region that are included in 

EPA’s modeling, 27 may experience compliance costs in excess of 1 percent of revenues in 

2014, based on our assumptions of how the affected states implement control measures to 

meet their emissions budgets as set forth in this rulemaking. 
16

Government entities projected to experience compliance costs in excess of 1 percent of 

revenues may have some potential for significant impact resulting from implementation of the 

Transport Rule. However, it is EPA’s position that because these government entities can pass 

on their costs of compliance to rate-payers, they will not be significantly affected. Furthermore, 

the decision to include only units greater than 25 MW in size exempts 380 government entities 

that would otherwise be potentially affected by the Transport Rule. 

1.5 Limitations and Uncertainties 

 Every analysis examining the potential benefits and costs of a change in 

environmental protection requirements is limited to some extent by data gaps, limitations in 

model capabilities (such as geographic coverage), and variability or uncertainties in the 

underlying scientific and economic studies used to configure the benefit and cost models. 

Despite the uncertainties, we believe this benefit-cost analysis provides a reasonable 

indication of the expected economic benefits and costs of the proposed Transport Rule. 

For this analysis, such uncertainties include possible errors in measurement and 

projection for variables such as population growth and baseline incidence rates; uncertainties 

associated with estimates of future-year emissions inventories and air quality; variability in 

the estimated relationships between changes in pollutant concentrations and the resulting 

changes in health and welfare effects; and uncertainties in exposure estimation. 

EPA’s cost estimates assume that all states in the proposed Transport Rule region 

participate in the programs that reduce SO2 and NOx emissions from the power industry. 

Below is a summary of the key uncertainties of the analysis: 

Costs 

 Analysis does not capture employment shifts as workers are retrained at the same 

company or re-employed elsewhere in the economy. 

 We do not include the costs of certain relatively small permitting costs associated with 

Title V that new program entrants face. 

 Technological innovation is not incorporated into these cost estimates. 
17

 Economic impacts do not take into response of electric power consumers to changes in 

electricity prices. While this response is likely to be of small magnitude, it may have 

some impact on the final estimate of private compliance costs. 

Benefits

 Most of the estimated PM-related benefits in this rule accrue to populations exposed to 

higher levels of PM2.5. Of these estimated PM-related mortalities avoided, about 80% 

occur among populations initially exposed to annual mean PM2.5 level of 10 μg/m3 and 

about 97% occur among those initially exposed to annual mean PM2.5 level of 7.5 μg/m3 ; 

these are the lowest air quality levels considered in the Laden et al. (2006) and Pope et al. 

(2002) studies, respectively. This fact is important, because as we estimate PM-related 

mortality among populations exposed to levels of PM2.5 that are successively lower, our 

confidence in the results diminishes. However, our analysis shows that the great majority 

of the impacts occur at higher exposures. 

 There are uncertainties related to the health impact functions used in the analysis. These 

include: within study variability; across study variation; the application of concentrationresponse (C-R) functions nationwide; extrapolation of impact functions across 

population; and various uncertainties in the C-R function, including causality and 

thresholds. Therefore, benefits may be under- or over-estimates. 

 Analysis is for 2014, and projecting key variables introduces uncertainty. Inherent in any 

analysis of future regulatory programs are uncertainties in projecting atmospheric 

conditions and source level emissions, as well as population, health baselines, incomes, 

technology, and other factors. 

 This analysis omits certain unquantified effects due to lack of data, time and resources. 

These unquantified endpoints include other health and ecosystem effects. EPA will 

continue to evaluate new methods and models and select those most appropriate for 

estimating the benefits of reductions in air pollution. Enhanced collaboration between air 

quality modelers, epidemiologists, toxicologists, ecologists, and economists should result 

in a more tightly integrated analytical framework for measuring benefits of air pollution 

policies. 

 PM2.5 mortality benefits represent a substantial proportion of total monetized benefits 

(over 90%), and these estimates have following key assumptions and uncertainties. 

1. The PM2.5 -related benefits of the alternative scenarios were derived through a 

benefit per-ton approach, which does not fully reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or 

other local factors that might lead to an over-estimate or under-estimate of the 

actual benefits of controlling SO2. 

2. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 
18

sources, but no clear scientific grounds exist for supporting differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied concentrations of 

PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality, we include a set of twelve estimates based on results of the expert 

elicitation study in addition to our core estimates. Even these multiple 

characterizations omit the uncertainty in air quality estimates, baseline incidence 

rates, populations exposed and transferability of the effect estimate to diverse 

locations. As a result, the reported confidence intervals and range of estimates 

give an incomplete picture about the overall uncertainty in the PM2.5 estimates. 

This information should be interpreted within the context of the larger uncertainty 

surrounding the entire analysis. 
19

1.6 References 

Bell, M.L., et al. 2004. Ozone and short-term mortality in 95 US urban communities, 1987-

2000. Journal of the American Medical Association. 292(19): p. 2372-8. 

Bell, M.L., F. Dominici, and J.M. Samet. 2005. A meta-analysis of time-series studies of 

ozone and mortality with comparison to the national morbidity, mortality, and air 

pollution study. Epidemiology. 16(4): p. 436-45. 

Ito, K., S.F. De Leon, and M. Lippmann. 2005. Associations between ozone and daily 

mortality: analysis and meta-analysis. Epidemiology. 16(4): p. 446-57. 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine 

Particulate Air Pollution and Mortality.” American Journal of Respiratory and 

Critical Care Medicine 173:667-672. Estimating the Public Health Benefits of 

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

Press. 

Levy JI, Baxter LK, Schwartz J. 2009. Uncertainty and variability in health-related damages 

from coal-fired power plants in the United States. Risk Anal. doi: 10.1111/j.1539-

6924.2009.01227.x [Online 9 Apr 2009] 

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

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

U.S. Environmental Protection Agency (EPA). September 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal 

Agencies on Preparation of Regulatory Analysis. 

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

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and 

Particulate Air Pollution in the United States.” Environmental Health Perspectives

105(6):608-612.
20

CHAPTER 2 

INTRODUCTION AND BACKGROUND 

2.1 Introduction 

 EPA is proposing actions to address the interstate transport of emissions of nitrogen 

oxides (NOx) and sulfur dioxide (SO2) that contribute significantly to nonattainment and 

maintenance problems with respect to the national ambient air quality standards (NAAQS) 

for fine particulate matter (PM2.5) that EPA promulgated in 1997 and 2006 and for 8-hour 

ozone that were promulgated in 1997. In this action, EPA is proposing to both identify and 

eliminate emissions within states in the eastern United States that significantly contribute to 

nonattainment and interfere with maintenance of the ozone and PM2.5 NAAQS in other 

downwind states. This document presents the health and welfare benefits of the proposed 

Transport Rule and compares the benefits of this rule to the estimated costs of implementing 

the rule in 2012 and 2014. This chapter contains background information relative to the rule 

and an outline of the chapters of the report. 

2.2 Background 

Clean Air Act (CAA) section 110(a)(2)(D)(i)(I) requires states to prohibit emissions 

that contribute significantly to nonattainment in, or interfere with maintenance by, any other 

state with respect to the National Ambient Air Quality Standards (NAAQS). In this 

proposed rule, the Environmental Protection Agency (EPA) is proposing actions to partially 

or fully address the interstate transport of emissions of sulfur dioxide (SO2), nitrogen oxides 

(NOx), and the fine particulate that they form in the atmosphere, that contribute significantly 

to nonattainment and interfere with maintenance with respect to the fine particulate matter 

(PM2.5) NAAQS promulgated in 1997 and 2006. EPA is also proposing actions to partially 

or fully address the interstate transport of NOx and the ozone that it forms in the atmosphere 

that contribute significantly to nonattainment and interfere with maintenance with respect to 

the 8-hour ozone NAAQS promulgated in 1997. 

 With this proposal, EPA is responding to the remand of the Clean Air Interstate Rule 

(CAIR) by the U.S. Court of Appeals for the D.C. Circuit in 2008. CAIR, promulgated May 
21

12, 2005 (70 FR 25162) and the CAIR federal implementation plans (FIPs), promulgated 

April 26, 2006 (71 FR 25328), aimed to address the interstate transport of pollutants that 

contributed significantly to downwind nonattainment of the PM2.5 and 8-hour ozone NAAQS 

promulgated in July 1997. In July 2008, the D.C. Circuit Court found CAIR and the CAIR 

FIPs unlawful. North Carolina v. EPA, 531 F.3d 896 (D.C. Cir. 2008). The Court’s original 

decision vacated CAIR. Id. at 929-30. However, the Court subsequently remanded CAIR to 

EPA without vacatur because it found that “allowing CAIR to remain in effect until it is 

replaced by a rule consistent with our opinion would at least temporarily preserve the 

environmental values covered by CAIR.” North Carolina v. EPA, 550 F.3d 1176, 1178 

(D.C. Cir. 2008). 

2.2.1 Methodology for Identifying Needed Reductions 

As described in section IV of the preamble for this proposed rule, EPA is proposing a 

state-specific methodology to identify specific reductions that states in the eastern United 

States must make to satisfy the CAA section 110(a)(2)(D)(i)(I) prohibition on emissions that 

significantly contribute to nonattainment or interfere with maintenance in a downwind state. 

To facilitate implementation of the requirement that significant contribution and interference 

with maintenance be eliminated, EPA developed state emissions budgets. These are new 

emissions budgets which are based on the Agency’s state-by-state analysis of each upwind 

state’s significant contribution to nonattainment and interference with maintenance 

downwind. A state’s emissions budget is the quantity of emissions that would remain after 

elimination of significant contribution and interference with maintenance in an average year, 

assuming no abnormal meteorology or disruptions in electricity supply. EPA proposes SO2

and NOx budgets for each state covered for the 24-hour and/or annual PM2.5 NAAQS. EPA 

also proposes an ozone season3

 NOx budget for each state covered for the 8-hour ozone 

NAAQS. 

 

3

 Consistent with the approach taken by the Ozone Transport Assessment Group (OTAG), the NOX SIP call, 

and the CAIR, we propose to define the ozone season, for purposes of emissions reduction requirements in this 

rule, as May through September. We recognize that this ozone season for regulatory requirements will have 

differences from the official state-specific monitoring season.
22

2.2.2 How Reductions Will Be Achieved, and Different Options to Do So 

EPA is proposing federal implementation plans (FIPs) to immediately implement the 

emissions reduction requirements. The FIPs would regulate electric generating units (EGUs) 

in the 32 covered states. EPA is proposing to regulate these sources through a program that 

uses state-specific budgets and allows intrastate and limited interstate trading. EPA is also 

taking comment on two alternative regulatory options. All three options would achieve the 

emission reductions necessary to address the emissions transport requirements in section 

110(a)(2)(D)(i)(I) of the Clean Air Act. 

2.2.3 States Covered by the Proposed Rule 

In this action, EPA proposes SO2 and NOx emissions controls in the following 26 

jurisdictions that contribute significantly to nonattainment in, or interfere with maintenance 

by, a downwind area with respect to the 24-hour PM2.5 NAAQS promulgated in September 

2006: Alabama, Connecticut, Delaware, District of Columbia, Georgia, Illinois, Indiana, 

Iowa, Kansas, Kentucky, Massachusetts, Maryland, Michigan, Minnesota, Missouri, 

Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, 

West Virginia, and Wisconsin. 

EPA proposes SO2 and NOx emissions controls in the following 24 jurisdictions that 

contribute significantly to nonattainment in, or interfere with maintenance by, a downwind 

area with respect to the annual PM2.5 NAAQS promulgated in July 1997: Alabama, 

Delaware, District of Columbia, Florida, Georgia, Illinois, Indiana, Iowa, Kentucky, 

Louisiana, Maryland, Michigan, Minnesota, Missouri, New Jersey, New York, North 

Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, West Virginia, and 

Wisconsin.

EPA also proposes ozone season NOx emissions controls in the following 26 

jurisdictions that contribute significantly to nonattainment in, or interfere with maintenance 

by, a downwind area with respect to the 8-hour ozone NAAQS promulgated in July 1997: 

Alabama, Arkansas, Connecticut, Delaware, District of Columbia, Florida, Georgia, Illinois, 

Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, New Jersey, New 

York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, 

Virginia, and West Virginia. 
23

As discussed above, EPA is proposing FIPs to directly regulate EGU SO2 and/or NOx

emissions in the 32 covered states. The proposed FIPs would require the 28 jurisdictions 

covered for purposes of the 24-hour and/or annual PM2.5 NAAQS to reduce SO2 and NOx

emissions by specified amounts. The proposed FIPs would require the 26 states covered for 

purposes of the 8-hour ozone NAAQS to reduce ozone season NOx emissions by specified 

amounts. For the PM2.5 NAAQS, EPA proposes two phases with an initial phase in 2012 and 

subsequent phase in 2012. For 8-hour ozone, EPA proposes a single phase that would start 

in 2012. 

As discussed in detail in section IV of the preamble, the proposed approach to 

significant contribution and interference with maintenance would group the 28 states covered 

for 24-hour and/or annual PM2.5 NAAQS in two tiers reflecting the stringency of SO2

reductions required to eliminate that state’s significant contribution and interference with 

maintenance. There would be a stringent SO2 tier comprising 15 states (“group 1”) and a 

moderate SO2 tier comprising 13 states (“group 2”) with uniform stringency within each 

tier.4 For these same 28 states, there would be one annual NOx tier with uniform stringency 

of NOx reductions across all 28 states. Similarly, for the 26 states covered for the 8-hour 

ozone NAAQS there would be one ozone season NOx tier with uniform stringency across all 

26 states.

The proposed stringent SO2 tier (“group 1”) would include Georgia, Illinois, Indiana, 

Iowa, Kentucky, Michigan, Missouri, New York, North Carolina, Ohio, Pennsylvania, 

Tennessee, Virginia, West Virginia, and Wisconsin. The proposed moderate SO2 tier 

(“group 2”) would include Alabama, Connecticut, Delaware, District of Columbia, Florida, 

Kansas, Louisiana, Maryland, Massachusetts, Minnesota, Nebraska, New Jersey, and South 

Carolina. 

For the 15 states in the stringent SO2 tier (“group 1”), the 2014 phase would 

substantially increase the SO2 reduction requirements (i.e., these states would have smaller 

SO2 emissions budgets starting in 2014), reflecting the greater reductions needed to eliminate 

significant contribution and interference with maintenance from these states with respect to 

 

4 With regard to interstate trading, the two SO2 stringency tiers lead to two exclusive SO2 trading groups.
24

the 24-hour PM2.5 NAAQS. For the 13 states in the moderate SO2 tier (“group 2”) the 2014 

SO2 emissions budgets would remain the same as the 2012 SO2 budgets for these states. 

The 2014 annual NOx emissions budgets for all 28 states covered for the 24-hour 

and/or annual PM2.5 NAAQS would remain the same as the 2012 annual NOx budgets. See 

Table 2-1 for proposed lists of covered states. 

Table 2-1 -- Lists of Covered States for PM2.5 and 8-Hour Ozone NAAQS 

State 

Covered for 24-hour 

and/or annual PM2.5 

Required to reduce SO2

and NOx 

Covered for 8-hour 

ozone

Required to reduce 

ozone season NOx

Alabama X X 

Arkansas X 

Connecticut X X 

Delaware X X 

District of Columbia X X 

Florida X X 

Georgia X X 

Illinois X X 

Indiana X X 

Iowa X 

Kansas X X 

Kentucky X X 

Louisiana X X 

Maryland X X 

Massachusetts X 

Michigan X X 
25

Minnesota X 

Mississippi X 

Missouri X 

Nebraska X 

New Jersey X X 

New York X X 

North Carolina X X 

Ohio X X 

Oklahoma X 

Pennsylvania X X 

South Carolina X X 

Tennessee X X 

Texas X 

Virginia X X 

West Virginia X X 

Wisconsin X 

TOTALS 28 26

The relevant regions for PM2.5 and ozone significant contribution are also depicted in 

the graphic in Figures 2-1 and 2-2, respectively. Maps are also available in Chapter 7 of this 

RIA. 
26

Figure 2-1 - PM2.5 Region (SO2 and Annual NOx States) Under the Proposed Transport 

Rule 
27

Figure 2-2. Transport Rule Ozone-season NOx States

Figure 2-2 - Ozone Region Under the Proposed Transport Rule 
28

2.3 Regulated Entities 

 This action proposes to directly regulate emissions of NOx and SO2, from electric 

generating units (EGUs) with capacity greater than 25 MW in the covered states. 

2.4 Baseline and Years of Analysis 

The proposed rule on which this analysis is based sets forth the requirements for 

states to address their significant contribution to downwind nonattainment of ozone and 

PM2.5 NAAQS and interference with maintenance. To address this significant contribution 

and interference with maintenance, EPA requires that certain states reduce their emissions of 

SO2 and NOx. The Agency considered all promulgated CAA requirements and known state 

actions in the baseline used to develop the estimates of benefits and costs for this rule. This 

baseline analysis takes into account emissions reductions associated with the implementation 

of all federal rules promulgated by December 2008 and assumes that the CAIR is not in 

effect. However, this baseline presents a unique situation. EPA has been directed to replace 

the CAIR; yet the CAIR remains in place and has led to significant emissions reductions in 

many states. 

A key step in the process of developing a 110(a)(2)(D)(i)(I) rule involves analyzing 

existing (base case) emissions to determine which states significantly contribute to 

downwind nonattainment and maintenance areas. EPA cannot prejudge at this stage which 

states will be affected by the rule. For example, a state affected by CAIR may not be 

affected by the new rule and after the new rule goes into effect, the CAIR requirements will 

no longer apply. For a state covered by CAIR but not covered by the new rule, the CAIR 

requirements would not be replaced with new requirements, and therefore an increase in 

emissions relative to present levels could occur in that state. More fundamentally, the court 

has made clear that, due to legal flaws, the CAIR rule cannot remain in place and must be 

replaced. If EPA’s base case analysis were to ignore this fact and assume that reductions 

from CAIR would continue indefinitely, areas that are in attainment solely due to controls 

required by CAIR would again face nonattainment problems, because the existing protection 

from upwind pollution would not be replaced. For these reasons, EPA cannot assume in its 

base case analysis, that the reductions required by CAIR will continue to be achieved. 
29

Following this logic, the 2012 base case shows emissions higher than current levels in 

some states. Because EPA has been directed to replace CAIR, EPA believes that for many 

states, the absence of the CAIR NOx program will lead to the status quo of the NOx Budget 

Program, which limits ozone-season NOX emissions and ensures the operation of NOX

controls in those states. Also, without the CAIR SO2 program, emission requirements in 

many areas would revert to the comparatively less stringent requirements of the Title IV 

Acid Rain program. As a result, SO2 emissions in many states would increase markedly in 

the 2012 base case relative to the present. Efforts to comply with ARP rules at the least-cost 

would occur in many cases without the operation of existing scrubbers through use of readily 

available, inexpensive Title IV allowances. Notably, all known controls that are required 

under state laws, NSPS, consent decrees, and other enforceable binding commitments 

through 2014 are accounted for in the base case. It is against this backdrop that the Transport 

Rule is analyzed and that significant contribution to nonattainment and interference with 

maintenance must be addressed. 

The model’s base case features an updated Title IV SO2 allowance bank assumption 

and incorporates updates related to the Energy Independence and Security Act of 2007. 

Many key assumptions, notably demand for electricity, reflect the 2008 Annual Energy 

Outlook from the Energy Information Administration (EIA). 5 In addition, the model 

includes policies affecting the power sector: the Title IV of the Clean Air Act (the Acid Rain 

Program); the NOx SIP Call; various New Source Review (NSR) settlements6; and several 

state rules7 affecting emissions of SO2 and NOx that were finalized through February 3, 

2009. IPM includes state rules that have been finalized and/or approved by a state’s 

 

5 For the final rule, EPA anticipates using an updated version of IPM that will reflect assumptions from AEO 

2010. Key differences will include lower assumptions about future electric demand and higher capital costs 

accounting for ARRA.

6 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company 

(Vectren), Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia 

Electric & Power Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power 

(AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio 

Edison, and Kentucky Utilities.

7 These include current and future state programs in Connecticut, Delaware, Georgia, Illinois, Maine, 

Maryland, Massachusetts, Minnesota, Missouri, New Hampshire, North Carolina, New Jersey, New York, 

Oregon, Texas, and Wisconsin.
30

legislature or environmental agency. 

The years 2012 and 2014 are the compliance years for the proposed rule, though as we 

explain in Chapters 5 and 7 we use 2015 as a proxy for compliance in 2014 for our benefits 

and economic impact analysis due to availability of modeling impacts in that year. We 

include analyses results for each year, but we do not include benefits and economic impact 

estimates for 2012 due to time constraints. All estimates presented in this report represent 

annualized estimates of the benefits and costs of the Transport Rule in 2012 and 2015 rather 

than the net present value of a stream of benefits and costs in these particular years of 

analysis.

2.5 Control Scenarios 

The option EPA is proposing for the FIPs ("State Budgets/Limited Trading") would 

utilize state-specific control budgets and allow for intrastate and limited interstate trading. 

This approach would assure environmental results while providing some limited flexibility 

for covered sources. The approach would also facilitate the transition from CAIR to the 

Transport Rule for implementing agencies and covered sources.

The first alternative remedy option for which EPA requests comment would use statespecific emissions budgets and allow intrastate trading, but prohibit interstate trading. The 

second alternative remedy option, for which EPA also requests comment, would use statespecific budgets and emission rate limits. 

The main difference between the three remedies lies in the kinds of flexibility they 

provide for compliance. State Budgets/Limited Trading allows sources to trade within state 

lines and, as long as state emissions remain within the budgets plus variability limits, across 

state lines as well. State Budgets/Intrastate Trading caps each state’s emissions at its budget 

without variability and only allows trading within (and not between) states. Under Direct 

Control, each EGU must meet an emission rate limit (company-wide within state averaging 

allowed), and a state’s emissions must remain within its budget plus variability. Further 

details on each of these remedies can be found in Section V. of the Transport Rule preamble. 
31

The proposed remedy option and the first alternative, both of which are cap-and-trade 

approaches, would use new allowance allocations developed on a different basis from CAIR. 

 Fossil-fuel electric generating units (EGUs) over 25 megawatt (MW) capacity within 

the proposed Transport Rule region would be covered by this action. 

2.6 Benefits of Emission Controls 

 The benefits of the proposed Transport Rule are discussed in Chapter 5 of this report. 

 Annual monetized benefits of $120 to 290 billion (3 percent discount rate) or $110 to 270 

billion (7 percent discount rate) are expected for the proposed rule in 2014. 

2.7 Cost of Emission Controls 

 EPA analyzed the costs to private industry of the proposed Transport Rule using the 

Integrated Planning Model (IPM). EPA has used this model in the past to analyze the 

impacts of regulations on the power sector and used an earlier version of this model to 

analyze the impacts of the CAIR rule. The social cost is measured using Hicksian equivalent 

variation and estimated using the EMPAX CGE model. IPM results are incorporated into the 

EMPAX model when calculating the social cost of the Transport Rule. EPA estimates the 

private industry costs of the rule to the power sector to be $3.7 billion in 2012 and $2.8 

billion in 2014 (2006 dollars). In estimating the net benefits (benefits – costs) of the rule, 

EPA uses social costs of the rule that represent the costs to society of this rule. These social 

costs include to the impact to industries affected by changes to electricity prices resulting 

from implementation of the proposed Transport Rule. The social costs of the rule are 

estimated by the EMPAX model to be $2.0 or $2.2 billion in 2015 (at 3 percent and 7 percent 

discount rates, respectively). A description of the methodology used to model the costs and 

economic impacts to the power sector is discussed in Chapter 7 of this report, and a 

description of the methodology used to estimate the social cost of the rule and model 

economic impacts outside of the power sector is discussed in Chapter 8 of this report. 
32

2.8 Organization of the Regulatory Impact Analysis 

 This report presents EPA’s analysis of the benefits, costs, and other economic effects 

of the proposed Transport Rule to fulfill the requirements of a Regulatory Impact Analysis 

(RIA). This RIA includes the following chapters: 

 Chapter 3, Emissions Impacts, describes the emission inventories and modeling 

that are essential inputs into the cost and benefit assessments. 

 Chapter 4, Air Quality Impacts, describes the air quality data and modeling that 

are important for assessing the effect on contributions to air quality from the 

remedy options applied in this proposed rule, and as inputs to the benefits 

assessment. 

 Chapter 5, Benefits Analysis and Results, describes the methodology and results 

of the benefits analysis 

 Chapter 6, Electric Power Sector Profile, describes the industry affected by the 

rule. 

 Chapter 7, Cost, Economic, and Energy Impacts, describes the modeling 

conducted to estimate the cost, economic, and energy impacts to the power sector. 

 Chapter 8, Macroeconomic Impacts and Social Costs, describes the modeling 

conducted to estimate the social cost of the rule as well as the economic impacts 

to industries outside of the power sector. 

 Chapter 9, Statutory and Executive Order Impact Analyses, describes the small 

business, unfunded mandates, paperwork reduction act, environmental justice, 

and other analyses conducted for the rule to meet statutory and Executive Order 

requirements. 

 Chapter 10, Comparison of Benefits and Costs, shows a comparison of the social 

benefits to social costs of the rule. 

 Appendix A, Human Health Benefits of Direct Control and Intrastate Trading 

Remedies 

 Appendix B, Analyses of Economic Impacts Outside of the Electric Power Sector 

– Intrastate Trading and Direct Control Remedies 
 33 

 Appendix C, Comparison of State Level Electrical Generating Unit Emissions 

Under Various Regulatory Alternatives To Reduce SO2 And NOx Emissions 

Under The Transport Rule 

 Appendix D, Integrated Planning Model Runs 

 Appendix E, Allowance Values for Emissions Trading Programs 

 

 
 34 

CHAPTER 3 

EMISSIONS IMPACTS 

This chapter summarizes the emissions inventories that are used to create emissions 

inputs to the air quality modeling that is described in Chapter 4. This chapter provides a 

summary of the baseline emissions inventories and the emissions reductions that were 

modeled for this rule. The emissions inventories are processed into a form that is required by 

the Comprehensive Air Quality Model with extensions (CAMx). CAMx is used to estimate 

base year, future baseline and post-control concentrations of ozone and PM and deposition of 

nitrogen and sulfur, which are combined with monitoring data to estimate population-level 

exposures to changes in ambient concentrations for use in estimating health and welfare 

effects. In the remainder of this Chapter we provide an overview of (1) the emissions 

components of the modeling platform, (2) the development of the 2005 base year emissions, 

(3) the development of 2014 future year base case emissions, and (4) the development of the 

future year control case emissions. 

3.1 Overview of Modeling Platform and Emissions Processing Performed 

The inputs to the air quality model; including emissions, meteorology, initial 

conditions, boundary conditions; along with the methods used to produce the inputs and the 

configuration of the air quality model are collectively known as a ‘modeling platform’. The 

2005-based air quality modeling platform used for the proposal includes 2005 base year 

emissions and 2005 meteorology for modeling ozone and PM2.5 with CAMx (see 

http://www.camx.com/). This platform provides an update to the now more historical data in 

the 2001-based platform used for CAIR that included 2001 emissions, 2001 meteorology for 

modeling PM2.5, and 1995 meteorology for modeling ozone. Details on the non-emissions 

portion of the modeling platform used for the RIA are provided in described in Chapter 4. In 

support of this proposal, EPA modeled the air quality in the East using a horizontal grid 

resolution of 12 x 12 km. This Eastern 12 km modeling domain was “nested” within a 
 35 

modeling domain covering the remainder of the lower 48 states using a grid resolution of 36 

x 36 km8, therefore the tables of emissions in this section cover the contiguous 48 states. 

 Emissions estimates were made for a 2005 base year and for the 2014 future year 

scenarios. All inventories include emissions from electric generating utilities (EGUs), 

nonEGU point sources, stationary nonpoint sources, onroad mobile sources, nonroad mobile 

sources and natural, biogenic emissions. These emissions were derived from the 2005 v4 

emissions modeling platform, described in the 2005-based, v4 platform document 

(http://www.epa.gov/ttn/chief/emch/index.html#2005). The Emissions Inventories Technical 

Support Document for Emissions Inventories for the Transport Rule (EITSD) provides more 

detail on (1) the development of the 2014 base case emissions inventories for all sectors, 

except EGUs and (2) the procedures followed to create emissions inputs to CAMx for each 

scenario modeled. For details on EPA’s projected emissions for the EGU sector, see Chapter 

7 of this RIA. 

 For each of the modeling scenarios conducted: 2005 base year, 2014 base case, and 

2014 control case, the emissions inventory files were processed using the Sparse Matrix 

Operator Kernel Emissions (SMOKE) Modeling System version 2.6 to produce the gridded 

model-ready emissions for input to CAMx. SMOKE was used to create the hourly, gridded 

emissions data for the species required by CAMx species to perform air quality modeling for 

all sectors, including biogenic emissions. See Chapter 4 for more details on the modeling 

performed with CAMx. 

3.2 Development of 2005 Base Year Emissions 

 Emissions inventory inputs representing the year 2005 were developed to provide a 

base year for forecasting future air quality. The emission source sectors and the basis for 

current and future-year inventories are listed and defined in Table 3-1. The 2005 National 

Emission Inventory (NEI), version 2 from October 6, 2008 was the starting point for the U.S. 

inventories used for the 2005 air quality modeling. This inventory includes 2005-specific 

data for most point and mobile sources, while most nonpoint data were carried forward from 

 

8 The air quality predictions from the 36 km Continental US (CONUS) domain were used to provide incoming 

“boundary” concentrations for the Eastern 12 km domain.
 36 

version 3 of the 2002 NEI. For more information on the 2005 National Emissions Inventory 

(NEI), upon which significant portions of the 2005 modeling platform are based, see 

http://www.epa.gov/ttn/chief/net/2005inventory.html. A 2006 Canadian inventory and a 

1999 Mexican inventory were the latest available data from these countries and were used for 

the portions of Canada and Mexico within the modeling domains. 

Table 3-1. Emissions Source Sectors for Current and Future-Year Inventories 

Platform Sector 2005 NEI 

Sector

Description and resolution of the data input to SMOKE

IPM sector: ptipm Point 2005v2 NEI point source EGUs mapped to the Integrated Planning 

Model (IPM) model using the National Electric Energy Database 

System (NEEDS) database. Day-specific continuous emission 

monitoring (CEM) emissions and non-CEM sources created for 

input into SMOKE. 

Non-IPM sector: 

ptnonipm

Point All 2005v2 NEI point source records not matched to the ptipm 

sector, annual resolution. Includes all aircraft emissions.

Average-fire 

sector: avefire 

N/A Average-year wildfire and prescribed fire emissions derived from 

the 2002-based Platform avefire sector, county and annual 

resolution.

Agricultural 

sector: ag

Nonpoint NH3 emissions from NEI nonpoint livestock and fertilizer 

application, county and annual resolution.

Area fugitive dust 

sector: afdust

Nonpoint PM10 and PM2.5 from fugitive dust sources from the NEI nonpoint 

inventory (e.g., building construction, road construction, paved 

roads, unpaved roads, agricultural dust), county and annual 

resolution.

Remaining 

nonpoint sector: 

nonpt

Nonpoint Primarily 2002 NEI nonpoint sources not otherwise included in 

other SMOKE sectors, county and annual resolution. Also includes 

2005 updated Residential Wood Combustion emissions and year 

2005 non-California WRAP oil and gas Phase II inventory.

Nonroad sector: 

nonroad

Mobile: 

Nonroad

Monthly nonroad emissions from the National Mobile Inventory 

Model (NMIM) using NONROAD2005 version nr05c-BondBase 

for all states except California. Monthly emissions for California 

created from annual emissions submitted by the California Air 

Resources Board (CARB) for the 2005v2 NEI.

Locomotive, and 

non-C3 

commercial 

marine: 

alm_no_c3

Mobile: 

Nonroad

Year 2002 non-rail maintenance locomotives, and category 1 and 

category 2 commercial marine vessel (CMV) emissions sources, 

county and annual resolution. Aircraft emissions are now included 

in the ptnonipm sector and category 3 emissions are now contained 

in the seca_c3 sector.
 37 

C3 commercial 

marine: seca_c3

Mobile : 

Nonroad

Annual point source formatted year 2005 category 3 (C3) CMV 

emissions, developed for the rule called “Control of Emissions 

from New Marine Compression-Ignition Engines at or Above 

30 Liters per Cylinder”, usually described as the Area (ECA) 

study, originally called SO2 (“S”) ECA (see 

http://www.epa.gov/otaq/oceanvessels.htm). 

Onroad 

California, 

NMIM-based, and 

MOVES sources 

not subject to 

temperature 

adjustments: 

on_noadj

Mobile: 

onroad

Three monthly, county-level components:

1) Onroad emissions from NMIM using MOBILE6.2, other 

than for California. 

2) California onroad, created using annual emissions submitted 

by CARB for the 2005v2 NEI.

3) Onroad gasoline non-motorcycle vehicle emissions from 

draft MOVES not subject to temperature adjustments: 

exhaust CO, NOx, VOC, some VOC Hazardous Air 

Pollutants (HAPs), and evaporative VOC and some VOC 

HAPs.

Onroad cold-start 

gasoline exhaust 

mode vehicle from 

MOVES subject 

to temperature 

adjustments: 

on_moves_startpm

Mobile: 

onroad

Monthly, county-level draft MOVES-based onroad non-motorcycle 

gasoline emissions subject to temperature adjustments. Limited to 

exhaust mode only for PM species and Naphthalene. California 

emissions not included. This sector is limited to cold start mode 

emissions that contain different temperature adjustment curves from 

running exhaust (see on_moves_runpm sector).

Onroad running 

gasoline exhaust 

mode vehicle from 

MOVES subject 

to temperature 

adjustments: 

on_moves_runpm

Mobile: 

onroad

Monthly, county-level draft MOVES-based onroad non-motorcycle 

gasoline emissions subject to temperature adjustments. Limited to 

exhaust mode only for PM species and Naphthalene. California 

emissions not included. This sector is limited to running mode 

emissions that contain different temperature adjustment curves from 

cold start exhaust (see on_moves_startpm sector).

Biogenic: biog N/A Hour-specific, grid cell-specific emissions generated from the 

BEIS3.14 model -includes emissions in Canada and Mexico.

Other point 

sources not from 

the NEI: othpt

N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase III 

1999 inventory, annual resolution. Also includes annual U.S. 

offshore oil 2005v2 NEI point source emissions.

Other point 

sources not from 

the NEI, Hg only: 

othpt_hg

N/A For 2005 only, the annual year 2000 Canada speciated mercury 

point source emissions. Note that the ‘_hg’ sectors were not 

included in the future-year modeling.

Other nonpoint 

and nonroad not 

from the NEI: 

othar

N/A Annual year 2006 Canada (province resolution) and year 1999 

Mexico Phase III (municipio resolution) nonpoint and nonroad 

mobile inventories, annual resolution.
 38 

Other nonpoint 

sources not from 

the NEI, Hg only: 

othar_hg

N/A For 2005 only, the annual year 2000 Canada speciated mercury from 

nonpoint sources.

Other onroad 

sources not from 

the NEI: othon

N/A Year 2006 Canada (province resolution) and year 1999 Mexico 

Phase III (municipio resolution) onroad mobile inventories, annual 

resolution.

 The onroad and nonroad emissions were primarily based on the National Mobile 

Inventory Model (NMIM) monthly, county, process level emissions from the 2005 NEI 

version 2 (v2). The 2005 onroad mobile emissions were augmented for onroad gasoline 

emissions sources with emissions based on a draft version of the Motor Vehicle Emissions 

Simulator (MOVES) (http://www.epa.gov/otaq/models/moves/) for carbon monoxide (CO), 

NOx, VOC, PM2.5, particulate matter less than ten microns (PM10), naphthalene, and some 

VOC HAPs9. To account for the temperature dependence of PM2.5, MOVES-based 

temperature adjustment factors were applied to gridded, hourly emissions using gridded, 

hourly meteorology. Additional information on this approach is available in the 2005-based 

platform documentation. 

 The 2005 annual NOx and SO2 emissions for sources in the ptipm sector as defined in 

Table 3-1 are based primarily on data from EPA’s Clean Air Markets Division’s Continuous 

Emissions Monitoring (CEM) program, with other pollutants estimated using emission 

factors and the CEM annual heat input. As noted in Table 3-1, the 2005 EGUs include those 

units operating in 2005 that are matched to the units modeled by the version of IPM used for 

this proposal, and include records with and without data submitted to the CEM program. For 

EGUs without CEMs, emissions were obtained from the state-submitted data in the NEI. 

 For the 2005 base year, the annual EGU NEI emissions in the ptipm sector were 

allocated to hourly emissions values needed for modeling based on the 2004, 2005, and 2006 

CEM data. The NOx CEM data were used to create NOx -specific profiles, the SO2 data were 

used to create SO2-specific profiles, and the heat input data were used to allocate all other 

pollutants. The three years of data were used to create monthly and hourly profiles by state, 

while the 2005 data were used to create profiles for allocating monthly emissions to daily 

 

9 The final version of MOVES was not available at the time we created the emissions for this proposed rule.
 39 

and hourly values. This approach to temporal allocation was used for both the 2005 base year 

and 2014 base and control emissions in order provide a temporal consistency across all 

scenarios modeled. 

 The nonpoint inventory was augmented with an oil and gas exploration inventory that 

includes emissions within the following states: Arizona, Colorado, Montana, Nevada, New 

Mexico, North Dakota, Oregon, South Dakota, Utah, and Wyoming. The commercial marine 

category 3 (C3) vessel emissions were augmented with gridded 2005 emissions from the 

previous modeling efforts for the rule called “Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder”. The 2005 point source 

daily wildfire and prescribed burning emissions were replaced with average-year countybased inventories. Additionally, the inventories were processed to provide the hourly, 

gridded emissions for the model-species needed by CAMx. All of these details are further 

described in the 2005-based platform documentation. 

 Tables 3-2 and 3-3 provide summaries of SO2 and NOx emissions by state by sector 

for the 2005 base year for those states within the Eastern 12 km modeling domain. 

Emissions for other states that are within the 36 km modeling domain are available in the 

EITSD. All sectors listed are defined in Table 3-1. In the tables, the EGU column 

summarizes all units matched to the IPM model (ptipm sector) and the nonEGU column is 

for other point source units (ptnonipm sector). The Nonpoint column shows emissions for all 

nonpoint stationary sources (nonpt, ag, and afdust sectors). The Nonroad column 

summarizes emissions for nonroad mobile sources, including aircraft, locomotive, and 

marine sources including the C3 commercial marine (nonroad, alm_no_c3, and seca_c3 

sectors). The Onroad column summarizes emissions for the combined NEI and draft 

MOVES-based emissions, in which emissions from the draft MOVES were used when 

available, and NEI emissions based on MOBILE6 were used for the remainder (on_noadj, 

on_moves_runpm, and on_moves_startpm sectors). Finally, the Fires column represents the 

average-year fire (avefire sector) emissions for wildfires and prescribed burning mentioned 

previously. 
 40 

Table 3-2. 2005 Base Year SO2 Emissions (tons/year) for Lower 48 States by Sector 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Alabama 460,123 70,346 52,325 6,397 3,199 983 593,372 

Arizona 52,733 23,966 2,571 6,154 2,909 2,888 91,221 

Arkansas 66,384 13,066 27,260 5,678 1,632 728 114,749 

California 622 33,097 77,672 101,270 4,935 6,735 224,330 

Colorado 64,174 1,549 6,810 4,897 2,526 1,719 81,675 

Connecticut 10,356 1,831 18,455 2,548 1,128 4 34,320 

Delaware 32,378 34,859 5,859 11,648 422 6 85,173 

District of Columbia 1,082 686 1,559 414 172 0 3,914 

Florida 417,321 57,475 70,490 93,543 10,285 7,018 656,131 

Georgia 616,054 56,116 56,829 13,331 5,690 2,010 750,031 

Idaho 0 17,151 2,915 2,304 794 3,845 27,010 

Illinois 330,382 156,154 5,395 19,302 5,716 20 516,969 

Indiana 878,978 95,200 59,775 9,436 3,981 24 1,047,396 

Iowa 130,264 61,241 19,832 8,838 1,702 25 221,902 

Kansas 136,520 13,142 36,381 8,035 1,824 103 196,005 

Kentucky 502,731 25,811 34,229 6,942 2,711 364 572,787 

Louisiana 109,851 165,737 2,378 73,233 2,399 892 354,489 

Maine 3,887 18,519 9,969 3,725 834 150 37,084 

Maryland 283,205 34,988 40,864 17,819 2,966 32 379,874 

Massachusetts 85,768 19,620 25,261 25,335 2,168 93 158,245 

Michigan 349,877 76,510 42,066 14,533 7,204 91 490,280 

Minnesota 101,666 25,169 14,747 10,410 2,558 631 155,181 

Mississippi 74,117 29,892 6,796 6,003 2,158 1,051 120,016 

Missouri 284,384 78,307 44,573 10,464 4,251 186 422,165 

Montana 19,715 11,056 2,600 3,813 767 1,422 39,373 

Nebraska 74,955 6,429 29,575 9,199 1,326 105 121,589 

Nevada 53,363 2,253 12,477 2,877 565 1,346 72,881 

New Hampshire 51,445 3,245 7,408 805 630 38 63,571 

New Jersey 57,044 7,640 10,726 23,484 2,486 61 101,441 

New Mexico 30,628 7,831 3,193 3,541 1,517 3,450 50,161 

New York 180,847 58,562 125,158 20,908 5,628 113 391,216 

North Carolina 512,231 66,150 22,020 42,743 5,341 696 649,181 

North Dakota 137,371 9,458 6,455 5,986 443 66 159,779 

Oklahoma 110,081 40,482 7,542 5,015 2,699 469 166,288 

Ohio 1,116,084 118,468 19,810 15,615 6,293 22 1,276,292 

Oregon 12,304 9,825 9,845 13,717 1,537 4,896 52,124 

Pennsylvania 1,002,202 85,411 68,349 11,972 5,363 32 1,173,328 

Rhode Island 176 2,743 3,365 2,494 208 1 8,987 

South Carolina 218,782 31,495 30,016 20,477 2,976 646 304,393 

South Dakota 12,215 1,698 10,347 3,412 511 498 28,682 

Tennessee 266,148 78,206 32,714 6,288 4,834 277 388,468 
 41 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Texas 534,949 223,625 109,215 52,749 13,470 1,178 935,187 

Utah 34,813 9,132 3,577 2,439 1,633 1,934 53,527 

Vermont 9 902 5,385 385 305 49 7,036 

Virginia 220,248 69,440 32,923 18,420 3,829 399 345,259 

Washington 3,409 24,211 7,254 28,137 2,823 407 66,241 

West Virginia 469,456 48,314 14,589 2,133 1,095 215 535,802 

Wyoming 89,874 22,321 6,721 2,674 663 1,106 123,359 

Wisconsin 180,200 66,807 6,369 7,129 3,110 70 263,685 

Grand Total 10,019,774 1,953,744 1,117,009 596,847 123,547 19,345 13,830,266 
 42 

Table 3-3. 2005 Base Year NOx Emissions (tons/year) for Lower 48 States by Sector 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Alabama 133,051 74,830 32,024 61,623 142,221 3,814 447,562 

Arizona 79,776 15975 8650 62711 159501 10532 337,145 

Arkansas 35,407 37,478 21,453 63,493 81,014 2,654 241,499 

California 6,992 90,687 121,882 523,800 665,225 24,563 1,433,149 

Colorado 73,909 20,971 43,652 50,856 109,231 6,271 304,890 

Connecticut 6,865 5,824 12,554 21,785 69,645 14 116,688 

Delaware 11,917 5,567 3,259 15,567 22,569 23 58,902 

District of Columbia 492 501 1,740 3,494 9,677 0 15,904 

Florida 217,263 53,778 29,533 277,888 460,474 25,600 1,064,537 

Georgia 111,017 53,297 38,919 95,175 279,449 7,955 585,812 

Idaho 19 10,354 30,317 22,087 34,858 14,024 111,659 

Illinois 127,923 97,504 47,645 223,697 276,507 71 773,347 

Indiana 213,503 73,647 30,185 110,100 187,426 88 614,949 

Iowa 72,806 39,299 15,150 92,965 91,795 90 312,105 

Kansas 90,220 70,785 42,286 86,553 76,062 378 366,285 

Kentucky 164,743 35,432 17,557 90,669 127,435 1,326 437,163 

Louisiana 63,791 165,162 27,559 301,170 112,889 3,254 673,824 

Maine 1,100 18,309 7,423 13,379 38,469 566 79,246 

Maryland 62,574 24,621 21,715 55,812 129,796 137 294,656 

Massachusetts 25,618 18,429 34,373 74,419 118,148 341 271,327 

Michigan 120,005 94,139 43,499 101,087 279,816 330 638,876 

Minnesota 83,836 64,438 56,700 115,873 146,138 2,300 469,286 

Mississippi 45,166 53,985 12,212 79,394 98,060 3,833 292,649 

Missouri 127,431 38,604 32,910 123,228 183,022 678 505,873 

Montana 39,858 5,356 14,415 40,687 32,312 5,187 137,815 

Nebraska 52,426 12,156 13,820 107,180 58,643 381 244,607 

Nevada 47,297 17,191 5,379 27,747 40,247 4,910 142,771 

New Hampshire 8,827 3,241 11,235 9,246 32,537 137 65,223 

New Jersey 30,114 20,598 26,393 88,486 157,736 223 323,550 

New Mexico 75,483 43,925 69,175 45,552 71,596 12,582 318,313 

New York 63,465 55,122 87,608 121,363 282,072 412 610,042 

North Carolina 111,576 44,502 18,869 135,936 225,756 11,424 548,064 

North Dakota 76,381 7,545 10,046 59,635 21,575 240 175,422 

Oklahoma 86,204 73,465 94,574 55,424 117,240 1,709 428,617 

Ohio 258,687 71,715 41,466 173,988 270,383 81 816,321 

Oregon 9,383 22,927 17,059 78,284 85,045 17,857 230,555 

Pennsylvania 176,870 89,208 53,435 118,774 266,649 117 705,053 

Rhode Island 545 2,164 2,964 7,798 13,456 4 26,930 

South Carolina 53,823 29,069 20,281 68,146 128,765 2,357 302,441 

South Dakota 15,650 5,035 5,766 30,324 24,850 1,817 83,442 

Tennessee 102,934 60,353 18,676 82,331 207,410 1,012 472,717 
 43 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Texas 176,170 292,806 274,338 377,246 615,715 4,890 1,741,166 

Utah 65,261 19,466 13,844 26,985 74,024 7,052 206,632 

Vermont 297 799 3,438 3,951 13,316 179 21,980 

Virginia 62,512 60,101 53,605 91,298 194,173 1,456 463,145 

Washington 17,634 25,427 16,911 121,014 145,871 1,484 328,341 

West Virginia 159,804 36,913 14,519 32,739 50,040 785 294,801 

Wisconsin 72,170 40,688 21,994 75,981 147,952 256 359,042 

Wyoming 89,315 30,516 40,480 35,482 27,084 4,035 226,912 

Grand Total 3,728,110 2,233,904 1,683,487 4,682,422 7,203,874 189,429 19,721,235 

3.3 Development of Future Year Base Case Emissions 

 The future base case scenarios represent predicted emissions in the absence of any 

further controls beyond those Federal measures already promulgated. For EGUs (ptipm 

sector), all state and other programs available at the time of modeling have been included. 

For mobile sources (on_noadj, on_moves_runpm, and on_moves_startpm sectors), all 

national measures available at the time of modeling have been included. The future base 

case scenarios do reflect projected economic changes and fuel usage for EGU and mobile 

sectors, as described in the EITSD. For nonEGU point (ptnonipm sector) and nonpoint 

stationary sources (nonpt, ag, and afdust sectors), any local control programs that might be 

necessary for areas to attain the 1997 PM2.5 NAAQS annual standard, 2006 PM NAAQS (24-

hour) standard, and the 1997 ozone NAAQS are not included in the future base case 

projections. This is because the nonattainment areas for the 1997 PM2.5 and ozone standards 

were not announced until 2004 and 2005 respectively, and the corresponding state 

implementation plans (SIPs) were not due until 2007 and 2008, thereby preventing the 

inclusion of these local measures in the 2005 emissions inventory. 

 Table 3-4 shows a summary of the 2005 and 2014 modeled base case emissions for 

the lower 48 states. Tables 3-6 and 3-7 below provide summaries of SO2 and NOx in the 

2014 base case for each sector for the 37 states included in the 12 km modeling domain. The 

EITSD provides summaries for carbon monoxide, volatile organic compounds, directly 

emitted PM2.5, and ammonia for each state in the nationwide 36 km modeling domain. For 

information on the topic of the significant contribution of some states on air quality issues in 

other states, please see Table 2-1. 
 44 

Table 3-4. Summary of Modeled Base Case Annual Emissions (tons/year) for 48 States 

by Sector 

Source Sector NOx Emissions 2005 2014

EGU Point 3,728,110 2,908,844

Non-EGU Point 2,233,904 2,201,601

Nonpoint 1,683,487 1,679,404

Nonroad 4,682,422 3,706,913

On-road 7,203,874 3,410,053

Average Fire 189,429 189,429

Total NOx, All Sources 19,721,235 14,096,244

 

Source Sector SO2 Emissions 

EGU Point 10,381,405 8,469,820

Non-EGU Point 2,116,137 1,923,949

Nonpoint 1,252,645 1,252,127

Nonroad 768,671 604,519

On-road 144,216 31,067

Average Fire 49,095 49,095

Total SO2, All Sources 14,712,170 12,330,575

 The 2014 base case EGU emissions were obtained from version 3.02 EISA of the 

Integrated Planning Model (IPM) (http://www.epa.gov/airmarkt/progsregs/epaipm/index.html). The IPM is a multiregional, dynamic, deterministic linear programming 

model of the U.S. electric power sector; version 3.02 EISA features an updated Title IV SO2

allowance bank assumption, reflects state rules and consent decrees through February 3, 

2009, and incorporates updates related to the Energy Independence and Security Act of 2007. 

 Units with advanced controls (e.g., scrubber, SCR) that were not required to run for 

compliance with Title IV, New Source Review (NSR), state settlements, or state-specific 

rules were allowed in IPM to decide on the basis of abatement cost minimization whether to 

operate those controls or to use allowances for compliance. Further details on the future year 
 45 

EGU emissions used for air quality modeling can be found in the IPM Documentation. Note 

that controls from the NOx SIP call were assumed to have been implemented by 2005 and 

captured in the 2005 NEI, and reductions from the Clean Air Interstate Rule are not included 

in the 2014 base case emissions. 

 Mobile source inventories of onroad and nonroad mobile emissions were created for 

2014 using a combination of the NMIM and draft MOVES models. The future onroad 

emissions reflect control programs including the Light-Duty Vehicle Tier 2 Rule, the Onroad 

Heavy-Duty Rule, and the Mobile Source Air Toxics (MSAT) final rule. Emission 

reductions and increases from the Renewable Fuel Standard version 2 (RFS2) are not 

included. The future case nonroad mobile emissions reductions for these years include 

reductions to locomotives, various nonroad engines including diesel engines and various 

marine engine types, fuel sulfur content, and evaporative emissions standards. A summary 

of the included mobile source control programs is shown in Table 3-5. A more 

comprehensive list of control programs included for mobile sources is available in the 

EITSD. 

 The 2014 onroad emissions were primarily based on the National Mobile Inventory 

Model (NMIM) monthly, county, process level emissions. The emissions from onroad 

gasoline sources were augmented with emissions based on the same preliminary version of 

MOVES as was used for 2005. The same MOVES-based PM2.5 temperature adjustment 

factors were also applied as in 2005 for running mode emissions; however, cold start 

emissions used year-specific temperature adjustment factors. 

 Nonroad mobile emissions were created only with NMIM using a consistent 

approach as was used for 2005, but emissions were calculated using NMIM future-year 

equipment population estimates and control programs for 2014. Emissions for locomotives 

and category 1 and 2 (C1 and C2) commercial marine vessels were derived for 2014 based 

on emissions published in the Locomotive Marine Rule, Regulatory Impact Assessment, 

Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final). 
 46 

Table 3-5. Summary of Mobile Source Control Programs Included in 2014 Base Case 

National Onroad Rules: 

Tier 2 Rule (February 28, 2000) 

Onroad Heavy-Duty Rule (February 24, 2009) 

Final Mobile Source Air Toxics Rule (MSAT2) (February 9, 2007) 

Renewable Fuel Standard (March 26, 2010) 

Local Onroad Programs: 

National Low Emission Vehicle Program (NLEV) (March 2, 1998) 

Ozone Transport Commission (OTC) LEV Program (January, 1995) 

National Nonroad Controls: 

Tier 1 nonroad diesel rule (June 17, 2004) 

Phase 1 nonroad SI rule (July 3, 1995) 

Marine SI rule (October 4, 1996) 

Nonroad diesel rule (October 23, 1998) 

Phase 2 nonroad nonhandheld SI rule (March 30, 1999) 

Phase 2 nonroad handheld SI rule (April 25, 2000) 

Nonroad large SI and recreational engine rule (November 8, 2002) 

Clean Air Nonroad Diesel Rule - Tier 4 (June 29, 2004) 

Locomotive and marine rule (May 6, 2008) 

Nonroad SI rule (October 8, 2008) 

Aircraft: 

Itinerant (ITN) operations at airports adjusted to year 2014 

Locomotives: 

Clean Air Nonroad Diesel Final Rule – Tier 4 (June 29, 2004) 

Locomotive rule (April 16, 2008) 

Locomotive and marine rule (May 6, 2008) 

Commercial Marine: 

Locomotive and marine rule (May 6, 2008) 

Clean Air Nonroad Diesel Final Rule – Tier 4 (June 29, 2004) 

Commercial Marine Rule (December 29, 1999) 

Tier 1 Commercial Marine Rule (February 28, 2003) 

 In the 2014 base case, we used the 2005 base year emissions for Canada and Mexico 

because future year emissions for sources in these countries were not available. 

 For nonEGU point sources, emissions were projected by including emissions 

reductions and increases from a variety of source data10. For nonEGU point sources, 

 

10 Controls from the NOX SIP call were assumed to have been in place by 2005 and captured in the 2005 NEI 

v2.
 47 

emissions were not grown using economic growth projections, but, rather were held constant 

at the emissions levels in 2005. Emissions reductions were applied to nonEGU point source 

to reflect known plant closures, refinery and other consent decrees, and reductions stemming 

from several Maximum Achievable Control Technology (MACT) standards. Since aircraft 

at airports were treated as point emissions sources in the 2005 NEI v2, we applied projection 

factors based on activity growth projected by the Federal Aviation Administration Terminal 

Area Forecast (TAF) system, published December 2008 for these sources. 

 Emissions from stationary nonpoint sources were projected using procedures specific 

to individual source categories. Refueling emissions were projected using the refueling 

results from the NMIM runs performed for the onroad mobile sector. Portable fuel container 

emissions were projected using estimates from previous rulemaking inventories compiled by 

the Office of Transportation and Air Quality (OTAQ). Emissions of ammonia and dust from 

animal operations were projected based on animal population data from the Department of 

Agriculture and EPA. Residential wood combustion was projected by replacement of 

obsolete woodstoves with new woodstoves and a 1 percent annual increase in fireplaces. 

Landfill emissions were projected using MACT controls. All other nonpoint sources were 

held constant between 2005 and the 2014 future year scenarios. 

Table 3-6. 2014 Base Case SO2 Emissions (tons/year) for Lower 48 States by SectorA

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Alabama 322,130 69,150 52,313 1,873 605 983 447,053

Arizona 20,945 23,982 2,566 51 738 2,888 51,170

Arkansas 88,187 13,055 27,256 142 347 728 129,715

California 5,052 24,869 77,614 108,132 2,002 6,735 224,404

Colorado 72,119 1,562 6,808 47 550 1,719 82,805 

Connecticut 5,512 1,834 18,440 1,294 340 4 27,424

Delaware 7,806 10,974 5,857 14,891 101 6 39,635

District of 

Columbia 0 686 1,559 4 42 0 2,291

Florida 192,903 57,521 70,480 108,579 2,159 7,018 438,660

Georgia 173,210 56,014 56,813 8,263 1,307 2,010 297,617

Idaho 1 17,153 2,912 21 177 3,845 24,109

Illinois 200,475 133,109 5,381 390 1,221 20 340,596

Indiana 804,294 95,037 59,764 193 810 24 960,122

Iowa 163,966 60,195 19,817 85 360 25 244,448
 48 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Kansas 65,125 13,048 36,375 54 313 103 115,018

Kentucky 739,592 23,804 34,210 258 528 364 798,756

Louisiana 94,824 151,216 2,372 78,097 470 892 327,871

Maine 11,650 18,520 9,945 4,215 160 150 44,640

Maryland 42,635 34,994 40,851 16,966 631 32 136,109

Massachusetts 16,299 19,624 25,237 32,043 594 93 93,890

Michigan 275,637 76,437 42,066 7,536 1,107 91 402,874

Minnesota 61,447 25,112 14,728 468 618 631 103,004

Mississippi 48,149 24,427 6,785 1,280 385 1,051 82,077

Missouri 500,649 77,086 44,543 214 796 186 623,474

Montana 16,863 7,597 2,593 24 115 1,422 28,614

Nebraska 115,695 6,431 29,570 55 217 105 152,073

Nevada 20,155 2,266 12,475 25 196 1,346 36,463

New 

Hampshire 6,608 3,246 7,393 45 148 38 17,478

New Jersey 37,669 6,756 10,712 26,589 799 61 82,586

New Mexico 13,708 7,834 3,190 24 280 3,450 28,486

New York 141,354 58,584 125,196 10,853 1,594 113 337,694

North 

Carolina 140,585 66,046 21,994 52,897 961 696 283,179

North Dakota 80,320 9,458 6,450 35 78 66 96,407

Ohio 841,194 105,123 19,810 2,085 1,171 22 969,405

Oklahoma 165,773 36,924 7,534 45 524 469 211,269

Oregon 13,366 9,831 9,846 14,530 397 4,896 52,866

Pennsylvania 972,977 76,256 68,324 4,117 1,169 32 1,122,875

Rhode Island 0 2,745 3,364 3,128 85 1 9,323

South 

Carolina 156,096 31,453 30,002 24,380 551 646 243,128

South Dakota 13,459 1,699 10,341 22 94 498 26,113

Tennessee 600,066 77,605 32,696 173 829 277 711,646

Texas 373,950 155,720 109,194 36,109 2,511 1,178 678,662

Utah 25,414 7,157 3,574 25 310 1,934 38,414

Vermont 0 903 5,380 7 101 49 6,440

Virginia 135,741 69,177 32,899 15,624 918 399 254,758

Washington 19,155 21,136 7,229 27,880 687 407 76,494

West Virginia 496,307 41,817 14,581 96 201 215 553,217

Wisconsin 117,253 66,456 6,370 638 675 70 191,462

Wyoming 53,505 22,320 6,718 17 95 1,106 83,761

Grand Total 8,469,820 1,923,949 1,252,127 604,519 31,067 49,094 12,330,575 A Emission estimates apply to all fossil Electrical Generating Units, including those with capacity < 25 MW 
 49 

Table 3-7. 2014 Base Case NOx Emissions (tons/year) for Lower 48 States by SectorA

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Alabama 118,420 74,622 31,939 45,932 67,011 3,814 341,738

Arizona 72,747 16,130 8,615 43,037 77,732 10,532 228,793

Arkansas 44,792 37,491 21,422 44,299 38,965 2,654 189,623

California 18,394 89,084 121,496 429,644 346,901 24,563 1,030,082

Colorado 61,641 21,139 43,556 35,480 59,980 6,271 228,067

Connecticut 2,821 5,854 12,451 14,410 31,534 14 67,084

Delaware 4,513 5,567 3,245 15,270 8,736 23 37,354

District of 

Columbia 1 501 1,738 2,398 3,929 0 8,567

Florida 180,801 55,343 29,457 278,920 225,478 25,600 795,599

Georgia 48,091 53,557 38,797 71,011 130,240 7,955 349,651

Idaho 398 10,367 30,294 15,832 20,727 14,024 91,642

Illinois 80,228 93,059 47,540 151,373 131,403 71 503,674

Indiana 200,899 73,523 30,107 76,024 94,217 88 474,858

Iowa 68,146 38,831 15,038 65,751 48,836 90 236,692

Kansas 78,920 70,730 42,238 61,613 35,950 378 289,829

Kentucky 148,509 34,979 17,413 65,805 57,759 1,326 325,791

Louisiana 45,457 161,766 27,515 274,697 52,360 3,254 565,049

Maine 2,535 18,316 7,257 13,169 18,061 566 59,904

Maryland 19,990 24,687 21,626 52,501 53,040 137 171,981

Massachusetts 6,619 18,527 34,207 75,654 46,748 341 182,096

Michigan 97,455 94,079 43,360 73,939 135,806 330 444,969

Minnesota 51,859 64,372 56,545 84,040 71,161 2,300 330,277

Mississippi 37,142 52,440 12,133 58,559 42,525 3,833 206,632

Missouri 82,979 38,744 32,677 88,233 90,001 678 333,312

Montana 36,800 5,368 14,359 28,367 14,161 5,187 104,242

Nebraska 52,970 12,173 13,779 75,252 27,856 381 182,411

Nevada 29,198 17,323 5,375 19,272 17,188 4,910 93,266

New 

Hampshire 2,515 3,255 11,129 6,587 16,260 137 39,883

New Jersey 16,268 19,089 26,298 78,875 63,254 223 204,007

New Mexico 51,340 43,953 69,146 31,864 34,564 12,582 243,449

New York 28,350 55,359 87,826 92,841 129,376 412 394,164

North 

Carolina 61,747 44,573 18,669 133,455 104,150 11,424 374,018

North Dakota 59,556 7,549 10,009 42,972 9,925 240 130,251

Ohio 164,945 69,157 41,352 120,900 122,426 81 518,861

Oklahoma 81,122 72,525 94,513 39,539 58,382 1,709 347,790

Oregon 13,889 22,985 17,081 68,854 51,973 17,857 192,639

Pennsylvania 196,151 84,111 53,246 83,885 118,122 117 535,632
 50 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

Rhode Island 281 2,186 2,957 7,384 6,772 4 19,584

South 

Carolina 47,512 28,969 20,271 62,400 62,996 2,357 224,505

South Dakota 15,514 5,039 5,722 22,021 12,254 1,817 62,367

Tennessee 68,779 59,694 18,542 59,145 104,711 1,012 311,883

Texas 166,177 282,509 274,163 289,605 241,009 4,890 1,258,353

Utah 64,088 19,285 13,824 18,576 35,500 7,052 158,325

Vermont 0 803 3,397 2,771 8,563 179 15,713

Virginia 32,115 60,216 53,464 75,461 92,291 1,456 315,003

Washington 18,374 24,825 16,728 106,915 83,318 1,484 251,644

West Virginia 100,103 35,700 14,459 23,798 22,863 785 197,708

Wisconsin 53,774 40,729 21,974 53,848 71,163 256 241,744

Wyoming 73,919 30,518 40,455 24,735 11,876 4,035 185,538

Grand Total 2,908,844 2,201,601 1,679,404 3,706,913 3,410,053 189,429 14,096,244 A Emission estimates apply to all fossil Electrical Generating Units, including those with capacity < 25 MW 

3.4 Development of Future Year Control Case Emissions 

 For the future year control case modeling, the emissions for all sectors were 

unchanged from the base case modeling except for those from EGUs. The IPM model was 

used by CAMD to prepare the 2014 control case EGU emissions as described in Chapter 7. 

The changes in EGU SO2 and NOx emissions as a result of the control case for the lower 48 

states are summarized in Table 3-8. State-specific summaries of EGU NOx and SO2 for the 

lower 48 states for the control case are shown in Tables 3-9 and 3-10, respectively. For EGU 

emission changes for each remedy analyzed, and for 2012 as well as 2014, please refer to 

Appendix C. Additional details on the changes that resulted from the control case are 

provided in the Transport Rule EITSD. 
 51 

Table 3-8. Summary of Emissions Changes for the Transport Rule in Lower 48 States 

Pollutant

Item NOX SO2

2014 EGU Emissions

Base Case EGU Emissions (tons) 2,908,844 8,469,820

Control EGU Emissions (tons) 2,089,744 4,045,465

Reductions to Base Case in Control Case (tons) 819,101 4,424,358

Percentage Reduction of Base EGU Emissions 28.2% 52.2%

Total 2014 Man-made Emissions*

Total Base Case Emissions (tons) 14,096,244 12,330,575

Total Control Case Emissions (tons) 13,277,143 7,906,217

Percentage Reduction of All Manmade Emissions 5.8% 35.9%

* In this table, man-made emissions includes average fires 

Table 3-9. State Specific Changes in Annual EGU NOx for the Lower 48 StatesA

State 2014 Base NOx

2014 Controlled 

NOx

EGU NOx 

Reduction 

(tons)

EGU NOx 

Reduction (%)

Alabama 118,420 61,259 57,161 48.3%

Arizona 72,747 72,705 42 0.1%

Arkansas 44,792 26,260 18,532 41.4%

California 18,394 18,429 -35 -0.2%

Colorado 61,641 62,018 -377 -0.6%

Connecticut 2,821 2,833 -12 -0.4%

Delaware 4,513 4,933 -420 -9.3%

District of Columbia 1 1 0 -3.7%

Florida 180,801 110,603 70,198 38.8%

Georgia 48,091 44,285 3,806 7.9%

Idaho 398 397 1 0.3%

Illinois 80,228 57,366 22,862 28.5%

Indiana 200,899 112,379 88,519 44.1%
 52 

State 2014 Base NOx

2014 Controlled 

NOx

EGU NOx 

Reduction 

(tons)

EGU NOx 

Reduction (%)

Iowa 68,146 52,986 15,160 22.2%

Kansas 78,920 39,958 38,963 49.4%

Kentucky 148,509 71,314 77,195 52.0%

Louisiana 45,457 37,156 8,301 18.3%

Maine 2,535 2,530 5 0.2%

Maryland 19,990 20,070 -80 -0.4%

Massachusetts 6,619 7,016 -397 -6.0%

Michigan 97,455 63,135 34,320 35.2%

Minnesota 51,859 35,426 16,433 31.7%

Mississippi 37,142 23,099 14,043 37.8%

Missouri 82,979 67,437 15,541 18.7%

Montana 36,800 36,789 10 0.0%

Nebraska 52,970 35,067 17,903 33.8%

Nevada 29,198 29,200 -2 0.0%

New Hampshire 2,515 2,456 59 2.3%

New Jersey 16,268 12,717 3,552 21.8%

New Mexico 51,340 51,358 -18 0.0%

New York 28,350 28,593 -243 -0.9%

North Carolina 61,747 59,663 2,085 3.4%

North Dakota 59,556 59,548 9 0.0%

Ohio 164,945 99,333 65,612 39.8%

Oklahoma 81,122 50,434 30,688 37.8%

Oregon 13,889 13,889 0 0.0%

Pennsylvania 196,151 114,884 81,267 41.4%

Rhode Island 281 278 4 1.4%

South Carolina 47,512 34,505 13,007 27.4%

South Dakota 15,514 15,509 5 0.0%

Tennessee 68,779 28,079 40,699 59.2%

Texas 166,177 148,002 18,175 10.9%

Utah 64,088 64,070 18 0.0%

Virginia 32,115 30,436 1,680 5.2%

Washington 18,374 18,359 15 0.1%

West Virginia 100,103 48,149 51,954 51.9%

Wisconsin 53,774 40,923 12,851 23.9%

Wyoming 73,919 73,908 10 0.0%

Total 2,908,844 2,089,743 819,101 28.2% A Emission estimates apply to all fossil Electrical Generating Units, including those with capacity < 25 MW 
 53 

Table 3-10. State Specific Changes in Annual EGU SO2 for the Lower 48 StatesA

State

2014 Base 

SO2

2014 

Controlled SO2

EGU SO2

Reduction (tons)

EGU SO2 

Reduction (%)

Alabama 322130 172198 149,932 46.5%

Arizona 20945 23477 -2,532 -12.1%

Arkansas 88187 119945 -31,758 -36.0%

California 5052 5052 0 0.0%

Colorado 72119 88324 -16,204 -22.5%

Connecticut 5512 2586 2,926 53.1%

Delaware 7806 8919 -1,113 -14.3%

District of Columbia 0 0 0 N/A

Florida 192903 137985 54,918 28.5%

Georgia 173210 92329 80,882 46.7%

Idaho 1 0 1 100.0%

Illinois 200475 164733 35,742 17.8%

Indiana 804294 240599 563,695 70.1%

Iowa 163966 102419 61,547 37.5%

Kansas 65125 51248 13,878 21.3%

Kentucky 739592 123831 615,761 83.3%

Louisiana 94824 94892 -67 -0.1%

Maine 11650 11669 -19 -0.2%

Maryland 42635 42756 -120 -0.3%

Massachusetts 16299 9340 6,959 42.7%

Michigan 275637 173414 102,223 37.1%

Minnesota 61447 48819 12,628 20.6%

Mississippi 48149 62356 -14,207 -29.5%

Missouri 500649 192644 308,004 61.5%

Montana 16863 19093 -2,229 -13.2%

Nebraska 115695 75094 40,601 35.1%

Nevada 20155 20531 -376 -1.9%

New Hampshire 6608 7290 -682 -10.3%

New Jersey 37669 14555 23,114 61.4%

New Mexico 13708 13027 681 5.0%

New York 141354 57047 84,307 59.6%

North Carolina 140585 96924 43,661 31.1%

North Dakota 80320 88320 -8,000 -10.0%

Ohio 841194 232948 608,245 72.3%

Oklahoma 165773 165994 -221 -0.1%

Oregon 13366 20187 -6,821 -51.0%

Pennsylvania 972977 153204 819,773 84.3%

Rhode Island 0 0 0 N/A

South Carolina 156096 131128 24,968 16.0%
 54 

South Dakota 13459 28897 -15,438 -114.7%

Tennessee 600066 106762 493,304 82.2%

Texas 373950 467765 -93,815 -25.1%

Utah 25414 29117 -3,703 -14.6%

Virginia 135741 57496 78,245 57.6%

Washington 19155 18863 292 1.5%

West Virginia 496307 127646 368,662 74.3%

Wisconsin 117253 85788 31,464 26.8%

Wyoming 53505 58254 -4,750 -8.9%

Total 8,469,820 4,045,465 4,424,358 52.2% A Emission estimates apply to all fossil Electrical Generating Units, including those with capacity < 25 MW 
 55 

CHAPTER 4 

 AIR QUALITY MODELING AND IMPACTS 

4.1 Air Quality Impacts 

 This section summarizes the methods for and results of estimating air quality for the 

2014 base case and control scenario for the purposes of the benefit analysis. EPA has 

focused on the health, welfare, and ecological effects that have been linked to air quality 

changes. These air quality changes include the following: 

1. Ambient particulate matter (PM10 and PM2.5)–as estimated using a nationalscale applications of the Comprehensive Air Quality Model with Extensions 

(CAMx; Environ, 2009); and 

2. Visibility degradation (i.e., regional haze), as developed using empirical 

estimates of light extinction coefficients and efficiencies in combination with 

CAMx modeled reductions in pollutant concentrations. 

 The air quality estimates in this section are based on the emission changes 

summarized in the preceding section. These air quality results are in turn associated with 

human populations and ecosystems to estimate changes in health and welfare effects. In 

Section 4.1.1, we describe the air quality modeling platform and in Section 4.2, we cover the 

impacts on PM2.5 and ozone. Lastly, in Section 4.3, we discuss the estimation of visibility 

degradation. 

4.1.1 Air Quality Modeling Platform 

We use the emissions inputs summarized above with national scale and regional scale 

application of the CAMx modeling system to estimate PM2.5 and ozone air quality in the 

contiguous U.S. CAMx is a three-dimensional grid-based Eulerian photochemical model 

designed to estimate PM2.5 and ozone concentrations over annual time periods. 

Consideration of the different processes that affect primary (directly emitted) and secondary 

(formed by atmospheric processes) PM2.5 in different locations is fundamental to 
 56 

understanding and assessing the effects of pollution control measures that affect PM2.5 and 

ozone concentrations at the surface.11 Because it accounts for spatial and temporal variations 

as well as differences in the reactivity of emissions, CAMx is useful for evaluating the 

impacts of the rule on PM2.5 and ozone concentrations. Version 5 of CAMx was employed 

for this Transport Rule modeling, as described in the Air Quality Modeling Technical 

Support Document (EPA, 2010). 

For this analysis we used CAMx to simulate air quality for every hour of every day of 

the year. These model applications required a variety of input files that contain information 

pertaining to the modeling domain and simulation period. In addition to the CAMx model, 

our modeling system includes (1) emissions for a 2005 base year and emissions for the 2014 

base case and control scenario, (2) meteorology for the year 2005, and (3) estimates of 

intercontinental transport (i.e., boundary concentrations) from a global photochemical model. 

 Using these data, CAMx generates hourly predictions of ozone and PM2.5 component 

species concentrations. As discussed in the Air Quality Modeling TSD, we use the relative 

predictions from the model by combining the 2005 base-year and each future-year scenario 

with speciated ambient air quality observations to determine the expected change in 2014 

concentrations due to the rule. After completing this process, we then calculated annual 

mean PM2.5 and seasonal mean ozone air quality metrics as inputs to the health and welfare 

C-R functions of the benefits analysis. 

4.1.1.1 Simulation Periods 

 For use in this benefits analysis, the simulation period modeled by CAMx included 

separate full-year application for each of the three emissions scenarios (i.e., 2005 base year 

and the 2014 base case and 2014 control scenario). 

4.1.1.2 Air Quality Modeling Domain 

Although air quality estimate are provided for the entire U.S., the focus of our 

analysis is on the Eastern U.S. since this is the geographic area of importance for this rule. 

 

11Given the focus of this rule on secondarily formed particles it is important to employ a Eulerian model such as 

CAMx. The impact of secondarily formed pollutants typically involves primary precursor emissions from a 

multitude of widely dispersed sources, and chemical and physical processes of pollutants are best addressed 

using an air quality model that employs an Eulerian grid model design.
 57 

The areas modeled (i.e., modeling domains) are segmented into rectangular blocks referred to 

as grid cells. The model actually predicts pollutant concentrations for each of these grid 

cells. Our modeling for the East (referred to as the Eastern regional scale domain) was 

performed at a horizontal resolution of 12 x 12 km. Modeling for the remainder of the U.S. 

(referred to as the national scale domain) was performed at a resolution of 36 x 36 km. The 

national and regional scale modeling domains contain 14 vertical layers with the top of the 

modeling domain at about 16,200 meters, or approximately 100 mb. The Eastern domain is 

nested within the National domain, as shown in Figure 4-1. 
 58 

Figure 4-1. National and Eastern U.S. air quality modeling domains. 

Nationwide 36 km 

Eastern 12 km 
 59 

4.1.1.3 Air Quality Model Inputs 

 CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates 

and meteorological data, and initial and boundary conditions. Separate emissions inventories 

were prepared for the 2005 base year and each future-year scenario. All other inputs were 

specified for the 2005 base year model application and remained unchanged for each futureyear modeling scenario. 

CAMx requires detailed emissions inventories containing temporally allocated 

emissions for each grid-cell in the modeling domain for each species being simulated. The 

previously described annual emission inventories were preprocessed into model-ready inputs 

through the SMOKE emissions preprocessing system. Meteorological inputs reflecting 2005 

conditions across the contiguous U.S. were derived from Version 5 of the Mesoscale Model 

(MM5). These inputs included horizontal wind components (i.e., speed and direction), 

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each 

vertical layer. Details of the annual 2005 MM5 modeling are provided in the Air Quality 

Modeling TSD. 

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry and transport model (GEOS-CHEM). The lateral 

boundary species concentrations varied with height and time (every 3 hours). Terrain 

elevations and land use information were obtained from the U.S. Geological Survey database 

at 10 km resolution and aggregated to the roughly 36 km horizontal resolution used for this 

CAMx application. The development of model inputs is discussed in greater detail in the Air 

Quality Modeling TSD, which is available in the docket for this rule. 

4.1.1.4 Air Quality Model Evaluation 

 An operational model performance evaluation for ozone and PM2.5 and its related 

speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon) was performed 

to estimate the ability of the CAMx modeling system to replicate 2005 base year 

concentrations. This evaluation principally comprises statistical assessments of model 

predictions versus observations paired in time and space on an hourly, daily, or weekly basis 
 60 

depending on the sampling period of measured data. Details on the evaluation methodology 

and the calculation of performance statistics are provided in the Air Quality Modeling TSD. 

Overall, the model performance statistics for ozone, sulfate, and nitrate from the CAMx 2005 

simulation are within or close to the ranges found in other recent applications. The 

normalized mean bias for 8-hour daily maximum ozone concentrations was -2.9 percent and 

the normalized mean error was 13.2 percent for the months of May through September 2005, 

based on an aggregate of all observed-predicted pairs within the 12 km modeling domain. 

The two PM2.5 species that are most relevant for today’s proposal are sulfate and nitrate. For 

the summer months of June though August, when observed sulfate concentrations are highest 

in the East, the model predictions of 24-hour average sulfate were lower than the 

corresponding measured values by 7 percent at urban sites and by 9 to 10 percent at rural 

sites in the IMPROVE12 and CASTNet13 monitoring networks, respectively. For the winter 

months of December through February, when observed nitrate concentrations are highest in 

the East, the model predictions of 24-hour average particulate nitrate were lower than the 

corresponding measured values by 12 percent at urban sites and by 4 percent at rural sites in 

the IMPROVE monitoring network. The model performance statistics by season for ozone 

and PM2.5 component species are provided in the Air Quality Modeling TSD. These model 

performance results give us confidence that our applications of CAMx using this 2005 

modeling platform provide a scientifically credible approach for assessing ozone and PM2.5

concentrations for the purposes of the Transport Rule. 

 

12 Interagency Monitoring of PROtected Visual Environments (IMPROVE). Debell, L.J., et. al. Spatial and 

Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United States: Report IV. 

November 2006.

13 Clean Air Status and Trends Network (CASTNet) 2005 Annual Report. EPA Office of Air and Radiation, 

Clean Air Markets Division. Washington, DC. December 2006.
 61 

4.2 Results for PM2.5 and Ozone 

4.2.1 Converting CAMx PM2.5 Outputs to Benefits Inputs

 CAMx generates predictions of hourly PM2.5 species concentrations for every grid. 

The species include a primary fraction and several secondary particles (e.g., sulfates, nitrates, 

and organics). PM2.5 is calculated as the sum of the primary and the secondary formed 

particles. Future-year estimates of PM2.5 were calculated using relative reduction factors 

(RRFs) applied to 2005 ambient PM2.5 species concentrations. Gridded fields of species 

concentrations were created by interpolating ambient data from the PM2.5 speciation network 

and IMPROVE data. The ambient data were interpolated to the 36 km and 12 km grid 

resolutions. 

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

modeling the PM2.5 standard (EPA, 2007). This guidance recommends that model 

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

species. The procedure for calculating future year PM2.5 values is called the Modeled 

Attainment Test Software (MATS). EPA used this procedure to estimate the ambient 

impacts of the Transport Rule emissions controls. For the purposes of projecting future 

PM2.5 concentrations for input to the benefits calculations, we applied the MATS procedure 

using the base year 2005 modeling results and each of the results from each of the 2014 base 

case and 2014 control scenario. In our application of MATS for PM2.5 we used temporally 

scaled speciated PM2.5 monitoring data from 2005 as the set of base-year measured 

concentrations. Temporal scaling is based on the ratios of model-predicted future case PM2.5 

species concentrations to the corresponding model-predicted 2005 concentrations. Output 

files from this process include both quarterly and annual mean PM2.5 mass concentrations 

which are then manipulated within SAS to produce a BenMAP input file containing 364 

daily values (created by replicating the quarterly mean values for each day of the appropriate 

season). 

 The MATS procedures documented in the Air Quality Modeling TSD are applicable 

for projecting future nonattainment and maintenance sites and downwind receptor areas for 

the transport analysis. Those procedures are similar as those performed for the PM benefits 

analysis in Chapter 5 with the following exceptions: 
 62 

1) The benefits analysis uses interpolated PM2.5 data that cover all of the grid cells in the 

modeling domain, whereas the nonattainment analysis is performed at each ambient 

monitoring site using measured PM2.5 data (only the species data are interpolated). 

2) The benefits analysis is anchored by the interpolated PM2.5 data from the single year of 

2005, whereas the nonattainment analysis uses design values from three, 3-year periods (i.e., 

2003-2005, 2004-2006, and 2005-2007) at individual monitoring sites. 

4.2.2 PM2.5 Air Quality Results

Table 4-1 summaries the projected ambient PM2.5 concentrations for the 2014 base 

case and 2014 impacts associated with rule. This table includes the annual mean 

concentrations averaged across all model grid cells in the East along with the average change 

between the 2014 base and control concentrations. We also provide the population-weighted 

average that better reflects the baseline levels and predicted changes for more populated 

areas of the East. This measure , therefore better reflects the potential benefits of these 

predicted changes through exposure changes to the affected populations. As shown, the 

average annual mean concentrations of PM2.5 across populated areas of the East declines by 

roughly 9.7 percent (or 6.27 μg/m3) in 2014. The population-weighted average mean 

concentration declined by 11 percent (or 1.21 μg/m3) in 2014. This indicates the rule 

generates greater absolute air quality improvements in more populated, urban areas. 
 63 

Table 4-1. Summary of Base Case PM2.5 Air Quality and Changes Due to the 

Transport Rule. 

2014

Statistic Base Case Control 

Case

Percent 

Changea

PM2.5 (μg/m3)

Minimum Annual Mean 1.94 1.95 -0.5%

Maximum Annual Mean 31.3 31.05 0.8%

Average Annual Mean 6.95 6.27 9.7%

Pop-Weighted Average Annual Mean b 10.81 1.21 11%

a

 The percent change is defined as the control case value minus the base case value multiplied by 100. A 

negative value denotes an increase in PM2.5 concentration.

b

 Calculated by summing the product of the projected CAMx grid-cell population and the estimated 

concentration, for that grid-cell and then dividing by the total population.

Table 4-2 provides information on the populations in 2014 that will experience 

improved PM air quality. Significant populations that live in areas with meaningful 

reductions in annual mean PM2.5 concentrations resulting from the rule. As shown, in 2014, 

about 20 percent of the U.S. population located in the modeling domain are predicted to 

experience reductions of greater than 1.75 μg/m3. Furthermore, over 43 percent of this 

population will benefit from reductions in annual mean PM2.5 concentrations of greater than 

1.25 μg/m3 and almost 88 percent will live in areas with reductions of greater than 0.5 μg/m3. 
 64 

Table 4-2. Distribution of PM2.5 Air Quality Improvements Over Population in 2014 

Due to the Transport Rule for the Eastern U.S.

Change in Annual Mean PM2.5

Concentrations (μg/m3

) 2014 Populationb

Number 

(millions) Percent (%)

0 > Δ PM2.5 Conc  0.25 3,332,940 1%

0.25 > Δ PM2.5 Conc  0.5 27,405,217 11%

0.5 > Δ PM2.5 Conc  0.75 39,549,835 16%

0.75 > Δ PM2.5 Conc  1.0 34,181,327 14%

1.0 > Δ PM2.5 Conc  1.25 33,590,794 14%

1.25 > Δ PM2.5 Conc  1.5 34,097,507 14%

1.5 > Δ PM2.5 Conc  1.75 21,029,053 9%

Δ PM2.5 Conc > 1.75 48,151,617 20%

a

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

b

 Population counts and percentages are for the fraction of the national population located in the eastern 37 state modeling domain (as 

shown in Figure 4-1) considered in modeling health benefits for the rule. 
 65 

4.2.3 Converting CAMx Outputs to Full-Season Profiles for Benefits Analysis 

 This study extracted hourly, surface-layer ozone concentrations for each grid-cell 

from the standard CAMx output file containing hourly average ozone values. These model 

predictions are used in conjunction with the observed concentrations obtained from the 

Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the 

entire ozone season.14,15 The predicted changes in ozone concentrations from the future-year 

base case to future-year control scenario serve as inputs to the health and welfare C-R 

functions of the benefits analysis (i.e., BenMAP). 

To estimate ozone-related health and welfare effects, full-season ozone data are 

required for every grid cell. Given available ozone monitoring data, we generated fullseason ozone profiles for each location in the contiguous 48 States in two steps: (1) we 

combine monitored observations and modeled ozone predictions to interpolate hourly ozone 

concentrations to a grid of 8 km by 8 km population grid-cells, and (2) we converted these 

 

14The ozone season for this analysis is defined as the 5-month period from May to September; however, to 

estimate certain crop yield benefits, the modeling results were extended to include months outside the 5-

month ozone season.

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

least 9 hourly observations per day (8 am to 8 pm) during the ozone season.
 66 

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

average.16,17 

4.2.4 Ozone Air Quality Results

 

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

Chapter 5 for a discussion of this model. 17This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation.

 This section provides a summary of the predicted ambient ozone concentrations from 

the CAMx model for the 2014 base case and changes associated with the rule. Table 4-3 

provides those ozone metrics for grid cells in the Eastern U.S. that enter the C-R functions 

for health benefits endpoints. The population-weighted average reflects the baseline levels 

and predicted changes for more populated areas of the nation. This measure, therefore, will 

better reflect the potential benefits of these predicted changes through exposure changes to 

these populations. 
 67 

Table 4-3. Summary of CAMx Derived Population-Weighted Ozone Season Air 

Quality Metrics for Health Benefits Endpoints Due to the Transport Rule for the 

Eastern U.S. 

 2014 

Statistica Base Case Change b Percent 

Change b

Population-Weighted Average (ppb) c

Daily 8-Hour Average Concentration 45.3 0.24 0.5% 

a

 This ozone metric is calculated at the CAMx grid-cell level for use in health effects estimates based on the results of spatial and temporal 

Voronoi Neighbor Averaging. 

b

 The change is defined as the control case value minus the base case value. The percent change is the “Change” divided by the “Base Case,” 

and then multiplied by 100 to convert the value to a percentage. 

c

 Calculated by summing the product of the projected CAMx grid-cell population and the estimated CAMx grid-cell seasonal ozone 

concentration, and then dividing by the total population. 

4.3 Visibility Degradation Estimates

 Visibility degradation is often directly proportional to decreases in light transmittal in 

the atmosphere. Scattering and absorption by both gases and particles decrease light 

transmittance. To quantify changes in visibility, our analysis computes a light-extinction 

coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is 

decreased per unit distance. This coefficient accounts for the scattering and absorption of 

light by both particles and gases, and accounts for the higher extinction efficiency of fine 

particles compared to coarse particles. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon (soot), and soil 

(Sisler, 1996). 

Based upon the light-extinction coefficient, we also calculated a unitless visibility 

index, called a “deciview,” which is used in the valuation of visibility. The deciview metric 

provides a scale for perceived visual changes over the entire range of conditions, from clear 
 68 

to hazy. Under many scenic conditions, the average person can generally perceive a change 

of one deciview. The higher the deciview value, the worse the visibility. Thus, an 

improvement in visibility is a decrease in deciview value. 

 Table 4-4 provides the visibility improvements, measured in annual average 

deciviews, expected to occur in the Eastern and Western U.S. As shown, Class I visibility 

regions in the Eastern U.S., including such regions as the Great Smoky Mountains and 

Shenandoah, are expected to see significant improvements in visibility. By 2014, such 

regions in the Eastern U.S. are expected to see improvements of over 1 deciview (9 percent), 

and such regions in the Western U.S. are expected to see improvements of over 0.04 

deciviews (or less than 1 percent). 

Table 4-4. Summary of Basecase Recreational Visibility and Changes by Region: 

(annual average deciviews)

2014 

 Class I Visibility Regions Base Case 

(Deciviews) 

Change in 

Annual 

Average 

(Deciviews) 

Percent 

Change in 

Annual 

Average (%) 

Eastern US 14.35 1.36 9 

Western US 9.62 0.04 < 1 
69

4.4 References

Environ, 2009. Comprehensive Air Quality Model with Extensions Version 5 User’s Guide. 

Environ International Corporation. Novato, CA. March 2009. 

Sisler, J.F. July 1996. Spatial and Seasonal Patterns and Long Term Variability of the 

Composition of the Haze in the United States: An Analysis of Data from the IMPROVE 

Network. Fort Collins, CO: Cooperative Institute for Research in the Atmosphere, 

Colorado State University. 

U.S. Environmental Protection Agency (EPA). 2007. Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. 

U.S. Environmental Protection Agency (EPA). 2010. Air Quality Modeling Technical Support 

Document for the Transport Rule. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. 
70

CHAPTER 5

   BENEFITS ANALYSIS AND RESULTS 

Synopsis 

This chapter contains a subset of the estimated health and welfare benefits of the 

proposed Transport Rule remedy in 2014. This rule is expected to yield significant reductions in 

SO2 and NOx from EGUs, which in turn would lower overall ambient levels of PM2.5 and ozone 

across much of the eastern U.S. In this chapter we quantify the health and welfare benefits 

resulting from these air quality improvements. 

We estimate the monetized benefits of the proposed remedy to be $120 billion to $290 

billion at a 3% discount rate and $110 billion to $270 billion at a 7% discount rate in 2014. The 

benefits of the alternative remedies may be found in the benefit-cost comparison chapter. All 

estimates are in 2006$. We estimate the benefits of the proposed remedy using modeled changes 

in ambient pollution concentrations while the benefits of the alternate remedies are based on a 

benefit per ton approach described below. This benefits analysis accounts for both decreases and 

increases in emissions across the country resulting from aspects of the proposed provisions of the 

rule from reductions in NOx and SO2. These estimates omit the benefits from several important 

categories, including ecosystem benefits and the direct health benefits from reducing exposure to 

NO2 and SO2 due to time constraints. While not quantified here, because the level of SO2 and 

NOx emission reductions in 2012 exceed those in 2014, we expect the total benefits in 2012 to 

exceed those in 2014. 

5.1 Overview 

This chapter contains a subset of the estimated health and welfare benefits of the 

proposed and alternate rule remedies for the Transport Rule in 2014. The Transport Rule is 

expected to yield significant aggregate reductions in SO2 and NOx from EGUs, which in turn 

would lower overall ambient levels of PM2.5 and ozone across much of the eastern U.S. The 

analysis in this chapter aims to characterize the benefits of these air quality changes by 

answering two key questions: 

1. What are the health and welfare effects of changes in ambient particulate matter (PM2.5) 

and ozone air quality resulting from reductions in precursors including NOx and SO2? 

2. What is the economic value of these effects?

In this analysis we consider an array of health and welfare impacts attributable to changes 
71

in PM2.5 and ozone air quality. The 2009 PM2.5 Integrated Science Assessment (U.S. EPA, 

2009d) and the 2006 ozone criteria document (U.S. EPA, 2006a) identify the human health 

effects associated with these ambient pollutants, which include premature mortality and a variety 

of morbidity effects associated with acute and chronic exposures. PM welfare effects include 

visibility impairment and materials damage. Ozone welfare effects include damages to 

agricultural and forestry sectors. NOx welfare effects include aquatic and terrestrial acidification 

and nutrient enrichment (U.S. EPA, 2008f). SO2 welfare effects include aquatic and terrestrial 

acidification and increased mercury methylation (U.S. EPA, 2008f). Though models exist for 

quantifying these ecosystem impacts, time and resource constraints precluded us from 

quantifying most of those effects in this analysis. 

Table 5-1 summarizes the total monetized benefits of the proposed remedy in 2014. This 

table reflects the economic value of the change in PM2.5 and ozone-related human health impacts 

occurring as a result of the proposed and alternate Transport Rule. 

Table 5-2 summarizes the human health and welfare benefits categories contained within 

the primary benefits estimate, those categories that were unquantified due to limited data or time. 
72

Table 5-1: Estimated monetized benefits of the proposed Transport Rule (billions of 

2006$)A

Benefits Estimate 

Within Transport 

RegionB

Outside 

Transport 

Region Total 

Pope et al. (2002) PM2.5 mortality and Bell et al. (2004) ozone mortality estimates 

Using a 3% discount 

rate 

$120 +B 

($10—$360) 

$1.1 +B 

($0.09—$3.3) 

$120 +B 

($10—$360) 

Using a 7% discount 

rate 

$110 +B 

($9—$330) 

$0.9 +B 

($0.08—$2.9) 

$110 +B 

($9—$330) 

Laden et al. (2006) PM2.5 mortality and Levy et al. (2005) ozone mortality estimates 

Using a 3% discount 

rate 

$290 +B 

($26—$840) 

$2.6 +B 

($0.2—$7.5) 

$290 +B 

($26—$840) 

Using a 7% discount 

rate 

$260 +B 

($23—$760) 

$2.4 +B 

($0.2—$6.8) 

$270 +B 

($24—$760) 

A For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of 

additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these 

endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were 

able to quantify. A detailed listing of unquantified health and welfare effects is provided in Table 5-2. 

Estimates here are subject to uncertainties discussed further in the body of the document 

B Rounded to two significant figures. 

The benefits analysis in this chapter relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in 

this analysis. As a means of characterizing this uncertainty we employ two primary techniques. 

First, we use Monte Carlo methods for characterizing random sampling error associated with the 

concentration response functions from epidemiological studies and economic valuation 

functions. Second, because this characterization of random statistical error may omit important 

sources of uncertainty we also employ the results of an expert elicitation on the relationship 

between premature mortality and ambient PM2.5 concentration (Roman et al., 2008); this 

provides additional insight into the likelihood of different outcomes and about the state of 

knowledge regarding the benefits estimates. Both approaches have different strengths and 

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

Given that reductions in premature mortality dominate the size of the overall monetized 

benefits, more focus on uncertainty in mortality-related benefits gives us greater confidence in 

our uncertainty characterization surrounding total benefits. Certain EPA RIA’s including the 

2008 Ozone NAAQS RIA (U.S. EPA, 2008a) contained a suite of sensitivity analyses, only 
73

some of which we include here due in part to time constraints. In particular, these analyses 

characterized the sensitivity of the monetized benefits to the specification of alternate cessation 

lags and income growth adjustment factors. The estimated benefits increased or decreased in 

proportion to the specification of alternate income growth adjustments and cessation lags, 

making it possible for readers to infer the sensitivity of the results in this RIA to these 

parameters by referring to the PM NAAQS RIA (2006d) and Ozone NAAQS RIA (2008a). 

For example, the use of an alternate lag structure would change the PM2.5-related 

mortality benefits discounted at 3% discounted by between 10.4% and –27%; when discounted 

at 7%, these benefits change by between 31% and -49%. When applying higher and lower 

income growth adjustments, the monetary value of PM2.5 and ozone-related premature changes 

between 30% and -10%; the value of chronic endpoints change between 5% and -2% and the 

value of acute endpoints change between 6% and -7%. 

Below we include a new analysis (Figures 5-19 to 5-21) in which we bin the estimated 

number of avoided PM2.5-related premature mortalities resulting from the implementation of the 

Transport Rule according to the projected 2014 baseline PM2.5 air quality levels. This 

presentation is consistent with our approach to applying PM2.5 mortality risk coefficients that 

have not been adjusted to incorporate an assumed threshold. The very large proportion of the 

avoided PM-related impacts we estimate in this analysis occur among populations exposed at or 

above the LML of each study, increasing our confidence in the PM mortality analysis. 

Approximately 80% of the avoided impacts occur at or above an annual mean PM2.5 level of 10 

μg/m3 (the LML of the Laden et al. 2006 study); about 97% occur at or above an annual mean 

PM2.5 level of 7.5 μg/m3 (the LML of the Pope et al. 2002 study). As we model mortality impacts 

among populations exposed to levels of PM2.5 that are successively lower than the LML of each 

study our confidence in the results diminishes. However, the analysis below confirms that the 

great majority of the impacts occur at or above each study’s LML.
74

Table 5-2: Human Health and Welfare Effects of Pollutants Affected by the Transport 

Rule 

Pollutant/ 

Effect Quantified and monetized in base estimate Unquantified 

Premature mortality based on cohort study 

estimatesb and expert elicitation estimates 

Low birth weight, pre-term birth and other reproductive 

outcomes 

Hospital admissions: respiratory and 

cardiovascular Pulmonary function 

Emergency room visits for asthma Chronic respiratory diseases other than chronic 

bronchitis 

Nonfatal heart attacks (myocardial 

infarctions) Non-asthma respiratory emergency room visits 

Lower and upper respiratory illness UVb exposure (+/-)c

Minor restricted activity days 

Work loss days 

Asthma exacerbations (among asthmatic 

populations 

Respiratory symptoms (among asthmatic 

populations) 

Infant mortality 

PM: 

healtha

 

Household soiling 

Visibility in Class I areas in SE, SW, and CA 

regions 

Visibility in residential areas 

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

NE, and Central regions 

PM: 

welfare 

UVb exposure (+/-)c

Global climate impactsc

Premature mortality based on short-term 

study estimates Chronic respiratory damage 

Hospital admissions: respiratory Premature aging of the lungs 

Emergency room visits for asthma Non-asthma respiratory emergency room visits 

Minor restricted activity days UVb exposure (+/-)c

Ozone: 

health 

School loss days 

Decreased outdoor worker productivity 

Yields for: 

--Commercial forests 

--Fruits and vegetables, and 

--Other commercial and noncommercial crops 

Damage to urban ornamental plants 

Recreational demand from damaged forest aesthetics 

Ozone: 

welfare 

 Ecosystem functions 

 UVb exposure (+/-)c 

Climate impacts 

Respiratory hospital admissions 

Respiratory emergency department visits 

 Asthma exacerbation 

Acute respiratory symptoms 

 Premature mortality 

NO2: 

health 

 Pulmonary function 

Commercial fishing and forestry from acidic deposition 

effects 

NOX: 

welfare 

 Commercial fishing, agriculture and forestry from 
75

nutrient deposition effects 

Recreation in terrestrial and estuarine ecosystems from 

nutrient deposition effects 

Other ecosystem services and existence values for 

currently healthy ecosystems 

Coastal eutrophication from nitrogen deposition effects 

 Respiratory hospital admissions 

Asthma emergency room visits 

 Asthma exacerbation 

Acute respiratory symptoms 

 Premature mortality 

SO2: 

health 

 Pulmonary function 

Commercial fishing and forestry from acidic deposition 

effects 

Recreation in terrestrial and aquatic ecosystems from 

acid deposition effects 

SOX: 

welfare 

Increased mercury methylation 

Incidence of neurological disorders 

Incidence of learning disabilities 

Incidences in developmental delays 

Mercury: 

health 

Potential cardiovascular effects including: 

--Altered blood pressure regulation 

--Increased heart rate variability 

--Incidences of Myocardial infarction 

Potential reproductive effects 

Impact on birds and mammals (e.g. reproductive 

Mercury: effects) 

welfare Impacts to commercial, subsistence and recreational 

fishing 

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

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

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

B

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

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

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

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

the primary analysis. 

C

 May result in benefits or disbenefits. 

  

The benefits analysis presented in this chapter incorporates an array of policy and technical 

changes that the Agency has adopted since the publication of the benefits chapter accompanying 

the promulgated CAIR in 2005 (U.S. EPA, 2005): 

1. Incorporation of additional long-term PM mortality studies. The 2005 CAIR analysis 

quantified PM-related mortality using a C-R function drawn the extended analysis of 

American Cancer Society (ACS) cohort, as reported in Pope et al. (2002). In this analysis 

we complement this estimate with a C-R function drawn from the Laden et al. (2006) 

reanalysis of the Harvard Six Cities (H6C) cohort. Rather than estimating PM-related 
76

mortality using a single estimate, we now report two core estimates based on these ACS 

and H6C studies. 

2. Inclusion of twelve PM-mortality estimates based on EPA’s expert elicitation study. As a 

means of characterizing uncertainty in the PM-mortality relationship, in 2005 EPA 

undertook an expert elicitation (Roman et al., 2008). The 2005 CAIR analysis included 

the results of the pilot expert elicitation. This analysis presents PM-mortality estimates 

based on the 12 risk estimates derived from the final elicitation.18

3. Quantification of short-term ozone mortality. The 2005 CAIR analysis considered shortterm ozone mortality in a sensitivity analysis. Consistent with recommendations from the 

2008 National Academy of Sciences report (NAS), we incorporate short-term ozone 

mortality estimates in our primary benefits estimate (NRC, 2008). 

4. Use of a revised Value of Statistical Life (VSL). The Agency continues to update its 

guidance on valuing mortality risk reductions and until a final report is available, EPA 

now uses a distribution of VSL as recommended in EPA’s guidance. The mean value of 

this distribution is $6.3 million (2006$). We discuss this issue in further depth below. 

5. Projection of baseline mortality rates. Beginning in late 2005, after the completion of the 

final CAIR benefits analysis, the Agency began projecting into the future county-level 

mortality rates (Abt, 2005). Mortality rates are a key input to the calculation of air 

pollution-related premature mortality. Using projected rates generally results in a lower 

number of estimated excess mortality because of projected increases in life expectancy 

and concurrent reductions in risk of death at younger ages. 

In general, for a given air quality change, the first four methodological changes increase, 

and the fifth decreases, the overall magnitude of the health impacts and monetized benefits 

compared to the approach used for the 2005 CAIR benefits analysis. 

5.2 Benefits Analysis Methods 

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health and 

 

18 As we discuss below, the characterization of PM‐related mortality using this expert elicitation responds in 

part to 2002 National Academy Sciences (NAS) recommendations regarding the propagation of uncertainty 

characterization throughout the benefits chapter.
77

welfare endpoints (specific effects that can be associated with changes in air quality) and assigns 

values to those changes assuming independence of the individual values. Total benefits are 

calculated simply as the sum of the values for all non-overlapping health and welfare endpoints. 

The “damage-function” approach is the standard method for assessing costs and benefits of 

environmental quality programs and has been used in several recent published analyses (Levy et 

al., 2009; Hubbell et al., 2009; Tagaris et al., 2009). 

To assess economic value in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, a health and 

welfare impact analysis must first be conducted to convert air quality changes into effects that 

can be assigned dollar values. 

For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked 

to ozone and PM. There may be other, indirect health impacts associated with implementing 

emissions controls, such as occupational health impacts for coal miners. 

The welfare impacts analysis is limited to changes in the environment that have a direct 

impact on human welfare. For this analysis, we are limited by the available data to examine 

impacts of changes in visibility in Class 1 areas. We also provide qualitative discussions of the 

impact of changes in other environmental and ecological effects, for example, changes in 

deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to place 

an economic value on these changes due to time and resource limitations. 

We note at the outset that EPA rarely has the time or resources to perform extensive new 

research to measure directly either the health outcomes or their values for regulatory analyses. 

Thus, similar to Kunzli et al. (2000) and other recent health impact analyses, our estimates are 

based on the best available methods of benefits transfer. Benefits transfer is the science and art 

of adapting primary research from similar contexts to obtain the most accurate measure of 

benefits for the environmental quality change under analysis. Adjustments are made for the level 

of environmental quality change, the socio-demographic and economic characteristics of the 

affected population, and other factors to improve the accuracy and robustness of benefits 

estimates. 
78

5.2.1 Health Impact Assessment 

The Health Impact Assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in adverse 

health impacts expected to result from population-level changes in exposure to pollutants (Levy 

et al. 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program 

(BenMAP) can systematize health impact analyses by applying a database of key input 

parameters, including health impact functions and population projections. Analysts have applied 

the HIA approach to estimate human health impacts resulting from hypothetical changes in 

pollutant levels (Hubbell et al. 2005; Davidson et al. 2007, Tagaris et al. 2009). EPA and others 

have relied upon this method to predict future changes in health impacts expected to result from 

the implementation of regulations affecting air quality (U.S. EPA, 2008a). 

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated projections of PM2.5 and ozone air quality and estimating the change in the spatial 

distribution of the ambient air quality; (2) determining the subsequent change in population-level 

exposure; (3) calculating health impacts by applying concentration-response relationships drawn 

from the epidemiological literature (Hubbell et al. 2009) to this change in population exposure. 

A typical health impact function might look as follows: 

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, or 

background, mortality rate for the given population of interest); Pop is the population affected by 

the change in air quality; x is the change in air quality; and β is the effect coefficient drawn 

from the epidemiological study. Tools such as BenMAP can systematize the HIA calculation 

process, allowing users to draw upon a library of existing air quality monitoring data, population 

data and health impact functions. 

Figure 5-1 provides a simplified overview of this approach. 
79

Figure 5-1: Illustration of BenMAP Approach 

5.2.2 Economic Valuation of Health Impacts

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante Willingness to Pay (WTP) 

for changes in risk. However, epidemiological studies generally provide estimates of the relative 

risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to 

use this data in a consistent framework is to convert probabilities to units of avoided statistical 

incidences. This measure is calculated by dividing individual WTP for a risk reduction by the 

related observed change in risk. For example, suppose a measure is able to reduce the risk of 

premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual 

WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality 

amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected 

population is automatically taken into account by the number of incidences predicted by 
80

epidemiological studies applied to the relevant population. The same type of calculation can 

produce values for statistical incidences of other health endpoints. 

For some health effects, such as hospital admissions, WTP estimates are generally not 

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

estimate. For example, for the valuation of hospital admissions we use the avoided medical costs 

as an estimate of the value of avoiding the health effects causing the admission. These cost of 

illness (COI) estimates generally (although not in every case) understate the true value of 

reductions in risk of a health effect. They tend to reflect the direct expenditures related to 

treatment but not the value of avoided pain and suffering from the health effect. 

We use the BenMAP model (Abt Associates, 2008) to estimate the health impacts and 

monetized health benefits for the proposed remedy. Figure 5-2 below shows the data inputs and 

outputs for the BenMAP model. 
81

Figure 5-2: Data inputs and outputs for the BenMAP model 

5.2.3 Benefit Per-Ton Estimates 

Benefit per-ton (BPT) estimates quantify the health impacts and monetized human health 

benefits of an incremental change in air pollution precursor emissions. In situations when we are 

unable to perform air quality modeling because of resource or time constraints, this approach can 

provide a reliable estimate of the benefits of emission reduction scenarios. EPA has used the 

benefit per-ton technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 

2008) and NO2 NAAQS RIA (U.S. EPA, 2010b). Time constraints prevented the Agency from 

modeling the air quality changes resulting from either the intrastate and direct control remedies 

or the more and less stringent SO2 caps and so we estimate a subset of these health benefits using 

PM2.5 benefit per-ton estimates. The assessment of the alternate scenarios omits ozone-related 

benefits for two reasons. First, the overall level of ozone-related benefits in the modeled case is 

Census 

Population 

Modeled 

Baseline and 

Post-Control 

2014 Ambient 

PM2.5 and O3

Concentrations 

PM2.5 & O3 Health 

Functions 

Economic 

Valuation 

Functions 

2014 

Population 

Projections 

PM2.5 & O3

Incremental Air 

Quality Change 

PM2.5 & O3-

Related Health 

Woods & 

Poole 

Population 

Projections 

Background 

Incidence and 

Prevalence Rates 

Monetized PM2.5

and O3-related 

Benefits

Blue identifies a user-selected input within the BenMAP model 

Green identifies a data input generated outside of the BenMAP 
82

relatively small compared to those associated with PM2.5 reductions (see table 5-17 below), due 

in part to the fairly modest summer time NOx emission reductions under this scenario. The level 

of summertime NOx emission reductions of the alternate scenarios are very similar to the 

modeled scenario, suggesting that the omission of ozone-related impacts would not greatly 

influence the overall level of benefits. Second, the complex non-linear chemistry of ozone 

formation introduces uncertainty to the development and application of a benefit per ton 

estimate. Taken together, these factors argued against developing an ozone benefit per ton 

estimate for this RIA. 

For this analysis, EPA applies PM2.5 BPT estimates that are methodologically consistent 

with those reported in Fann et al. (2009), but have been adjusted for this analysis to better match 

the spatial distribution of air quality changes projected for the Transport Rule. To derive the BPT 

estimates for this analysis, we: 

1. Quantified the PM2.5 -related human and monetized health benefits of the SO2 emission 

reductions of the proposed remedy. We first quantified the health impacts and monetized 

benefits of total PM2.5 mass formed from the SO2 reductions of the proposed remedy, 

allowing us to isolate the PM air quality impacts from SO2 reductions alone.19 This 

procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-related 

benefits of incremental changes in SO2 emissions. Because reductions in NOx emissions 

are relatively small in each scenario, and previous EPA modeling indicates that PM2.5

formation is less sensitive to NOx emission reductions on a per-μg/m3

 basis (Fann et al, 

2009), we did not quantify the NOx-related PM2.5 changes. 

2. Divided the health impacts and monetized benefits by the emission reduction. This 

calculation yields BPT estimates for PM-related SO2. 

The resulting BPT estimates were then multiplied by the projected SO2 emission reductions 

for the Direct Control and Interstate Trading scenarios to produce an estimate of the PM- and 

 

19 The Transport Rule includes both SO2 and NOx emissions reductions. In general SO2 is a precursor to particulate 

sulfate and NOx is a precursor to particulate nitrate. However, there are also several interactions between the PM2.5

precursors which cannot be easily quantified. For example, under conditions in which SO2 levels are reduced by a 

substantial margin, "nitrate replacement" may occur. This occurs when particulate ammonium sulfate concentrations 

are reduced, thereby freeing up excess gaseous ammonia. The excess ammonia is then available to react with 

gaseous nitric acid to form particulate nitrate. The impact of nitrate replacement is also affected by concurrent NOx 

reductions. NOx reductions can lead to decreases in nitrate, which competes with the process of nitrate replacement. 

 NOx reductions can also lead to reductions in photochemical by-products which can reduce both particulate sulfate 

and secondary organic carbon PM concentrations. Due to the complex nature of these interactions, EPA performed a 

sensitivity modeling analysis in which only SO2 emissions were reduced. We calculated benefits from this air 

quality modeling run to generate an SO2-only benefit per ton estimate. The results of the SO2-only sensitivity run 

may be found in the EPA Benefits TSD [Docket No. EPA-HQ-OAR-2010-0491]
83

ozone-related health impacts and monetized benefits. There is no analogous approach for 

estimating a BPT for visibility, and so the benefits of the alternative remedies omit this important 

monetized benefit. 

5.3 Uncertainty Characterization

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

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

both in this and preceding chapters, many inputs were used to derive the estimate of benefits for 

the proposed remedy, including emission inventories, air quality models (with their associated 

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

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

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

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

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

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

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

emission levels can lead to large impacts on total benefits. 

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

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

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

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

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

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

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

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

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

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

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

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

with the concentration response functions from epidemiological studies and random effects 

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

functions. Monte Carlo simulation uses random sampling from distributions of parameters to 
84

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

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

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

epidemiological studies determined the distributions for individual effect estimates. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies. In our results, we report credible intervals based on these standard errors, reflecting the 

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

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

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

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

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

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

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

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

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

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

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

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

uncertainty in the estimates. This information should be interpreted within the context of the 
85

larger uncertainty surrounding the entire analysis. 

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

literature to that point improved the understanding of ozone-related mortality. The NAS found 

that short-term ozone exposure was likely to contribute to ozone-related mortality (NRC, 2008) 

and issued a series of recommendations to EPA, including that the Agency should: 

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

(NMMAPS) as well as meta-analytic studies. 

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

to short-term exposure. 

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

mortality. 

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

is responsive to these NRC recommendations and generally consistent with EPA’s recent ozone 

reconsideration analysis (U.S. EPA, 2010a). 

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

assessment are the following: 

 gaps in scientific data and inquiry; 

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

introduced through differences in study design and statistical modeling; 

 errors in measurement and projection for variables such as population growth rates; 

 errors due to misspecification of model structures, including the use of surrogate 

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

simplification of complex functions; and 

 biases due to omissions or other research limitations. 

In Table 5-3 we summarize some of the key uncertainties in the benefits analysis. 
86

Table 5-3. Primary Sources of Uncertainty in the Benefits Analysis 

1. Uncertainties Associated with Impact Functions 

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

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

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

- Correct functional form of each impact function. 

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

source epidemiological study. 

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

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

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

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

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

urban to rural areas. 

3. Uncertainties Associated with PM Mortality Risk 

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

evidence. 

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

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

times in the year versus peak exposures. 

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

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

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

4. Uncertainties Associated with Possible Lagged Effects 

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

PM levels that would occur in a single year is uncertain as well as the portion that might occur in 

subsequent years. 

5. Uncertainties Associated with Baseline Incidence Rates 

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

may not accurately represent the actual location-specific rates. 

- Current baseline incidence rates may not approximate well baseline incidence rates in 2014. 

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

demographics. 

6. Uncertainties Associated with Economic Valuation 

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

therefore have uncertainty surrounding them. 

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

because of differences in income or other factors. 

7. Uncertainties Associated with Aggregation of Monetized Benefits 

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

unquantified or unmonetized benefits are not included. 
87

5.4 Benefits Analysis Data Inputs

In Figure 5-2, we summarized the key data inputs to the health impact and economic 

valuation estimate. Below we summarize the data sources for each of these inputs, including 

demographic projections, effect coefficients, incidence rates and economic valuation. We 

indicate where we have updated key data inputs since the 2005 CAIR benefits analysis. 

5.4.1 Demographic Data 

Quantified and monetized human health impacts depend on the demographic characteristics 

of the population, including age, location, and income. We use projections based on economic 

forecasting models developed by Woods and Poole, Inc (Woods and Poole, 2008). The Woods 

and Poole (WP) database contains county-level projections of population by age, sex, and race 

out to 2030. Projections in each county are determined simultaneously with every other county 

in the United States to take into account patterns of economic growth and migration. The sum of 

growth in county-level populations is constrained to equal a previously determined national 

population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to 

WP, linking county-level growth projections together and constraining to a national-level total 

growth avoids potential errors introduced by forecasting each county independently. County 

projections are developed in a four-stage process: 

1. First, national-level variables such as income, employment, and populations are 

forecasted. 

2. Second, employment projections are made for 172 economic areas defined by the Bureau 

of Economic Analysis, using an “export-base” approach, which relies on linking 

industrial-sector production of non-locally consumed production items, such as outputs 

from mining, agriculture, and manufacturing with the national economy. The exportbased approach requires estimation of demand equations or calculation of historical 

growth rates for output and employment by sector. 

3. Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method based 

on fertility and mortality in each area. 

4. Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each region or 
88

county are determined by aging the population by single year of age by sex and race for 

each year through 2014 based on historical rates of mortality, fertility, and migration. 

5.4.2 Effect Coefficients

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with EPA’s Integrated 

Science Assessments (which replace the Criteria Document), with input and advice from the 

EPA Science Advisory Board - Health Effects Subcommittee (SAB-HES), a scientific review 

panel specifically established to provide advice on the use of the scientific literature in 

developing benefits analyses for air pollution regulations (http://www.epa.gov/sab/). In general, 

we follow a weight of evidence approach, based on the biological plausibility of effects, 

availability of concentration-response functions from well conducted peer-reviewed 

epidemiological studies, cohesiveness of results across studies, and a focus on endpoints 

reflecting public health impacts (like hospital admissions) rather than physiological responses 

(such as changes in clinical measures like Forced Expiratory Volume (FEV1)). 

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology 

studies provide direct concentration-response relationships which can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment. 

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between PM and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 5-4. These criteria include consideration 

of whether the study was peer-reviewed, the match between the pollutant studied and the 

pollutant of interest, the study design and location, and characteristics of the study population, 

among other considerations. The selection of C-R functions for the benefits analysis is guided by 

the goal of achieving a balance between comprehensiveness and scientific defensibility. In 

general, the use of results from more than a single study can provide a more robust estimate of 

the relationship between a pollutant and a given health effect. However, there are often 

differences between studies examining the same endpoint, making it difficult to pool the results 

in a consistent manner. For example, studies may examine different pollutants or different age 

groups. For this reason, we consider very carefully the set of studies available examining each 
89

endpoint and select a consistent subset that provides a good balance of population coverage and 

match with the pollutant of interest. In many cases, either because of a lack of multiple studies, 

consistency problems, or clear superiority in the quality or comprehensiveness of one study over 

others, a single published study is selected as the basis of the effect estimate.  
90

Table 5-4. Criteria Used when Selecting C-R functions 

Consideration Comments 

Peer-Reviewed 

Research 

Peer-reviewed research is preferred to research that has not undergone the peer-review 

process. 

Study Type 

Among studies that consider chronic exposure (e.g., over a year or longer), prospective 

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

Study Period 

Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. More recent studies 

are also preferred because of possible changes in pollution mixes, medical care, and 

lifestyle over time. However, when there are only a few studies available, studies from all 

years will be included. 

Population Attributes 

The most technically appropriate measures of benefits would be based on impact functions 

that cover the entire sensitive population but allow for heterogeneity across age or other 

relevant demographic factors. In the absence of effect estimates specific to age, sex, 

preexisting condition status, or other relevant factors, it may be appropriate to select effect 

estimates that cover the broadest population to match with the desired outcome of the 

analysis, which is total national-level health impacts. When available, multi-city studies 

are preferred to single city studies because they provide a more generalizable 

representation of the C-R function. 

Study Size 

Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, either through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children). 

Study Location 

U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, and 

lifestyle. 

Pollutants Included in 

Model 

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is 

important to use properly specified impact functions that include both pollutants. Using 

single-pollutant models in cases where both pollutants are expected to affect a health 

outcome can lead to double-counting when pollutants are correlated. 

Measure of PM 

For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically 

Valuable Health 

Effects 

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. These health effects are not 

quantified in this analysis. 

Non-overlapping 

Endpoints 

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 
91

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Technical Appendices provides details of the procedures used to 

combine multiple impact functions (Abt Associates, 2008). In general, we used fixed or random 

effects models to pool estimates from different studies of the same endpoint. Fixed effects 

pooling simply weights each study’s estimate by the inverse variance, giving more weight to 

studies with greater statistical power (lower variance). Random effects pooling accounts for both 

within-study variance and between-study variability, due, for example, to differences in 

population susceptibility. We used the fixed effects model as our null hypothesis and then 

determined whether the data suggest that we should reject this null hypothesis, in which case we 

would use the random effects model. Pooled impact functions are used to estimate hospital 

admissions and asthma exacerbations. For more details on methods used to pool incidence 

estimates, see the BenMAP Manual Appendices (Abt Associates, 2008), which are available 

with the BenMAP software at http://www.epa.gov/benmap.html. 

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available. 

The specific studies from which effect estimates for the primary analysis are drawn are 

included in Table 5-5. We highlight in blue those studies that have been added since the 2005 

CAIR benefits analysis and incorporated into the central benefits estimate. In all cases where 

effect estimates are drawn directly from epidemiological studies, standard errors are used as a 

partial representation of the uncertainty in the size of the effect estimate. Below we provide the 

basis for selecting these studies.
92

Table 5-5. Health Endpoints and Epidemiological Studies Used to Quantify Health 

Impactsa 

Endpoint Pollutant Study 

Study 

Population 

Premature Mortality 

Premature mortality—daily 

time series 

O3 

(8-hour 

max) 

Bell et al.(2004) (NMMAPS study) 

Huang et al. (2004) (multi-city) 

Schwartz (2005) (multi-city) 

Meta-analyses:

Bell et al. (2005) 

Ito et al. (2005) 

Levy et al. (2005) 

All ages 

Premature mortality—cohort 

study, all-cause 

PM2.5 

(annual 

avg) 

Pope et al. (2002) 

Laden et al. (2006) 

>29 years 

>25 years 

Premature mortality, total 

exposures 

PM2.5 

(annual 

avg) 

Expert Elicitation (Roman et al., 2008) >24 years 

Premature mortality—allcause 

PM2.5 

(annual 

avg) 

Woodruff et al. (2006) Infant (<1 year) 

Chronic Illness 

Chronic bronchitis 

PM2.5 

(annual 

avg) 

Abbey et al. (1995) >26 years 

Nonfatal heart attacks 

PM2.5 

(24-hour 

avg) 

Peters et al. (2001) Adults (>18 

years) 

Hospital Admissions 

Pooled estimate: 

Schwartz (1995)—ICD 460–519 (all resp) 

Schwartz (1994a; 1994b)—ICD 480–486 

(pneumonia) 

Moolgavkar et al. (1997)—ICD 480–487 

(pneumonia) 

Schwartz (1994b)—ICD 491–492, 494–496 

(COPD) 

Moolgavkar et al. (1997)—ICD 490–496 

(COPD) 

>64 years 

O3 

(8-hour 

max) 

Burnett et al. (2001) <2 years 

PM2.5 

(24-hour 

avg) 

Pooled estimate:

Moolgavkar (2003)—ICD 490–496 (COPD) 

Ito (2003)—ICD 490–496 (COPD) 

>64 years 

PM2.5 

(24-hour 

avg) 

Moolgavkar (2000)—ICD 490–496 (COPD) 20–64 years 

PM2.5 

(24-hour 

avg) 

Ito (2003)—ICD 480–486 (pneumonia) >64 years 

Respiratory 

PM2.5 

(24-hour Sheppard (2003)—ICD 493 (asthma) <65 years 
93

Endpoint Pollutant Study 

Study 

Population 

avg) 

PM2.5 

(24-hour 

avg) 

Pooled estimate:

Moolgavkar (2003)—ICD 390–429 (all 

cardiovascular) 

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

heart disease, dysrhythmia, heart failure) 

Cardiovascular >64 years 

PM2.5 

(24-hour 

avg) 

Moolgavkar (2000)—ICD 390–429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER visits O3 

(8-hour 

max) 

Pooled estimate: 

Peel et al. (2005) 

Wilson et al.(2005) 

All ages 

All ages 

 PM2.5 

(24-hour 

avg) 

Norris et al. (1999) 0–18 years 

Other Health Endpoints 

Acute bronchitis 

PM2.5 

(annual 

avg) 

Dockery et al. (1996) 

8–12 years 

Upper respiratory symptoms 

PM10 

(24-hour 

avg) 

Pope et al. (1991) 

Asthmatics, 9–

11 years 

Lower respiratory symptoms 

PM2.5 

(24-hour 

avg) 

Schwartz and Neas (2000) 

7–14 years 

Asthma exacerbations 

PM2.5 

(24-hour 

avg) 

Pooled estimate: 

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

of breath) 

Vedal et al. (1998) (cough) 

6–18 yearsb

Work loss days 

PM2.5 

(24-hour 

avg) 

Ostro (1987) 

18–65 years 

School absence days 

O3 

(8-hour 

max) 

Pooled estimate: 

Gilliland et al. (2001) 

Chen et al. (2000) 

5–17 yearsc

O3 

(8-hour 

max) 

Ostro and Rothschild (1989) 

18–65 years 

Minor Restricted Activity 

Days (MRADs) PM2.5 

(24-hour 

avg) 

Ostro and Rothschild (1989) 

18–65 years 

a

 Studies or air quality metrics highlighted in blue represent updates incorporated since the 2005 CAIR RIA 

b

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

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

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

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

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

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

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

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

absences for all school-aged children based on the biological similarity between children aged 5 to 17.
94

5.4.2.1 PM2.5 Premature Mortality Effect Coefficients

Both long- and short-term exposures to ambient levels of PM2.5 air pollution have been 

associated with increased risk of premature mortality. The size of the mortality risk estimates 

from epidemiological studies, the serious nature of the effect itself, and the high monetary value 

ascribed to prolonging life make mortality risk reduction the most significant health endpoint 

quantified in this analysis. 

Although a number of uncertainties remain to be addressed by continued research (NRC, 

2002), a substantial body of published scientific literature documents the correlation between 

elevated PM2.5 concentrations and increased mortality rates (U.S. EPA, 2009d). Time-series 

methods have been used to relate short-term (often day-to-day) changes in PM2.5 concentrations 

and changes in daily mortality rates up to several days after a period of elevated PM2.5 

concentrations. Cohort methods have been used to examine the potential relationship between 

community-level PM exposures over multiple years (i.e., long-term exposures) and communitylevel annual mortality rates. Researchers have found statistically significant associations between 

PM2.5 and premature mortality using both types of studies. In general, the risk estimates based on 

the cohort studies are larger than those derived from time-series studies. Cohort analyses are 

thought to better capture the full public health impact of exposure to air pollution over time, 

because they account for the effects of long-term exposures and possibly some component of 

short-term exposures (Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues 

surrounding the estimation of PM2.5-related premature mortality. To demonstrate the sensitivity 

of the benefits estimates to the specific sources of information regarding the impact of PM2.5

exposures on the risk of premature death, we are providing estimates in our results tables based 

on studies derived from the epidemiological literature and from the EPA sponsored expert 

elicitation. The epidemiological studies from which these estimates are drawn are described 

below. The expert elicitation project and the derivation of effect estimates from the expert 

elicitation results are described in the 2006 PM2.5 NAAQS RIA and Roman et al. (2008). In the 

interest of brevity we do not repeat those details here. However, Figure 5-18 summarizes the 

estimated PM2.5-related premature mortalities avoided using risk estimates drawn from the 

expert elicitation. 

Over a dozen epidemiological studies have found significant associations between 

various measures of long-term exposure to PM and elevated rates of annual mortality, beginning 

with Lave and Seskin (1977). Most of the published studies found positive (but not always 

statistically significant) associations with available PM indices such as total suspended particles 
95

(TSP). However, exploration of alternative model specifications sometimes raised questions 

about causal relationships (e.g., Lipfert et al., 1989). These early “ecological cross-sectional” 

studies (Lave and Seskin, 1977; Ozkaynak and Thurston, 1987) were criticized for a number of 

methodological limitations, particularly for inadequate control at the individual level for 

variables that are potentially important in causing mortality, such as wealth, smoking, and diet. 

Over the last 17 years, several studies using “prospective cohort” designs have been 

published that appear to be consistent with the earlier body of literature. These new “prospective 

cohort” studies reflect a significant improvement over the earlier work because they include 

individual level information with respect to health status and residence. The most extensive 

analyses have been based on data from two prospective cohort groups, often referred to as the 

Harvard “Six-Cities Study” (Dockery et al., 1993; Laden et al., 2006) and the “American Cancer 

Society or ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004, Krewski et al. 

2009); these studies have found consistent relationships between fine particle indicators and 

premature mortality across multiple locations in the United States. A third major data set comes 

from the California-based 7th Day Adventist Study (e.g., Abbey et al., 1999), which reported 

associations between long-term PM exposure and mortality in men. Results from this cohort, 

however, have been inconsistent, and the air quality results are not geographically representative 

of most of the United States, and the lifestyle of the population is not reflective of much of the 

U.S. population. Analysis is also available for a cohort of adult male veterans diagnosed with 

hypertension has been examined (Lipfert et al., 2000; Lipfert et al., 2003, 2006). The 

characteristics of this group differ from the cohorts in the Six-Cities, ACS, and 7th Day 

Adventist studies with respect to income, race, health status, and smoking status. Unlike 

previous long-term analyses, this study found some associations between mortality and ozone 

but found inconsistent results for PM indicators. Because of the selective nature of the 

population in the veteran’s cohort, we have chosen not to include any effect estimates from the 

Lipfert et al. (2000) study in our benefits assessment. 

Given their consistent results and broad geographic coverage, and importance in 

informing the NAAQS development process, the Six-Cities and ACS data have been particularly 

important in benefits analyses. The credibility of these two studies is further enhanced by the fact 

that the initial published studies (Pope et al., 1995 and Dockery et al., 1993) were subject to 

extensive reexamination and reanalysis by an independent team of scientific experts 

commissioned by the Health Effect Institute (HEI) (Krewski et al., 2000). The final results of the 

reanalysis were then independently peer reviewed by a Special Panel of the HEI Health Review 

Committee. The results of these reanalyses confirmed and expanded the conclusions of the 
96

original investigators. While the HEI reexamination lends credibility to the original studies, it 

also highlights sensitivities concerning the relative impact of various pollutants, such as SO2, the 

potential role of education in mediating the association between pollution and mortality, and the 

influence of spatial correlation modeling. Further confirmation and extension of the findings of 

the 1993 Six City Study and the 1995 ACS study were recently completed using more recent air 

quality and a longer follow-up period for the ACS cohort was published over the past several 

years (Pope et al., 2002, 2004; Laden et al., 2006, Krewski et al. 2009). The follow up to the 

Harvard Six City Study both confirmed the effect size from the first analysis and provided 

additional confirmation that reductions in PM2.5 are likely to result in reductions in the risk of 

premature death. This additional evidence stems from the observed reductions in PM2.5 in each 

city during the extended follow-up period. Laden et al. (2006) found that mortality rates 

consistently went down at a rate proportionate to the observed reductions in PM2.5. 

A number of additional analyses have been conducted on the ACS cohort data (Jarrett et 

al., 2009; Pope et al., 2009). These studies have continued to find a strong significant 

relationship between PM2.5 and mortality outcomes and life expectancy. Specifically, much of 

the recent research has suggested a stronger relationship between cardiovascular mortality and 

lung cancer mortality with PM2.5, and a less significant relationship between respiratory-related 

mortality and PM2.5. The extended analyses of the ACS cohort data (Krewski et al. 2009) 

provides additional refinements to the analysis of PM-related mortality by (a) extend the followup period by 2 years to the year 2000, for a total of 18 years; (b) incorporate ecological., or 

neighborhood-level co-variates so as to better estimate personal exposure; (c) perform an 

extensive spatial analysis using land use regression modeling. These additional refinements may 

make this analysis well-suited for the assessment of PM-related mortality for EPA benefits 

analyses. 

In developing and improving the methods for estimating and valuing the potential 

reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel 

recommended using long-term prospective cohort studies in estimating mortality risk reduction 

(U.S. EPA-SAB, 1999b). This recommendation has been confirmed by a report from the 

National Research Council, which stated that “it is essential to use the cohort studies in benefits 

analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). More 

specifically, the SAB recommended emphasis on the ACS study because it includes a much 

larger sample size and longer exposure interval and covers more locations (e.g., 50 cities 

compared to the Six Cities Study) than other studies of its kind. Because of the refinements in 

the extended follow-up analysis, the SAB-HES recommended using the Pope et al. (2002) study 
97

as the basis for the primary mortality estimate for adults and suggests that alternate estimates of 

mortality generated using other cohort and time-series studies could be included as part of the 

sensitivity analysis (U.S. EPA-SAB, 2004b). The PM NAAQS Risk and Exposure Assessment 

(U.S EPA, 2010) utilized risk coefficients drawn from the Krewski et al. (2009) study. In a 

December of 2009 consultation with the SAB-HES, the Agency proposed utilizing the Krewski 

et al. (2009) extended analysis of the ACS cohort data. The panel is scheduled to issue an 

advisory in early 2010. 

As noted above, since 2004 SAB review, an extended follow-up of the Harvard Six cities 

study has been published (Laden et al., 2006) and in recent RIAs (see for example the SO2

NAAQS, PM NAAQS, CAIR and Nonroad Diesel RIAs), we have included this estimate of 

mortality impacts based on application of the C-R function derived from this study. We use this 

specific estimate to represent the Six Cities study because it both reflects among the most up-todate science and was cited by many of the experts in their elicitation responses. It is clear from 

the expert elicitation that the results published in Laden et al. (2006) are potentially influential, 

and in fact the expert elicitation results encompass within their range the estimates from both the 

Pope et al. (2002) and Laden et al. (2006) studies (see Figure 5-18 below). These are logical 

choices for anchor points in our presentation because, while both studies are well designed and 

peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for 

using both studies to generate benefits estimates. 

5.4.2.2 Ozone Premature Mortality Effect Coefficients 

While particulate matter is the criteria pollutant most clearly associated with premature 

mortality, recent research suggests that short-term repeated ozone exposure also likely 

contributes to premature death. The 2006 Ozone Criteria Document found that “[c]onsistent 

with observed ozone-related increases in respiratory- and cardiovascular-related morbidity, 

several newer multi-city studies, single-city studies, and several meta-analyses of these studies 

have provided relatively strong epidemiologic evidence for associations between short-term 

ozone exposure and all-cause mortality, even after adjustment for the influence of season and 

PM” (U.S. EPA, 2006). The epidemiologic data are also supported by recent experimental data 

from both animal and human studies, which provide evidence suggestive of plausible pathways 

by which risk of respiratory or cardiovascular morbidity and mortality could be increased by 

ambient ozone. With respect to short-term exposure, the Ozone Criteria Document concluded, 

“This overall body of evidence is highly suggestive that ozone directly or indirectly contributes 
98

to non-accidental and cardiopulmonary-related mortality, but additional research is needed to 

more fully establish underlying mechanisms by which such effects occur” (U.S. EPA, 2006). 

With respect to the time-series studies, the conclusion regarding the relationship between 

short-term ozone exposure and premature mortality is based, in part, upon recent city-specific 

time-series studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) 

analysis in Los Angeles.20 This conclusion is also based on recent meta-analyses by Bell et al. 

(2005), Ito et al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, 

Mortality, and Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically 

sought to disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 

2006 Criteria Document states that “the results from these meta-analyses, as well as several 

single- and multiple-city studies, indicate that co-pollutants generally do not appear to 

substantially confound the association between ozone and mortality” (p. 7-103). However, 

CASAC raised questions about the implications of these time-series results in a policy context. 

Specifically, CASAC emphasized that “...while the time-series study design is a powerful tool to 

detect very small effects that could not be detected using other designs, it is also a blunt tool” 

(U.S. EPA-SAB, 2006). They point to findings (e.g., Stieb et al., 2002, 2003) that indicated 

associations between premature mortality and all of the criteria pollutants, indicating that 

“findings of time-series studies do not seem to allow us to confidently attribute observed effects 

to individual pollutants” (id.). They note that “not only is the interpretation of these associations 

complicated by the fact that the day-to-day variation in concentrations of these pollutants is, to a 

varying degree, determined by meteorology, the pollutants are often part of a large and highly 

correlated mix of pollutants, only a very few of which are measured” (id.). Even with these 

uncertainties, the CASAC Ozone Panel, in its review of EPA’s Staff Paper, found “...premature 

total non-accidental and cardiorespiratory mortality for inclusion in the quantitative risk 

assessment to be appropriate.” 

In 2006 the EPA requested an NAS study to answer four key questions regarding ozone 

mortality: (1) how did the epidemiological literature to that point improve our understanding of 

the size of the ozone mortality effect? (2) How best can EPA quantify the level of ozone 

mortality impacts from short-term exposure? (3) How might EPA estimate the change in life 

 

20 For an exhaustive review of the city-specific time-series studies considered in the ozone staff paper, see: U.S. 

Environmental Protection Agency, 2007. Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information. Prepared by the Office of Air and Radiation. 

Available at http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36.
99

expectancy? (4) What methods should EPA use to estimate the monetary value of changes in 

ozone-related mortality risk and life expectancy? 

In 2008 the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding 

the quantification and valuation of ozone-related short-term mortality. Chief among these was 

that “...short-term exposure to ambient ozone is likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multicity and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008). 

Recent evidence also suggests a relationship between long-term exposure to ozone and 

premature respiratory mortality in the ACS cohort (Jerrett et al. 2009). Jerrett and colleagues find 

that long-term exposure to ozone is linked to respiratory premature mortality in a two-pollutant 

model that controls for PM2.5. This is the first long-term cohort study to have observed such a 

relationship. In a December of 2009 consultation with the SAB-HES, the Agency proposed 

utilizing the Jerrett et al. (2009) analysis of the ACS cohort data. The panel is scheduled to issue 

an advisory in early 2010. 

In view of the findings of the Criteria document and the NAS panel, we include used 

estimates of short-term ozone mortality from the Bell et al. (2004) NMMAPS analysis, the 

Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect 

estimates from the three meta-analyses (Bell et al. 2005, Levy et al. 2005 and Ito et al. 2005). 

5.4.2.3 Chronic Bronchitis 

CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 

months a year for several years in a row. CB affects an estimated 5 percent of the U.S. 

population (American Lung Association, 1999). A limited number of studies have estimated the 

impact of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) 

provide evidence that long-term PM exposure gives rise to the development of CB in the United 

States. Because the Transport Rule is expected to reduce primarily PM2.5, this analysis uses only 

the Abbey et al. (1995) study, because it is the only study focusing on the relationship between 

PM2.5 and new incidences of CB. 
100

5.4.2.4 Nonfatal Myocardial Infarctions (Heart Attacks) 

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent study 

by Peters et al. (2001) as the basis for the impact function estimating the relationship between 

PM2.5 and nonfatal heart attacks. Peters et al. is the only available U.S. study to provide a 

specific estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar 

(2000), show a consistent relationship between all cardiovascular hospital admissions, including 

those for nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on long-term 

health costs and earnings, we provide a separate estimate for nonfatal heart attacks. The estimate 

used in the Transport Rule analysis is based on the single available U.S. effect estimate. The 

finding of a specific impact on heart attacks is consistent with hospital admission and other 

studies showing relationships between fine particles and cardiovascular effects both within and 

outside the United States. Several epidemiologic studies (Liao et al., 1999; Gold et al., 2000; 

Magari et al., 2001) have shown that heart rate variability (an indicator of how much the heart is 

able to speed up or slow down in response to momentary stresses) is negatively related to PM 

levels. Heart rate variability is a risk factor for heart attacks and other coronary heart diseases 

(Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; Tsuji et al., 1996). As such, 

significant impacts of PM on heart rate variability are consistent with an increased risk of heart 

attacks. 

5.4.2.5 Hospital and Emergency Room Admissions 

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. Because of this, many of the hospital admission endpoints use pooled impact 

functions based on the results of a number of studies. In addition, some studies have examined 

the relationship between air pollution and emergency room visits. Since most emergency room 

visits do not result in an admission to the hospital (the majority of people going to the emergency 

room are treated and return home), we treat hospital admissions and emergency room visits 

separately, taking account of the fraction of emergency room visits that are admitted to the 

hospital. 

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much evidence linking ozone or PM 

with other types of hospital admissions. The only type of emergency room visits that have been 

consistently linked to ozone and PM in the United States are asthma-related visits. 
101

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2003) and Ito (2003). Additional published studies show 

a statistically significant relationship between PM10 and cardiovascular hospital admissions. 

However, given that the control options we are analyzing are expected to reduce primarily PM2.5, 

we focus on the two studies that examine PM2.5. Both of these studies provide an effect estimate 

for populations over 65, allowing us to pool the impact functions for this age group. Only 

Moolgavkar (2000) provided a separate effect estimate for populations 20 to 64.21 Total 

cardiovascular hospital admissions are thus the sum of the pooled estimate for populations over 

65 and the single study estimate for populations 20 to 64. Cardiovascular hospital admissions 

include admissions for myocardial infarctions. To avoid double-counting benefits from 

reductions in myocardial infarctions when applying the impact function for cardiovascular 

hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove 

admissions for myocardial infarctions. 

To estimate total avoided incidences of respiratory hospital admissions, we used impact 

functions for several respiratory causes, including chronic obstructive pulmonary disease 

(COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published 

studies show a statistically significant relationship between PM10 and respiratory hospital 

admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and Ito (2003) 

provide effect estimates for COPD in populations over 65, allowing us to pool the impact 

functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for 

populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate 

for populations over 65 and the single study estimate for populations 20 to 64. Only Ito (2003) 

estimated pneumonia and only for the population 65 and older. In addition, Sheppard (2003) 

provided an effect estimate for asthma hospital admissions for populations under age 65. Total 

avoided incidences of PM-related respiratory-related hospital admissions are the sum of COPD, 

pneumonia, and asthma admissions. 

To estimate the effects of PM air pollution reductions on asthma-related ER visits, we 

use the effect estimate from a study of children 18 and under by Norris et al. (1999). As noted 

earlier, there is another study by Schwartz examining a broader age group (less than 65), but the 

Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al. (1999) effect 

 

21Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence 

criteria. However, given that no other estimates are available for this age group, we chose to use the existing 

study. Given the very small (<5 percent) difference in the effect estimates for people 65 and older with

cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choice to 

introduce much bias.
102

estimate because it better matched the pollutant of interest. Because children tend to have higher 

rates of hospitalization for asthma relative to adults under 65, we will likely capture the majority 

of the impact of PM2.5 on asthma emergency room visits in populations under 65, although there 

may still be significant impacts in the adult population under 65. 

To estimate avoided incidences of respiratory hospital admissions associated with ozone, 

we used a number of studies examining hospital admissions for a range of respiratory illnesses, 

including pneumonia and COPD. Two age groups, adults over 65 and children under 2, were 

examined. For adults over 65, Schwartz (1995) provides effect estimates for two different cities 

relating ozone and hospital admissions for all respiratory causes (defined as ICD codes 460–

519). Impact functions based on these studies were pooled first before being pooled with other 

studies. Two studies (Moolgavkar et al., 1997; Schwartz, 1994a) examine ozone and pneumonia 

hospital admissions in Minneapolis. One additional study (Schwartz, 1994b) examines ozone 

and pneumonia hospital admissions in Detroit. The impact functions for Minneapolis were 

pooled together first, and the resulting impact function was then pooled with the impact function 

for Detroit. This avoids assigning too much weight to the information coming from one city. 

For COPD hospital admissions, two studies are available: Moolgavkar et al. (1997), conducted 

in Minneapolis, and Schwartz (1994b), conducted in Detroit. These two studies were pooled 

together. To estimate total respiratory hospital admissions for adults over 65, COPD admissions 

were added to pneumonia admissions, and the result was pooled with the Schwartz (1995) 

estimate of total respiratory admissions. Burnett et al. (2001) is the only study providing an 

effect estimate for respiratory hospital admissions in children under 2. 

We used two studies as the source of the concentration-response functions we used to 

estimate the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et al. 

(2005) and Wilson et al. (2005). We estimated the change in ER visits using the effect 

estimate(s) from each study and then pooled the results using the random effects pooling 

technique (see Abt, 2005). The Peel et al. study (2005) estimated asthma-related ER visits for all 

ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating 

equations, the authors found a marginal association between the maximum daily 8-hour average 

ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a 

single pollutant model. Wilson et al. (2005) examined the relationship between ER visits for 

respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 

1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used in the 

analysis, the authors restricted the ozone data incorporated into the model to the months ozone 

levels are usually measured, the spring-summer months (April through September). Using the 
103

generalized additive model, Wilson et al. (2005) found a significant association between the 

maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no 

significant association for Manchester. Similar to the approach used to generate effect estimates 

for hospital admissions, we used random effects pooling to combine the results across the 

individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. 

(2005) Manchester estimates were not significant at the 95 percent level, and thus, the 

confidence interval for the pooled incidence estimate based on these studies includes negative 

values. This is an artifact of the statistical power of the studies, and the negative values in the 

tails of the estimated effect distributions do not represent improvements in health as ozone 

concentrations are increased. Instead, these should be viewed as a measure of uncertainty due to 

limitations in the statistical power of the study. We included both hospital admissions and ER 

visits as separate endpoints associated with ozone exposure because our estimates of hospital 

admission costs do not include the costs of ER visits and most asthma ER visits do not result in a 

hospital admission. 

5.4.2.6 Acute Health Events and School/Work Loss Days 

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to ambient levels of 

ozone and PM. The sources for the effect estimates used to quantify these effects are described 

below. 

Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes of 

acute bronchitis annually (American Lung Association, 2002c). Acute bronchitis is 

characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a 

number of days. According to the MedlinePlus medical encyclopedia,22 with the exception of 

cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of 

acute bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate 

developed from Dockery et al. (1996). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 

7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000). 

Because asthmatics have greater sensitivity to stimuli (including air pollution), children 

with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or 

stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air 

 

22See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 
104

pollution on upper respiratory symptoms has thus focused on effects in asthmatics. Incidences of 

upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect 

estimate developed from Pope et al. (1991). 

Health effects from air pollution can also result in missed days of work (either from 

personal symptoms or from caring for a sick family member). Days of work lost due to PM2.5

were estimated using an effect estimate developed from Ostro (1987). Children may also be 

absent from school because of respiratory or other diseases caused by exposure to air pollution. 

Most studies examining school absence rates have found little or no association with PM2.5, but 

several studies have found a significant association between ozone levels and school absence 

rates. We used two recent studies, Gilliland et al. (2001) and Chen et al. (2000), to estimate 

changes in absences (school loss days) due to changes in ozone levels. The Gilliland et al. study 

estimated the incidence of new periods of absence, while the Chen et al. study examined absence 

on a given day. We converted the Gilliland estimate to days of absence by multiplying the 

absence periods by the average duration of an absence. We estimated an average duration of 

school absence of 1.6 days by dividing the average daily school absence rate from Chen et al. 

(2000) and Ransom and Pope (1992) by the episodic absence rate from Gilliland et al. (2001). 

This provides estimates from Chen et al. (2000) and Gilliland et al. (2001), which can be pooled 

to provide an overall estimate. 

MRAD result when individuals reduce most usual daily activities and replace them with 

less strenuous activities or rest, yet not to the point of missing work or school. For example, a 

mechanic who would usually be doing physical work most of the day will instead spend the day 

at a desk doing paper and phone work because of difficulty breathing or chest pain. The effect of 

PM2.5 and ozone on MRAD was estimated using an effect estimate derived from Ostro and 

Rothschild (1989). 
105

For the Transport Rule, we have followed the SAB-HES recommendations regarding 

asthma exacerbations in developing the primary estimate. To prevent double-counting, we 

focused the estimation on asthma exacerbations occurring in children and excluded adults from 

the calculation.23 Asthma exacerbations occurring in adults are assumed to be captured in the 

general population endpoints such as work loss days and MRADs. Consequently, if we had 

included an adult-specific asthma exacerbation estimate, we would likely double-count incidence 

for this endpoint. However, because the general population endpoints do not cover children 

(with regard to asthmatic effects), an analysis focused specifically on asthma exacerbations for 

children (6 to 18 years of age) could be conducted without concern for double-counting. 

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 

2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed 

a group of 138 African-American children in Los Angeles for 13 weeks, recording daily 

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see 

below). Vedal et al. (1998) followed a group of elementary school children, including 74 

asthmatics, located on the west coast of Vancouver Island for 18 months including 

measurements of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms 

(e.g., cough, phlegm, wheeze, chest tightness) through the use of daily diaries. Association 

between PM10 and respiratory symptoms for the asthmatic population was only reported for two 

 

23Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about double 

counting of benefits. Concerns over double counting stem from the fact that studies of the general population also 

include asthmatics, so estimates based solely on the asthmatic population cannot be directly added to the general 

population numbers without double counting. In one specific case (upper respiratory symptoms in children), the 

only study available is limited to asthmatic children, so this endpoint can be readily included in the calculation of 

total benefits. However, other endpoints, such as lower respiratory symptoms and MRADs, are estimated for the 

total population that includes asthmatics. Therefore, to simply add predictions of asthma-related symptoms 

generated for the population of asthmatics to these total population-based estimates could result in double 

counting, especially if they evaluate similar endpoints. The SAB-HES, in commenting on the analytical blueprint 

for 812, acknowledged these challenges in evaluating asthmatic symptoms and appropriately adding them into the 

primary analysis (SAB-HES, 2004). However, despite these challenges, the SAB-HES recommends the addition 

of asthma-related symptoms (i.e., asthma exacerbations) to the primary analysis, provided that the studies use the 

panel study approach and that they have comparable design and baseline frequencies in both asthma prevalence 

and exacerbation rates. Note also, that the SAB-HES, while supporting the incorporation of asthma exacerbation 

estimates, does not believe that the association between ambient air pollution, including ozone and PM, and the 

new onset of asthma is sufficiently strong to support inclusion of this asthma-related endpoint in the primary 

estimate. 
106

endpoints: cough and PEF. Because it is difficult to translate PEF measures into clearly defined 

health endpoints that can be monetized, we only included the cough-related effect estimate from 

this study in quantifying asthma exacerbations. We employed the following pooling approach in 

combining estimates generated using effect estimates from the two studies to produce a single 

asthma exacerbation incidence estimate. First, we pooled the separate incidence estimates for 

shortness of breath, wheeze, and cough generated using effect estimates from the Ostro et al. 

study, because each of these endpoints is aimed at capturing the same overall endpoint (asthma 

exacerbations) and there could be overlap in their predictions. The pooled estimate from the 

Ostro et al. study is then pooled with the cough-related estimate generated using the Vedal study. 

 The rationale for this second pooling step is similar to the first; both studies are attempting to 

quantify the same overall endpoint (asthma exacerbations). 

5.4.2.7 School Absences 

Children may be absent from school due to respiratory or other acute diseases caused, or 

aggravated by, exposure to air pollution. Several studies have found a significant association 

between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen 

et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The 

Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al. 

study examined daily absence rates. We converted the Gilliland et al. estimate to days of absence 

by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days 

as the average duration of a school absence, the result of dividing the average daily school 

absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence 

duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is converted 

into 1.6 absence days. 

Following advice from the National Research Council (2002), we calculated reductions 

in school absences for the full population of school age children, ages five to 17. This is 

consistent with recent peer-reviewed literature on estimating the impact of ozone exposure on 

school absences (Hall et al. 2003). We estimated the change in school absences using both Chen 

et al. (2000) and Gilliland et al. (2001) and then, similar to hospital admissions and ER visits, 

pooled the results using the random effects pooling procedure. 

5.4.2.8 Outdoor Worker Productivity 

To monetize benefits associated with increased worker productivity resulting from 

improved ozone air quality, we used information reported in Crocker and Horst (1981). Crocker 
107

and Horst examined the impacts of ozone exposure on the productivity of outdoor citrus workers. 

The study measured productivity impacts. Worker productivity is measuring the value of the loss 

in productivity for a worker who is at work on a particular day, but due to ozone, cannot work as 

hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction 

workers. Here, productivity impacts are measured as the change in income associated with a 

change in ozone exposure, given as the elasticity of income with respect to ozone concentration. 

The reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in 

income. Given the national median daily income for outdoor workers engaged in strenuous 

activity reported by the U.S. Census Bureau (2002), $81 per day (2006$), a ten percent reduction 

in ozone yields about $0.97 in increased daily wages. We adjust the national median daily 

income estimate to reflect regional variations in income using a factor based on the ratio of 

county median household income to national median household income. No information was 

available for quantifying the uncertainty associated with the central valuation estimate. 

Therefore, no uncertainty analysis was conducted for this endpoint. 

5.4.3 Baseline Incidence Estimates 

Epidemiological studies of the association between pollution levels and adverse health 

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

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

example, a typical result might be that a 10 ppb decrease in daily ozone levels might, in turn, 

decrease hospital admissions by 3 percent. The baseline incidence of the health effect is 

necessary to convert this relative change into a number of cases. A baseline incidence rate is the 

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

corresponds to baseline pollutant levels in that location. To derive the total baseline incidence 

per year, this rate must be multiplied by the corresponding population number. For example, if 

the baseline incidence rate is the number of cases per year per million people, that number must 

be multiplied by the millions of people in the total population. 

Table 5-6 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentration-response 

functions to individual age groups and then summed over the relevant age range to provide an 

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

to a lack of more spatially disaggregated data. Whenever possible, the national rates used are 

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

For some studies, however, the only available incidence information comes from the studies 
108

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

incidence at the national level. Regional incidence rates are available for hospital admissions, 

and county-level data are available for premature mortality. We have projected mortality rates 

such that future mortality rates are consistent with our projections of population growth (Abt 

Associates, 2008); this represents a change from the 2005 CAIR analysis, which used static 

rates. 

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 5-7 lists the prevalence rates used to determine the applicable population for 

asthma symptom endpoints. Note that these reflect current asthma prevalence and assume no 

change in prevalence rates in future years. We again highlight in blue those rates that have been 

updated since the publication of the 2005 CAIR RIA. 
109

Table 5-6: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population 

Rates 

Endpoint Parameter Value Sourcea

Mortality Daily or annual mortality rate 

projected to 2015 

Age-, cause-, 

and countyspecific rate 

CDC Wonder (1996–1998) 

U.S. Census bureau 

Hospitalizations Daily hospitalization rate 

Age-, region-, 

and causespecific rate 1999 NHDS public use data filesb

Asthma ER Visits Daily asthma ER visit rate 

Age- and 

region- specific 

visit rate 

2000 NHAMCS public use data 

filesc

; 1999 NHDS public use data 

filesb

Chronic Bronchitis 

Annual prevalence rate per 

person 

 Aged 18–44 

 Aged 45–64 

 Aged 65 and older 

0.0367 

0.0505 

0.0587 

1999 NHIS (American Lung 

Association, 2002b, Table 4) 

Annual incidence rate per 

person 0.00378 Abbey et al. (1993, Table 3) 

Nonfatal Myocardial 

Infarction (heart 

attacks) 

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+ 

 Northeast 

 Midwest 

 South 

 West 

0.0000159 

0.0000135 

0.0000111 

0.0000100 

1999 NHDS public use data filesb

; 

adjusted by 0.93 for probability of 

surviving after 28 days (Rosamond 

et al., 1999) 

Incidence (and prevalence) 

among asthmatic AfricanAmerican children 

 daily wheeze 

 daily cough 

 daily dyspnea 

0.076 (0.173) 

0.067 (0.145) 

0.037 (0.074) 

Ostro et al. (2001) 

Asthma Exacerbations 

Prevalence among asthmatic 

children 

 daily wheeze 

 daily cough 

 daily dyspnea 

0.038 

0.086 

0.045 

Vedal et al. (1998) 

Acute Bronchitis Annual bronchitis incidence 

rate, children 0.043 American Lung Association (2002c, 

Table 11) 

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

childrend

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD incidence rate per 

person (18–65) 

 Aged 18–24 

 Aged 25–44 

 Aged 45–64 

0.00540 

0.00678 

0.00492 

1996 HIS (Adams, Hendershot, and 

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

Bureau of the Census (2000) 
110

School Loss Days Rate per person per year, 

assuming 180 school days per 

year 

9.9 

National Center for Education 

Statistics (1996) and 1996 HIS 

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

Minor RestrictedActivity Days Daily MRAD incidence rate 

per person 

0.02137 Ostro and Rothschild (1989, p. 243) 

a The following abbreviations are used to describe the national surveys conducted by the National Center for Health 

Statistics: HIS refers to the National Health Interview Survey; NHDS—National Hospital Discharge Survey; 

NHAMCS—National Hospital Ambulatory Medical Care Survey. 

b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/. 

c See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/. 

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

Table 5-7. Asthma Prevalence Rates Used for this Analysis

Asthma Prevalence Rates 

Population Group Value Source 

All Ages 0.0386 American Lung Association (2002a, Table 7)—based on 1999 HIS 

< 18 0.0527 American Lung Association (2002a, Table 7)—based on 1999 HIS 

5–17 0.0567 American Lung Association (2002a, Table 7)—based on 1999 HIS 

18–44 0.0371 American Lung Association (2002a, Table 7)—based on 1999 HIS 

45–64 0.0333 American Lung Association (2002a, Table 7)—based on 1999 HIS 

65+ 0.0221 American Lung Association (2002a, Table 7)—based on 1999 HIS 

Male, 27+ 0.021 2000 HIS public use data filesa

African American, 5 to 17 0.0726 American Lung Association (2002a, Table 9)—based on 1999 HIS 

African American, <18 0.0735 American Lung Association (2002a, Table 9)—based on 1999 HIS 

a

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

5.4.4 Economic Valuation Estimates

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

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

WTP for changes in risk of a health effect rather than WTP for a health effect that would occur 

with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect that is avoided because of a reduction in air pollution. 

We converted those to units of avoided statistical incidence for ease of presentation. We 
111

calculated the value of avoided statistical incidences by dividing individual WTP for a risk 

reduction by the related observed change in risk. 

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

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

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

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

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We 

provide unit values for health endpoints (along with information on the distribution of the unit 

value) in Table 5-8. All values are in constant year 2006 dollars, adjusted for growth in real 

income out to 2014 using projections provided by Standard and Poor’s. Economic theory argues 

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

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

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

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

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

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

wage rates. For these two reasons, these cost of illness estimates may underestimate the 

economic value of avoided health impacts in 2014. The discussion below provides additional 

details on ozone and PM2.5-related related endpoints. 

5.4.4.1 Mortality Valuation 

Following the advice of the EEAC of the SAB, EPA currently uses the VSL approach in 

calculating the primary estimate of mortality benefits, because we believe this calculation 

provides the most reasonable single estimate of an individual’s willingness to trade off money 

for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary 

measure for the value of small changes in mortality risk experienced by a large number of 

people. For a period of time (2004-2008), the Office of Air and Radiation (OAR) valued 

mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited 

analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to 

$10 million (2000$) consistent with two meta-analyses of the wage-risk literature. The $1 

million value represented the lower end of the interquartile range from the Mrozek and Taylor 

(2002) meta-analysis of 33 studies. The $10 million value represented the upper end of the 

interquartile range from the Viscusi and Aldy (2003) meta-analysis of 43 studies. The mean 

estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 million 
112

estimated in the Kochi et al. (2006) meta-analysis. However, the Agency neither changed its 

official guidance on the use of VSL in rule-makings nor subjected the interim estimate to a 

scientific peer-review process through the Science Advisory Board (SAB) or other peer-review 

group. 

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by EPA and the SAB on combining estimates from the various 

data sources. In addition, the Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the 

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, 

appropriate meta-analytic techniques to combine estimates from unique data sources and 

different studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007). 

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the 

Guidelines for Preparing Economic Analyses (U.S. EPA, 2000)24 while the Agency continues its 

efforts to update its guidance on this issue. This approach calculates a mean value across VSL 

estimates derived from 26 labor market and contingent valuation studies published between 1974 

and 1991. The mean VSL across these studies is $6.3 million (2000$).25 The Agency is 

committed to using scientifically sound, appropriately reviewed evidence in valuing mortality 

risk reductions and has made significant progress in responding to the SAB-EEAC’s specific 

recommendations. The Agency anticipates presenting results from this effort to the SAB-EEAC 

in Spring 2010 and that draft guidance will be available shortly thereafter. 

As indicated in the previous section on quantification of premature mortality benefits, we 

assumed for this analysis that some of the incidences of premature mortality related to PM 

exposures occur in a distributed fashion over the 20 years following exposure. To take this into 

 

24 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008d), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 

near future. Therefore, this report does not represent final agency policy. 

25 In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income 

growth to 2014. After applying these adjustments to the $6.3 million value, the VSL is $7.8M. 
113

account in the valuation of reductions in premature mortality, we applied an annual 3% discount 

rate to the value of premature mortality occurring in future years.26

The economics literature concerning the appropriate method for valuing reductions in 

premature mortality risk is still developing. The adoption of a value for the projected reduction 

in the risk of premature mortality is the subject of continuing discussion within the economics 

and public policy analysis community. EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics, we use a single VSL for all reductions in mortality 

risk. 

Although there are several differences between the labor market studies EPA uses to 

derive a VSL estimate and the PM air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward adjustments. 

Table 5-11 lists some of these differences and the expected effect on the VSL estimate for air 

pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment 

factors, EPA believes it is reasonable to continue to use the $6.3 million value while 

acknowledging the significant limitations and uncertainties in the available literature. 

 

26 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. EPA adopted a 3% discount rate for its base estimate in this case to reflect reliance on a 

“social rate of time preference” discounting concept. We have also calculated benefits and costs using a 7% rate 

consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet 

regulatory requirements. In this case, the benefit and cost estimates were not significantly affected by the choice 

of discount rate. Further discussion of this topic appears in EPA’s Guidelines for Preparing Economic Analyses

(EPA, 2000b).
114

Table 5-8: Expected Impact on Estimated Benefits of Premature Mortality Reductions of 

Differences Between Factors Used in Developing Applied VSL and Theoretically 

Appropriate VSL 

Attribute Expected Direction of Bias 

Age Uncertain, perhaps overestimate 

Life Expectancy/Health Status Uncertain, perhaps overestimate 

Attitudes Toward Risk Underestimate 

Income Uncertain 

Voluntary vs. Involuntary Uncertain, perhaps underestimate 

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate 

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised EPA to “continue to use a wage-risk-based VSL 

as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of 

these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be 

made is the timing of the risk” (U.S. EPA, 2000a). In developing our primary estimate of the 

benefits of premature mortality reductions, we have followed this advice and discounted over the 

lag period between exposure and premature mortality. 

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits of the Transport Rule. In addition, in prior analyses, EPA has identified 

valuation of mortality-related benefits as the largest contributor to the range of uncertainty in 

monetized benefits (U.S. EPA, 1999b).27 Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand the 

various types of economic approaches available for valuing reductions in mortality risk. Such an 

assessment also requires an understanding of how alternative valuation approaches reflect that 

some individuals may be more susceptible to air pollution-induced mortality or reflect 

differences in the nature of the risk presented by air pollution relative to the risks studied in the 

relevant economics literature. 

 

27 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM 

mortality expert elicitation may result in different conclusions about the relative contribution of sources of 

uncertainty.
115

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival plus WTP to improve other individuals’ survival rates. 

The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

 Each individual’s survival curve, or the probability of surviving beyond a given age, should 

shift as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

such characteristics as age, health state, and the current age to which the individual is likely to 

survive. 

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

study we value reductions in premature mortality risk using the VSL approach. 

Other uncertainties specific to premature mortality valuation include the following: 

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs 

and data used in the existing literature, 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 hedonic wage approach. Certain characteristics of 

both the population affected and the mortality risk facing that population are believed to 

affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP 

based on the current VSL literature 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 the 

extent to which the risks being valued are similar and the extent to which the subjects in 

the studies are similar to the population affected by changes in pollution concentrations. 
116

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the PM NAAQS 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 provides a result that the average WTP for a reduction in 

mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting 

from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is 

linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP 

of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in 

the study). Under the assumption of linearity, the estimate of the VSL does not depend 

on the particular amount of risk reduction being valued. This assumption has been shown 

to be reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998). 

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may 

be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air 

pollution-related risks are incurred involuntarily. Some evidence suggests that people 

will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. If 

this is the case, WTP estimates based on wage-risk studies may understate WTP to 

reduce involuntarily incurred air pollution-related mortality risks. 

 Sudden versus protracted death: A final important difference related to the nature of the 

risk may be that some workplace mortality risks tend to involve sudden, catastrophic 

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that 

the mortality risks addressed in this assessment are associated with longer periods of 

illness or greater pain and suffering than are the risks addressed in the valuation 

literature, the WTP measurements employed in the present analysis would reflect a 

downward bias. 

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed 

in hedonic studies reflects the preferences of the marginal worker (i.e., that worker who 

demands the highest compensation for his risk reduction). This worker must have either 

a higher workplace risk than the average worker, a lower risk tolerance than the average 

worker, or both. However, the risk estimate used in hedonic studies is generally based on 
117

average risk, so the VSL may be upwardly biased because the wage differential and risk 

measures do not match. 

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age. 

5.4.4.2 Chronic Bronchitis Valuation 

The best available estimate of WTP to avoid a case of CB comes from Viscusi, Magat, 

and Huber (1991). The Viscusi, Magat, and Huber study, however, describes a severe case of 

CB to the survey respondents. We therefore employ an estimate of WTP to avoid a pollutionrelated case of CB, based on adjusting the Viscusi, Magat, and Huber (1991) estimate of the 

WTP to avoid a severe case. This is done to account for the likelihood that an average case of 

pollution-related CB is not as severe. The adjustment is made by applying the elasticity of WTP 

with respect to severity reported in the Krupnick and Cropper (1992) study. Details of this 

adjustment procedure are provided in the Benefits Technical Support Document (TSD) for the 

Nonroad Diesel rulemaking (Abt Associates, 2003). 

We use the mean of a distribution of WTP estimates as the central tendency estimate of 

WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates 

uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, 

Magat, and Huber; the severity level of an average pollution-related case of CB (relative to that 

of the case described by Viscusi, Magat, and Huber); and the elasticity of WTP with respect to 

severity of the illness. Based on assumptions about the distributions of each of these three 

uncertain components, we derive a distribution of WTP to avoid a pollution-related case of CB 

by statistical uncertainty analysis techniques. The expected value (i.e., mean) of this 

distribution, which is about $340,000 (2006$), is taken as the central tendency estimate of WTP 

to avoid a PM-related case of CB. 

5.4.4.3 Nonfatal Myocardial Infarctions Valuation

The Agency has recently incorporated into its analyses the impact of air pollution on the 

expected number of nonfatal heart attacks, although it has examined the impact of reductions in 

other related cardiovascular endpoints. We were not able to identify a suitable WTP value for 
118

reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two 

components: the direct medical costs and the opportunity cost (lost earnings) associated with the 

illness event. Because the costs associated with a myocardial infarction extend beyond the initial 

event itself, we consider costs incurred over several years. Using age-specific annual lost 

earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a 

present discounted value in lost earnings (in 2006$) over 5 years due to a myocardial infarction 

of $8,774 for someone between the ages of 25 and 44, $12,932 for someone between the ages of 

45 and 54, and $74,746 for someone between the ages of 55 and 65. The corresponding agespecific estimates of lost earnings (in 2006$) using a 7% discount rate are $7,855, $11,578, and 

$66,920, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for 

populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates 

for these age groups. 

We found three possible sources in the literature of estimates of the direct medical costs 

of myocardial infarction: 

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,211 (in 1986$) for people who were admitted to the hospital and 

survived hospitalization. (There does not appear to be any discounting used.) Wittels et 

al. was used to value coronary heart disease in the 812 Retrospective Analysis of the 

Clean Air Act. Using the CPI-U for medical care, the Wittels estimate is $144,111 in 

year 2006$. This estimated cost is based on a medical cost model, which incorporated 

therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” 

as consultants). The model used medical data and medical decision algorithms to 

estimate the probabilities of certain events and/or medical procedures being used. The 

authors note that the average length of hospitalization for acute myocardial infarction has 

decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 

1983). Wittels et al. used 10 days as the average in their study. It is unclear how much 

further the length of stay for myocardial infarction may have decreased from 1983 to the 

present. The average length of stay for ICD code 410 (myocardial infarction) in the year2000 Agency for Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days. 

However, this may include patients who died in the hospital (not included among our 

nonfatal myocardial infarction cases), whose length of stay was therefore substantially 

shorter than it would be if they had not died. 

 Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $64,003 in 2006$ 

for myocardial infarction patients, using statistical prediction (regression) models to 

estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were 

included. 
119

 Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal 

myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. Converting 

to year 2006$, that would be $30,102 for a 5-year period (without discounting) or 

$38,113 for a 10-year period. 

In summary, the three different studies provided significantly different values (see Table 

5-9). 

Table 5-9: Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks 

Study Direct Medical Costs (2006$) Over an x-Year Period, for x = 

Wittels et al. (1990) $144,111a

 5 

Russell et al. (1998) $30,102b 5 

Eisenstein et al. (2001) $64,003b

 10 

Russell et al. (1998) $38,113b

 10 

a Wittels et al. (1990) did not appear to discount costs incurred in future years. 

b Using a 3% discount rate. Discounted values as reported in the study. 

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from Wittels 

et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

$65,902, and added it to the 5-year opportunity cost estimate. The resulting estimates are given 

in Table 5-10. 

Table 5-10: Estimated Costs Over a 5-Year Period (in 2006$) of a Nonfatal Myocardial 

Infarction 

Age Group Opportunity Cost Medical Costa Total Cost 

0–24 $0 $84,955 $84,955 

25–44 $10,757b $84,955 $95,713 

45–54 $15,855b

 $84,955 $100,811 

55–65 $91,647b

 $84,955 $176,602 

> 65 $0 $84,955 $84,955 

a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). 

b From Cropper and Krupnick (1990), using a 3% discount rate. 
120

5.4.4.4 Hospital Admissions Valuation 

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost 

productivity) typically are used as conservative, or lower bound, estimates. These estimates are 

biased downward, because they do not include the willingness-to-pay value of avoiding pain and 

suffering. 

The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates 

used in this analysis consist of estimated hospital charges and the estimated opportunity cost of 

time spent in the hospital (based on the average length of a hospital stay for the illness). We 

based all estimates of hospital charges and length of stays on statistics provided by the Agency 

for Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day 

spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized 

individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median 

weekly wage by five and inflated the result to year 2006$ using the CPI-U “all items.” The 

resulting estimate is $127.93. The total cost-of-illness estimate for an ICD code-specific hospital 

stay lasting n days, then, was the mean hospital charge plus $127.93 multiplied by n. 

Table 5-11: Unit Values for Economic Valuation of Health Endpoints (2006$) 

Central Estimate of Value Per Statistical 

Incidence 

Health Endpoint 

2000 

Income Level 

2014 Income Level Derivation of 

Distributions of 

Estimates 

Premature Mortality (Value of a Statistical 

Life) 

$6,300,000 $7,800,000 EPA currently 

recommends a central 

VSL of $6.3m (2000$) 

based on a Weibull 

distribution fitted to 26 

published VSL 

estimates (5 contingent 

valuation and 21 labor 

market studies). The 

underlying studies, the 

distribution 

parameters, and other 

useful information are 

available in Appendix 

B of EPA's current 

Guidelines for 

Preparing Economic 

Analyses (U.S. EPA, 

2000). 
121

Chronic Bronchitis (CB) $340,000 $460,000 The WTP to avoid a 

case of pollutionrelated CB is 

calculated as where x 

is the severity of an 

average CB case, 

WTP13 is the WTP for 

a severe case of CB, 

and $ is the parameter 

relating WTP to 

severity, based on the 

regression results 

reported in Krupnick 

and Cropper (1992). 

The distribution of 

WTP for an average 

severity-level case of 

CB was generated by 

Monte Carlo methods, 

drawing from each of 

three distributions: (1) 

WTP to avoid a severe 

case of CB is assigned 

a 1/9 probability of 

being each of the first 

nine deciles of the 

distribution of WTP 

responses in Viscusi et 

al. (1991); (2) the 

severity of a pollutionrelated case of CB 

(relative to the case 

described in the 

Viscusi study) is 

assumed to have a 

triangular distribution, 

with the most likely 

value at severity level 

6.5 and endpoints at 

1.0 and 12.0; and (3) 

the constant in the 

elasticity of WTP with 

respect to severity is 

normally distributed 

with mean = 0.18 and 

standard deviation = 

0.0669 (from Krupnick 

and Cropper [1992]). 

This process and the 

rationale for choosing 
122

it is described in detail 

in the Costs and 

Benefits of the Clean 

Air Act, 1990 to 2010 

(U.S. EPA, 1999b). 

Nonfatal Myocardial Infarction (heart attack) 

 3% discount rate

 Age 0–24 

 Age 25–44 

 Age 45–54 

 Age 55–65 

 Age 66 and over 

 7% discount rate

 Age 0–24 

 Age 25–44 

 Age 45–54 

 Age 55–65 

 Age 66 and over 

$79,685 

$88,975 

$93,897 

$167,532 

$79,685 

$77,769 

$87,126 

$91,559 

$157,477 

$77,769 

$79,685 

$88,975 

$93,897 

$167,532 

$79,685 

$77,769 

$87,126 

$91,559 

$157,477 

$77,769 

No distributional 

information available. 

Age-specific cost-ofillness values reflect 

lost earnings and direct 

medical costs over a 5-

year period following a 

nonfatal MI. Lost 

earnings estimates are 

based on Cropper and 

Krupnick (1990). 

Direct medical costs 

are based on simple 

average of estimates 

from Russell et al. 

(1998) and Wittels et 

al. (1990). 

Lost earnings: 

Cropper and Krupnick 

(1990). Present 

discounted value of 5 

years of lost earnings: 

age of onset: at 3%

25–44 $8,774 $7,85

45–54 $12,932 11,57

55–65 $74,746 66,92

Direct medical 

expenses: An average 

of: 

1. Wittels et al. 

(1990) 

($102,658—no 

discounting) 

2. Russell et al. 

(1998), 5-year 

period ($22,331 at 

3% discount rate; 

$21,113 at 7% 

discount rate) 

Hospital Admissions 

Chronic Obstructive Pulmonary Disease 

(COPD) 

$16,606 $16,606 No distributional 

information available. 

The COI estimates 
123

(lost earnings plus 

direct medical costs) 

are based on ICD-9 

code-level information 

(e.g., average hospital 

care costs, average 

length of hospital stay, 

and weighted share of 

total COPD category 

illnesses) reported in 

Agency for Healthcare 

Research and Quality 

(2000) 

(www.ahrq.gov). 

Asthma Admissions $8,900 $8,900 No distributional 

information available. 

The COI estimates 

(lost earnings plus 

direct medical costs) 

are based on ICD-9 

code-level information 

(e.g., average hospital 

care costs, average 

length of hospital stay, 

and weighted share of 

total asthma category 

illnesses) reported in 

Agency for Healthcare 

Research and Quality 

(2000) 

(www.ahrq.gov). 

All Cardiovascular $24,668 $24,668 No distributional 

information available. 

The COI estimates 

(lost earnings plus 

direct medical costs) 

are based on ICD-9 

code-level information 

(e.g., average hospital 

care costs, average 

length of hospital stay, 

and weighted share of 

total cardiovascular 

category illnesses) 

reported in Agency for 

Healthcare Research 

and Quality (2000) 

(www.ahrq.gov). 
124

All respiratory (ages 65+) $24,622 $24,622 No distributions 

available. The COI 

point estimates (lost 

earnings plus direct 

medical costs) are 

based on ICD-9 code 

level information (e.g., 

average hospital care 

costs, average length 

of hospital stay, and 

weighted share of total 

COPD category 

illnesses) reported in 

Agency for Healthcare 

Research and Quality, 

2000 (www.ahrq.gov). 

All respiratory (ages 0–2) $10,385 $10,385 No distributions 

available. The COI 

point estimates (lost 

earnings plus direct 

medical costs) are 

based on ICD-9 code 

level information (e.g., 

average hospital care 

costs, average length 

of hospital stay, and 

weighted share of total 

COPD category 

illnesses) reported in 

Agency for Healthcare 

Research and Quality, 

2000 (www.ahrq.gov). 

Emergency Room Visits for Asthma $384 $384 No distributional 

information available. 

Simple average of two 

unit COI values: 

(1) $311.55, from 

Smith et al. (1997) and 

(2) $260.67, from 

Stanford et al. (1999). 

Respiratory Ailments Not Requiring Hospitalization 

Upper Respiratory Symptoms (URS) $30 $30 Combinations of the 

three symptoms for 

which WTP estimates 

are available that 

closely match those 

listed by Pope et al. 

result in seven 
125

different “symptom 

clusters,” each 

describing a “type” of 

URS. A dollar value 

was derived for each 

type of URS, using 

mid-range estimates of 

WTP (IEc, 1994) to 

avoid each symptom in 

the cluster and 

assuming additivity of 

WTPs. In the absence 

of information 

surrounding the 

frequency with which 

each of the seven types 

of URS occurs within 

the URS symptom 

complex, we assumed 

a uniform distribution 

between $9.2 and 

$43.1. 

Lower Respiratory Symptoms (LRS) $16 $19 Combinations of the 

four symptoms for 

which WTP estimates 

are available that 

closely match those 

listed by Schwartz et 

al. result in 11 different 

“symptom clusters,” 

each describing a 

“type” of LRS. A 

dollar value was 

derived for each type 

of LRS, using midrange estimates of 

WTP (IEc, 1994) to 

avoid each symptom in 

the cluster and 

assuming additivity of 

WTPs. The dollar 

value for LRS is the 

average of the dollar 

values for the 11 

different types of LRS. 

In the absence of 

information 

surrounding the 

frequency with which 

each of the 11 types of 
126

LRS occurs within the 

LRS symptom 

complex, we assumed 

a uniform distribution 

between $6.9 and 

$24.46. 

Asthma Exacerbations $43 $53 Asthma exacerbations 

are valued at $45 per 

incidence, based on the 

mean of average WTP 

estimates for the four 

severity definitions of 

a “bad asthma day,” 

described in Rowe and 

Chestnut (1986). This 

study surveyed 

asthmatics to estimate 

WTP for avoidance of 

a “bad asthma day,” as 

defined by the 

subjects. For purposes 

of valuation, an asthma 

exacerbation is 

assumed to be 

equivalent to a day in 

which asthma is 

moderate or worse as 

reported in the Rowe 

and Chestnut (1986) 

study. The value is 

assumed have a 

uniform distribution 

between $15.6 and 

$70.8. 

Acute Bronchitis $360 $440 Assumes a 6-day 

episode, with the 

distribution of the daily 

value specified as 

uniform with the low 

and high values based 

on those recommended 

for related respiratory 

symptoms in Neumann 

et al. (1994). The low 

daily estimate of $10 is 

the sum of the midrange values 

recommended by IEc 

(1994) for two 
127

symptoms believed to 

be associated with 

acute bronchitis: 

coughing and chest 

tightness. The high 

daily estimate was 

taken to be twice the 

value of a minor 

respiratory restrictedactivity day, or $110. 

Work Loss Days (WLDs) Variable (U.S. 

median = $130) 

Variable (U.S. median 

= $130) 

No distribution 

available. Point 

estimate is based on 

county-specific median 

annual wages divided 

by 50 (assuming 2 

weeks of vacation) and 

then by 5—to get 

median daily wage. 

U.S. Year 2000 

Census, compiled by 

Geolytics, Inc. 

Minor Restricted Activity Days (MRADs) $51 $62 Median WTP estimate 

to avoid one MRAD 

from Tolley et al. 

(1986). Distribution is 

assumed to be 

triangular with a 

minimum of $22 and a 

maximum of $83, with 

a most likely value of 

$52. Range is based on 

assumption that value 

should exceed WTP 

for a single mild 

symptom (the highest 

estimate for a single 

symptom—for eye 

irritation—is $16.00) 

and be less than that 

for a WLD. The 

triangular distribution 

acknowledges that the 

actual value is likely to 

be closer to the point 

estimate than either 

extreme. 
128

School Absence Days $89 $89 No distribution 

available 

5.4.4.5 Asthma-Related Emergency Room Visits Valuation 

To value asthma emergency room visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency room visits in 1987, at a total 

cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2006$, that 

cost was $400.88 (using the CPI-U for medical care to adjust to 2006$). The second estimate 

comes from Stanford et al. (1999), who reported the cost of an average asthma-related 

emergency room visit at $335.14, based on 1996–1997 data. A simple average of the two 

estimates yields a (rounded) unit value of $368. 

5.4.4.6 Minor Restricted Activity Days Valuation 

No studies are reported to have estimated WTP to avoid a minor restricted activity 

day. However, one of EPA’s contractors, IEc (1994) has derived an estimate of willingness 

to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. 

(1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. The 

IEc estimate of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or 

about $62.04 (2006$). 

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted 

activity days, it is likely that most MRADs associated with ozone exposure are, in fact, minor 

respiratory restricted activity days. For the purpose of valuing this health endpoint, we used 

the estimate of mean WTP to avoid a minor respiratory restricted activity day. 

5.4.4.7 School Absences Valuation 

To value a school absence, we: (1) estimated the probability that if a school child 

stays home from school, a parent will have to stay home from work to care for the child; and 

(2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of 

families with school-age children in which both parents work, and we valued a school-loss 
129

day as the probability that such a day also would result in a work-loss day. We calculated this 

value by multiplying the proportion of households with school-age children by a measure of 

lost wages. 

We used this method in the absence of a preferable WTP method. However, this 

approach suffers from several uncertainties. First, it omits willingness to pay to avoid the 

symptoms/illness that resulted in the school absence; second, it effectively gives zero value 

to school absences that do not result in work-loss days; and third, it uses conservative 

assumptions about the wages of the parent staying home with the child. Finally, this method 

assumes that parents are unable to work from home. If this is not a valid assumption, then 

there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working 

parents, the female parent will stay home with a sick child. From the Statistical Abstract of 

the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single, 

married and “other” (widowed, divorced or separated) working women with children; and (2) 

the rates of participation in the workforce of single, married and “other” women with 

children. From these two sets of statistics, we calculated a weighted average participation 

rate of 72.85 percent. 

Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick 

child) is based on the year 2006 median weekly wage among women ages 25 and older (U.S. 

Census Bureau, 2001). This median weekly wage is $655. Dividing by five gives an 

estimated median daily wage of $131. To estimate the expected lost wages on a day when a 

mother has to stay home with a school-age child, we first estimated the probability that the 

mother is in the workforce then multiplied that estimate by the daily wage she would lose by 

missing a workday: 72.85 percent times $131, for a total loss of $95.43. This valuation 

approach is similar to that used by Hall et al. (2003). 

5.4.4.8 Visibility Valuation 

Reductions in NO2 and SO2 emissions along with the secondary formation of PM2.5 

would improve the level of visibility throughout the United States because these suspended 

particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009d). 
130

Visibility has direct significance to people’s enjoyment of daily activities and their overall 

sense of wellbeing (U.S. EPA, 2009d). Individuals value visibility both in the places they 

live and work, in the places they travel to for recreational purposes, and at sites of unique 

public value, such as the Great Smokey Mountains National Park. This section discusses the 

measurement of the economic benefits of improved visibility. 

Visual air quality (VAQ) is commonly measured as either light extinction, which is 

defined as the loss of light per unit of distance in terms of inverse megameters (Mm-1) or the 

deciview (dv) metric (Pitchford and Malm, 1993), which is a logarithmic function of 

extinction. Extinction and deciviews are physical measures of the amount of visibility 

impairment (e.g., the amount of “haze”), with both extinction and deciview increasing as the 

amount of haze increases. Pitchford and Malm characterize a change of one deciview as “a 

small but perceptible scenic change under many circumstances.” Light extinction is the 

optical characteristic of the atmosphere that occurs when light is either scattered or absorbed, 

which converts the light to heat. Particulate matter and gases can both scatter and absorb 

light. Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler, 1996). The extent to which any amount of 

light extinction affects a person’s ability to view a scene depends on both scene and light 

characteristics. For example, the appearance of a nearby object (i.e. a building) is generally 

less sensitive to a change in light extinction than the appearance of a similar object at a 

greater distance. See Figure 5-3 for an illustration of the important factors affecting 

visibility. 
131

Figure 5-3: Important factors involved in seeing a scenic vista (Malm, 1999) 

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other 

Federal land managers, and State organizations in the U.S., the U.S. EPA has supported 

visibility monitoring in national parks and wilderness areas since 1988. The monitoring 

network known as IMPROVE (Interagency Monitoring of Protected Visual Environments) 

now includes 150 sites that represent almost all of the Class I areas across the country (see 

Figure 5-4) (U.S. EPA, 2009d). 
132

Figure 5-4: Mandatory Class I Areas in the U.S. 

Annual average visibility conditions (reflecting light extinction due to both 

anthropogenic and non-anthropogenic sources) vary regionally across the U.S. (U.S. EPA, 

2009d). The rural East generally has higher levels of impairment than remote sites in the 

West, with the exception of urban-influenced sites such as San Gorgonio Wilderness (CA) 

and Point Reyes National Seashore (CA), which have annual average levels comparable to 

certain sites in the Northeast (U.S. EPA, 2004). Higher visibility impairment levels in the 

East are due to generally higher concentrations of fine particles, particularly sulfates, and 

higher average relative humidity levels. While visibility trends have improved in most Class 

I areas, the recent data show that these areas continue to suffer from visibility impairment. In 

eastern parks, average visual range has decreased from 90 miles to 15-25 miles, and in the 

West, visual range has decreased from 140 miles to 35-90 miles (U.S. EPA, 2004; U.S. EPA, 

1999b). 
133

 EPA distinguishes benefits from two categories of visibility changes: residential 

visibility and recreational visibility. In both cases economic benefits are believed to consist 

of use values and nonuse values. Use values include the aesthetic benefits of better visibility, 

improved road and air safety, and enhanced recreation in activities like hunting and 

birdwatching. Nonuse values are based on people’s beliefs that the environment ought to 

exist free of human-induced haze. Nonuse values may be more important for recreational 

areas, particularly national parks and monuments. 

Residential visibility benefits are those that occur from visibility changes in urban, 

suburban, and rural areas. In previous assessments, EPA used a study on residential 

visibility valuation conducted in 1990 (McClelland et al., 1993). Subsequently, EPA 

designated the McClelland et al. study as significantly less reliable for regulatory benefit-cost 

analysis consistent with SAB advice (U.S. EPA-SAB, 1999). Although a wide range of 

published, peer-review literature supports a non-zero value for residential visibility 

(Brookshire et al., 1982; Rae, 1983; Tolley et al., 1986; Chestnut and Rowe, 1990c; 

McClleand et al., 1993; Loehman et al., 1994), the residential visibility benefits have not 

been calculated in this analysis. 

For recreational visibility, only one existing study provides defensible monetary 

estimates of the value of visibility changes in a 1988 survey on recreational visibility value 

(Chestnut and Rowe, 1990a; 1990b). Although there are a number of other studies in the 

literature, they were conducted in the early 1980s and did not use methods that are 

considered defensible by current standards. The Chestnut and Rowe study uses the CV 

method. There has been a great deal of controversy and significant development of both 

theoretical and empirical knowledge about how to conduct CV surveys in the past decade. In 

EPA’s judgment, the Chestnut and Rowe study contains many of the elements of a valid CV 

study and is sufficiently reliable to serve as the basis for monetary estimates of the benefits 

of visibility changes in recreational areas.28 This study serves as an essential input to our 

 

28 An SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe 

study is the best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did 

not formally approve use of these estimates because of concerns about the peer-reviewed status of the study. 

EPA believes the study has received adequate review and has been cited in numerous peer-reviewed 

publications (Chestnut and Dennis, 1997).
134

estimates of the benefits of recreational visibility improvements in the primary benefits 

estimates. 

For the purposes of this analysis, recreational visibility improvements are defined as 

those that occur specifically in federal Class I areas.29 A key distinction between 

recreational and residential benefits is that only those people living in residential areas are 

assumed to receive benefits from residential visibility, while all households in the United 

States are assumed to derive some benefit from improvements in Class I areas. Values are 

assumed to be higher if the Class I area is located close to their home.30 The Chestnut and 

Rowe study measured the demand for visibility in Class I areas managed by the National 

Park Service (NPS) in three broad regions of the country: California, the Southwest, and the 

Southeast. Respondents in five states were asked about their WTP to protect national parks 

or NPS-managed wilderness areas within a particular region. The survey used photographs 

reflecting different visibility levels in the specified recreational areas. The visibility levels in 

these photographs were later converted to deciviews for the current analysis. The survey 

data collected were used to estimate a WTP equation for improved visibility. In addition to 

the visibility change variable, the estimating equation also included household income as an 

explanatory variable. 

The Chestnut and Rowe study did not measure values for visibility improvement in 

Class I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the 

United States. We can infer the value of visibility changes in the other Class I areas by 

transferring values of visibility changes at Class I areas in the study regions. A complete 

description of the benefits transfer method used to infer values for visibility changes in Class 

I areas outside the study regions is provided in the Benefits TSD for the Nonroad Diesel 

rulemaking (Abt Associates, 2003). 

 

29 The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility 

protection.

30 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the 

Nonroad Diesel rulemaking (Abt Associates, 2003).
135

The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b), although 

representing the best available estimates, has a number of limitations. These include the 

following: 

 The age of the study (late 1980s) will increase the uncertainty about the 

correspondence of the estimated values to those that might be provided by current or 

future populations. 

 The survey focused only on populations in five states, so the application of the 

estimated values to populations outside those states requires that preferences of 

populations in the five surveyed states be similar to those of non-surveyed states. 

 There is an inherent difficulty in separating values expressed for visibility 

improvements from an overall value for improved air quality. The Chestnut and 

Rowe study attempted to control for this by informing respondents that “other 

households are being asked about visibility, human health, and vegetation protections 

in urban areas and at national parks in other regions.” However, most of the 

respondents did not feel that they were able to segregate visibility at national parks 

entirely from residential visibility and health effects. 

 It is not clear exactly what visibility improvements the respondents to the Chestnut 

and Rowe survey were valuing. The WTP question asked about changes in average 

visibility, but the survey respondents were shown photographs of only summertime 

conditions, when visibility is generally at its worst. It is possible that the respondents 

believed those visibility conditions held year-round, in which case they would have 

been valuing much larger overall improvements in visibility than what otherwise 

would be the case. For the purpose of the benefits analysis for this rule, EPA assumed 

that respondents provided values for changes in annual average visibility. Because 

most policies will result in a shift in the distribution of visibility (usually affecting the 

worst days more than the best days), the annual average may not be the most relevant 

metric for policy analysis. 

 The survey did not include reminders of possible substitutes (e.g., visibility at other 

parks) or budget constraints. These reminders are considered to be best practice for 

stated preference surveys. 

 The Chestnut and Rowe survey focused on visibility improvements in and around 

national parks and wilderness areas. The survey also focused on visibility 

improvements of national parks in the southwest United States. Given that national 

parks and wilderness areas exhibit unique characteristics, it is not clear whether the 
136

WTP estimate obtained from Chestnut and Rowe can be transferred to other national 

parks and wilderness areas, without introducing additional uncertainty. 

In general, the survey design and implementation reflect the period in which the 

survey was conducted. Since that time, many improvements to the stated preference 

methodology have been developed. As future survey efforts are completed, EPA will 

incorporate values for visibility improvements reflecting the improved survey designs. 

The estimated relationship from the Chestnut and Rowe study is only directly 

applicable to the populations represented by survey respondents. EPA used benefits transfer 

methodology to extrapolate these results to the population affected by the reductions in 

precursor emissions associated with this rule. A general WTP equation for improved 

visibility (measured in deciviews) was developed as a function of the baseline level of 

visibility, the magnitude of the visibility improvement, and household income. The 

behavioral parameters of this equation were taken from analysis of the Chestnut and Rowe 

data. These parameters were used to calibrate WTP for the visibility changes resulting from 

this rule. The method for developing calibrated WTP functions is based on the approach 

developed by Smith et al. (2002). Available evidence indicates that households are willing to 

pay more for a given visibility improvement as their income increases (Chestnut, 1997). The 

benefits estimates here incorporate Chestnut’s estimate that a 1% increase in income is 

associated with a 0.9% increase in WTP for a given change in visibility. A more detailed 

explanation of the visibility benefits methodology is provided in Appendix I of the PM 

NAAQS RIA (U.S. EPA, 2006). 

One major source of uncertainty for the visibility benefits estimate is the benefits 

transfer process used. Judgments used to choose the functional form and key parameters of 

the estimating equation for WTP for the affected population could have significant effects on 

the size of the estimates. Assumptions about how individuals respond to changes in visibility 

that are either very small or outside the range covered in the Chestnut and Rowe study could 

also affect the results. 

 In addition, our estimate of visibility benefits is incomplete. For example, we 

anticipate improvement in visibility in residential areas within the Transport Rule region for 

which we are currently unable to monetize benefits, such as the Northeastern and Central 
137

regions of the U.S. The value of visibility benefits in areas where we were unable to 

monetize benefits could also be substantial. EPA requests public comment on the approach 

taken here to quantify the monetary value of changes in visibility in Class I areas. 

5.4.4.9 Growth in WTP Reflecting National Income Growth Over Time 

Our analysis accounts for expected growth in real income over time. Economic 

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

real incomes increase. There is substantial empirical evidence that the income elasticity31 of 

WTP for health risk reductions is positive, although there is uncertainty about its exact value. 

 Thus, as real income increases, the WTP for environmental improvements also increases. 

Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% 

higher real income level implies a 10% higher WTP to reduce risk changes), empirical 

evidence suggests that income elasticity is substantially less than one and thus relatively 

inelastic. As real income rises, the WTP value also rises but at a slower rate than real 

income. 

The effects of real income changes on WTP estimates can influence benefits 

estimates in two different ways: through real income growth between the year a WTP study 

was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. 

Empirical evidence of the effect of real income on WTP gathered to date is based on studies 

examining the former. The Environmental Economics Advisory Committee (EEAC) of the 

Science Advisory Board (SAB) advised EPA to adjust WTP for increases in real income over 

time but not to adjust WTP to account for cross-sectional income differences “because of the 

sensitivity of making such distinctions, and because of insufficient evidence available at 

present” (U.S. EPA-SAB, 2000a). A recent advisory by another committee associated with 

the SAB, the Advisory Council on Clean Air Compliance Analysis, has provided conflicting 

advice. While agreeing with “the general principle that the willingness to pay to reduce 

mortality risks is likely to increase with growth in real income (U.S. EPA-SAB, 2004a, p. 

52)” and that “The same increase should be assumed for the WTP for serious nonfatal health 

 

31 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income.
138

effects (U.S. EPA-SAB, 2004a, p. 52),” they note that “given the limitations and 

uncertainties in the available empirical evidence, the Council does not support the use of the 

proposed adjustments for aggregate income growth as part of the primary analysis (U.S. 

EPA-SAB, 2004a, p. 53).” Until these conflicting advisories have been reconciled, EPA will 

continue to adjust valuation estimates to reflect income growth using the methods described 

below, while providing sensitivity analyses for alternative income growth adjustment factors. 

Based on a review of the available income elasticity literature, we adjusted the 

valuation of human health benefits upward to account for projected growth in real U.S. 

income. Faced with a dearth of estimates of income elasticities derived from time-series 

studies, we applied estimates derived from cross-sectional studies in our analysis. Details of 

the procedure can be found in Kleckner and Neumann (1999). An abbreviated description of 

the procedure we used to account for WTP for real income growth between 1990 and 2014 is 

presented below. 

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe 

and chronic health effects, and premature mortality. Note that because of the variety of 

empirical sources used in deriving the income elasticities, there may appear to be 

inconsistencies in the magnitudes of the income elasticities relative to the severity of the 

effects (a priori one might expect that more severe outcomes would show less income 

elasticity of WTP). We have not imposed any additional restrictions on the empirical 

estimates of income elasticity. One explanation for the seeming inconsistency is the 

difference in timing of conditions. WTP for minor illnesses is often expressed as a short 

term payment to avoid a single episode. WTP for major illnesses and mortality risk 

reductions are based on longer term measures of payment (such as wages or annual income). 

 Economic theory suggests that relationships become more elastic as the length of time 

grows, reflecting the ability to adjust spending over a longer time period. Based on this 

theory, it would be expected that WTP for reducing long term risks would be more elastic 

than WTP for reducing short term risks. We also expect that the WTP for improved visibility 

in Class I areas would increase with growth in real income. The relative magnitude of the 

income elasticity of WTP for visibility compared with those for health effects suggests that 
139

visibility is not as much of a necessity as health, thus, WTP is more elastic with respect to 

income. The elasticity values used to adjust estimates of benefits in 2014 are presented in 

Table 5-12. 

Table 5-12: Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate 

Minor Health Effect 0.14 

Severe and Chronic Health Effects 0.45 

Premature Mortality 0.40 

Visibility 0.90 

a Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI 

estimates are assigned an adjustment factor of 1.0. 

In addition to elasticity estimates, projections of real gross domestic product (GDP) 

and populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita 

income growth. For consistency with the emissions and benefits modeling, we used national 

population estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates 

(Hollman, Mulder, and Kallan, 2000). These population estimates are based on application 

of a cohort-component model applied to 1990 U.S. Census data projections (U.S. Bureau of 

Census, 2000). For the years between 2000 and 2014, we applied growth rates based on the 

U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. 

We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 

1990 to 2010.32 We used projections of real GDP (in chained 1996 dollars) provided by 

Standard and Poor’s (2000) for the years 2010 to 2014.33

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 5-13. Benefits for each of the categories (minor health effects, 

 

32 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/ 

tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections 

for 2007 to 2010 are based on average GDP growth rates between 1999 and 2007.

33 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the 

relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP 

based on the Bureau of Economic Analysis projections.
140

severe and chronic health effects, premature mortality, and visibility) are adjusted by 

multiplying the unadjusted benefits by the appropriate adjustment factor. Note that, for 

premature mortality, we applied the income adjustment factor to the present discounted value 

of the stream of avoided mortalities occurring over the lag period. Also note that because of 

a lack of data on the dependence of COI and income, and a lack of data on projected growth 

in average wages, no adjustments are made to benefits based on the COI approach or to work 

loss days and worker productivity. This assumption leads us to underpredict benefits in 

future years because it is likely that increases in real U.S. income would also result in 

increased COI (due, for example, to increases in wages paid to medical workers) and 

increased cost of work loss days and lost worker productivity (reflecting that if worker 

incomes are higher, the losses resulting from reduced worker production would also be 

higher). 

Table 5-13: Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2014 

Minor Health Effect 1.04 

Severe and Chronic Health Effects 1.16 

Premature Mortality 1.14 

Visibility 1.35 

a

 Based on elasticity values reported in Table 5-3, U.S. Census population projections, and projections of 

real GDP per capita. 

5.5 Unquantified Health and Welfare Benefits 

This analysis is limited by the available data and resources. As such, we are not able 

to quantify several welfare benefit categories, as shown in Table 5-2. In this section, we 

provide a qualitative assessment of some of the primary welfare benefit categories from 

reducing NO2 and SO2 emissions: health and ecosystem benefits of reducing nitrogen and 

sulfur emissions and deposition and vegetation benefits from reducing ozone.34 While we 

were unable to quantify how large these benefits might be as a result of the emission 

 

34 Some quantitative estimates of the total value of certain recreational and environmental goods given current 

and historic emission levels are provided below. They do not reflect benefits that would accrue as a result of this 

result. However, these values would be expected to increase as emissions are decreased a result of this rule. 
141

reductions achieved by this rule , previous EPA assessments show that these benefits could 

be substantial (U.S. EPA, 2008f; U.S. EPA, 2009c; U.S. EPA, 2007b; U.S. EPA, 1999b). 

The omission of these endpoints from the monetized results should not imply that the 

impacts are small or unimportant. 

5.5.1 Ecosystem Services 

Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. EPA has defined ecological goods and services as the 

“outputs of ecological functions or processes that directly or indirectly contribute to social 

welfare or have the potential to do so in the future. Some outputs may be bought and sold, 

but most are not marketed” (U.S. EPA, 2006b). Figure 5-5 provides the Millennium 

Ecosystem Assessment’s schematic demonstrating the connections between the categories of 

ecosystem services and human well-being. The interrelatedness of these categories means 

that any one ecosystem may provide multiple services. Changes in these services can affect 

human well-being by affecting security, health, social relationships, and access to basic 

material goods (MEA, 2005). 

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are 

classified into four main categories: 

1. Provisioning: Products obtained from ecosystems, such as the production of food and 

water 

2. Regulating: Benefits obtained from the regulation of ecosystem processes, such as the 

control of climate and disease 

3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual 

enrichment, cognitive development, reflection, recreation, and aesthetic experiences 

4. Supporting: Services necessary for the production of all other ecosystem services, 

such as nutrient cycles and crop pollination
142

Figure 5-5: Linkages between categories of ecosystem services and components of 

human well-being from Millennium Ecosystem Assessment (MEA, 2005)

The monetization of ecosystem services generally involves estimating the value of 

ecological goods and services based on what people are willing to pay (WTP) to increase 

ecological services or by what people are willing to accept (WTA) in compensation for 

reductions in them (U.S. EPA, 2006b). There are three primary approaches for estimating 

the monetary value of ecosystem services: market-based approaches, revealed preference 

methods, and stated preference methods (U.S. EPA, 2006b). Because economic valuation of 

ecosystem services can be difficult, nonmonetary valuation using biophysical measurements 

and concepts also can be used. An example of a nonmonetary valuation method is the use of 

relative-value indicators (e.g., a flow chart indicating uses of a water body, such as boatable, 

fishable, swimmable, etc.). It is necessary to recognize that in the analysis of the 

environmental responses associated with any particular policy or environmental management 

action, only a subset of the ecosystem services likely to be affected are readily identified. Of 

those ecosystem services that are identified, only a subset of the changes can be quantified. 

Within those services whose changes can be quantified, only a few will likely be monetized, 

and many will remain nonmonetized. The stepwise concept leading up to the valuation of 

ecosystems services is graphically depicted in Figure 5-6. 
143

Figure 5-6: Schematic of the benefits assessment process (U.S. EPA, 2006b)

5.5.2 Ecosystem Benefits of Reduced Nitrogen and Sulfur Deposition 

5.5.2.1 Science of Deposition 

Nitrogen and sulfur emissions occur over large regions of North America. Once 

these pollutants are lofted to the middle and upper troposphere, they typically have a much 

longer lifetime and, with the generally stronger winds at these altitudes, can be transported 

long distances from their source regions. The length scale of this transport is highly variable 

owing to differing chemical and meteorological conditions encountered along the transport 

path (U.S. EPA, 2008f).. Sulfur is primarily emitted as SO2, and nitrogen can be emitted as 

NO, NO2, or NH3. Secondary particles are formed from NOx and SOX gaseous emissions and 

associated chemical reactions in the atmosphere. Deposition can occur in either a wet (i.e., 

rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., gases or particles). Together these 

emissions are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing 

to the problems of acidification, nutrient enrichment, and methylmercury production as 

represented in Figure 5-7. Although there is some evidence that nitrogen deposition may 

have positive effects on agricultural and forest output through passive fertilization, it is likely 

that the overall value is very small relative to other health and welfare effects. 
144

Figure 5-7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

The lifetimes of particles vary with particle size. Accumulation-mode particles such 

as sulfates are kept in suspension by normal air motions and have a lower deposition velocity 

than coarse-mode particles; they can be transported thousands of kilometers and remain in 

the atmosphere for a number of days. They are removed from the atmosphere primarily by 

cloud processes. Particulates affect acid deposition by serving as cloud condensation nuclei 

and contribute directly to the acidification of rain. In addition, the gas-phase species that 

NO2 Atmospheric 

Fate and Transport 

Deposition

Processes

Acidification Nutrient 

Enrichment

Aquatic Terrestrial Aquatic Terrestrial

SO2 Atmospheric 

Fate and Transport 

Deposition 

Processes 

Acidification

MeHg Production

Aquatic Terrestrial Aquatic Terrestrial
145

lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in 

NO2 and SO2 emissions will decrease both acid deposition and PM concentrations, but not 

necessarily in a linear fashion. (U.S. EPA, 2008f). Sulfuric acid is also deposited on 

surfaces by dry deposition and can contribute to environmental effects (U.S. EPA, 2008f). 

5.5.2.2 Ecological Effects of Acidification 

Deposition of nitrogen and sulfur can cause acidification, which alters 

biogeochemistry and affects animal and plant life in terrestrial and aquatic ecosystems across 

the U.S. Soil acidification is a natural process, but is often accelerated by acidifying 

deposition, which can decrease concentrations of exchangeable base cations in soils (U.S. 

EPA, 2008f). Major terrestrial effects include a decline in sensitive tree species, such as red 

spruce (Picea rubens) and sugar maple (Acer saccharum) (U.S. EPA, 2008f). Biological 

effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity and 

decreased ability of plant roots to take up base cations (U.S. EPA, 2008f). Decreases in the 

acid neutralizing capacity and increases in inorganic aluminum concentration contribute to 

declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems 

(U.S. EPA, 2008f). 

Geology (particularly surficial geology) is the principal factor governing the 

sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur 

deposition (U.S. EPA, 2008f). Geologic formations having low base cation supply generally 

underlie the watersheds of acid-sensitive lakes and streams. Other factors contribute to the 

sensitivity of soils and surface waters to acidifying deposition, including topography, soil 

chemistry, land use, and hydrologic flow path (U.S. EPA, 2008f). 

5.5.2.3 Aquatic Ecosystems

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at 

various trophic levels. These studies indicate that aquatic biota have been affected by 

acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems. 

Effects have been most clearly documented for fish, aquatic insects, other invertebrates, and 

algae. Biological effects are primarily attributable to a combination of low pH and high 
146

inorganic aluminum concentrations. Such conditions occur more frequently during rainfall 

and snowmelt that cause high flows of water and less commonly during low-flow conditions, 

except where chronic acidity conditions are severe. Biological effects of episodes include 

reduced fish condition factor35, changes in species composition and declines in aquatic 

species richness across multiple taxa, ecosystems and regions. These conditions may also 

result in direct fish mortality (Van Sickle et al., 1996). Biological effects in aquatic 

ecosystems can be divided into two major categories: effects on health, vigor, and 

reproductive success; and effects on biodiversity. Surface water with ANC values greater 

than 50 μeq/L generally provides moderate protection for most fish (i.e., brook trout, others) 

and other aquatic organisms (U.S. EPA, 2009c). Table 5-14 provides a summary of the 

biological effects experienced at various ANC levels. 

 

35 Condition factor is an index that describes the relationship between fish weight and length, and is one 

measure of sublethal acidification stress that has been used to quantify effects of acidification on an individual 

fish (U.S.EPA, 2008f).
147

Table 5-14: Aquatic Status Categories

Category Label ANC Levels Expected Ecological Effects 

Acute 

Concern 

<0 micro 

equivalent per 

Liter (μeq/L) 

Near complete loss of fish populations is expected. Planktonic communities 

have extremely low diversity and are dominated by acidophilic forms. The 

number of individuals in plankton species that are present is greatly reduced. 

Severe 

Concern 0–20 μeq/L 

Highly sensitive to episodic acidification. During episodes of high acidifying 

deposition, brook trout populations may experience lethal effects. Diversity 

and distribution of zooplankton communities decline sharply. 

Elevated 

Concern 20–50 μeq/L 

Fish species richness is greatly reduced (i.e., more than half of expected 

species can be missing). On average, brook trout populations experience 

sublethal effects, including loss of health, reproduction capacity, and fitness. 

Diversity and distribution of zooplankton communities decline. 

Moderate 

Concern 50–100 μeq/L 

Fish species richness begins to decline (i.e., sensitive species are lost from 

lakes). Brook trout populations are sensitive and variable, with possible 

sublethal effects. Diversity and distribution of zooplankton communities also 

begin to decline as species that are sensitive to acidifying deposition are 

affected. 

Low 

Concern >100 μeq/L 

Fish species richness may be unaffected. Reproducing brook trout 

populations are expected where habitat is suitable. Zooplankton communities 

are unaffected and exhibit expected diversity and distribution. 

A number of national and regional assessments have been conducted to estimate the 

distribution and extent of surface water acidity in the U.S (U.S. EPA, 2008f). As a result, 

several regions of the U.S. have been identified as containing a large number of lakes and 

streams that are seriously impacted by acidification. Figure 5-8 illustrates those areas of the 

U.S. where aquatic ecosystems are at risk from acidification. 
148

Figure 5-8: Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008f)

Because acidification primarily affects the diversity and abundance of aquatic biota, it 

also affects the ecosystem services that are derived from the fish and other aquatic life found 

in these surface waters. 

While acidification is unlikely to have serious negative effects on, for example, water 

supplies, it can limit the productivity of surface waters as a source of food (i.e., fish). In the 

northeastern United States, the surface waters affected by acidification are not a major source 

of commercially raised or caught fish; however, they are a source of food for some 

recreational and subsistence fishermen and for other consumers. For example, there is 

evidence that certain population subgroups in the northeastern United States, such as the 

Hmong and Chippewa ethnic groups, have particularly high rates of self-caught fish 

consumption (Hutchison and Kraft, 1994; Peterson et al., 1994). However, it is not known if 

and how their consumption patterns are affected by the reductions in available fish 

populations caused by surface water acidification. 
149

Inland surface waters support several cultural services, including aesthetic and 

educational services and recreational fishing. Recreational fishing in lakes and streams is 

among the most popular outdoor recreational activities in the northeastern United States. 

Based on studies conducted in the northeastern United States, Kaval and Loomis (2003) 

estimated average consumer surplus values per day of $36 for recreational fishing (in 2007 

dollars); therefore, the implied total annual value of freshwater fishing in the northeastern 

United States was $5.1 billion in 2006.36 For recreation days, consumer surplus value is most 

commonly measured using recreation demand, travel cost models. 

Another estimate of the overarching ecological benefits associated with reducing lake 

acidification levels in Adirondacks National Park can be derived from the contingent 

valuation (CV) survey (Banzhaf et al., 2006), which elicited values for specific 

improvements in acidification-related water quality and ecological conditions in Adirondack 

lakes. The survey described a base version with minor improvements said to result from the 

program, and a scope version with large improvements due to the program and a gradually 

worsening status quo. After adapting and transferring the results of this study and converting 

the 10-year annual payments to permanent annual payments using discount rates of 3% and 

5%, the WTP estimates ranged from $48 to $107 per year per household (in 2004 dollars) for 

the base version and $54 to $154 for the scope version. Using these estimates, the aggregate 

annual benefits of eliminating all anthropogenic sources of NOx and SOx emissions were 

estimated to range from $291 million to $829 million (U.S. EPA, 2009c).37

In addition, inland surface waters provide a number of regulating services associated 

with hydrological and climate regulation by providing environments that sustain aquatic food 

webs. These services are disrupted by the toxic effects of acidification on fish and other 

aquatic life. Although it is difficult to quantify these services and how they are affected by 

acidification, some of these services may be captured through measures of provisioning and 

cultural services. 

 

36 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 

37 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
150

5.5.2.4 Terrestrial Ecosystems 

Acidifying deposition has altered major biogeochemical processes in the U.S. by 

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching 

from soil to drainage waters, depleting base cations (especially calcium and magnesium) 

from soils, and increasing the mobility of aluminum. Inorganic aluminum is toxic to some 

tree roots. Plants affected by high levels of aluminum from the soil often have reduced root 

growth, which restricts the ability of the plant to take up water and nutrients, especially 

calcium (U. S. EPA, 2008f). These direct effects can, in turn, influence the response of these 

plants to climatic stresses such as droughts and cold temperatures. They can also influence 

the sensitivity of plants to other stresses, including insect pests and disease (Joslin et al., 

1992) leading to increased mortality of canopy trees. In the U.S., terrestrial effects of 

acidification are best described for forested ecosystems (especially red spruce and sugar 

maple ecosystems) with additional information on other plant communities, including shrubs 

and lichen (U.S. EPA, 2008f). 

Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial 

acidification, which is the greatest concern regarding nitrogen and sulfur deposition U.S. 

EPA (2008f). Figure 5-9 depicts the areas across the U.S. that are potentially sensitive to 

terrestrial acidification.
151

Figure 5-9: Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008f) 

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing 

gradual losses of base cation nutrients from the soil due to accelerated leaching from 

acidifying deposition. This change in nutrient availability may reduce the quality of forest 

nutrition over the long term. Evidence suggests that red spruce and sugar maple in some 

areas in the eastern U.S. have experienced declining health because of this deposition. For 

red spruce, (Picea rubens) dieback or decline has been observed across high elevation 

landscapes of the northeastern U.S., and to a lesser extent, the southeastern U.S., and 

acidifying deposition has been implicated as a causal factor (DeHayes et al., 1999). Figure 

5-10 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S. 
152

Figure 5-10: Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern 

U.S. (U.S. EPA, 2008f)

Terrestrial acidification affects several important ecological endpoints, including 

declines in habitat for threatened and endangered species (cultural), declines in forest 

aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil 

erosion and reductions in water retention (cultural and regulating). 

Forests in the northeastern United States provide several important and valuable 

provisioning services in the form of tree products. Sugar maples are a particularly important 

commercial hardwood tree species, providing timber and maple syrup. In the United States, 

sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006), and annual 

production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% 

of worldwide production. The total annual value of U.S. production in these years was 

approximately $160 million (NASS, 2008). Red spruce is also used in a variety of products 

including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of 

red spruce saw timber from timberland in the United States was over 300 million board feet 

in 2006 (USFS, 2006). 
153

Forests in the northeastern United States are also an important source of cultural 

ecosystem services—nonuse (i.e., existence value for threatened and endangered species), 

recreational, and aesthetic services. Red spruce forests are home to two federally listed 

species and one delisted species: 

1. Spruce-fir moss spider (Microhexura montivaga)—endangered 

2. Rock gnome lichen (Gymnoderma lineare)—endangered 

3. Virginia northern flying squirrel (Glaucomys sabrinus fuscus)—delisted, but 

important 

Forestlands support a wide variety of outdoor recreational activities, including 

fishing, hiking, camping, off-road driving, hunting, and wildlife viewing. Regional statistics 

on recreational activities that are specifically forest based are not available; however, more 

general data on outdoor recreation provide some insights into the overall level of recreational 

services provided by forests. More than 30% of the U.S. adult population visited a 

wilderness or primitive area during the previous year and engaged in day hiking (Cordell et 

al., 2008). From 1999 to 2004, 16% of adults in the northeastern United States participated 

in off-road vehicle recreation, for an average of 27 days per year (Cordell et al., 2005). The 

average consumer surplus value per day of off-road driving in the United States was $25 (in 

2007 dollars), and the implied total annual value of off-road driving recreation in the 

northeastern United States was more than $9 billion (Kaval and Loomis, 2003). More than 

5% of adults in the northeastern United States participated in nearly 84 million hunting days 

(U.S. FWS and U.S. Census Bureau, 2007). Ten percent of adults in northeastern states 

participated in wildlife viewing away from home on 122 million days in 2006. For these 

recreational activities in the northeastern United States, Kaval and Loomis (2003) estimated 

average consumer surplus values per day of $52 for hunting and $34 for wildlife viewing (in 

2007 dollars). The implied total annual value of hunting and wildlife viewing in the 

northeastern United States was, therefore, $4.4 billion and $4.2 billion, respectively, in 2006. 

As previously mentioned, it is difficult to estimate the portion of these recreational 

services that are specifically attributable to forests and to the health of specific tree species. 

However, one recreational activity that is directly dependent on forest conditions is fall color 

viewing. Sugar maple trees, in particular, are known for their bright colors and are, 
154

therefore, an essential aesthetic component of most fall color landscapes. A survey of 

residents in the Great Lakes area found that roughly 30% of residents reported at least one 

trip in the previous year involving fall color viewing (Spencer and Holecek, 2007). In a 

separate study conducted in Vermont, Brown (2002) reported that more than 22% of 

households visiting Vermont in 2001 made the trip primarily for viewing fall colors. 

Two studies estimated values for protecting high-elevation spruce forests in the 

southern Appalachian Mountains. Kramer et al. (2003) conducted a contingent valuation 

study estimating households’ WTP for programs to protect remaining high-elevation spruce 

forests from damages associated with air pollution and insect infestation. Median household 

WTP was estimated to be roughly $29 (in 2007 dollars) for a smaller program, and $44 for 

the more extensive program. Jenkins et al. (2002) conducted a very similar study in seven 

Southern Appalachian states on a potential program to maintain forest conditions at status 

quo levels. The overall mean annual WTP for the forest protection programs was $208 (in 

2007 dollars). Multiplying the average WTP estimate from these studies by the total number 

of households in the seven-state Appalachian region results in an aggregate annual range of 

$470 million to $3.4 billion for avoiding a significant decline in the health of high-elevation 

spruce forests in the Southern Appalachian region.38

Forests in the northeastern United States also support and provide a wide variety of 

valuable regulating services, including soil stabilization and erosion control, water 

regulation, and climate regulation. The total value of these ecosystem services is very 

difficult to quantify in a meaningful way, as is the reduction in the value of these services 

associated with total nitrogen and sulfur deposition. As terrestrial acidification contributes to 

root damages, reduced biomass growth, and tree mortality, all of these services are likely to 

be affected; however, the magnitude of these impacts is currently very uncertain. 

5.5.3 Ecological Effects Associated with the Role of Sulfate in Mercury Methylation 

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated 

compound, methylmercury (U.S. EPA, 2008f). The contaminant is concentrated in higher 

 

38 These estimates reflect the marginal value of the service for the hypothetical program described in the survey, 

not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.
155

trophic levels, including fish eaten by humans. Experimental evidence has established that 

only inconsequential amounts of methylmercury can be produced in the absence of sulfate 

(U.S. EPA, 2008f). Many variables influence how much mercury accumulates in fish, but 

elevated mercury levels in fish can only occur where substantial amounts of methylmercury 

are present (U.S. EPA, 2008f). Current evidence indicates that in watersheds where mercury 

is present, increased sulfate deposition very likely results in methylmercury accumulation in 

fish (Drevnick et al., 2007; Munthe et al., 2007). The ISA for Oxides of Nitrogen and Sulfur: 

Ecological Criteria ISA concluded that evidence is sufficient to infer a casual relationship 

between sulfur deposition and increased mercury methylation in wetlands and aquatic 

environments (U.S. EPA, 2008f). 

Establishing the quantitative relationship between sulfate and mercury methylation in 

natural settings is difficult because of the presence of multiple interacting factors in aquatic 

and terrestrial environments, including wetlands, aquatic environments where sulfate, sulfurreducing bacteria (SRB), and inorganic mercury are present (U.S. EPA, 2008f). These are 

the three primary requirements for bacterially-mediated conversion to methylmercury. 

Additional factors affecting conversion include the presence of anoxic conditions, 

temperature, the presence and types of organic matter, the presence and types of mercurybinding species, and watershed effects (e.g., watershed type, land cover, water body 

limnology, and runoff loading). With regard to methylmercury, the highest concentrations in 

the environment generally occur at or near the sedimentary surface, below the oxic–anoxic 

boundary. Although mercury methylation can occur within the water column, there is 

generally a far greater contribution of mercury methylation from sediments because of 

anoxia and of greater concentrations of SRB, substrate, and sulfate. Figure 5-15 depicts the 

mercury cycle. 
156

Figure 5-15: The mercury cycle in an ecosystem (USGS, 2006)

Figure 5-16 illustrates a map of mercury-sensitive watersheds based on sulfate 

concentrations, ANC, levels of dissolved organic carbon and pH, mercury species 

concentrations, and soil types to gauge the methylation sensitivity (Myers et al., 2007). 
157

Figure 5.16: Preliminary USGS map of mercury methylation–sensitive watersheds 

(Myers et al., 2007)

Decreases in sulfate deposition/emissions have already shown reductions in 

methylmercury (U.S. EPA, 2008f). Observed decreases in methylmercury fish tissue 

concentrations have been linked to decreased acidification and declining sulfate and mercury 

deposition (Hrabik and Watras, 2002; Drevnick et al., 2007). 

In the U.S., consumption of fish and shellfish are the main sources of methylmercury 

exposure to humans. Methylmercury builds up more in some types of fish and shellfish than 

in others. The levels of methylmercury in high and shellfish vary widely depending on what 

they eat, how long they live, and how high they are in the food chain. Most fish, including 

ocean species and local freshwater fish, contain some methylmercury. For example, in 

recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish 

samples contained some methylmercury. 
158

State-level fish consumption advisories for mercury are based on state criteria, many 

of which are based on EPA’s fish tissue criterion for methylmercury (U.S. EPA, 2001) or on 

U.S. Food and Drug Administration’s action levels (U.S. FDA, 2001). In 2008, there were 

3,361 fish advisories issued at least in part for mercury contamination (80% of all fish 

advisories), covering 16.8 million lake acres (40% of total lake acreage) and 1.3 million river 

miles (35% of total river miles) over all 50 states, one U.S. territory, and 3 tribes (U.S. EPA, 

2009f). Recently, the U.S. Geological Survey (USGS) examined mercury levels in toppredator fish, bed sediment, and water from 291 streams across the U.S. (Scudder et al., 

2009). USGS detected mercury contamination in every fish sampled, and the concentration 

of mercury in fish exceeded EPA’s criterion in 27% of the sites sampled. 

 The ecosystem service most directly affected by sulfate-mediated mercury 

methylation is the provision of fish for consumption as a food source. This service is of 

particular importance to groups engaged in subsistence fishing, pregnant women and young 

children. While it is not possible to quantify the reduction in fish consumption due to the 

presence of methylmercury in fish from sulfur deposition, it is likely, given the number of 

state advisories and the EPA/FDA guidelines (U.S. EPA/FDA, 2004) on consumption for 

pregnant women and young children, that this service is negatively affected. 

Research shows that most people’s fish consumption does not cause a mercuryrelated health concern. However, certain people may be at higher risk because of their 

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their 

families who rely heavily on fish for a substantial part of their diet). It has been 

demonstrated that high levels of methylmercury in the bloodstream of unborn babies and 

young children may harm the developing nervous system, making the child less able to think 

and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidneys, 

lungs, and immune system of people of all ages. The majority of fish consumed in the U.S. 

are ocean species. The methylmercury concentrations in ocean fish species are primarily 
159

influences by the global mercury pool. However, the methylmercury found in local fish can 

be due, at least partly, to mercury emissions from local sources. 

Several studies suggest that the methylmercury content of fish may reduce these 

cardio-protective effects of fish consumption. Some of these studies also suggest that 

methylmercury may cause adverse effects to the cardiovascular system. For example, the 

NRC (2000) review of the literature concerning methylmercury health effects took note of 

two epidemiological studies that found an association between dietary exposure to 

methylmercury and adverse cardiovascular effects.39 Moreover, in a study of 1,833 males in 

Finland aged 42 to 60 years, Solonen et al. (1995) observed a relationship between 

methylmercury exposure via fish consumption and acute myocardial infarction (AMI or heart 

attacks), coronary heart disease, cardiovascular disease, and all-cause mortality.40 The NRC 

also noted a study of 917 seven year old children in the Faroe Islands, whose initial exposure 

to methylmercury was in utero although post natal exposures may have occurred as well. At 

seven years of age, these children exhibited an increase in blood pressure and a decrease in 

heart rate variability.41 Based on these and other studies, NRC concluded in 2000 that, while 

“the data base is not as extensive for cardiovascular effects as it is for other end points (i.e. 

neurologic effects) the cardiovascular system appears to be a target for methylmercury 

toxicity.”42 

 Since publication of the NRC report there have been some 30 published papers 

 

39 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National 

Academies Press. Washington, DC. pp.168-173.

40Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the 

risk of myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” 

Circulation, 91 (3):645-655.

41Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P., 1999. “Prenatal Methylmercury 

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375.

42National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National 

Academies Press. Washington, DC. p. 229.
160

presenting the findings of studies that have examined the possible cardiovascular effects of 

methylmercury exposure. These studies include epidemiological, toxicological, and 

toxicokinetic investigations. Over a dozen review papers have also been published. If there 

is a causal relationship between methylmercury exposure and adverse cardiovascular effects, 

then reducing exposure to methylmercury would result in public health benefits from reduced 

cardiovascular effects. 

In early 2010, EPA sponsored a workshop in which a group of experts were asked to 

assess the plausibility of a causal relationship between methylmercury exposure and 

cardiovascular health effects and to advise EPA on methodologies for estimating population 

level cardiovascular health impacts of reduced methylmercury exposure. The report from that 

workshop is in preparation. 

Because establishing the quantitative relationship between sulfate and mercury 

methylation in natural settings is difficult, we were unable to model the changes in the 

methylation process, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from 

reducing sulfate emissions in this rule. 

5.5.4 Ecological Effects Associated with Gaseous Sulfur Dioxide 

Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving 

adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO2 penetrates 

into leaves through to the stomata, although there is evidence for limited pathways via the 

cuticle. Pollutants must be transported from the bulk air to the leaf boundary layer in order 

to get to the stomata. When the stomata are closed, as occurs under dark or drought 

conditions, resistance to gas uptake is very high and the plant has a very low degree of 

susceptibility to injury. In contrast, mosses and lichens do not have a protective cuticle 

barrier to gaseous pollutants or stomates and are generally more sensitive to gaseous sulfur 

and nitrogen than vascular plants (U.S. EPA, 2008f). Acute foliar injury usually happens 
161

within hours of exposure, involves a rapid absorption of a toxic dose, and involves collapse 

or necrosis of plant tissues. Another type of visible injury is termed chronic injury and is 

usually a result of variable SO2 exposures over the growing season. Besides foliar injury, 

chronic exposure to low SO2 concentrations can result in reduced photosynthesis, growth, 

and yield of plants (U.S. EPA, 2008f). These effects are cumulative over the season and are 

often not associated with visible foliar injury. As with foliar injury, these effects vary among 

species and growing environment. SO2 is also considered the primary factor causing the 

death of lichens in many urban and industrial areas (Hutchinson et al., 1996). 

In addition to the role of sulfate deposition on methylation, the technologies installed 

to reduce emissions of NOx and SO2 associated with this proposed rule would also reduce 

mercury emissions. EPA recently commissioned an information collection request that will 

soon provide greatly improved power industry mercury emissions estimates that will enable 

the Agency to better estimate mercury emissions changes from its air emissions control 

actions. For this reason, the Agency did not estimate Hg changes in this rule and will instead 

wait for these new data which will be available in the near future. Due to time and resource 

limitations, we were unable in any event to model mercury dispersion, deposition, 

methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from 

reducing these mercury emissions. 

5.5.5 Nitrogen Enrichment 

5.5.5.1 Aquatic Enrichment 

One of the main adverse ecological effects resulting from N deposition, particularly 

in the Mid-Atlantic region of the United States, is the effect associated with nutrient 

enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the 

National Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries 

(13%) suffered from moderately high or high levels of eutrophication due to excessive inputs 

of both N and phosphorus, and a majority of these estuaries are located in the coastal area 

from North Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid-Atlantic 
162

region, the contribution of atmospheric distribution to total N loads is estimated to range 

between 10% and 58% (Valigura et al., 2001). 

Eutrophication in estuaries is associated with a range of adverse ecological effects. 

The conceptual framework developed by NOAA emphasizes four main types of 

eutrophication effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss of 

submerged aquatic vegetation (SAV), and low water clarity. Low DO disrupts aquatic 

habitats, causing stress to fish and shellfish, which, in the short-term, can lead to episodic 

fish kills and, in the long-term, can damage overall growth in fish and shellfish populations. 

Low DO also degrades the aesthetic qualities of surface water. In addition to often being 

toxic to fish and shellfish, and leading to fish kills and aesthetic impairments of estuaries, 

HABs can, in some instances, also be harmful to human health. SAV provides critical 

habitat for many aquatic species in estuaries and, in some instances, can also protect 

shorelines by reducing wave strength; therefore, declines in SAV due to nutrient enrichment 

are an important source of concern. Low water clarity is the result of accumulations of both 

algae and sediments in estuarine waters. In addition to contributing to declines in SAV, high 

levels of turbidity also degrade the aesthetic qualities of the estuarine environment. 

Estuaries in the eastern United States are an important source of food production, in 

particular fish and shellfish production. The estuaries are capable of supporting large stocks 

of resident commercial species, and they serve as the breeding grounds and interim habitat 

for several migratory species. To provide an indication of the magnitude of provisioning 

services associated with coastal fisheries, from 2005 to 2007, the average value of total catch 

was $1.5 billion per year. It is not known, however, what percentage of this value is directly 

attributable to or dependent upon the estuaries in these states. 

In addition to affecting provisioning services through commercial fish harvests, 

eutrophication in estuaries may also affect the demand for seafood. For example, a wellpublicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved 

thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost 

seafood sales for 360 seafood firms in Maryland in the months following the outbreak 

(Lipton, 1999). 
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Estuaries in the United States also provide an important and substantial variety of 

cultural ecosystem services, including water-based recreational and aesthetic services. The 

water quality in the estuary directly affects the quality of these experiences. For example, 

there were 26 million days of saltwater fishing coastal states from North Carolina to 

Massachusetts in 2006 (FWA and Census, 2007). Assuming an average consumer surplus 

value for a fishing day at $36 (in 2007 dollars) in the Northeast and $87 in the Southeast 

(Kaval and Loomis, 2003), the aggregate value was approximately $1.3 billion (in 2007 

dollars). 43 In addition, almost 6 million adults participated in motorboating in coastal states 

from North Carolina to Massachusetts, for a total of nearly 63 million days annually during 

1999–2000 (Leeworthy and Wiley, 2001). Using a national daily value estimate of $32 (in 

2007 dollars) for motorboating (Kaval and Loomis (2003), the aggregate value of these 

coastal motorboating outings was $2 billion per year. 44 Almost 7 million participated in 

birdwatching for 175 million days per year, and more than 3 million participated in visits to 

non-beach coastal waterside areas. 

Estuaries and marshes have the potential to support a wide range of regulating 

services, including climate, biological, and water regulation; pollution detoxification; erosion 

prevention; and protection against natural hazards from declines in SAV (MEA, 2005). SAV 

can help reduce wave energy levels and thus protect shorelines against excessive erosion, 

which increases the risks of episodic flooding and associated damages to near-shore 

properties or public infrastructure or even contribute to shoreline retreat. 

5.5.5.2 Terrestrial Enrichment 

Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess 

of natural background levels, either through atmospheric deposition or direct application. 

Evidence presented in the Integrated Science Assessment (U.S. EPA, 2008f) supports a causal 

relationship between atmospheric N deposition and biogeochemical cycling and fluxes of N 

and carbon in terrestrial systems. Furthermore, evidence summarized in the report supports a 

causal link between atmospheric N deposition and changes in the types and number of species 

 

43 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 

44 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
164

and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as 

a result, it may take as much as 50 years or more to see changes in ecosystem conditions and 

indicators. This long time scale also affects the timing of the ecosystem service changes. 

One of the main provisioning services potentially affected by N deposition is grazing 

opportunities offered by grasslands for livestock production in the Central U.S. Although N 

deposition on these grasslands can offer supplementary nutritive value and promote overall 

grass production, there are concerns that fertilization may favor invasive grasses and shift the 

species composition away from native grasses. This process may ultimately reduce the 

productivity of grasslands for livestock production. Losses due to invasive grasses can be 

significant; for example, based on a bioeconomic model of cattle grazing in the upper Great 

Plains, Leitch, Leistritz, and Bangsund (1996) and Leistritz, Bangsund, and Hodur (2004) 

estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, 

and Wyoming. 45 However, the contribution of N deposition to these losses is still uncertain. 

5.5.6 Benefits of Reducing Ozone Effects on Vegetation and Ecosystems 

Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006a; Fox 

and Mickler, 1996). In terms of forest productivity and ecosystem diversity, ozone may be 

the pollutant with the greatest potential for regional-scale forest impacts (U.S. EPA, 2006a). 

Studies have demonstrated repeatedly that ozone concentrations commonly observed in 

polluted areas can have substantial impacts on plant function (De Steiguer et al., 1990; Pye, 

1988).

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Like carbon dioxide (CO2) and other gaseous substances, ozone 

enters plant tissues primarily through the stomata in leaves in a process called “uptake” 

(Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), 

or its reaction products, reaches the interior of plant cells, it can inhibit or damage essential 

cellular components and functions, including enzyme activities, lipids, and cellular 

membranes, disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns 

 

45 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
165

(U.S. EPA, 2006a; Tingey and Taylor, 1982). With fewer resources available, the plant 

reallocates existing resources away from root growth and storage, above ground growth or 

yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced 

growth and/or reproduction. Studies have shown that plants stressed in these ways may 

exhibit a general loss of vigor, which can lead to secondary impacts that modify plants' 

responses to other environmental factors. Specifically, plants may become more sensitive to 

other air pollutants, or more susceptible to disease, pest infestation, harsh weather (e.g., 

drought, frost) and other environmental stresses, which can all produce a loss in plant vigor 

in ozone-sensitive species that over time may lead to premature plant death. Furthermore, 

there is evidence that ozone can interfere with the formation of mycorrhiza, essential 

symbiotic fungi associated with the roots of most terrestrial plants, by reducing the amount 

of carbon available for transfer from the host to the symbiont (U.S. EPA, 2006a).

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant 

damage described above. Foliar injury is usually the first visible sign of injury to plants from 

ozone exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). 

 When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, 

and/or increased leaf senescence (accelerated leaf aging). Because ozone damage can consist 

of visible injury to leaves, it can also reduce the aesthetic value of ornamental vegetation and 

trees in urban landscapes, and negatively affects scenic vistas in protected natural areas. 

 Ozone can produce both acute and chronic injury in sensitive species depending on 

the concentration level and the duration of the exposure. Ozone effects also tend to 

accumulate over the growing season of the plant, so that even lower concentrations 

experienced for a longer duration have the potential to create chronic stress on sensitive 

vegetation. Not all plants, however, are equally sensitive to ozone. Much of the variation in 

sensitivity between individual plants or whole species is related to the plant’s ability to 

regulate the extent of gas exchange via leaf stomata (e.g., avoidance of ozone uptake through 

closure of stomata) (U.S. EPA, 2006a; Winner, 1994). After injuries have occurred, plants 

may be capable of repairing the damage to a limited extent (U.S. EPA, 2006a). Because of 

the differing sensitivities among plants to ozone, ozone pollution can also exert a selective 

pressure that leads to changes in plant community composition. Given the range of plant 
166

sensitivities and the fact that numerous other environmental factors modify plant uptake and 

response to ozone, it is not possible to identify threshold values above which ozone is 

consistently toxic for all plants. 

 Because plants are at the base of the food web in many ecosystems, changes to the 

plant community can affect associated organisms and ecosystems (including the suitability of 

habitats that support threatened or endangered species and below ground organisms living in 

the root zone). Ozone impacts at the community and ecosystem level vary widely depending 

upon numerous factors, including concentration and temporal variation of tropospheric 

ozone, species composition, soil properties and climatic factors (U.S. EPA, 2006a). In most 

instances, responses to chronic or recurrent exposure in forested ecosystems are subtle and 

not observable for many years. These injuries can cause stand-level forest decline in 

sensitive ecosystems (U.S. EPA, 2006a, McBride et al., 1985; Miller et al., 1982). It is not 

yet possible to predict ecosystem responses to ozone with much certainty; however, 

considerable knowledge of potential ecosystem responses has been acquired through longterm observations in highly damaged forests in the United States (U.S EPA, 2006a). 

5.5.6.1 Ozone Effects on Forests 

Air pollution can affect the environment and affect ecological systems, leading to 

changes in the ecological community and influencing the diversity, health, and vigor of 

individual species (U.S. EPA, 2006a). Ozone has been shown in numerous studies to have a 

strong effect on the health of many plants, including a variety of commercial and ecologically 

important forest tree species throughout the United States (U.S. EPA, 2007b). 

In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest 

Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program, 

formerly known as Forest Health Monitoring, FIA examines ozone injury to ozone-sensitive 

plant species at ground monitoring sites in forestland across the country (excluding woodlots 

and urban trees). FIA looks for damage on the foliage of ozone-sensitive forest plant species 

at each site that meets certain minimum criteria. Because ozone injury is cumulative over the 

course of the growing season, examinations are conducted in July and August, when ozone 

injury is typically highest. 
167

Monitoring of ozone injury to plants by the USDA Forest Service has expanded over 

the last 10 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 

41 states in 2002. The data underlying the indictor in Figure 5-13 are based on averages of 

all observations collected in 2002, the latest year for which data are publicly available at the 

time the study was conducted, and are broken down by U.S. EPA Regions. Ozone damage to 

forest plants is classified using a subjective five-category biosite index based on expert 

opinion, but designed to be equivalent from site to site. Ranges of biosite values translate to 

no injury, low or moderate foliar injury (visible foliar injury to highly sensitive or 

moderately sensitive plants, respectively), and high or severe foliar injury, which would be 

expected to result in tree-level or ecosystem-level responses, respectively (U.S. EPA, 2006a; 

Coulston, 2004). The highest percentages of observed high and severe foliar injury, which 

are most likely to be associated with tree or ecosystem-level responses, are primarily found 

in the Mid-Atlantic and Southeast regions. 
168

Figure 5-13: Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002a, b 

Assessing the impact of ground-level ozone on forests in the eastern United States 

involves understanding the risks to sensitive tree species from ambient ozone concentrations 

and accounting for the prevalence of those species within the forest. As a way to quantify 

the risks to particular plants from ground-level ozone, scientists have developed ozoneexposure/tree-response functions by exposing tree seedlings to different ozone levels and 

measuring reductions in growth as “biomass loss.” Typically, seedlings are used because 

they are easy to manipulate and measure their growth loss from ozone pollution. The 

mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are 
169

identical, and the decreases predicted using the seedlings should be related to the decrease in 

overall plant fitness for mature trees, but the magnitude of the effect may be higher or lower 

depending on the tree species (Chappelka and Samuelson, 1998). In areas where certain 

ozone-sensitive species dominate the forest community, the biomass loss from ozone can be 

significant. Significant biomass loss can be defined as a more than 2% annual biomass loss, 

which would cause long term ecological harm as the short-term negative effects on seedlings 

compound to affect long-term forest health (Heck, 1997). 

Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), and eastern white pine 

(Pinus strobus). Ozone-exposure/tree-response functions have been developed for each of 

these tree species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus 

ponderosa) (U.S. EPA, 2007b). Other common tree species, such as oak (Quercus spp.) and 

hickory (Carya spp.), are not as sensitive to ozone. Consequently, with knowledge of the 

distribution of sensitive species and the level of ozone at particular locations, it is possible to 

estimate a “biomass loss” for each species across their range. As shown in Figure 5-14, 

current ambient levels of ozone are associated with significant biomass loss across large 

geographic areas (U.S. EPA, 2009b). However, this information is unavailable this rule. 

To estimate the biomass loss for forest ecosystems across the eastern United States, 

the biomass loss for each of the seven tree species was calculated using the three-month, 12-

hour W126 exposure metric at each location, along with each tree’s individual C-R 

functions. The W126 exposure metric was calculated using monitored ozone data from 

CASTNET and AQS sites, and a three-year average was used to mitigate the effect of 

variations in meteorological and soil moisture conditions. The biomass loss estimate for 

each species was then multiplied by its prevalence in the forest community using the U.S. 

Department of Agriculture (USDA) Forest Service IV index of tree abundance calculated 

from Forest Inventory and Analysis (FIA) measurements (Prasad, 2003). Sources of 

uncertainty include the ozone-exposure/plant-response functions, the tree abundance index, 

and other factors (e.g., soil moisture). Although these factors were not considered, they can 

affect ozone damage (Chappelka, 1998). 
170

Figure 5-14: Estimated Black Cherry, Yellow Poplar, Sugar Maple, Eastern White 

Pine, Virginia Pine, Red Maple, and Quaking Aspen Biomass Loss due to Current 

Ozone Exposure, 2006-2008 (U.S. EPA, 2009b) 

Ozone damage to the plants including the trees and understory in a forest can affect 

the ability of the forest to sustain suitable habitat for associated species particularly 

threatened and endangered species that have existence value – a nonuse ecosystem service - 

for the public. Similarly, damage to trees and the loss of biomass can affect the forest’s 

provisioning services in the form of timber for various commercial uses. In addition, ozone 

can cause discoloration of leaves and more rapid senescence (early shedding of leaves), 

which could negatively affect fall-color tourism because the fall foliage would be less 

available or less attractive. Beyond the aesthetic damage to fall color vistas, forests provide 

the public with many other recreational and educational services that may be impacted by 

reduced forest health including hiking, wildlife viewing (including bird watching), camping, 

picnicking, and hunting. Another potential effect of biomass loss in forests is the subsequent 

loss of climate regulation service in the form of reduced ability to sequester carbon (Felzer et 

al., 2005). 
171

5.5.6.2 Ozone Effects on Crops and Urban Ornamentals 

Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). Damage to crops from ozone exposures includes yield losses (i.e., in terms of 

weight, number, or size of the plant part that is harvested), as well as changes in crop quality 

(i.e., physical appearance, chemical composition, or the ability to withstand storage) (U.S. 

EPA, 2007b). The most extensive field experiments, conducted under the National Crop 

Loss Assessment Network (NCLAN) examined 15 species and numerous cultivars. The 

NCLAN results show that “several economically important crop species are sensitive to 

ozone levels typical of those found in the United States” (U.S. EPA, 2006a). In addition, 

economic studies have shown reduced economic benefits as a result of predicted reductions 

in crop yields, directly affecting the amount and quality of the provisioning service provided 

by the crops in question, associated with observed ozone levels (Kopp et al., 1985; Adams et 

al., 1986; Adams et al., 1989). According to the Ozone Staff Paper, there has been no 

evidence that crops are becoming more tolerant of ozone (U.S. EPA, 2007b). Using the 

Agriculture Simulation Model (AGSIM) (Taylor, 1994) to calculate the agricultural benefits 

of reductions in ozone exposure, U.S. EPA estimated that meeting a W126 standard of 21 

ppm-hr would produce monetized benefits of approximately $160 million to $300 million 

(inflated to 2006 dollars) (U.S. EPA, 2007b). 46

Urban ornamentals are an additional vegetation category likely to experience some 

degree of negative effects associated with exposure to ambient ozone levels. Because ozone 

causes visible foliar injury, the aesthetic value of ornamentals (such as petunia, geranium, 

and poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007b). Sensitive 

ornamental species would require more frequent replacement and/or increased maintenance 

(fertilizer or pesticide application) to maintain the desired appearance because of exposure to 

ambient ozone (U.S. EPA, 2007b). In addition, many businesses rely on healthy-looking 

vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, 

farmers of leafy crops, etc.) and a variety of ornamental species have been listed as sensitive 

 

46 These estimates illustrate the value of vegetation effects from a substantial reduction of ozone concentrations, 

not the marginal change in ozone concentrations anticipated a result of the emission reductions achieved by this 

rule. 
172

to ozone (Abt Associates, 1995). The ornamental landscaping industry is valued at more 

than $30 billion (inflated to 2006 dollars) annually, by both private property owners/tenants 

and by governmental units responsible for public areas (Abt Associates, 1995). Therefore, 

urban ornamentals represent a potentially large unquantified benefit category. This aesthetic 

damage may affect the enjoyment of urban parks by the public and homeowners’ enjoyment 

of their landscaping and gardening activities. In the absence of adequate exposure-response 

functions and economic damage functions for the potential range of effects relevant to these 

types of vegetation, we cannot conduct a quantitative analysis to estimate these effects. 

5.5.7 Unquantified SO2 and NO2 -Related Human Health Benefits 

Following an extensive evaluation of health evidence from epidemiologic and 

laboratory studies, the Integrated Science Assessment for Sulfur Dioxide concluded that 

there is a causal relationship between respiratory health effects and short-term exposure to 

SO2 (U.S. EPA, 2008). The immediate effect of SO2 on the respiratory system in humans is 

bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely resulting 

from preexisting inflammation associated with this disease. A clear concentration-response 

relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality 

was “suggestive of a causal relationship” because it is difficult to attribute the mortality risk 

effects to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in 

reporting a relationship between SO2 exposure and mortality, there was a lack of robustness 

of the observed associations to adjustment for pollutants. We did not quantify these benefits 

due to time constraints. 

Epidemiological researchers have associated NO2 exposure with adverse health 

effects in numerous toxicological, clinical and epidemiological studies, as described in the 

Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report) (U.S. 
173

EPA, 2008c). The NO2 ISA provides a comprehensive review of the current evidence of 

health and environmental effects of NO2. The NO2 ISA concluded that the evidence “is 

sufficient to infer a likely causal relationship between short-term NO2 exposure and adverse 

effects on the respiratory system” (ISA, section 5.3.2.1). These epidemiologic and 

experimental studies encompass a number of endpoints including [Emergency Department 

(ED)] visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway 

inflammation, and lung function. Effect estimates from epidemiologic studies conducted in 

the United States and Canada generally indicate a 2-20% increase in risks for ED visits and 

hospital admissions and higher risks for respiratory symptoms (ISA, section 5.4). The NO2

ISA concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship” because it is 

difficult to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated 

that studies consistently reported a relationship between NO2 exposure and mortality, the 

effect was generally smaller than that for other pollutants such as PM. We did not quantify 

these benefits due to time constraints. 

5.6 Social Cost of Carbon and Greenhouse Gas Benefits 

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions 

using recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the 

monetized damages associated with an incremental increase in carbon emissions in a given 

year. It is intended to include (but is not limited to) changes in net agricultural productivity, 

human health, property damages from increased flood risk, and the value of ecosystem 

services due to climate change. The SCC estimates used in this analysis were developed 

through an interagency process that included EPA and other executive branch entities, and 

concluded in February 2010. EPA first used these SCC estimates in the benefits analysis for 

the final joint EPA/DOT Rulemaking to establish Light-Duty Vehicle Greenhouse Gas 

Emission Standards and Corporate Average Fuel Economy Standards; see the rule’s 

preamble for discussion about application of SCC (75 FR 25324; 5/7/10). The SCC 

Technical Support Document (SCC TSD) provides a complete discussion of the methods 
174

used to develop these SCC estimates.47 

The interagency group selected four SCC values for use in regulatory analyses, which 

we have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions48 in 

2010, in 2007 dollars. The first three values are based on the average SCC from three 

integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs 

at several discount rates are included because the literature shows that the SCC is quite 

sensitive to assumptions about the discount rate, and because no consensus exists on the 

appropriate rate to use in an intergenerational context. The fourth value is the 95th percentile 

of the SCC from all three models at a 3 percent discount rate. It is included to represent 

higher-than-expected impacts from temperature change further out in the tails of the SCC 

distribution. Low probability, high impact events are incorporated into all of the SCC values 

through explicit consideration of their effects in two of the three models as well as the use of 

a probability density function for equilibrium climate sensitivity. Treating climate sensitivity 

probabilistically results in more high temperature outcomes, which in turn lead to higher 

projections of damages. 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the 

SCC directly using the three integrated assessment models rather than assuming a constant 

annual growth rate. This helps to ensure that the estimates are internally consistent with other 

modeling assumptions. The SCC estimates for the analysis years of 2014, in 2006 dollars 

are provided in Table 5-15. 

 

47 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also 

available at http://www.epa.gov/otaq/climate/regulations.htm

48 The interagency group decided that these estimates apply only to CO2 emissions. Given that warming 

profiles and impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group 

concluded “transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents 

by the SCC, would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13). 
175

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the 

National Academies of Science (NRC 2009) points out that any assessment will suffer from 

uncertainty, speculation, and lack of information about (1) future emissions of greenhouse 

gases, (2) the effects of past and future emissions on the climate system, (3) the impact of 

changes in climate on the physical and biological environment, and (4) the translation of 

these environmental impacts into economic damages. As a result, any effort to quantify and 

monetize the harms associated with climate change will raise serious questions of science, 

economics, and ethics and should be viewed as provisional. 

The interagency group noted a number of limitations to the SCC analysis, including 

the incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding 

risk aversion. The limited amount of research linking climate impacts to economic damages 

makes the interagency modeling exercise even more difficult. The interagency group hopes 

that over time researchers and modelers will work to fill these gaps and that the SCC 

estimates used for regulatory analysis by the Federal government will continue to evolve 

with improvements in modeling. Additional details on these limitations are discussed in the 

SCC TSD. 

In light of these limitations, the interagency group has committed to updating the 

current estimates as the science and economic understanding of climate change and its 

impacts on society improves over time. Specifically, the interagency group has set a 

preliminary goal of revisiting the SCC values within two years or at such time as 

substantially updated models become available, and to continue to support research in this 

area. 

Applying the global SCC estimates to the estimated reductions in CO2 emissions for 

the range of policy scenarios, we estimate the dollar value of the climate related benefits 

captured by the models for each analysis year. For internal consistency, the annual benefits 

are discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 
176

5%, 3%, and 2.5%) rather than 3% and 7%.49 These estimates are provided in Table 5-16. 

Table 5-15. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014 (in 

2006$)a

Discount Rate and Statistic SCC estimate

5% Average $5.4

3% Average $22.7

2.5% Average $36.7

3% 95%ile $69.2 a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 5-16. Monetized Benefits of CO2 Emissions Reductions in 2014 (in millions of 

2006$)a

Discount Rate and Statistic SCC estimate

5% Average $82

3% Average $350

2.5% Average $560

3% 95%ile $1,100 a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

5.7 Benefits Results

 Applying the impact and valuation functions described previously in this chapter to 

the estimated changes in ozone and PM yields estimates of the changes in physical damages 

(e.g., premature mortalities, cases, admissions, and change in light extinction) and the 

associated monetary values for those changes. Estimates of physical health impacts among 

those states in either the ozone or PM2.5 trading region, or outside the trading region, are 

presented in Table 5-15. Monetized values for both health and welfare endpoints within the 

trading region are presented in Table 5-16, along with total aggregate monetized benefits. All 

of the monetary benefits are in constant-year 2006 dollars. The PM2.5-related benefits of the 

Direct Control and Intrastate Trading scenarios were within about 5% of the preferred 

 

49 It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates.
177

remedy. The results of this analysis may be found in Appendix A. The benefits of the more 

and less stringent SO2 sensitivity analyses may be found in the Chapter 10 cost-benefit 

comparison chapter.
178

Table 5-17: Estimated Reduction in Incidence of Adverse Health Effects of the 

Proposed remedy (95% confidence intervals)A

Health Effect Within transport region 

Beyond transport 

region Total 

PM-Related endpoints 

Premature Mortality 

Pope et al. (2002) (age 

>30) 

14,000 

(4,000—24,000) 

130 

(35—220) 

14,000 

(4,000—25,000) 

Laden et al. (2006) (age 

>25) 

36,000 

(17,000—55,000) 

320 

(150—500) 

36,000 

(17,000—56,000) 

Infant (< 1 year) 59 

(-66—180) 

0.3 

(-0.3—0.8) 

59 

(-66—180) 

Chronic Bronchitis 9,200 

(310—18,000) 

89 

(3—160) 

9,200 

(320—18,000) 

Non-fatal heart attacks (age > 

18) 

22,000 

(5,700—39,000) 

250 

(64—440) 

23,000 

(5,800—39,000) 

Hospital admissions—

respiratory 

(all ages) 

3,500 

(1,400—5,500) 

35 

(14—56) 

3,500 

(1,400—5,500) 

Hospital admissions—

cardiovascular (age > 18) 

7,500 

(5,200—8,800) 

76 

(51—93) 

7,500 

(5,200—8,900) 

Emergency room visits for 

asthma 

(age < 18) 

14,000 

(7,100—21,000) 

71 

(36—110) 

14,000 

(7,200—21,000) 

Acute bronchitis 

(age 8-12) 

21,000 

(-4,800—46,000) 

150 

(33—320) 

21,000 

(-4,800—46,000) 

Lower respiratory symptoms 

(age 7-14) 

250,000 

(98,000—400,000) 

1,700 

(670—2,800) 

250,000 

(98,000—400,000) 

Upper respiratory symptoms 

(asthmatics age 9-18) 

190,000 

(36,000—350,000) 

1,300 

(250—2,400) 

190,000 

(36,000—350,000) 

Asthma exacerbation 

(asthmatics 6-18) 

230,000 

(8,300—800,000) 

1,700 

(11—5,700) 

240,000 

(8,300—800,000) 

Lost work days 

(ages 18-65) 

1,800,000 

(1,500,000—2,000,000) 

14,000 

(12,000—17,000) 

1,800,000 

(1,500,000—2,000,000) 

Minor restricted-activity days 

(ages 18-65) 

10,000,000 

(8,600,000—12,000,000) 

86,000 

(71,000—100,000) 

11,000,000 

(8,600,000—12,000,000) 
179

Ozone-related endpoints

Premature mortality 

Bell et al. (2004) (all 

ages) 

50 

(16—83) 

0.6 

(0.2—1) 

50 

(17—84) 

Schwartz et al. (2005) 

(all ages) 

76 

(23—130) 

1 

(0.2—2) 

77 

(24—130) M

ulti-city 

and 

NMMAPS- Huang et al. (2005) 

(all ages) 

83 

(31—130) 

1 

(0.3—2) 

84 

(31—140) 

Ito et al. (2005) (all 

ages) 

220 

(130—310) 

3 

(2—4) 

230 

(140—320) 

Bell et al. (2005) (all 

ages) 

160 

(76—250) 

2 

(1—3) 

160 

(77—250) 

Meta-analyses 

Levy et al. (2005) (all 

ages) 

230 

(160—300) 

3 

(2—4) 

230 

(160—300) 

Hospital admissions—

respiratory causes (ages > 65) 

380 

(-18—730) 

4 

(-0.4—9) 

390 

(-18—740) 

Hospital admissions—

respiratory causes (ages <2) 

290 

(130—460) 

4 

(1—6) 

300 

(130—460) 

Emergency room visits for 

asthma (all ages) 

230 

(-30—730) 

2 

(-0.4—8) 

230 

(-30—730) 

Minor restricted-activity days 

(ages 18-65) 

300,000 

(120,000—480,000) 

3,700 

(1,300—6,100) 

300,000 

(130,000—480,000) 

School absence days 110,000 

(38,000—160,000) 

1,300 

(380—2,100) 

110,000 

(38,000—160,000) 

A Estimates rounded to two significant figures; column values will not sum to total value. B

 The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these

health impacts and do not suggest that increases in air pollution exposure result in decreased health impacts.
180

Table 5-18: Estimated Economic Value of Health and Welfare Benefits (95% 

confidence intervals, billions of 2006$)A

Health Effect Pollutant 

Within transport 

region 

Beyond transport 

regionB Total 

Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality 

estimates) 

 3% discount rate PM2.5 & O3

$110 

($8.8—$330) 

$0.1 

($0.08—$3) 

$110 

($8.8—$340) 

 7% discount rate PM2.5 & O3

$100 

($7.9—$300) 

$0.09 

($0.07—$2.7) 

$100 

($7.9—$300) 

Premature Mortality (Laden et al. 2006 PM mortality and Levy et al. 2005 ozone mortality 

estimates) 

 3% discount rate PM2.5 & O3

$280 

($25—$810) 

$2.5 

($0.2—$7.3) 

$280 

($25—$820) 

 7% discount rate PM2.5 & O3

$250 

($22—$300) 

$2.3 

($0.2—$6.6) 

$260 

($22—$310) 

Chronic Bronchitis PM2.5

$4.3 

($0.2—$20) 

$0.04 

($0.002--$0.2) 

$4.3 

($0.2—$20) 

Non-fatal heart attacks 

 3% discount rate PM2.5

$2.5 

($0.4—$6) 

$0.03 

($0.005—$0.07) 

$2.5 

($0.4—$6) 

 7% discount rate PM2.5

$2.4 

($0.4—$5.9) 

$0.03 

($0.005—$0.07) 

$2.4 

($0.4—$5.9) 

Hospital admissions—

respiratory PM2.5 & O3

$0.06 

($0.03—$0.1) 

$0.00006 

($0.00003—$0.001) 

$0.06 

($0.03—$0.1) 

Hospital admissions—

cardiovascular PM2.5

$0.2 

($0.1—$0.3) 

$0.002 

($0.001—$0.003) 

$0.2 

($0.1—$0.3) 

Emergency room visits 

for asthma PM2.5 & O3

$0.005 

($0.002—$0.008) --- $0.005 

($0.002—$0.008) 

Acute bronchitis PM2.5

$0.009 

(-$0.0004—$0.03)c --- $0.009 

(-$0.0004—$0.03) 

Lower respiratory 

symptoms PM2.5

$0.005 

($0.002—$0.009) --- $0.005 

($0.002—$0.009) 

Upper respiratory 

symptoms PM2.5

$0.006 

($0.001—$0.014) --- $0.006 

($0.001—$0.014) 

Asthma exacerbation PM2.5

$0.012 

($0.001--$0.046) --- $0.012 

($0.001--$0.046) 

Lost work days PM2.5

$0.2 

($0.19—$0.24) 

$0.002 

($0.0002--$0.002) 

$0.2 

($0.19—$0.24) 

School loss days O3

$0.01 

($0.004—$0.013) --- $0.01 

($0.004—$0.013) 

Minor restricted-activity 

days PM2.5 & O3

$0.64 

($0.34—$0.97) 

$0.005 

($0.003—$0.008) 

$0.64 

($0.34—$0.97) 

Recreational visibility, 

Class I areas PM2.5 $3.5 $0.03 $3.6 

Social cost of carbon (3% 

discount rate, 2014 

value) 

CO2 $0.35 
181

Monetized total Benefits 

(Pope et al. 2002 PM2.5 mortality and Bell et al. 2004 ozone mortality estimates) 

3% discount rate PM2.5, O3

$120 

($10—$360) 

$1.1 

($0.09—$3.3) 

$120 

($10—$360) 

7% discount rate PM2.5, O3 

$110 

($9—$330) 

$0.9 

($0.08—$2.9) 

$110 

($9—$330) 

Monetized total Benefits 

(Laden et al. 2006 PM2.5 mortality and Levy et al. 2005 ozone mortality estimates) 

3% discount rate PM2.5, O3

$290 

($26—$840) 

$2.6 

($0.2—$7.5) 

$290 

($26—$840) 

7% discount rate PM2.5, O3 

$260 

($23—$760) 

$2.4 

($0.2—$6.8) 

$270 

($24—$760) A Estimates rounded to two significant figures. 

B Monetary value of endpoints marked with dashes are < $100,000. States included in transport region may be 

found in chapter 2. 

C The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.

Not all known PM- and ozone-related health and welfare effects could be quantified or 

monetized. The monetized value of these unquantified effects is represented by adding an 

unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus 

equal to the subset of monetized PM- and ozone-related health and welfare benefits plus B, 

the sum of the nonmonetized health and welfare benefits; this B represents both uncertainty 

and a bias in this analysis, as it reflects those benefits categories that we are unable quantify 

in this analysis. 

 Total monetized benefits are dominated by benefits of mortality risk reductions. The 

primary analysis projects that the proposed remedy will result in between 14,000 and 36,000 

PM2.5 and ozone-related avoided premature deaths annually in 2014. Our estimate of total 

monetized benefits in 2014 proposed remedy is between $120 billion and $290 billion using 

a 3 percent discount rate and between $110 billion and $270 using a 7 percent discount rate. 

Health benefits account for between 97 and 99 percent of total benefits depending on the 

PM2.5 and ozone mortality estimates used, in part because we are unable to quantify most of 

the non-health benefits. The monetized benefit associated with reductions in the risk of 

premature mortality, which accounts for between $110 and $280 billion in 2014, depending 

again on the PM and ozone mortality risk estimates used, is between 90 and 96 percent of 
182

total monetized health benefits. The next largest benefit is for reductions in chronic illness 

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

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

visibility, MRADs, work loss days, school absence days, and worker productivity account for 

the majority of the remaining benefits. The remaining categories each account for a small 

percentage of total benefit; however, they represent a large number of avoided incidences 

affecting many individuals. A comparison of the incidence table to the monetary benefits 

table reveals that there is not always a close correspondence between the number of 

incidences avoided for a given endpoint and the monetary value associated with that 

endpoint. For example, there are almost 100 times more work loss days than premature 

mortalities, yet work loss days account for only a very small fraction of total monetized 

benefits. This reflects the fact that many of the less severe health effects, while more 

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

such as hospital admissions, are valued using a proxy measure of WTP. As such, the true 

value of these effects may be higher than that reported in Table 5-16. 

Figures 5-15 and 5-16 illustrates the geographic distribution of avoided PM2.5 and 

ozone-related mortalities estimated to result from the proposed remedy. Figure 5-17 plots the 

cumulative distribution of reductions in all-cause premature mortality attributable to 

reductions in PM2.5 and ozone resulting from the proposed remedy. Among the 10 counties 

containing the most populous cities in the U.S., the three experiencing the largest reduction 

in the percentage of PM2.5 and ozone-related premature mortality are located within the 

Transport Rule region: New York, Chicago and Philadelphia. While not quantified in this 

RIA, we expect the Transport Rule to produce important public health benefits for 

populations living in Canada. Approximately 90% of the Canadian population lives within 

100 miles of the U.S. border, suggesting that some of the air quality improvements projected 

in areas near the U.S.-Canada border would be enjoyed by Canadian populations as well. A 

recent analysis (Chestnut and Mills, 2005) of the U.S. Acid Rain Program estimates annual 

benefits of the program in 2010 to both Canada and the United States at $122 billion and 

costs for that year at $3 billion (2000$)—a 40-to-1 benefit/cost ratio. These quantified 

benefits in the United States and Canada are the result of improved air quality prolonging 

lives, reducing heart attacks and other cardiovascular and respiratory problems, and 
183

improving visibility. The complete report is available in volume 77, issue 3, of the Journal of 

Environmental Management. 

These figures show that while there are very large health benefits throughout most of 

the East, there could be several areas where a very small disbenefit could result if further 

governmental actions do not occur to address them in the future. There are several upcoming 

planned federal actions that could lead to further large reductions throughout the US of 

ambient levels of fine particles and ozone. Additionally, state actions to address regional 

haze in the near future and the existing NAAQS for fine particles and ozone could address 

these situations. There are also other state actions, such as the recent Colorado Clean Air – 

Clean Jobs Act of April 2010 that is likely to convert much of the front range coal-fired 

generation in Colorado to natural gas in the near future.
184

Figure 5-15: Estimated reduction in excess PM2.5-related premature mortalities 

estimated to occur in each county in 2014 as a result of the proposed remedy.
185

Figure 5-16: Estimated reduction in excess ozone-related premature mortalities 

estimated to occur in each county in 2014 as a result of the proposed remedy
186

Figure 5-17: Cumulative percentage of the reduction in all-cause mortality attributable 

to reductions in PM2.5 and Ozone resulting from the proposed remedy by county in 

2014A

A Bell et al. 2005 ozone mortality estimate and Pope et al. 2002 PM2.5 mortality estimates. 

Figure 5-18 summarizes an array of PM2.5-related monetized benefits estimates based 

on alternative epidemiology and expert-derived PM-mortality estimate as well as the sum of 

ozone-related benefits using the Bell et al. (2004) mortality estimate. 

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

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed 

by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold loglinear model most adequately portrays the PM-mortality concentration-response relationship 
187

while recognizing potential uncertainty about the exact shape of the concentration-response 

function. Consistent with this finding, we have conformed the threshold sensitivity analysis 

to the current state of the PM science improved upon our previous approach for estimating 

the sensitivity of the benefits estimates to the presence of an assumed threshold by 

incorporating a new “Lowest Measured Level” (LML) assessment. 

This approach summarizes the distribution of avoided PM mortality impacts 

according to the baseline (i.e. pre-Transport Rule) PM2.5 levels experienced by the 

population receiving the PM2.5 mortality benefit (Figure 5-19). We identify on this figure the 

lowest air quality levels measured in each of the two primary epidemiological studies EPA 

uses to quantify PM-related mortality. This information allows readers to determine the 

portion of PM-related mortality benefits occurring above or below the LML of each study; in 

general, our confidence in the estimated PM mortality decreases as we consider air quality 

levels further below the LML in the two epidemiological studies. While the LML analysis 

provides some insight into the level of uncertainty in the estimated PM mortality benefits, 

EPA does not view the LML as a threshold and continues to quantify PM-related mortality 

impacts using a full range of modeled air quality concentrations. 

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

analysis occur among populations exposed at or above the LML of each study (Figures 5-20 

and 5-21), increasing our confidence in the PM mortality analysis. Approximately 80% of 

the avoided impacts occur at or above an annual mean PM2.5 level of 10 μg/m3

 (the LML of 

the Laden et al. 2006 study); about 97% occur at or above an annual mean PM2.5 level of 7.5 

μg/m3 (the LML of the Pope et al. 2002 study). As we model mortality impacts among 

populations exposed to levels of PM2.5 that are successively lower than the LML of each 

study our confidence in the results diminishes. However, the analysis above confirms that the 

great majority of the impacts occur at or above each study’s LML. 

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

areas with an annual mean PM level of 8 ug/m3, we would place greater confidence in 

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

this study. Conversely, we would place equal confidence when estimating mortality impacts 

among populations living in locations where the annual mean PM levels are above 10 ug/m3 

because this value is at or above the LML of each study. 
188

Finally, Figure 5-22 illustrates the percentage of population exposed to different 

levels of annual mean PM2.5 levels in the baseline and after the implementation of the 

Transport Rule in 2014. The Transport Rule reduces overall PM2.5 levels substantially, 

particularly among highly exposed populations located within the states covered by the rule. 

Locations of the U.S. where annual mean PM levels are below the lowest measured level of 

the Pope study--western states in particular--are generally unaffected by the rule. However, 

for populations in the far western portion of the Transport Rule region, where annual mean 

PM2.5 concentrations are below 7.5 ug/m3, there are benefits of the rule, although the 

relative magnitude of those benefits compared to benefits in the majority of the areas covered 

by the Transport Rule is small. In these areas there is lower confidence in the magnitude of 

the benefits associated with reductions in long-term PM2.5. In addition, we note that prior 

to the implementation of the Transport Rule, 89% of the population live in areas where 

PM2.5 levels are projected to be above the lowest measured levels of the Pope study. Taken 

together, this information increases our confidence in the estimated mortality reductions for 

this rule. 

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

interpreting the overall level PM-related benefits, as discussed earlier in this chapter, EPA 

believes that both cohort-based mortality estimates are suitable for use in air pollution health 

impact analyses. When estimating PM mortality impacts using risk coefficients drawn from 

the Laden et al. analysis of the Harvard Six Cities and the Pope et al. analysis of the 

American Cancer Society cohorts there are innumerable other attributes that may affect the 

size of the reported risk estimates—including differences in population demographics, the 

size of the cohort, activity patterns and particle composition among others. The LML 

assessment presented here provides a limited representation of one key difference between 

the two studies. 
189

Figure 5-18: Estimated PM2.5- related premature mortalities avoided according to 

epidemiology or expert-derived PM mortality risk estimateA

A Column total equals sum of PM2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate. 
190

Figure 5-19: Distribution of PM2.5-related mortality impacts by baseline PM2.5 levels, 

PM2.5 epidemiology study and lowest measured level (LML) of each study 
191

Figure 5-20: Percentage of total PM-related mortalities avoided by baseline air quality 

level
192

Figure 5-21: Cumulative percentage of total PM-related mortalities avoided by baseline 

air quality level 
193

Figure 5-22: Cumulative percentage of adult population at annual mean PM2.5 levels 

(pre- and post-2014 Transport Rule) 

5.8 Discussion 

This analysis demonstrates the significant health and welfare benefits of the 

Transport Rule. We estimate that by 2014 the rule will have reduced the number of PM2.5 and 

ozone-related premature mortalities by between 14,000 and 36,000, produce substantial nonmortality benefits and significantly improve visibility in Class 1 areas. This rule promises to 

yield significant welfare impacts as well, though the quantification of those endpoints in this 

RIA is incomplete. These significant health and welfare benefits suggest the important role 

that pollution from the EGU sector plays in the public health impacts of air pollution. 

Inherent in any complex RIA such as this one are multiple sources of uncertainty. 

Some of these we characterized through our quantification of statistical error in the 

concentration response relationships and our use of the expert elicitation-derived PM 

mortality functions. Others, including the projection of atmospheric conditions and sourcelevel emissions, the projection of baseline morbidity rates, incomes and technological 
194

development are unquantified. When evaluated within the context of these uncertainties, the 

health impact and monetized benefits estimates in this RIA can provide useful information 

regarding the public health impacts attributable to EGUs. 
195

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CHAPTER 6 

ELECTRIC POWER SECTOR PROFILE 

This chapter discusses important aspects of the power sector that relate to the 

Transport Rule, including the types of power-sector sources affected by the Transport Rule, 

and provides background on the power sector and electric generating units (EGUs). In 

addition, this chapter provides some historical background on EPA regulation of and future 

projections for the power sector. 

6.1 Power Sector Overview 

 The production and delivery of electricity to customers consists of three distinct 

segments: generation, transmission, and distribution. 

6.1.1 Generation 

 Electricity generation is the first process in the delivery of electricity to consumers. 

Most of the capacity for generating electricity involves creating heat to rotate turbines which, 

in turn, create electricity. The power sector consists of over 17,000 generating units, 

comprising fossil-fuel-fired units, nuclear units, and hydroelectric and other renewable 

sources dispersed throughout the country (see Table 6-1). 
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Table 6-1. Existing Electricity Generating Capacity by Energy Source, 2008 

Energy Source Number of 

Generators

Generator 

Nameplate 

Capacity (MW) 

Generator Net 

Summer 

Capacity (MW) 

Coal 1,445 337,300 313,322

Petroleum 3,768 63,655 57,445

Natural Gas 5,467 454,611 397,432

Other Gases 102 2,262 1,995

Nuclear 104 106,147 100,755

Hydroelectric Conventional 3,996 77,731 77,930

Wind Farms 494 24,980 24,651

Solar Thermal and Photovoltaic Projects 89 539 536

Wood and Wood Derived Fuels 353 7,730 6,864

Geothermal 228 3,281 2,256

Other Biomass 1,412 4,854 4,186

Pumped Storage 151 20,355 21,858

Other 49 1,042 942

Total 17,658 1,104,486 1,010,171

Source: EIA Electric Power Annual 2008, Table 1.2 

These electric generating sources provide electricity for commercial, industrial, and 

residential uses, each of which consumes roughly a quarter to a third of the total electricity 

produced (see Table 6-2). Some of these uses are highly variable, such as heating and air 

conditioning in residential and commercial buildings, while others are relatively constant, 

such as industrial processes that operate 24 hours a day. 

Table 6-2. Total U.S. Electric Power Industry Retail Sales in 2008 (Billion kWh) 

Sales/Direct Use 

(Billion kWh) 

Share of Total End 

Use 

Residential 1,380 35% 

Commercial 1,336 34% 

Industrial 1,009 26% Retail Sales 

Transportation 8 0.2% 

Direct Use 173 4% 

Total End Use 3,906 100% 

 Source: EIA Electric Power Annual 2008, Table 7.2 
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In 2008, electric generating sources produced 4,157 billion kWh to meet electricity 

demand. Roughly 70 percent of this electricity was produced through the combustion of 

fossil fuels, primarily coal and natural gas, with coal accounting for approximately half of the 

total (see Table 6-3). 

Table 6-3. Electricity Net Generation in 2008 (Billion kWh) 

Net Generation 

(Billion kWh)

Fuel Source 

Share

Coal 1,986 48% 

Petroleum 32 1% 

Natural Gas 883 21% 

Other Gases 12 0.3% 

Nuclear 806 20% 

Hydroelectric 255 6% 

Other 146 4% 

Total 4,119 100% 

Source: EIA Electric Power Annual 2008, Table 1.1 

Note: Retail sales and net generation are not equal because net generation includes 

net exported electricity and loss of electricity that occurs through transmission and 

distribution. 

Coal-fired generating units typically supply “base-load” electricity, the portion of 

electricity loads which are continually present, and typically operate throughout the day. 

Along with nuclear generation, these coal units meet the part of demand that is relatively 

constant. Gas-fired generation, on the other hand, is more often used to meet the variable 

portion of the electricity load and typically supplies “peak” power, when there is increased 

demand for electricity (for example, when businesses operate throughout the day or when 

people return home from work and run appliances and heating/air-conditioning), versus late 

at night or very early morning, when demand for electricity is reduced. 
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6.1.2 Transmission 

Transmission is the term used to describe the movement of electricity over a network 

of high voltage lines, from electric generators to substations where power is stepped down 

for local distribution. In the US and Canada, there are three separate interconnected 

networks of high voltage transmission lines50, each operating at a common frequency. 

Within each of these transmission networks, there are multiple areas where the operation of 

power plants is monitored and controlled to ensure that electricity generation and load are 

kept in balance. In some areas, the operation of the transmission system is under the control 

of a single regional operator; in others, individual utilities coordinate the operations of their 

generation, transmission, and distribution systems to balance their common generation and 

load needs. 

6.1.3 Distribution 

Distribution of electricity involves networks of lower voltage lines and substations 

that take the higher voltage power from the transmission system and step it down to lower 

voltage levels to match the needs of customers. The transmission and distribution system is 

the classic example of a natural monopoly, in part because it is not practical to have more 

than one set of lines running from the electricity generating sources to substations or from 

substations to residences and business, and in part because both transmission and generation 

are characterized by very high capital costs and very low variable operating costs. 

Transmission has generally been developed by the larger vertically integrated utilities 

that typically operate generation and distribution networks. Distribution is handled by a 

large number of utilities that often purchase and sell electricity, but do not generate it. 

Transmission and distribution have been considered differently from generation in efforts to 

restructure the industry. As discussed below, electricity restructuring has focused primarily 

on efforts to reorganize the industry to encourage competition in the generation segment of 

the industry, including ensuring open access of generation to the transmission and 

distribution services needed to deliver power to consumers. In many state efforts, this has 

also included separating generation assets from transmission and distribution assets into 

 

50 These three network interconnections are the western US and Canada, corresponding approximately to the 

area west of the Rocky Mountains; eastern US and Canada, not including most of Texas; and a third network 

operating in most of Texas.
223

separate economic entities. Transmission and distribution remain price-regulated throughout 

the country based on the cost of service. 

6.2 Deregulation and Restructuring 

 The process of restructuring and deregulation of wholesale and retail electric markets 

has changed the structure of the electric power industry. In addition to reorganizing asset 

management between companies, restructuring sought a functional unbundling of the 

generation, transmission, distribution, and ancillary services the power sector has historically 

provided, with the aim of enhancing competition in the generation segment of the industry. 

Beginning in the 1970s, government policy shifted against traditional regulatory 

approaches and in favor of deregulation for many important industries, including 

transportation (notably commercial airlines), communications, and energy, which were all 

thought to be natural monopolies (prior to 1970) that warranted governmental control of 

pricing. However, deregulation efforts in the power sector were most active during the 

1990s. Some of the primary drivers for deregulation of electric power included the desire for 

more efficient investment choices, the possibility of lower electric rates, reduced costs of 

combustion turbine technology that opened the door for more companies to sell power, and 

complexity of monitoring utilities’ cost of service and establishing cost-based rates for 

various customer classes (see Figure 6-1). 

The pace of restructuring in the electric power industry slowed significantly in 

response to market volatility and financial turmoil associated with bankruptcy filings of key 

energy companies in California. By the end of 2001, restructuring had either been delayed or 

suspended in eight states that previously enacted legislation or issued regulatory orders for its 

implementation (shown as “Suspended” in Figure 6-1 below). Another 18 other states that 

had seriously explored the possibility of deregulation in 2000 reported no legislative or 

regulatory activity in 2001 (DOE, EIA, 2003a) (“Not Active” in Figure 6-1 below). 

Currently, there are 15 states where price deregulation of generation (restructuring) has 

occurred (“Active” in Figure 6-1 below). Thirteen of these states are in the Transport Rule 

region. The effort is more or less at a standstill; there have been no recent proposals to the 
224

Federal Energy Regulatory Commission (FERC) for actions aimed at wider restructuring, 

and no new states have begun retail deregulation activity. 

Figure 6-1. Status of State Electricity Industry Restructuring Activities 

Source: EIA http://www.eia.doe.gov/cneaf/electricity/page/restructuring/restructure_elect.html (January 2010). 

6.3 Pollution and EPA Regulation of Emissions 

 The burning of fossil fuels, which generates about 70 percent of our electricity 

nationwide, results in air emissions of SO2 and NOx, important precursors in the formation of 

fine particles and ozone (NOx only). The power sector is a major contributor of both these 

pollutants, and reductions of SO2 and NOx emissions are critical to EPA’s efforts to bring 

about attainment with the fine particle and ozone NAAQS through programs like the 

Transport Rule. In 2008, the power sector accounted for 66 percent of total nationwide SO2

emissions and 18 percent of total nationwide NOx emissions (see Figure 6-2). 
225

Figure 6-2. Emissions of SO2 and NOx from the Power Sector (2008) 

Source: EPA http://www.epa.gov/ttn/chief/trends/ 

Different types of fossil fuel-fired units vary widely in their air emissions levels for 

SO2 and NOx, particularly when uncontrolled. For coal-fired units, NOx emissions rates can 

vary from under 0.05 lbs/mmBtu (for a unit with selective catalytic reduction (SCR) for NOx

removal) to over 1 lb/mmBtu for an uncontrolled cyclone boiler. NOx emissions from 

coal-fired power plants are formed during combustion and are a result of both nitrogen in 

coal and nitrogen in the air. SO2 emissions rates can vary from under 0.1 lbs/mmBtu (for 

some units with flue gas desulfurization (FGD) for SO2 removal) to over 5 lbs/mmBtu for 

units burning higher sulfur coal without any pollution controls. For an uncontrolled coal 

plant, SO2 emissions are directly related to the amount of sulfur in the coal. 

Oil- and gas-fired units also have a wide range of NOx emissions depending on both 

the plant type and the controls installed. Gas-fired units with SCR can have emissions rates 

under 0.01 lbs/mmBtu, while completely uncontrolled units can have emissions rates in 

excess of 0.5 lbs/mmBtu. Gas-fired units have very little SO2 emissions. NOx emissions 

rates on oil-fired units can range from under 0.1 lbs/mmBtu (for units with new combustion 

controls) to over 0.6 lbs/mmBtu for units without combustion controls. SO2 emissions for 

oil-fired units can range from under 0.1 lbs/mmBtu for units burning low sulfur distillate oil 

Nitrogen Oxides

Power Sector (18%)

Sulfur Dioxide

Power Sector (66%)
226

to over 2 lbs/mmBtu for units burning high sulfur residual oil. 

6.4 Pollution Control Technologies 

 There are three options for reducing SO2 emissions from coal-burning power plants. 

Units may switch from higher to lower sulfur coal, blend higher sulfur coal with lower sulfur 

coal, or use FGD, commonly referred to as scrubbers. According to data submitted to EPA 

for compliance with the Title IV Acid Rain Program, the SO2 emissions rates for coal-fired 

units without controls varied from under 0.4 lbs/mmBtu to over 5 lbs/mmBtu depending on 

the type of coal combusted. With controls, rates range from as low as 0.03 lbs/mmBtu to 

close to 1 lb/mmBtu. 

It is generally easier to switch to a coal within the same rank (e.g., bituminous or 

sub-bituminous) because these coals will have similar heat contents and other characteristics. 

 Switching completely to sub-bituminous coal (which typically has lower sulfur content) 

from bituminous coal is likely to require some modifications to the unit. Limited blending of 

sub-bituminous coal with bituminous coal can often be done with fewer modifications. 

The two most commonly used scrubber types include wet scrubbers and spray dryers, 

also known as dry scrubbers. Wet scrubbers can use a variety of sorbents to capture SO2, 

including limestone and magnesium-enhanced lime. The choice of sorbent can affect the 

performance, size, and capital and operating costs of the scrubber. New wet scrubbers 

typically achieve at least 95 percent SO2 removal. Spray dryers use lime-based slurry and 

can achieve over 90 percent removal. 

One method of reducing NOx emissions is through the use of combustion controls 

(such as low NOx burners and over-fire air). Combustion controls adjust the coal combustion 

conditions to those where less formation of NOx occurs. Post-combustion controls remove 

the NOx after it has been formed. The most common post-combustion control is SCR. In 

SCR systems ammonia (NH3) is injected, which combines with the NOx in the flue gas, to 

form nitrogen and water and uses a catalyst to enhance the reaction. These systems can 

reduce NOx by 90 percent and achieve emissions rates of around 0.06 lbs/mmBtu. Another 

post-combustion control is Selective Non-catalytic Reduction (SNCR). In this technology 
227

NOx also is removed by injecting ammonia, but no catalyst is used. SNCR systems can 

reduce NOx by up to 40 percent. 

Some of the same control options available to coal-fired units are also applicable to 

units fueled by oil or gas. Combustion controls, SCR, and SNCR can also be applied to oiland gas-fired boilers for NOx control. Combustion controls and SCR are also routinely used 

for NOx control on gas turbines. 

For more detail on the cost and performance assumptions of pollution controls, see 

the documentation for the Integrated Planning Model (IPM), a dynamic linear programming 

model that EPA uses to examine air pollution control policies for SO2 and NOx throughout 

the contiguous United States for the entire power system. Documentation for IPM can be 

found at www.epa.gov/airmarkets/epa-ipm and in the TSD “Updates to EPA Base Case 

v3.02 EISA Using the Integrated Planning Model.” 

6.5 Regulation of the Power Sector 

 At the federal level, efforts to reduce emissions of SO2 have been occurring since 

1970. Policy makers have recognized the need to address these harmful emissions, and 

incremental steps have been taken to ensure that the country meets air quality standards. The 

Transport Rule is the next step towards realizing attainment of the national standards for 

PM2.5 and ozone. 

 Even before widespread regulation of SO2 and NOx for the power sector, total 

suspended particulate matter (TSP) was a related target of state and federal action. Because 

larger particulates are visible as dark smoke from smokestacks, most states had regulations 

by 1970 limiting the opacity of emissions. Requirements for taller smokestacks also 

mitigated local impacts of TSP. Notably, such regulations effectively addressed largediameter, filterable particulate matter rather than condensable particulate matter (such as 

PM2.5) associated with SO2 and NOx emissions, which are not visible at the smokestack and 

have impacts far from their sources. 
228

Federal regulation of SO2 and NOx emissions at power plants began with the 1970 

Clean Air Act. The Act required the Agency to develop New Source Performance Standards 

(NSPS) for a number of source categories including coal-fired power plants. The first NSPS 

for power plants (subpart D) required new units to limit SO2 emissions either by using 

scrubbers or by using low sulfur coal. NOx was required to be limited through the use of low 

NOx burners. A new NSPS (subpart Da), promulgated in 1978, tightened the standards for 

SO2, requiring scrubbers on all new units. 

The 1990 Clean Air Act Amendments (CAAA) placed a number of new requirements 

on power plants. The Acid Rain Program, established under Title IV of the 1990 CAAA, 

requires major reductions of SO2 and NOx emissions. The SO2 program sets a permanent cap 

on the total amount of SO2 that can be emitted by electric power plants in the contiguous 

United States at about one-half of the amount of SO2 these sources emitted in 1980. Using a 

market-based cap and trade mechanism allows flexibility for individual combustion units to 

select their own methods of compliance with the SO2 reduction requirements. The program 

uses a more traditional approach to NOx emissions limitations for certain coal-fired electric 

utility boilers, with the objective of achieving a 2 million ton reduction from projected NOx

emission levels that would have been emitted in 2000 without implementation of Title IV. 

The Acid Rain Program comprises two phases for SO2 and NOx. Phase I applied 

primarily to the largest coal-fired electric generating sources from 1995 through 1999 for 

SO2 and from 1996 through 1999 for NOx. Phase II for both pollutants began in 2000. For 

SO2, it applies to thousands of combustion units generating electricity nationwide; for NOx it 

generally applies to affected units that burned coal during 1990 through 1995. The Acid 

Rain Program has led to the installation of a number of scrubbers on existing coal-fired units 

as well as significant fuel switching to lower sulfur coals. Under the NOx provisions of Title 

IV, most existing coal-fired units installed low NOx burners.

The CAAA also placed much greater emphasis on control of NOx to reduce ozone 

nonattainment. This led to the formation of several regional NOx trading programs as well as 

intrastate NOx trading programs in states such as Texas. The northeastern states of the 

Ozone Transport Commission (OTC) required existing sources to meet Reasonably 

Available Control Technology (RACT) limits on NOx in 1995 and in 1999 began an ozone-
229

season cap and trade program to achieve deeper reductions. In 1998, EPA promulgated 

regulations (the NOx SIP Call) that required 21 states in the eastern United States and the 

District of Columbia to reduce NOx emissions that contributed to nonattainment in 

downwind states using the cap and trade approach. This program began in May of 2003 and 

has resulted in the installation of significant amounts of selective catalytic reduction. 

The Clean Air Interstate Rule (CAIR) built on EPA’s efforts in the NOx SIP call to 

address specifically interstate pollution transport for ozone, and was EPA’s first attempt to 

address interstate pollution transport for PM2.5. It required significant reductions in 

emissions of SO2 and NOx in 28 states and the District of Columbia (see Figure 6-3 below). 

EGUs were found to be a major source of the SO2 and NOx emissions which contributed to 

fine particle concentrations and ozone problems downwind. Although the D.C. Circuit 

remanded the rule to EPA in 2008, it did so without vacatur, allowing the rule to remain in 

effect while EPA addresses the remand. Thus, CAIR is continuing to help states address 

ozone and PM2.5 nonattainment and improve visibility by reducing transported precursors of 

SO2 and NOx through the implementation of three separate cap and trade compliance 

programs for annual NOx, ozone season NOx, and annual SO2 emissions from power plants. 
230

Figure 6-3. States Covered under the Clean Air Interstate Rule 

Perhaps in anticipation of complying with CAIR, especially the more stringent 

second phase that was set to begin in 2015, several sources have recently been installing or 

planning to install advanced controls for SO2 and NOx to begin operating in the 2010 to 2015 

timeframe. Many EPA New Source Review (NSR) settlements also require controls in those 

years, as do state rules in Georgia, Illinois, and Maryland. States like North Carolina, New 

York, Connecticut, Massachusetts, and Delaware have also moved to control these emissions 

to address nonattainment. Thus both federal and state efforts are continuing to bring about 

sizeable reductions in SO2 and NOx from the power sector. Section 7-1 below discusses how 

these recent activities are reflected in the Transport Rule base case. Details of the NSR 

settlements and state controls can be found in the IPM documentation referenced earlier. 
231

6.6 Price Elasticity of Electricity 

 Electricity performs a vital and high-value function in the economy; as a result, 

electricity consumers are generally unable or unwilling to alter consumption as the price 

increases. Demand for electricity, especially in the short run, is not very sensitive to changes 

in prices and is considered relatively price inelastic, although some demand reduction does 

occur. With that in mind, EPA modeling does not incorporate a “demand response” in its 

electric generation modeling (Chapter 7) to any increases in electricity prices because of the 

reasons mentioned. Electricity demand is considered to be constant in EPA modeling 

applications and the reduction in production costs that would result from lower demand is not 

considered. This leads to some overstatement in the private compliance costs that EPA 

estimates. Notably, the “compliance costs” are the changes in the electric power generation 

costs in the base case and pollution control options that are evaluated in Chapter 7. In simple 

terms, it is the resource costs of what the power industry will directly expend to comply with 

EPA’s requirements. This is not the “social cost” of the rule which has been separately 

explained and estimated in EMPAX modeling in Chapter 8. 

6.7 Reference 

EIA Electric Power Annual 2008. DOE/EIA-0348 (2008). Available at: 

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm 
232

CHAPTER 7 

COST, ECONOMIC, AND ENERGY IMPACTS 

This chapter reports the cost, economic, and energy impact analysis performed for the 

Transport Rule. EPA used the Integrated Planning Model (IPM), developed by ICF 

Consulting, to conduct its analysis. IPM is a dynamic linear programming model that can be 

used to examine air pollution control policies for SO2 and NOx (as well as other air 

pollutants) throughout the contiguous United States for the entire power system. 

Documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm, 

and updates specific to Transport Rule modeling are in the TSD “Updates to EPA Base Case 

v3.02 EISA Using the Integrated Planning Model.” 

7.1 Background 

Over the last decade, EPA has on several occasions used IPM to consider control 

options for reducing power-sector SO2 and NOx for regional transport. These, along with the 

alternative remedies analyzed alongside the proposed remedy below, both provide context 

and suggest alternative approaches to the Transport Rule proposed remedy (see Keohane 

2009, 34–35 and Wagner 2009, 59). 

Many EPA analyses with IPM have focused on legislative changes with national 

programs, such as EPA’s IPM analyses of the Clean Air Planning Act (S.843 in 108th 

Congress), the Clean Power Act (S.150 in 109th Congress), the Clear Skies Act of 2005 

(S.131 in 109th Congress), the Clear Skies Act of 2003 (S.485 in 108th Congress), and the 

Clear Skies Manager's Mark (of S.131). These analyses are available at EPA’s website: 

(www.epa.gov/airmarkets/progsregs/cair/multi.html). EPA’s IPM analysis for CAIR is 

another example, in this case dealing with a regulatory approach focusing on the eastern US: 

(www.epa.gov/airmarkets/progsregs/epa-ipm/cair/index.html). 

In addition, EPA conducted extensive state-by-state analysis of control levels and 

associated emissions projections related to identifying significant contribution to 
233

nonattainment and interference with maintenance for the Transport Rule. More details on 

this analysis can be found in the Significant Contribution Approach TSD. 

As discussed in section 6.5, this proposed rule comes during a period when many new 

SO2 and NOx controls are being installed. Many are needed for compliance with NSR 

settlements and state rules, while others may have been planned in expectation of CAIR. 

Because CAIR remains in effect until it is replaced, emission reductions continue in the 

eastern US. 

The base case in this RIA assumes that CAIR is not in effect, but does take into 

account emissions reductions associated with the implementation of all federal rules, state 

rules and statutes, and other binding, enforceable commitments finalized by February 3, 

2009, that the power industry has for installing and operating SO2 and NOx emissions 

controls in the timeframe covered in the analysis. 

EPA has made these base case assumptions recognizing that a key step in the process 

of developing a 110(a)(2)(D)(i)(I) rule, such as the Transport Rule, involves analyzing 

existing (base case) emissions to determine which states significantly contribute to 

downwind nonattainment and maintenance areas. EPA cannot prejudge at this stage which 

states will be affected by the rule. For example, a state affected by CAIR may not be 

affected by the new rule and after the new rule goes into effect, the CAIR requirements will 

no longer apply. For a state covered by CAIR but not covered by the new rule, the CAIR 

requirements would not be replaced with new requirements, and therefore an increase in 

emissions relative to present levels could occur in that state. More fundamentally, the court 

has made clear that, due to legal flaws, the CAIR rule cannot remain in place and must be 

replaced. If EPA’s base case analysis were to ignore this fact and assume that reductions 

from CAIR would continue indefinitely, areas that are in attainment solely due to controls 

required by CAIR would again face nonattainment problems, because the existing protection 

from upwind pollution would not be replaced. For these reasons, EPA cannot assume in its 

base case analysis that the reductions required by CAIR will continue to be achieved. 

Following this logic, the 2012 base case shows emissions higher than current levels in 

some states. Because EPA has been directed to replace CAIR, EPA believes that for many 
234

states, the absence of the CAIR NOx program will lead to reversion to the NOx Budget 

Trading Program (NBP), which substantially limits ozone-season NOx emissions from 

electric power generation in a major part of the Eastern US and ensures the operation of NOx

controls in 20 covered states and the District of Columbia. The base case contains the NBP. 

Also, without the CAIR SO2 program, there would remain the broad federal SO2 emissions 

requirements for electric generation from fossil fuels in the lower 48 states for the 

comparatively less stringent CAA Title IV Acid Rain program. As a result, SO2 emissions in 

many states would increase markedly in the 2012 base case relative to the present. Efforts to 

comply with ARP rules at the least-cost would occur in many cases solely through use of 

currently readily available, inexpensive Title IV allowances and without the operation of 

some existing scrubbers that do not have other binding enforceable requirements. Notably, 

all known controls for both SO2 and NOx that are required under state laws, NSPS, consent 

decrees, and other enforceable, binding commitments through 2014 are accounted for in the 

base case. These requirements are quite substantial in maintaining the operation of much of 

the existing advanced controls in place. It is against this backdrop that the Transport Rule is 

analyzed and that significant contribution to nonattainment and interference with 

maintenance must be addressed. 

The model’s base case features an updated Title IV SO2 allowance bank assumption 

and incorporates updates related to the Energy Independence and Security Act of 2007. 

Many key assumptions, notably demand for electricity, reflect the 2008 Annual Energy 

Outlook from the Energy Information Administration (EIA).51 In addition, the model 

includes policies affecting the power sector: the Title IV of the Clean Air Act (the Acid Rain 

Program); the NOx SIP Call; various New Source Review (NSR) settlements52; and several 

 

51 For the final rule, EPA anticipates using an updated version of IPM that will reflect assumptions from AEO 

2010.

52 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company 

(Vectren), Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia 

Electric & Power Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power 

(AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio 

Edison, and Kentucky Utilities. These agreements lay out specific NOx, SO2, and other emissions controls for 

the fleets of these major Eastern companies by specified dates. Many of the pollution controls (e.g FGDs and 

SCRs) are required between 2010 and 2014.
235

state rules53 affecting emissions of SO2 and NOx that were finalized through February 3, 

2009. IPM includes state rules that have been finalized and/or approved by a state’s 

legislature or environmental agency. The IPM documentation TSD contains details on all of 

these other binding enforceable commitments for installation and operation of advanced NOx

and SO2 pollution controls across much of the Eastern US. In providing the results of the 

EPA’s analysis with IPM, the Agency is using the results for the model’s 2015 run year 

(which covers years 2014, 2015, 2016, and 2017) as a proxy for 2014 results. IPM provides 

actual results for 2012, 2015, and 2020. 

 To address air quality problems and improve public health and the environment, EPA 

is proposing the Transport Rule. The proposed Transport Rule requires annual SO2 and NOx

reductions in 27 states and the District of Columbia, and also requires ozone season NOx

reductions in 25 States and the District of Columbia. Many of the Transport Rule States are 

affected by both the annual SO2 and NOx reduction requirements and the ozone season NOx

requirements. 

The rule would affect roughly 5,000 fossil fuel-fired units with a nameplate capacity 

greater than 25 MW. These sources accounted for roughly 84 percent of nationwide SO2

emissions and 73 percent of nationwide NOx emissions in 2008 (see Table 7-1). 

 

53 These include current and future state programs in Connecticut, Delaware, Georgia, Illinois, Maine, 

Maryland, Massachusetts, Minnesota, Missouri, New Hampshire, North Carolina, New Jersey, New York, 

Oregon, Texas, and Wisconsin the cover NOx and SO2 emissions controls.
236

Table 7-1. Annual Emissions of SO2 and NOx in 2008 and Percentage of Emissions in 

the Transport Rule Affected Region (tons) 

SO2 NOx

Transport Rule Annual NOx and SO2 States 6,439,067 2,267,008 

Nationwide (Contiguous 48 States) 7,620,588 3,098,267 

Emissions of Transport Rule States as 

Percentage of Nationwide Emissions 

84% 73% 

Source: EPA emissions data from all reporting units. 

Note: Transport Rule annual NOX and SO2 states include Alabama, Connecticut, Delaware, Florida, Georgia, 

Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, 

Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, 

Virginia, West Virginia, and Wisconsin. 

For SO2 and annual NOx, EPA modeled control requirements beginning in 2012 for 

the 27 eastern states shown in blue and green in Figure 7-1 below. In 15 of those states 

(shown in blue), more stringent SO2 requirements begin in 2014. For ozone-season NOx, 

separate ozone-season requirements were applied to the 25 states shown in blue in Figure 7-

2. Many of the Transport Rule states are affected by both the annual SO2 and NOx reduction 

requirements and the ozone-season (May–September) NOx requirements. Tables 7-2 and 7-3 

show the emission budgets allotted to each state. For further discussion about the scope and 

requirements of the Transport Rule, see the Transport Rule preamble or Chapter 2 of this 

RIA. 
237

Figure 7-1. Transport Rule Annual NOx and SO2 States
238

Figure 7-2. Transport Rule Ozone-season NOx States
239

Table 7-2. Transport Rule Annual NOx and SO2 State Emission Budgets (tons) 

State SO2, 2012 and 

2013 

SO2, 2014 and 

Later 

NOx Annual, 

All Years 

Alabama 161,871 161,871 69,169 

Connecticut 3,059 3,059 2,775 

Delaware 7,784 7,784 6,206 

District of Columbia 337 337 170 

Florida 161,739 161,739 120,001 

Georgia 233,260 85,717 73,801 

Illinois 208,957 151,530 56,040 

Indiana 400,378 201,412 115,687 

Iowa 94,052 86,088 46,068 

Kansas 57,275 57,275 51,321 

Kentucky 219,549 113,844 74,117 

Louisiana 90,477 90,477 43,946 

Maryland 39,665 39,665 17,044 

Massachusetts 7,902 7,902 5,960 

Michigan 251,337 155,675 64,932 

Minnesota 47,101 47,101 41,322 

Missouri 203,689 158,764 57,681 

Nebraska 71,598 71,598 43,228 

New Jersey 11,291 11,291 11,826 

New York 66,542 42,041 23,341 

North Carolina 111,485 81,859 51,800 

Ohio 464,964 178,307 97,313 

Pennsylvania 388,612 141,693 113,903 

South Carolina 116,483 116,483 33,882 

Tennessee 100,007 100,007 28,362 

Virginia 72,595 40,785 29,581 

West Virginia 205,422 119,016 51,990 

Wisconsin 96,439 66,683 44,846 

Group 1 SO2 States 3,117,288 1,723,421 

Group 2 SO2 States 776,582 776,582 

Total 3,893,870 2,500,003 1,376,312 
240

Table 7-3. Ozone-season NOx State Emission Budgets (tons) 

State 

NOx Ozone 

Season, All 

Years 

Alabama 29,738 

Arkansas 16,660 

Connecticut 1,315 

Delaware 2,450 

District of Columbia 105 

Florida 56,939 

Georgia 32,144 

Illinois 23,570 

Indiana 49,987 

Kansas 21,433 

Kentucky 30,908 

Louisiana 21,220 

Maryland 7,232 

Michigan 28,253 

Mississippi 16,530 

New Jersey 5,269 

New York 11,090 

North Carolina 23,539 

Ohio 40,661 

Oklahoma 37,087 

Pennsylvania 48,271 

South Carolina 15,222 

Tennessee 11,575 

Texas 75,574 

Virginia 12,608 

West Virginia 22,234 

Total 641,614 

EPA modeling54 shows that coal-fired and oil/gas-fired generation will continue to 

play an important part of the electricity generating portfolio in the United States. Electricity 

 

54 EPA uses the IPM to make power-sector forecasts about emissions, costs, and other key factors of the power 

sector. Industry projections presented here are from EPA’s base case scenario. For more information about 

IPM, see http://www.epa.gov/airmarkets/progsregs/epa-ipm/.
241

demand is anticipated to grow by roughly 1 percent a year, and total electricity demand is 

projected to be 4,333 billion kWh by 2014. Table 7-4 shows current electricity generation 

and projected levels in 2012, 2014, and 2020 using EPA IPM modeling. The increasing 

growth of coal-fired generation and decline of generation from units using natural gas and oil 

results primarily due to the relative prices of the fuels in EPA’s IPM forecast and the energy 

efficiency of the generation technologies in producing electricity. IPM in essence is using 

EIA’s Annual Energy Outlook for 2008’s electric demand forecast for the US and estimating 

the mix of fossil generation based on its on fuel price projections and assumptions on 

performance and costs of electric generation technologies. The base case assumption that 

CAIR is not in effect does have some modest influence on the fossil generation mix in this 

forecast, because CAIR had increased the costs of coal-fired generation relatively more than 

it had increased the costs to generation units that burned oil or natural gas.55

Table 7-4. 2008 Electricity Net Generation and EPA Base Case Projections for 2012, 

2014 and 2020 for the Contiguous 48 States (Billion kWh) 

2008 2012 2014 2020

Coal 1,967 2,232 2,418 2,629

Oil 21 24 15 14

Natural Gas 798 743 632 570

Other 1,171 1,266 1,269 1,332

Total 3,957 4,266 4,333 4,546

Source: 2008 data from EIA Electric Power Annual 2008, Table 1.1 (adjusted to represent the 

Contiguous 48 States for consistency with projections, which are from the Integrated Planning 

Model run by EPA, 2010).

While EPA is proposing one particular remedy for the Transport Rule, State 

Budgets/Limited Trading, it is also requesting comment on the alternatives of State 

Budgets/Intrastate Trading and Direct Control. These two alternatives represent the range of 

myriad alternatives considered by EPA for this rule. While their features and impacts 

inevitably differ in some respects, the alternatives are distinct ways to try to achieve the same 

emissions reductions as the proposed remedy. 

 

55 For the same time period, the Energy Information Administration’s Annual Energy Outlook for 2010 shows a 

small amount of growth in coal-fired electric generation, no growth in electric generation using oil, and a similar 

trend in generation from natural gas, which declines through 2014 and increases between 2014 and 2020.
242

The main difference between the three remedies lies in the kinds of flexibility they 

provide for compliance. State Budgets/Limited Trading allows sources to trade within state 

lines and, as long as state emissions remain within the budgets plus variability limits, across 

state lines as well. State Budgets/Intrastate Trading caps each state’s emissions at its budget 

without variability and only allows trading within (and not between) states. Under Direct 

Control, each EGU must meet an emission rate limit, and a state’s emissions must remain 

within its budget plus variability. Even though 2012 rate limits are designed to be met 

without installing unplanned controls, individual rates still offer less flexibility than trading. 

Details on the derivation of unit-specific rate limits for Direct Control can be found in the 

TSD, “State Budgets, Unit Allocations, and Unit Emissions Rates.” 

Even under Direct Control, the proposed rule provides some flexibility. Notably, in 

addition to allowing total emissions up to the budget plus variability limit, units in the same 

state within the same company are allowed to average their rates together. As described in 

section 7.11 below, intra-state, intra-company averaging is not modeled in IPM, while the 

variability provision is. Further details on Direct Control, State Budgets/Intrastate Trading, 

and the preferred State Budgets/Limited Trading can be found in Section V. of the Transport 

Rule preamble. 

As noted above, IPM has been used for evaluating the economic and emission 

impacts of environmental policies for over a decade. The economic modeling presented in 

this chapter has been developed for specific analyses of the power sector. Thus, the model 

has been designed to reflect the industry as accurately as possible. As a result, EPA has used 

discount rates in IPM that are appropriate for the various types of investments and other costs 

that the power sector incurs. The primary real discount rate is 5.5 % for pollution control 

retrofits, fuel costs, allowance prices, and most generation technologies’ capital and 

operating expenses.56 The discount rates used in IPM differ from discount rates used in other 

RIA analyses done for the Transport Rule, particularly the discount rates used in the benefits 

and macroeconomic analyses that are assumed to be social discount rates. (See Chapters 5 

and 8 where social discount rates of 3 % and 7 % are used.) EPA uses the best available 

information from utilities, financial institutions, debt rating agencies, and government 

 

56 For renewable and most natural gas technologies a 6.1 % discount rate is used.
243

statistics as the basis for the discount rates used for power sector modeling in IPM. These 

discount rates have undergone review by the power sector and the Energy Information 

Administration. 

More detail on IPM can be found in the model documentation, which provides 

additional information on the assumptions discussed here as well as all other assumptions 

and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa-ipm). Updates 

specific to Transport Rule modeling are also in the TSD “Updates to EPA Base Case v3.02 

EISA Using the Integrated Planning Model.” 

7.2 Projected SO2 and NOx Emissions and Reductions 

Both the proposed Transport Rule remedy and the alternative remedies achieve 

substantial emissions reductions. Under each of these remedies, EPA projects annual SO2

emission reductions of greater than 60 percent and annual NOx emissions reductions of 

greater than 33 percent in the respective remedy regions by 2014 relative to the base case. 

Additionally, EPA projects ozone-season NOx reductions of greater than 15 percent in the 

Transport Rule region (see Table 7-5). On the other hand, differences among the remedies 

the incentive for emissions banking can lead to slight differences in the timing of SO2

reductions. The following section describes the emission results for each remedy. 

In Figure 7-3 below, the results of EPA modeling of State Budgets/Limited Trading, 

the Transport Rule proposed remedy, show that substantial SO2 emissions reductions occur 

in the Midwest and Mid-Atlantic regions of the country. Because banking of allowances is 

allowed to encourage early reductions, 2012 SO2 reductions are greater overall than state 

budgets alone would require in that year. For many coal-fired electric generation units 

throughout the region it is economically advantageous to make extra emissions reductions in 

2012 through fuel switching to have allowances to later use or sell in 2014 and beyond. This 

is when the Transport Rule becomes more stringent and when electric generators will also 

need to meet higher electric demand. Because of the banking provisions, the relative 

economics of making pollution reductions below the emissions cap levels in 2012 versus 

making emissions reductions later favor doing more in 2012. Annual NOx emissions 
244

reductions occur across the Transport Rule region (see Figure 7-4), and with the Transport 

Rule, ozone-season NOx emissions reductions are lower than they would have been with the 

NOx SIP Call (base case) (see Figure 7-5). 

Table 7-5. Projected Emissions of SO2 and NOx with the Base Casea

 (No Further 

Controls) and with Transport Rule Options (Million Tons) 

Base Case 

State 

Budgets/Limited 

Trading 

State 

Budgets/Intrastate 

Trading Direct Control 

2012 2014 2012 2014 2012 2014 2012 2014 

 Coverage 

2012 Base Case 2014 Base Case Transport Rule Emission Reductions Transport Rule Emission Reductions Transport Rule Emission Reductions Transport Rule Emission Reductions Transport Rule Emission Reductions Transport Rule Emission Reductions 

SO2

Contiguous 

48 States 9.5 8.5 4.8 4.7 4.1 4.4 4.6 4.9 4.2 4.3 5.1 4.4 4.1 4.4 

(annual) 

Transport 

Rule States 8.5 7.4 3.6 4.9 2.8 4.6 3.4 5.1 2.9 4.5 3.9 4.6 2.8 4.6 

 

NOx

Contiguous 

48 States 3.0 3.0 2.2 0.8 2.2 0.8 2.2 0.8 2.2 0.8 2.2 0.8 2.2 0.9 

(annual) 

Transport 

Rule States 2.2 2.1 1.4 0.7 1.4 0.7 1.4 0.7 1.4 0.8 1.4 0.7 1.4 0.8 

 

NOx

Contiguous 

48 States 1.1 1.1 1.0 0.1 1.0 0.1 1.0 0.1 1.0 0.2 1.0 0.1 1.0 0.2 

(summer) 

Transport 

Rule States 0.7 0.7 0.6 0.1 0.6 0.1 0.6 0.1 0.6 0.1 0.6 0.1 0.6 0.1 

Note: Numbers may not add due to rounding. The emissions data presented here are EPA modeling results 

and the Transport Rule region includes States modeled for the annual SO2 and NOx requirements. “Summer” is 

from May 1–September 30, which is the ozone season. 

a

Base case includes Title IV Acid Rain Program, NOx SIP Call, and State rules through February 3, 2009. 

Source: Integrated Planning Model run by EPA, 2010.
245

Figure 7-3. SO2 Emissions from the Power Sector in 2012 and 2014 with and without 

the Transport Rule (State Budgets/Limited Trading) 

Source: EPA, IPM, 2010.
246

Figure 7-4. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and 

without the Transport Rule (State Budgets/Limited Trading) 

Source: EPA, IPM, 2010.
247

Figure 7-5. Ozone-season NOx Emissions from the Power Sector in 2012 and 2014 with 

and without State Budgets/Limited Trading 

Source: EPA, IPM, 2010. 
248

 Emissions for State Budgets/Intrastate Trading are shown in Table 7-5 above and in 

Figures 7-6, 7-7, and 7-8 below. Compared to the proposed remedy, State Budgets/Intrastate 

Trading achieves slightly more SO2 reduction in 2012 (and slightly less in 2014), as Table 7-

5 shows above. For this remedy, each state’s emissions were restricted to the state budget 

without variability. Without the opportunity for even limited trading of allowances across 

state borders, more banking was projected in some states than in the proposed remedy. In 

other states, more immediate emissions reductions (relative to the base case) are projected; 

because sources cannot purchase allowances from outside their own state, state budgets must 

be met exactly. Both of these factors lead to slightly greater SO2 reductions in 2012 than in 

the State Budgets/Limited Trading.

Figure 7-6. SO2 Emissions from the Power Sector in 2012 and 2014 with and without 

State Budgets/Intrastate Trading 

Source: EPA, IPM, 2010. 
249

Figure 7-7. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and 

without State Budgets/Intrastate Trading 

Source: EPA, IPM, 2010. 
250

Figure 7-8. Ozone-season NOx Emissions from the Power Sector in 2012 and 2014 with 

and without State Budgets/Intrastate Trading 

Source: EPA, IPM, 2010. 
251

 Emissions for Direct Control are shown in Table 7-5 above and in Figures 7-9, 7-10, 

and 7-11 below. Compared to State Budgets/Limited Trading, Direct Control results in less 

SO2 reduction in 2012 (see Table 7-5). Because it does not allow banking for early 

reductions, the Direct Control alternative does not result in reductions below state budgets in 

2012. The absence of banking does not lead to lower emissions relative to the other proposed 

remedies in 2014 because total emissions in each state can still be as high as the state’s 

budget plus variability limit with the Direct Control option. 

Figure 7-9. SO2 Emissions from the Power Sector in 2012 and 2014 with and without 

Direct Control 

Source: EPA, IPM, 2010. 
252

Figure 7-10. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and 

without Direct Control 

Source: EPA, IPM, 2010.
253

Figure 7-11. Ozone-season NOx Emissions from the Power Sector in 2012 and 2014 with 

and without Direct Control 

Source: EPA, IPM, 2010. 

7.3 Overview of Costs and Other Impacts 

 As shown above in Figures 7-1 and 7-2, the Transport Rule directly affects 27 states 

and the District of Columbia in controlling pollution related to fine particles. For ozone, it 

also affects a distinct but overlapping group of 25 states and the District of Columbia. The 

states in one or both of these regions constitute most of the fossil-fuel-fired generation and 

capacity in the contiguous US, especially coal-fired (see Tables 7-6 and 7-7 below). 
254

Table 7-6. Fossil-fuel Generation Nationwide and in the Transport Region 

 2008 Generation (Thousand GWh) 

 Contiguous 

48 States 

Transport 

Rule Fine 

Particle Area 

Transport 

Rule Ozone 

Area 

Coal-fired 1,967 1,477 1,487 

Gas-fired 798 331 518 

Oil-fired 21 20 18 

Sum 2,786 1,828 2,022 

Source: EIA Electric Power Monthly with data for December 2009, 

Tables 1.7.B, 1.8.B, 1.10.B. 

Table 7-7. Fossil-fuel Capacity Nationwide and in the Transport Region 

2008 Capacity (GW) 

 Contiguous 

48 States 

Transport 

Rule Fine 

Particle Area 

Transport 

Rule Ozone 

Area 

Pulverized Coal 309 240 237 

Combined Cycle 190 95 131 

Other Oil/Gas 249 160 191 

Sum 748 496 559 

Source: EPA's NEEDS v3.02ARRA. 

 While most impacts of the Transport Rule affect the covered states themselves, 

national impacts are important. Because the electric grid is connected irrespective of state 

boundaries, effects on electrical generation in one state have spillover effects in other states. 

Likewise, because the Transport Rule states have the vast majority of coal-fired generation, 

changes in their coal consumption and demand affect coal prices nationwide. In some cases, 

such as retail electricity prices and the operation of pollution controls, nationwide 

information would not be as relevant as regional totals. But for most of the following 

sections, nationwide projections provide a more complete picture of the Transport Rule’s 

impacts. 
255

7.4 Projected Compliance Costs 

The power industry’s “compliance costs” are the changes in electric power generation 

costs in the base case and alternative pollution control approaches that are examined in this 

chapter. In simple terms, these costs are the resource costs of what the power industry will 

directly expend to comply with EPA’s requirements. This is not the “social cost” of the 

control approaches, which is separately explained and estimated in EMPAX modeling in 

Chapter 8 that follows. 

EPA projects that the annual incremental compliance costs of the Transport Rule 

proposed remedy (State Budgets/Limited Trading) are $3.7 billion in 2012 and $2.8 billion in 

2014 (see Table 7-8 below). Another measure of this impact is the change in electricity 

prices (discussed in section 7.9). Costs generally are higher in 2012 than in 2014 because of 

reduced compliance flexibility in 2012, which is too soon for sources to retrofit new FGD 

and SCR that were not already planned. 

Table 7-8. Annualized Compliance Cost of the Transport Rule 

 State 

Budgets/Limited 

Trading 

State 

Budgets/Intrastate 

Trading 

Direct Control 

Annualized Cost 2012 2014 2012 2014 2012 2014 

(billions of 2006$) $3.7 $2.8 $4.2 $2.7 $4.3 $3.4 

Note: Numbers rounded to the nearest hundred million for annualized cost. 

Source: Integrated Planning Model run by EPA. 

Though based on the same state budgets as State Budgets/Limited Trading, State 

Budgets/Intrastate Trading costs approximately $0.5 billion dollars more in 2012, as Table 7-

8 shows above. As mentioned above in the context of emissions reductions, more banking is 

projected in some states and more immediate emissions reductions (relative to the base case) 

in others, both because the flexibility of trading between states is not available. These 

factors drive 2012 costs higher than those of State Budgets/Limited Trading. 

The allowance prices of the two alternatives that allow emissions trading are provided 

in Appendix E. 

The Direct Control alternative remedy costs $0.6 billion more than the proposed 
256

remedy in 2012 and 2014. Based on source-specific emissions rates, it provides less 

flexibility for compliance than either of the trading remedies. For example, demand for the 

lowest-sulfur grades of coal is higher in the Direct Control case even relative to the nextlowest grades. In this case, many unscrubbed units have rates that require either using the 

lowest-sulfur grade of coal or installing new controls, even though slightly higher-sulfur coal 

would likely be economical for some if trading were an option.

7.5 Projected Approaches to Emissions Reductions 

Emission reductions of NOx and SO2 for the alternative pollution control remedies 

that EPA examined are achieved through a combination of compliance options by electric 

generation units using fossil fuels in the Transport Region. These actions include full 

operation of existing controls that are in place that were noneconomic to operate under the 

base case, additional pollution control installations, coal switching (including blending of 

coals), and generation shifts towards more efficient electricity producing units and loweremitting generation technologies (e.g., some reduction of coal-fired generation with an 

increase of generation from natural gas). Notably, only coal-fired generation units actually 

install and operate added pollution controls in response to control alternatives. In 2012 and 

2014, there are similar, but somewhat different sets of actions that EPA’s modeling predicts 

will occur. 

In 2012 in the State Budgets/Limited Trading option (preferred approach), a small 

shift from coal-fired and oil generation to greater use of natural gas lowers emissions a small 

amount (see Table 7-12). NOx emissions reductions largely occur when on coal-fired units, 

selective catalytic reduction (SCR) controls that were only required to operate in the ozone 

season in the base case now operate throughout the year (see Table 7-11). Additionally, in 

states that were not in the original CAIR program but are covered in summer ozone season 

program of this rule there is a modest amount of NOx reduction stemming from low- NOx

burners that are installed on coal-fired units. SO2 emissions reductions result from the 

relatively high allowances values for the SO2 programs, which makes it economic for all 

FGDs (scrubbers) that EPA estimates are in place by 2012 to operate under the preferred 

control option. High allowance values also lead to considerable coal switching to lower 
257

sulfur coals as the least cost compliance approach for many coal-fired units without FGDs. 

Examining the changes in use of coals with differing sulfur contents as laid out in 

Table 7-9 below is helpful in considering what EPA estimates will occur from the base case 

to the preferred approach in the rule. For coal-fired generation units that do not operate 

FGDs in the base case, about 30 % of power generated by these units is from high sulfur 

coals; 10 % of the power generated is from high-medium sulfur coals; and 15 % of the power 

generated is from low-medium sulfur coals. The percentages for power generation from 

cleaner coals are close to 1 %, 15 %, and 30 % for low sulfur bituminous, low sulfur 

subbituminous, and very low sulfur subbituminous respectively. Under the preferred 

approach, for the units that do not have FGDs, power generation from the units using high, 

high-medium, and low-medium sulfur coals changes to about 2 %, 5 %, and 30 %, 

respectively. For the cleaner coals, the percentages of power generation from units without 

FGDs under the preferred approach were about 3 %, less than 1 %, and 60 % for low-sulfur 

bituminous, low sulfur subbituminous, and very low sulfur subbituminous respectively.

Table 7-9. Coal Sulfur Categories (lbs/mmBTU) 

High sulfur > 1.2 

Subbituminous Low sulfur 0.7 to 1.2 

Very low sulfur < 0.7 

High sulfur > 2 

High-medium sulfur 1.4 to 2 

Low-medium sulfur 0.9 to 1.4 Bituminous 

Low sulfur < 0.9 

Lignite All grades 0.6 to 4 

Table 7-10 shows total coal use among both scrubbed and unscrubbed EGUs in the 

states subject to the proposed SO2 programs. The preferred remedy approach (State 

Budgets/Limited Trading) is associated with only a slight reduction (1% in 2014) in total 

coal use among these units compared to the base case. More importantly, the table reinforces 

that the preferred approach drives increased overall use of cleaner bituminous and 

subbituminous coals, especially very low sulfur subbituminous. This trend appears even 
258

when, as in this table, coal use of scrubbed units and very small units are included. 

Table 7-10. Coal Use by Sulfur Category in the PM2.5 Transport Region 

for the Base Case and Preferred Approach* 

(thousand short tons) 

 Subbituminous Bituminous 

 

Lignite 

High 

sulfur 

Low 

sulfur 

Very 

low 

sulfur 

High 

sulfur 

Highmedium 

sulfur 

Lowmedium 

sulfur 

Low 

sulfur 

Total 

Base case 3,911 3,405 125,460 176,479 285,511 106,575 72,952 4,507 778,800 

2012 Preferred 

approach 3,911 2,143 63,858 246,828 258,847 101,383 89,826 10,518 777,315 

Base case 3,883 6,664 110,357 193,885 331,913 69,060 85,248 5,143 806,153 

2014 Preferred 

approach 3,883 4,823 64,434 242,821 294,305 84,519 91,672 11,558 798,014 

*These coal usage results are for the 28 states covered by the rule in the trading program to reduce SO2 emissions. 

In 2014, the preferred approach to the Transport Rule is projected to result in the 

operation of an additional 40 GW of flue gas desulfurization (scrubbers) for SO2 control on 

existing coal-fired generation capacity and the year-round operation of an additional 51 GW 

of selective catalytic reduction technology (SCR) for NOx control on existing coal-fired 

generation capacity by 2014 (see Table 7-11). This accounts for the vast majority of the NOx

reduction. A small number of coal-fired units also install selective non-catalytic reduction 

technology (SNCR) for NOx control under the preferred approach to the Transport Rule. 

For SO2, the reductions from the added FGDs are also supplemented by the continued 

and expanded use of relatively cleaner coals at uncontrolled units. Notably, many of the 

FGDs that are coming into operation are built due to control requirements other than the 

Transport Rule and only 14 GWs of capacity with newly constructed FGDs are resulting 

from the preferred approach. 
259

Table 7-11. Advanced Pollution Controls on Coal-fired Generation by Technology with 

the Base Case (No Further Controls) and with Transport Rule Options (GW) 

Base Case 

State Budgets/Limited 

Trading 

State 

Budgets/Intrastate 

Trading Direct Control 

2012 2014 2012 2014 2012 2014 2012 2014 

Total 

Capacity 

(GW) Capacity Controlled Year-round (GW) 

Control Technology Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region Contiguous 48 States Transport Region 

FGD 194 146 162 117 202 139 193 147 241 179 191 145 238 176 191 145 247 185 

SCR 140 136 88 83 117 106 133 128 167 157 134 129 168 157 137 132 171 160 

Note: For FGD, the “Transport Region” comprises 28 states and District of Columbia as shown in Figure 7-1. For SCR, 

the “Transport Region” includes both these states and those under the ozone-season NOx program as shown in Figure 7-2 

above. All totals refer to coal-fired generating capacity. 

Source: Parsed files from the Integrated Planning Model run by EPA, 2010.

7.6 Projected Generation Mix 

Table 7-12 and Figure 7-12 show the generation mix with the Transport Rule. Coalfired generation and natural-gas-fired generation are projected to remain relatively 

unchanged because of the phased-in nature of the Transport Rule, which allows industry the 

appropriate amount of time to install the necessary pollution controls. Both the base case and 

all three remedies show shifts away from oil and natural gas generation and toward increased 

coal generation between 2012 and 2014. 
260

Table 7-12. Generation Mix with the Base Case (No Further Controls) and with 

Transport Rule Options (Thousand GWh) 

2008 2012 2014 Historical Base Case State Budgets/Limited Trading Change from Base State Budgets/Intrastate Trading Change from Base Direct Control Change from Base Base Case State Budgets/Limited Trading Change from Base State Budgets/Intrastate Trading 

Change from Base Direct Control Change from Base 

Coal 1,967 2,232 2,223 -0.4% 2,21

1 -1% 2,226 -0.3% 2,41

8 2,400 -1% 2,396 -1% 2,407 -0.5% 

Oil 21 24 19 -20% 16 -32% 15 -36% 15 11 -30% 10 -37% 11 -30% 

Natural 

Gas 798 743 751 1% 761 2% 756 2% 632 641 1% 643 2% 639 1% 

Other 1,171 1,266 1,273 1% 1,27

7 1% 1,269 0.2% 1,269 1,282 1% 1,284 1% 1,276 1% 

Total 3,957 4,266 4,266 0.0% 4,26

5 0.0% 4,266 0.0% 4,33

4 4,333 0.0% 4,333 0.0% 4,333 0.0% 

Note: Numbers may not add due to rounding. 

Source: 2008 data from EIA Electric Power Monthly with data for December 2009, Tables 1.6.B 1.7.B, 1.8.B, 1.10.B; 2012 and 2014

projections are from the Integrated Planning Model run by EPA, 2010. 
261

Figure 7-12. Generation Mix with the Base Case (No Further Controls) and with 

Transport Rule Options 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Base Case

State

Budgets/Limited

TradingState

Budgets/Intrastate

Trading

Direct Control

Base Case

State

Budgets/Limited

TradingState

Budgets/Intrastate

Trading

Direct Control

2012 2014

Thousand GWh

Other

Renewables

Hydro

Nuclear

Oil

Gas

Coal

Source: 2008 data derived from EIA U.S. Coal Supply and Demand: 2008 Review, Table 1; 2012 and 2014 

projections from the Integrated Planning Model run by EPA, 2010. 

Relative to the base case, about 1.2 GW of coal-fired capacity is projected to be 

uneconomic to maintain (less than 1 percent of all coal-fired capacity in the Transport Rule 

states) by 2014. Uneconomic units, for the most part, are small and infrequently used 

generating units that are dispersed throughout the states covered in the Transport Rule. In 

practice, units projected to be uneconomic to maintain may be “mothballed,” retired, or kept 

in service to ensure transmission reliability in certain parts of the grid. EPA modeling is 

unable to distinguish between these potential outcomes. IPM can only predict that specific 

generating units are uneconomic to maintain, based on their fuel, operating and fixed costs, 

and whether they are needed to meet both demand and reliability reserve requirements. 

Though similar to the proposed remedy, the alternative remedies result in slightly 

different projections of uneconomic units. State Budgets/Intrastate Trading results in 1.6 
262

GW of such capacity by 2014, 0.4 GW more than in State Budgets/Limited Trading. In 

contrast, Direct Control yields only 0.5 GW in 2014 (0.7 GW less than the proposed 

remedy). 

7.7 Projected Capacity Additions 

In addition, EPA projects that most future growth in electric demand will be met with 

a combination of new natural gas- and coal-fired capacity (see Table 7-13). This occurs in 

the base case, under the proposed Transport Rule, and under both alternative remedies. 

Table 7-13. Total Coal-fired, Natural Gas-fired, Oil-fired and Renewable Generation 

Capacity by 2025 (GW) 

 2008 Base Case 

State 

Budgets/Limited 

Trading 

State 

Budgets/Intrastate 

Trading 

Direct 

Control 

Pulverized Coal 309 387 386 386 387 

Combined Cycle 

Turbines 190 229 228 228 228 

Other Oil/Gas 249 240 240 240 241 

Renewables 30 49 49 49 49 

Source: 2008 data from EPA's NEEDS v3.02ARRA. Projections from Integrated Planning Model run by EPA. 

Note: “Renewables” include biomass, geothermal, solar, and wind electric generation capacity. 

7.8 Projected Coal Production for the Electric Power Sector 

Coal production for electricity generation is expected to increase relative to current 

levels, with or without the Transport Rule (see Table 7-14). The reductions in emissions 

from the power sector will be met through the installation and operation of pollution controls 

for SO2 and NOx removal. Many of these pollution controls can achieve SO2 removal rates 

of 95 percent or greater, which allows industry to rely more heavily on local bituminous coal 

in the eastern and central parts of the country that has a higher sulfur content and is less 

expensive to transport than western subbituminous coal. 
263

Table 7-14. Coal Production for the Electric Power Sector with the Base Case (No 

Further Controls) and with Transport Rule Options (Million Tons) 

 Historical Base Case 

State 

Budgets/Limited 

Trading 

State 

Budgets/Intrastate 

Trading Direct Control 

Supply 

Area 2008 2012 2014 2012 2014 2012 2014 2012 2014 

Appalachia 362 304 317 307 329 309 328 305 323 

Interior 136 163 185 144 158 128 161 138 160 

West 588 601 654 613 661 621 659 622 666 

Total 1086 1068 1156 1064 1148 1058 1148 1065 1149 

Source: 2008 data derived from EIA data. All projections from Integrated Planning Model run by EPA. 

http://www.eia.doe.gov/cneaf/coal/page/special/tbl1.html 

7.9 Projected Retail Electricity Prices 

Retail electricity prices for the Transport Rule region are projected to increase a small 

amount with the Transport Rule (see Table 7-15).

Table 7-15. Projected Regional Retail Electricity Prices with the Base Case (No 

Further Controls) and with Transport Rule Options (2006 Mills/kWh) 

Year 

Base 

Case 

State 

Budgets/Limited 

Trading 

Change 

from 

Base 

State 

Budgets/Intrastate 

Trading 

Percent 

Change 

Direct 

Control 

Change 

from 

Base 

2012 86.2 88.3 2.5% 88.6 2.9% 88.0 2.1% 

2014 85.6 86.9 1.5% 87.1 1.8% 86.7 1.4% 

Source: EPA’s Retail Electricity Price Model. 

Regional retail electricity prices are projected to be 1 to 3 percent higher with the Transport 

Rule. Retail electricity prices by NERC region are provided in Table 7-16 (see Figure 7-13). 

 These results show increases in retail prices for the NERC regions in the eastern part of the 

country. By 2014, retail electricity prices in the regions directly affected by the Transport 

Rule are projected to be roughly 1.5 percent higher with the Transport Rule (Table 7-15). 
264

Table 7-16. Retail Electricity Prices by NERC Region with the Base Case (No Further 

Controls) and with Transport Rule Options (2006 Mills/kWh) 

 

Historical Base Case State Budgets/Limited Trading State Budgets/Limited Trading Change from Base State Budgets/ Intrastate Trading State Budgets/ Intrastate Trading from Base Direct Control Direct Control Change from Base 

Power 

Region 

Primary 

States 

included 

2000 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 

ECAR 

(1) 

OH, MI, 

IN, KY, 

WV, PA 

68 64 66 67 69 4% 5% 69 69 7% 5% 66 68 3% 3% 

ERCOT 

(2) 

TX 77 109 102 111 103 1% 1% 111 103 1% 1% 111 103 1% 1% 

MAAC 

(3) 

PA, NJ, 

MD, DC, 

DE 

96 105 103 111 103 6% 0% 108 105 3% 2% 109 104 4% 1% 

MAIN 

(4) 

IL, MO, 

WI 

73 66 70 69 73 4% 4% 70 73 6% 4% 68 72 3% 2% 

MAPP 

(5) 

MN, IA, 

SD, ND, 

NE 

68 72 74 74 75 2% 1% 74 75 2% 1% 74 75 2% 2% 

NY 

(6) 

NY 124 143 143 144 144 1% 1% 145 144 1% 1% 144 144 1% 1% 

NE 

(7) 

VT, NH, 

ME, MA, 

CT, RI 

107 126 123 127 124 1% 0% 127 124 1% 1% 127 124 1% 0% 

FRCC 

(8) 

FL 81 105 104 105 104 1% 0% 106 104 1% 0% 106 104 1% 0% 

STV (9) VA, NC, 

SC, GA, 

AL, MS, 

TN, AR, 

LA 

71 72 73 74 74 2% 1% 74 74 2% 2% 74 74 2% 1% 

SPP 

(10) 

KS, OK, 

MO 

71 90 84 93 85 4% 1% 93 85 3% 1% 93 85 3% 1% 

Regionwide 79 86 86 88 87 3% 2% 89 87 3% 2% 88 87 2% 1% 

Source: EPA’s Retail Electricity Price Model based on the Integrated Planning Model run by EPA, 2010. 2000 

prices are from EIA Annual Energy Outlook 2003 (adjusted to 2006 dollars). 
265

Figure 7-13. NERC Power Regions 

 

7.10 Projected Fuel Price Impacts 

The impacts of the Transport Rule on coal prices and natural gas prices before 

shipment are shown below in Tables 7-17 and 7-18. The proposed remedy and two 

alternative remedies have the same effect on natural gas prices, but somewhat different 

effects on coal prices, reflecting differing effects of the remedies on mix of coal types used 

based on their sulfur content. Overall, average coal price changes are related to increased 

demand for a wide variety of coals, with the dominant factor being increased use of lowersulfur coals. For example, under Direct Control, complying with unit-specific emission rates 

drives many uncontrolled units to demand only the lowest-sulfur grade of coal available. 

Conversely, in the proposed remedy, the incentive of the SO2 allowance markets not only 

influences the relative demand for every coal grade (allowing cost-effective coal blending) 
266

but also affects other decisions such as dispatch, leading to an outcome with a less 

pronounced effect on any single coal grade. Commensurate with its flexibility relative to the 

other remedies, State Budgets/Intrastate Trading, in which units in different states have no 

opportunity to trade, leads to a greater average price increase than in the proposed remedy, 

but significantly less than under Direct Control. 

Table 7-17. Henry Hub Natural Gas Prices and Minemouth Coal Prices with the Base 

Case (No Further Controls) and with Transport Rule Options (2006 $/MMBtu) 

 

Base Case 

State Budgets/ 

Limited Trading 

Percentage Change 

from Base 

State Budgets/ 

Intrastate Trading 

Percentage Change 

from Base 

Direct 

Control 

Percentage Change 

from Base 

Fuel 2008 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 

Natural Gas 8.42 6.50 6.07 6.60 6.10 1.5% 0.5% 6.60 6.10 1.5% 0.5% 6.60 6.10 1.5% 0.5% 

Coal 1.50 0.94 0.93 1.03 0.98 9.9% 4.7% 1.03 0.98 10.1% 4.8% 1.03 0.98 9.6% 5.3% 

Source: Historical data from: Platts Gas Daily; EIA Electric Power Annual 2008, Table 3.5; EIA Annual Coal 

Report 2008 Table 28; 2012 and 2014 projections from the Integrated Planning Model run by EPA, 2010. 

Table 7-18. Average Delivered Natural Gas and Coal Prices with the Base Case (No 

Further Controls) and with Transport Rule Options (2006 $/MMBtu) 

 

Base Case 

State Budgets/ 

Limited Trading 

Percentage Change 

from Base 

State Budgets/ 

Intrastate Trading 

Percentage Change 

from Base 

Direct 

Control 

Percentage Change 

from Base 

Fuel 2008 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 

Natural Gas 9.06 6.57 6.09 6.68 6.12 1.7% 0.5% 6.68 6.12 1.7% 0.5% 6.69 6.12 1.8% 0.5% 

Coal 1.97 1.59 1.56 1.70 1.62 6.9% 3.8% 1.71 1.62 7.5% 3.8% 1.73 1.63 8.8% 4.5% 

Source: EIA Electric Power Annual 2008, Table 3.5; 2012 and 2014 projections from the Integrated Planning Model 

run by EPA, 2010. 

7.11 Key Differences in EPA Model Runs for Transport Rule Modeling 

 As previously stated, the emissions, cost, air quality, and benefits analyses done for 

the Transport Rule are from a modeling scenario that requires annual SO2 and NOx

reductions in 27 states and ozone season NOx requirements in 25 states (See Figures 7-1 and 

7-2). This modeling differs from the proposed Transport Rule because the District of 

Columbia is included neither in the annual SO2 and NOx requirements nor in the ozone 

season NOx requirement. Modeled units in the District of Columbia include two small 

facilities, one of which has only units below 25 MW capacity. EPA believes the addition of 
267

emissions limits in the District of Columbia would have little to no effect on the modeling 

results. 

 Also, the modeling of the Direct Control alternative remedy did not include an 

emissions averaging provision, due to its dependence on EGU ownership information. 

Because this provision provides additional compliance flexibility, EPA believes it would not 

increase modeled compliance costs for this remedy and would have little to no effect on total 

emissions. 2012 budgets largely reflect installed controls, planned controls, and fuel 

switching that would lead to actual emission rates similar to allowable emission rates, so the 

effect of not modeling firm averaging on estimated cost impacts from IPM in the first phase 

are likely to not be substantial. In the second phase modeling, additional scrubbers are 

required based on which EGUs IPM projects have the lowest-cost opportunities for installing 

incremental scrubbers (which are also likely to be similar to utility projections of lowest-cost 

controls). Therefore, EPA believes that even if the flexibility provisions were modeled, they 

would not significantly impact modeled cost in either the first or second phase. Notably, in 

reality this flexibility could provide cost savings for dealing with unplanned outages, sudden 

price changes, and other dynamic costs of power-sector operation not modeled in IPM. 

7.12 Projected Primary PM and Carbon Dioxide Emissions from Power Plants 

IPM does not project primary PM emissions from power plants. These emissions are 

calculated using IPM outputs and emission factors. Fuel use (heat input) as projected by 

IPM is multiplied by PM emission factors to determine PM emissions. Primary PM 

emissions are calculated by adding the filterable PM and condensable PM emissions. 

Filterable PM emissions for each unit are based on historical information regarding 

installed emissions controls and types of fuel burned and ash content. Condensable PM 

emission factors are based on existing SO2 and PM controls, plant and fuel type. 

This methodology tends to underpredict reductions in filterable PM emissions 

between the base case and the control case (especially when a unit does not have a high 

removal efficiency ESP or baghouse) because no changes are assumed in the emission 
268

factors even if a unit is projected to install a control such as an FGD, which could lead to a 

decrease in filterable PM emissions. 

For condensable PM emissions, emission factors were changed between the base case 

and the control case to reflect SO2 controls projected to be installed in the control case. 

Although EPA used the best emission factors available for its analysis, these emission factors 

did not account for the potential changes in condensable PM emissions due to the installation 

and operation of SCRs. The formation of additional condensable PM (in the form of SO3 and 

H2SO4) in units with SCRs depends on a number of factors, including coal sulfur content, 

combustion conditions and characteristics of the catalyst used in the SCR, and is likely to 

vary widely from unit to unit. SCRs are generally designed and operated so that they 

minimize increases in condensable PM. This limitation leads to an overprediction of 

reductions in condensable PM emissions for units with SCRs. For a more complete 

description of the methodologies used to post-process PM emissions from IPM, see “IPM 

ORL File Generation Methodology,” October 2007. 

IPM provides EPA estimates of carbon dioxide (CO2) emissions for fossil fuel 

electric generation as a standard output from the model, enabling consideration of the 

changes in CO2 emissions that result from pollution control alternatives.57 EPA found that the 

State Budget/Limited Trading option (preferred approach) lowered CO2 emissions from the 

Base Case in 2014 by 15.3 million metric tons. This occurs due to reductions in coal and oil 

use and greater use of natural gas and non-fossil sources of electric generation (e.g., biomass 

cogeneration and nuclear generation, with one fewer unit retiring.) 

EPA is not using IPM to project the impacts of this proposed rule on mercury. EPA 

recently commissioned an information collection request that will soon provide greatly 

improved power industry mercury emissions estimates that will enable the Agency to better 

estimate mercury emissions changes from its air emissions control actions. For this reason, 

the Agency did not estimate mercury changes in this rule and will instead wait for these new 

data, which will be available in the near future. 

 

57 The CO2 emissions factors for fossil fuels used in IPM are: oil = 173.9 lbs/mmbtu, natural gas = 117 

lbs/mmbtu, bituminous coal = 202–205 lbs/mmbtu, subbituminous coal = 208–211 lbs/mmbtu, and lignite = 

211–217 lbs/mmbtu.
269

7.13 Limitations of Analysis 

EPA’s modeling is based on its best judgment for various input assumptions that are 

uncertain. Assumptions for future fuel prices and electricity demand growth deserve 

particular attention because of the importance of these two key model inputs to the power 

sector. As a general matter, the Agency selects the best available information from available 

engineering studies of air pollution controls and has set up what it believes is the most 

reasonable modeling framework for analyzing the cost, emission changes, and other impacts 

of regulatory controls. 

The annualized cost estimates of the private compliance costs that are provided in this 

analysis are meant to show the increase in production (engineering) costs to the power sector 

of the Transport Rule proposed remedy and major alternatives. In simple terms, the private 

compliance costs that are presented are the annual increase in revenues required for the 

industry to be as well off after the Transport Rule is implemented as before. To estimate 

these annualized costs, EPA uses a conventional and widely-accepted approach that is 

commonplace in economic analysis of power sector costs for estimating engineering costs in 

annual terms. For estimating annualized costs, EPA has applied a capital recovery factor 

(CRF) multiplier to capital investments and added that to the annual incremental operating 

expenses. The CRF is derived from estimates of the cost of capital (private discount rate), 

the amount of insurance coverage required, local property taxes, and the life of capital. The 

private compliance costs presented earlier are EPA’s best estimate of the direct private 

compliance costs of the Transport Rule. 

The annualized cost of the Transport Rule, as quantified here, is EPA’s best 

assessment of the cost of implementing the Transport Rule. These costs are generated from 

rigorous economic modeling of changes in the power sector due to the Transport Rule. This 

type of analysis using IPM has undergone peer review and federal courts have upheld 

regulations covering the power sector that have relied on IPM’s cost analysis. 

The direct private compliance cost includes, but is not limited to, capital investments 

in pollution controls, operating expenses of the pollution controls, investments in new 
270

generating sources, and additional fuel expenditures. EPA believes that the cost assumptions 

used for the Transport Rule reflect, as closely as possible, the best information available to 

the Agency today. The relatively small cost associated with monitoring emissions, reporting, 

and record keeping for affected sources is not included in these annualized cost estimates, 

but EPA has done a separate analysis and estimated the cost to be approximately $28 million 

(see Section 9.3., Paperwork Reduction Act). 

Cost estimates for the Transport Rule are based on results from ICF’s Integrated 

Planning Model. The model minimizes the costs of producing electricity (including 

abatement costs) while meeting load demand and other constraints (full documentation for 

IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm and in the TSD 

“Updates to EPA Base Case v3.02 EISA Using the Integrated Planning Model”). The 

structure of the model assumes that the electric utility industry will be able to meet the 

environmental emission caps at least cost. Montgomery (1972) has shown that this least cost 

solution corresponds to the equilibrium of an emission permit system. See also Atkinson and 

Tietenburg (1982), Krupnick et al. (1980), and McGartland and Oates (1985). However, to 

the extent that transaction and/or search costs, combined with institutional barriers, restrict 

the ability of utilities to exhaust all the gains from emissions trading, costs are 

underestimated by the model. Utilities in the IPM model also have “perfect foresight.” To 

the extent that utilities misjudge future conditions affecting the economics of pollution 

control, costs may be understated as well. 

The “perfect foresight” of the model is also relevant in the context of the assurance 

provisions required in the proposed remedy (State Budgets/Limited Trading) and Direct 

Control. Because of the sizeable penalties associated with violating assurance provisions, 

EPA believes it will be economical for units to comply with the provisions. EPA modeled 

these provisions, which restrict emissions from a state to the budget plus variability limits on 

a 1-year and 3-year rolling average basis, as state-specific emissions caps set at the budget 

plus 3-year average variability. The Power Sector Variability Technical Support Document 

contains further details on these assurance provisions. 

Modeling the assurance provisions as caps means that the model must meet the same 

limit each year, but it also allows the model to optimize with perfect foresight of present and 
271

future limits. While the model minimizes production costs while meeting required 

generation and reserve margin, sources in reality may choose to make greater emissions 

reductions than required in exchange for more certainty about emissions variability. IPM 

captures the cost associated with making required reductions in each state, but because of its 

“perfect foresight,” the model likely cannot capture the true benefit to sources of having a 

range of allowed variability. 

From another vantage point, this modeling analysis does not take into account the 

potential for advancements in the capabilities of pollution control technologies for SO2 and 

NOx removal as well as reductions in their costs over time. Market-based cap and trade 

regulation serves to promote innovation and the development of new and cheaper 

technologies. As an example, cost estimates of the Acid Rain SO2 trading program by 

Resources for the Future (RFF) and MIT’s Center for Energy and Environmental Policy 

Research (CEEPR) have been as much as 83 percent lower than originally projected by the 

EPA (see Carlson et al., 2000; Ellerman, 2003). It is important to note that the original 

analysis for the Acid Rain Program done by EPA also relied on an optimization model like 

IPM. Ex ante, EPA cost estimates of roughly $2.7 to $6.2 billion58 in 1989 were an 

overestimate of the costs of the program in part because of the limitation of economic 

modeling to predict technological improvement of pollution controls and other compliance 

options such as fuel switching. Ex post estimates of the annual cost of the Acid Rain SO2

trading program range from $1.0 to $1.4 billion. Harrington et al. have examined cost 

analyses of EPA programs and found a tendency for predicted costs to overstate actual 

implementation costs in market-based programs (Harrington, Morgenstern, and Nelson, 

2000). In recognition of this, EPA’s mobile source program uses adjusted engineering cost 

estimates of pollution control equipment and installation costs to account for this fact, which 

EPA has not done in this case.59 The Agency is considering approaches to make this 

adjustment in the future, or at least to be able to provide a sense of the rough amount by 

which costs could be overstated in the analysis that has occurred. 

 

58 2010 Phase II cost estimate in $1995.

59 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy-Duty 

Diesel Vehicle Rules (2000).
272

EPA’s latest update of IPM incorporates state rules or regulations and various NSR 

settlements adopted through February 3, 2009. Documentation for IPM can be found at 

http://www.epa.gov/airmarkets/progsregs/epa-ipm and in the TSD “Updates to EPA Base 

Case v3.02 EISA Using the Integrated Planning Model.”. Any state or settlement action 

since that time has not been accounted for in our analysis in this chapter. 

As configured in this application, IPM does not take into account demand response 

(i.e., consumer reaction to electricity prices). The increased retail electricity prices shown in 

Tables 7-15 and 7-16 would prompt end users to curtail (to some extent) their use of 

electricity and encourage them to use substitutes.60 The response would lessen the demand 

for electricity, resulting in electricity price increases slightly lower than IPM predicts, which 

would also reduce generation and emissions. Because of demand response, certain 

unquantified negative costs (i.e., savings) result from the reduced resource costs of producing 

less electricity because of the lower quantity demanded. To some degree, these saved 

resource costs will offset the additional costs of pollution controls and fuel switching that we 

would anticipate with the Transport Rule. Although the reduction in electricity use is likely 

to be small, the cost savings from such a large industry61 is not insignificant. EIA analysis 

examining multi-pollutant legislation under consideration in 2003 indicates that the 

annualized costs of the Transport Rule may be overstated substantially by not considering 

demand response, depending on the magnitude and coverage of the price increases.62

On balance, after consideration of various unquantified costs (and savings that are 

possible), EPA believes that the annual private compliance costs that we have estimated are 

more likely to overstate the future annual compliance costs that industry will incur, rather 

than understate those costs. 

 

60 The degree of substitution/curtailment depends on the costs and performance of the goods that substitute for 

more energy consuming goods, which is reflected in the demand elasticity.

61 Investor-owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA).

62 See “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean Air Planning Act of 2003.” 

Energy Information Administration. September, 2003. EIA modeling indicated that the Clear Skies Act of 

2003 (a nationwide cap and trade program for SO2, NOX, and mercury), demand response could lower present 

value costs by as much as 47% below what it would have been without an emission constraint similar to the 

Transport Rule.
273

7.14 Significant Energy Impact 

The Transport Rule as proposed has significant impact according to E.O. 13211: 

Actions that Significantly Affect Energy Supply, Distribution, or Use. Under the provisions 

of this proposed rule, EPA projects that approximately 1.2 GW of coal-fired generation may 

be removed from operation by 2014 under the proposed remedy. In practice, however, the 

units projected to be uneconomic to maintain may be “mothballed,” retired, or kept in service 

to ensure transmission reliability in certain parts of the grid. These units are predominantly 

small and infrequently-used generating units dispersed throughout the area affected by the 

rule. Assumptions of higher natural gas prices or electricity demand would create a greater 

incentive to keep these units operational. 

The EPA estimates that there are several fuel price increases resulting from the 

proposed remedy in the Transport Rule. The EPA projects that the average retail electricity 

price could increase nationally by about 2.5 percent in 2012 and 1.5 percent in 2014. This is 

generally less of an increase than often occurs with fluctuating fuel prices and other market 

factors. Related to this, delivered coal prices increase by about 7 percent in 2012 and 4 

percent in 2014 as a result of higher demand for lower-sulfur coals. The EPA also projects 

that delivered natural gas prices will increase by less than 1.7 percent in 2012 and 0.5 percent 

in 2014 and that natural gas use for electricity generation will increase by less than 73 

million million cubic feet (mcf) by 2014. The price increase is also within the range we 

regularly see in delivered natural gas prices. Finally, the EPA projects coal production for 

use by the power sector, a large component of total coal production, will decrease by 3 

million tons in 2012 and 9 million tons in 2014 from the base case levels, which is a 

relatively small amount compared to the more than one billion tons of coal produced for 

utility use each year. The EPA does not believe that this rule will have any other impacts 

that exceed the significance criteria. 

The EPA believes that a number of features of the rulemaking serve to reduce its 

impact on energy supply. First, the trading programs in State Budgets/Limited Trading 

provide considerable flexibility to the power sector and enable industry to comply with the 

emission reduction requirements in the most cost-effective manner, thus minimizing overall 
274

costs and the ultimate impact on energy supply. Second, the more stringent budgets for SO2

are set in two phases, providing adequate time for EGUs to install pollution controls. In 

addition, both the operational flexibility of trading and the ability to bank allowances for 

future years helps industry plan for and ensure reliability in the electrical system. 
275

7.15 References 

Atkinson, S., and T. Tietenberg. 1982. “The Empirical Properties of Two Classes of Design 

for Transferable Discharge Permit Markets.” Journal of Environmental Economics 

and Management 9:101-121 

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur 

Dioxide Control by Electric Utilities: What Are the Gains from Trade?” Journal of 

Political Economy 108(#6):1292-1326. 

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2

Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy 

and Environmental Policy Research. 

EIA Annual Coal Report 2008. DOE/EIA-0584 (2008). Available at: 

http://www.eia.doe.gov/cneaf/coal/page/acr/acr_sum.html 

EIA Annual Energy Outlook 2003. DOE/EIA-0383 (2003). Available at: 

http://www.eia.doe.gov/oiaf/archive/aeo03/index.html 

EIA Electric Power Annual 2008. DOE/EIA-0348 (2008). Available at: 

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm 

EIA Electric Power Monthly March 2010 with Data for December 2009. DOE/EIA-0226 

(2010/03). Available at: http://www.eia.doe.gov/cneaf/electricity/epm/epm_sum.html 

Freme, Fred. 2009. U.S. Coal Supply and Demand: 2008 Review. EIA. Available at: 

http://www.eia.doe.gov/cneaf/coal/page/special/tbl1.html 

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory 

Cost Estimates.” Journal of Policy Analysis and Management 19(2):297-322. 

Keohane, Nathaniel O. 2009. “The Technocratic and Democratic Functions of the CAIR 

Regulatory Analysis.” In Reforming Regulatory Impact Analysis, eds. Winston 

Harrington, Lisa Heinzerling, and Richard D. Morgenstern, 33–55. Washington, DC: 

Resources for the Future. 

Krupnick, A., W. Oates, and E. Van De Verg. 1980. “On Marketable Air Pollution Permits: 

The Case for a System of Pollution Offsets.” Journal of Environmental Economics 

and Management 10:233-47. 

Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning 
276

Curves on Clean Air Act Compliance Costs.” Industrial Economics Incorporated. 

McGartland, A., and W. Oates. 1985. “Marketable Permits for the Prevention of 

Environmental Deterioration.” Journal of Environmental Economics and 

Management 12:207-228. 

Montgomery, W. David. 1972. “Markets in Licenses and Efficient Pollution Control 

Programs.” Journal of Economic Theory 5(3):395-418. 

Wagner, Wendy E. 2009. “The CAIR RIA: Advocacy Dressed Up as Policy Analysis.” In 

Reforming Regulatory Impact Analysis, eds. Winston Harrington, Lisa Heinzerling, 

and Richard D. Morgenstern, 56–81. Washington, DC: Resources for the Future.
277

CHAPTER 8 

MACROECONOMIC IMPACTS AND SOCIAL COSTS 

EPA prepares an economic impact analysis (EIA) to provide decision makers with a 

measure of the social costs of using resources to comply with a program (EPA, 2000). The 

social costs can then be compared with estimated social benefits. As noted in EPA’s (2000) 

Guidelines for Preparing Economic Analyses, several tools are available to estimate social 

costs and range from simple direct compliance cost methods to the development of a more 

complex market analysis. The Office of Air Quality Planning and Standards (OAQPS) 

adopted an economy-wide market analysis as described in the Office’s Economic Resource 

Manual (EPA, 1999)63 and uses the latest EMPAX computable general equilibrium modeling 

system. 

The Economic Model for Policy Analysis (EMPAX) was first developed in 2000 to 

support economic analysis of EPA’s maximum achievable control technology (MACT) rules 

for combustion sources (reciprocating internal combustion engines, industrial boilers, and 

turbines). The initial framework consisted of a national multi-market partial-equilibrium 

model with linkages only between manufacturing industries and the energy sector. Modified 

versions of EMPAX were subsequently used to analyze economic impacts of strategies for 

improving air quality in the Southern Appalachian mountain region as part of efforts in 2002 

associated with the Southern Appalachian Mountain Initiative (SAMI). Later work extended 

its scope to cover all aspects of the U.S. economy with regional detail. 

Since large-scale environmental policies also indirectly influence current and future 

input uses, income, and household consumption patterns, EPA subsequently updated the 

model system to include a complete set of economic linkages among all industrial and energy 

sectors as well as households that supply factors of production such as labor and purchase 

goods (i.e., a computable general equilibrium [CGE] framework). As a result, EMPAX is 

now a dynamic general equilibrium model that traces economic impacts as they are 

transmitted across time and throughout the economy. EMPAX-CGE underwent peer review 

 

63 This document is available on the Internet at http://www.epa.gov/ttn/ecas/analguid.html. 
278

in 2006; detailed model documentation and results of the peer review can be accessed at the 

following Web site: http://www.epa.gov/ttn/ecas/EMPAXCGE.htm. 

8.1 EMPAX Computable General Equilibrium (CGE) Model: Overview 

EMPAX-CGE is a dynamic, intertemporally optimizing model that solves in 5-year 

intervals from 2010 to 2050. It uses the classical Arrow-Debreu general equilibrium 

framework wherein households maximize utility subject to budget constraints, and firms 

maximize profits subject to technology constraints. The model structure, in which agents are 

assumed to have perfect foresight and to maximize utility across all time periods, allows 

agents to modify behavior in anticipation of future policy changes, unlike dynamic recursive 

models that assume agents do not react until a policy has been implemented. 

Nested constant elasticity of substitution (CES) functions are used to portray 

substitution possibilities available to producers and consumers. Figure 8-1 illustrates this 

general framework and gives a broad characterization of the model.64 Along with the 

underlying data, these nesting structures and associated substitution elasticities determine the 

effects that will be estimated for policies. These nesting structures and elasticities used in 

EMPAX-CGE are generally based on the Emissions Prediction and Policy Analysis (EPPA) 

Model developed at the Massachusetts Institute of Technology (Paltsev et al., 2005). This 

updated version of the EPPA model incorporates some extensions over the EPPA version 

documented in Babiker et al. (2001), such as specification of transportation purchases by 

households. These updates to transportation choices have been incorporated in this version of 

EMPAX-CGE as shown on the left-hand side of Figure 8-1. Although the two models 

continue to have different focuses (EPPA is a recursive dynamic, international model focused 

on national-level climate change policies), both are intended to simulate how agents will 

respond to environmental policies; thus, EPPA provides a strong basis to develop the 

theoretical structure of EMPAX-CGE. 

 

64 Although it is not illustrated in Figure 8-1, some differences across industries exist in their handling of 

energy inputs. In addition, the agriculture and fossil fuel sectors in EMPAX-CGE contain equations that account 

for the presence of fixed inputs to production (land and fossil-fuel resources, respectively).
279

Figure 8-1. General Production and Consumption Nesting Structure in EMPAX-CGE

Consumption Leisure

Consumption is a CobbDouglas composite of 

transportation plus goods. 

Domestic Foreign 

Domestic goods are a CES 

composite of local goods and 

goods from other U.S. regions. 

Local 

Output 

Regional 

Output 

KLE Intermediates 

Most producer goods use 

fixed proportions of 

intermediate inputs and KLE. 

Energy Value Added 

Capital Labor 

Energy (5 

Types) 

KLE is a CES 

composite of 

energy (E) and 

value-added (K – 

capital and L - 

labor). 

Value added is a CobbDouglas composite of 

capital and labor (KL). 

 Utility

Intermediate inputs are the 

30 types of non-energy 

goods, in fixed proportion 

for each industry. 

Energy 

Consumption of 

energy is a CES 

composite of 

energy and other 

consumption 

goods. 

Energy (E) is a 

CES composite of 5 

energy types. The 

structure of this 

function varies across 

industries. 

Imports are a CES 

composite across 

foreign supply sources. 

Purchased 

Transport 

Personal 

Transport 

Transportation Consumption Goods 

Petroleum 

Services Manufactured 

Goods

Goods and 

Services 

Transportation is a 

CES composite of 

personal vehicle 

transport and 

purchased transport. 

Personal vehicles 

use fuel and 

goods/services. 

Each consumption good is 

a CES composite of 

foreign and domestically 

produced goods. 

(30 types) (5 types) 

Household utility is a Constant 

Elasticity of Substitution (CES) 

function of consumption and leisure. 
280

Given this basic similarity, EMPAX-CGE has adopted a comparable structure. 

EMPAX-CGE is programmed in the GAMS65 language (Generalized Algebraic Modeling 

System) and solved as a mixed complementarity problem (MCP)66 using MPSGE software 

(Mathematical Programming Subsystem for General Equilibrium).67 The PATH solver from 

GAMS is used to solve the MCP equations generated by MPSGE. 

8.1.1 Data Sources 

The economic data come from state-level information provided by the Minnesota 

IMPLAN Group (2006),68 and energy data come from the Energy Information 

Administration (EIA).69 Forecasts for economic growth are taken from EIA’s Annual Energy 

Outlook 2009 Updated Reference Case (AEO) and Global Insight (2007).70 Although 

IMPLAN data contain information on the value of energy production and consumption in 

dollars, these data are replaced with EIA data since the policies being investigated by 

EMPAX-CGE typically focus on energy markets, making it essential to include the best 

possible characterization of these markets in the model. Although the IMPLAN data are 

developed from a variety of government data sources at the U.S. Bureau of Economic 

Analysis and U.S. Bureau of Labor Statistics, these data do not always agree with energy 

information collected by EIA directly from manufacturers and electric utilities. 

EMPAX-CGE combines these economic and energy data to create a balanced social 

accounting matrix (SAM) that provides a baseline characterization of the economy. The 

SAM contains data on the value of output in each sector, payments for factors of production 

 

65 See Brooke, Kendrick, and Meeraus (1996) for a description of GAMS (http://www.gams.com/).

66 Solving EMPAX-CGE as an MCP problem implies that complementary slackness is a feature of the 

equilibrium solution. In other words, any firm in operation will earn zero economic profits, and any unprofitable 

firms will cease operations. Similarly, for any commodity with a positive price, supply will equal demand, or 

conversely any good in excess supply will have a zero price.

67 See Rutherford (1999) for MPSGE documentation (http://www.mpsge.org/).

68 See http://www.implan.com/index.html for a description of the Minnesota IMPLAN Group and its data.

69 These EIA sources include AEO 2007, the Manufacturing Energy Consumption Survey, State Energy Data 

Report, State Energy Price and Expenditure Report, and various annual industry profiles. 

70 See http://www.globalinsight.com/ProductsServices/ProductDetail1100.htm for a description of the Global 

Insight U.S. State Forecasting Service.
281

and intermediate inputs by each sector, household income and consumption, government 

purchases, investment, and trade flows. A balanced SAM for the baseline year consistent 

with the desired sectoral and regional aggregation is produced using procedures developed 

by Babiker and Rutherford (1997) and described in Rutherford and Paltsev (2000). This 

methodology relies on optimization techniques to maintain the calculated energy statistics (in 

both quantity and value terms) while minimizing any changes needed in the other economic 

data to create a new balanced SAM based on EIA/IMPLAN data for the baseline model year 

(in essence, industry production functions are adjusted, if necessary, to account for 

discrepancies between EIA energy data and IMPLAN economic data by matching the energy 

data and adjusting the use of nonenergy inputs so that the industry is in balance, that is, the 

value of inputs to production equals the value of output). 

These data are used to define economic conditions in 50 states within the United 

States (plus the District of Columbia), each of which contains 80 industries. Prior to solving 

EMPAX-CGE, the states and industries are aggregated up to the categories to be included in 

the analysis. Aggregated regions have been selected to capture important differences across 

the country in electricity generation technologies, while industry aggregations are controlled 

by available energy consumption data. 

Table 8-1 presents the 35 industry categories included in EMPAX-CGE for policy 

analysis. Their focus is on maintaining as much detail in the energy intensive and 

manufacturing sectors71 as is allowed by available energy consumption data and 

computational limits of dynamic CGE models. In addition, the electricity industry is 

separated into fossil fuel generation and nonfossil generation, which is necessary because 

many electricity policies affect only fossil-fired electricity. 

Figure 8-2 shows the five regions run in EMPAX-CGE in this analysis, which have 

been defined based on the expected regional distribution of policy impacts, availability of 

economic and energy data, and computational limits on model size. These regions have been 

constructed from the underlying state-level database designed to follow, as closely as 

 

71 Energy-intensive industry categories are based on EIA definitions of energy-intensive manufacturers in the 

Assumptions for the Annual Energy Outlook 2007.
282

possible, the electricity market regions defined by the North American Electric Reliability 

Council (NERC).72 

8.1.2 Production Functions 

All productive markets are assumed to be perfectly competitive and have production 

technologies that exhibit constant returns to scale, except for the agriculture and natural 

resource extracting sectors, which have decreasing returns to scale because they use factors 

in fixed supply (land and fossil fuels, respectively). The electricity industry is separated into 

two distinct sectors: fossil fuel generation and nonfossil generation. This allows tracking of 

variables such as heat rates for fossil-fired utilities (in BTUs of energy input per kilowatt 

hour of electricity output). 

All markets must clear (i.e., supply must equal demand in every sector) in every 

period, and the income of each agent in the model must equal their factor endowments plus 

any net transfers. Along with the underlying data, the nesting structures shown in Figure 8-1 

and associated substitution elasticities define current production technologies and possible 

alternatives.

 

72 Economic data and information on nonelectricity energy markets are generally available only at the state 

level, which necessitates an approximation of the NERC regions that follows state boundaries.
283

Table 8-1. Industries in Dynamic EMPAX-CGE

EMPAX Industry NAICS Classifications 

Energy 

Coal 2121 

Crude oila

 211111, 4861 

Electricity (fossil) 2211 

Electricity (nonfossil) 2211 

Natural gas 211112, 2212, 4862 

Petroleum refiningb

 324, 48691 

General 

Agriculture 11 

Mining (w/o coal, crude, gas) 21 

Construction 23 

Manufacturing 

Food products 311 

Textiles and apparel 313, 314, 315, 316 

Lumber 321 

Paper and allied 322 

Printing 323 

Chemicals 325 

Plastic & rubber 326 

Glass 3272 

Cement 3273 

Other minerals 3271, 3274, 3279 

Iron and steel 3311, 3312 

Aluminum 3313 

Other primary metals 3314, 3316 

Fabricated metal products 332 

Manufacturing equipment 333 

Computers & communication equipment 334 

Electronic equipment 335 

Transportation equipment 336 

Miscellaneous remaining 312, 337, 339 

Services 

Wholesale & retail trade 42, 44, 45 

Transportationc

 481–488 

Information 51 

Finance & real estate 52, 54 

Business/professional 53, 55, 56 

Education (w/public) 61 

Health care (w/public) 62 

Other services 71, 72, 81, 92 
284

a

Although NAICS 211111 covers crude oil and gas extraction, the gas component of this sector is moved to the 

natural gas industry. 

b

Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas 

industry. 

c

The petroleum refining industry provided oil in delivered terms, which includes pipeline transport. 

Figure 8-2. Regions Defined in Dynamic EMPAX-CGE 

8.1.3 Utility Functions 

Each region in the dynamic version of EMPAX-CGE contains four representative 

households, classified by income, that maximize intertemporal utility over all time periods in 

the model subject to budget constraints, where the income groups are 

▪ $0 to $14,999, 

▪ $15,000 to $29,999, 

▪ $30,000 to $49,999, and 
285

▪ $50,000 and above. 

These representative households are endowed with factors of production, including 

labor, capital, natural resources, and land inputs to agricultural production. Factor prices are 

equal to the marginal revenue received by firms from employing an additional unit of labor 

or capital. The value of factors owned by each representative household depends on factor 

use implied by production within each region. Income from sales of these productive factors 

is allocated to purchases of consumption goods to maximize welfare. 

Within each time period, intratemporal utility received by a household is formed from 

consumption of goods and leisure. All consumption goods are combined using a CobbDouglas structure to form an aggregate consumption good. This composite good is then 

combined with leisure time to produce household utility. The elasticity of substitution 

between consumption goods and leisure depends on empirical estimates of labor supply 

elasticities and indicates how willing households are to trade off leisure time for 

consumption. Over time, households consider the discounted present value of utility received 

from all periods’ consumption of goods and leisure. 

Following standard conventions of CGE models, factors of production are assumed to 

be mobile among sectors within regions, but migration of productive factors is not allowed 

across regions. This assumption is necessary to calculate welfare changes for the 

representative household located in each region in EMPAX-CGE. EMPAX-CGE also 

assumes that ownership of natural resources and capital embodied in nonfossil electricity 

generation are spread across the United States through capital markets. 

8.1.3.1 Welfare Measures 

To analyze the social benefits and costs of policy alternatives, EMPAX uses a 

willingness-to-pay measure known as a Hicksian equivalent variation (EV). EV reflects the 

additional money that a household would need (at original prices p0

 and income m0) to make 

it as well off with the new policy; the amount is “equivalent” to the changes in the utility 

households receive from consumption and leisure time. 
286

EV = u(p0

; p′,m′) − u(p0; p0, m0) = u(p0; p′,m′) − m0 

where 

p0

 = the baseline prices 

m0 = baseline income 

p′ = with policy prices 

m′ = with policy income 

For example, under a policy that makes households worse off, EV represents the maximum 

amount of money the household would be willing to pay to avoid the policy. Through this 

analysis, we use this metric to measure the policy’s social costs. It is important to emphasize 

the measure does not incorporate any environmental benefits associated with air quality 

improvements. 

8.1.4 Treatment of Trade 

In EMPAX-CGE, all goods and services are assumed to be composite, differentiated 

“Armington” goods made up of locally manufactured commodities and imported goods. 

Output of local industries is initially separated into output destined for local consumption by 

producers or households and output destined for export. This local output is then combined 

with goods from other regions in the United States using Armington trade elasticities that 

indicate agents make relatively little distinction between output from firms located within 

their region and output from firms in other regions within the United States. Finally, the 

domestic composite goods are aggregated with imports from foreign sources using lower 

trade elasticities to capture the fact that foreign imports are more differentiated from 

domestic output than are imports from other regional suppliers in the United States. 

8.1.5 Tax Rates and Distortions 

Taxes and associated distortions in economic behavior have been included in 

EMPAX-CGE because theoretical and empirical literature found that taxes can substantially 

alter estimated policy costs (e.g., Bovenberg and Goulder [1996]; Goulder and Williams 
287

[2003]). For example, existing labor taxes distort economic choices because they encourage 

people to work below the levels they would choose in an economy without labor taxes and 

reduce economic efficiency.73 When environmental policies raise production costs for firms 

and the price of goods and services, people may choose to work even less; the additional 

economic costs from this decision have been described as the “tax interaction” effect. 

EMPAX-CGE considers these interaction effects by using tax data from several 

sources and by explicitly modeling household labor supply decisions. The IMPLAN 

economic database provides information on taxes such as indirect business taxes (all sales 

and excise taxes) and social security taxes. However, since IMPLAN reports factor payments 

for labor and capital at their gross of tax values, we use additional data sources to determine 

personal income and capital tax rates. Information from the TAXSIM model at the National 

Bureau of Economic Research (Feenberg and Coutts, 1993), along with user cost-of-capital 

calculations from Fullerton and Rogers (1993), are used to establish tax rates. Elasticity 

parameters describing labor supply choice ultimately determine how distortionary existing 

taxes are in the CGE model. EMPAX-CGE currently uses elasticities based on the relevant 

literature (i.e., 0.4 for the compensated labor supply elasticity and 0.15 for the 

uncompensated labor supply elasticity). These elasticity values give an overall marginal 

excess burden associated with the existing tax structure of approximately 0.3. 

8.1.6 Intertemporal Dynamics and Economic Growth 

EMPAX-CGE includes four sources of economic growth: technological change from

improvements in energy efficiency, growth in the available labor supply (from both 

population growth and changes in labor productivity), increases in stocks of natural 

resources, and capital accumulation. Energy consumption per unit of output tends to decline 

over time because of improvements in production technologies and energy conservation. 

These changes in energy use per unit of output are modeled as autonomous energy efficiency 

improvements (AEEIs), which are used to replicate energy consumption forecasts by 

 

73 These efficiency losses are often expressed in terms of overall marginal excess burden—the cost associated 

with raising an additional dollar of tax revenue. Estimates range from $0.10 to $0.35 per dollar (Ballard, 

Shoven, and Whalley, 1985).
288

industry and fuel from EIA.74 The AEEI values provide the means for matching expected 

trends in energy consumption that have been taken from the AEO forecasts. They alter the 

amount of energy needed to produce a given quantity of output by incorporating 

improvements in energy efficiency and conservation. Labor force and regional economic 

growth, electricity generation, changes in available natural resources, and resource prices are 

also based on the AEO forecasts. 

Savings provide the basis for capital formation and are motivated through people’s 

expectations about future needs for capital. Savings and investment decisions made by 

households determine aggregate capital stocks in EMPAX-CGE. The IMPLAN data set 

provides details on the types of goods and services used to produce the investment goods 

underlying each region’s capital stocks. Adjustment dynamics associated with formation of 

capital are controlled by using quadratic adjustment costs experienced when installing new 

capital, which imply that real costs are experienced to build and install new capital 

equipment. 

Prior to investigating policy scenarios, it is necessary to establish a baseline path for 

the economy that incorporates economic growth and technology changes that are expected to 

occur in the absence of the policy actions. Beginning from the initial balanced SAM data set, 

the model is calibrated to replicate forecasts from the AEO 2009 (Updated Reference Case 

Version, March 2009). Upon incorporating these forecasts, EMPAX-CGE is solved to 

generate a baseline based on them through 2030. Once this baseline is established, it is 

possible to run the “counterfactual” policy experiments discussed below. 

8.1.7 Linkage with the Integrated Planning Model 

Although CGE models have been used extensively to analyze climate policies that 

limit carbon emissions from electricity production, some other types of utility-emissions 

policies are more difficult to consider. Unlike carbon dioxide, emissions of pollutants such as 

SO2, NOx, and mercury are not necessarily proportional to fuel use. These types of emissions 

 

74 See Babiker et al. (2001) for a discussion of how this methodology was used in the EPPA model (EPPA 

assumes that AEEI parameters are the same across all industries in a country, while AEEI values in 

EMPAX-CGE are industry specific).
289

can be lowered by a variety of methods, but a CGE model cannot adequately capture the 

boiler-specific nature of these decisions and their costs and effects. Combining the strengths 

of the Integrated Planning Model (IPM) (disaggregated unit-level analyses of electricity 

policies) with the strengths of CGE models (macroeconomic effects of environmental 

policies) allows investigation of economy-wide implications of policies that would normally 

be hard to estimate consistently and effectively. IPM provides EMPAX with several 

electricity market outcomes needed to evaluate macroeconomic implications of policies. 

IPM also provides information on generation costs in terms of capital costs, fixed 

operating costs, and variable operating costs. For EMPAX to effectively incorporate these 

IPM data on changes in costs, they have to be expressed in terms of the productive inputs 

used in CGE models (i.e., capital, labor, and material inputs produced by other industries). 

Rather than assume these costs represent a proportional scaling up of all inputs to the 

electricity industry in EMPAX, we use Nestor and Pasurka (1995) data on purchases made 

by industries for environmental protection reasons to allocate these additional expenditures 

across inputs within EMPAX (discussed in the EMPAX model documentation). Once these 

expenditures are specified, the incremental costs from IPM can be used to adjust the 

production technologies and input purchases by electricity generation in the CGE model. 

Among the many results provided by IPM, several can potentially have significant 

implications for the rest of the economy including changes in electricity prices, fuel 

consumption by utilities, fuel prices, and changes in electricity production expenditures. 

EMPAX is capable of simultaneously incorporating some of all of these IPM findings, 

depending on the desired type and degree of linkage between the two models. At the 

regional level, EMPAX can match changes estimated by IPM for the following variables: 

electricity prices (percentage change in retail prices) 

coal and gas consumption for electricity (percentage changes in BTUs) 

coal and gas prices (percentage changes in prices) 

coal and gas expenditures ($ changes—BTUs of energy input times $/MMBTU) 
290

capital costs ($ changes) 

fixed operating costs ($ changes) 

variable operating costs ($ changes) 

In addition, EMPAX-CGE can control electricity output to simulate the fixed demand 

used by IPM, or it can determine how changes in electricity prices will affect demand for 

electricity and hence electricity generation levels. 

The IPM model calculates these variables for 26 NERC subregions. EMPAX uses 

information on generation levels for these subregions to aggregate the IPM results into the 

five regions used within EMPAX. Wholesale electricity prices are then matched to the 

changes shown by IPM. Fuel consumption by utilities in physical units (BTUs) is adjusted 

by the percentage changes in the IPM results. Fuel prices paid by both industries and 

households are also changed by the amounts estimated by IPM (the coal and gas market 

modules of IPM cover all fuel consumers, not merely utilities, so prices paid by all agents in 

EMPAX are adjusted). 

8.1.8 Qualifications 

Caveats that can typically be applied to CGE analyses, including this one, cover 

issues such as transitional dynamics in the economy. CGE models such as EMPAX, which 

assume foresight on the part of businesses and households, will allow agents to adapt to 

anticipated policy impacts coming in the future. These adaptations may occur more quickly 

than if agents adopted a wait-and-see approach to new regulations. The alternative, recursivedynamic structure used in CGE models such as MIT EPPA imply that no anticipation or 

adjustments will occur until the policy is in place, which tends to overstate the costs of 

policies. 

In addition to transition dynamics, while CGE models are ideally suited for analyzing 

broad, economy-wide impacts of policies, they are not able to examine firm-specific impacts 

on profits/losses or estimate how policies might affect particular types of disadvantaged 

households. Similarly, environmental justice and other distributional concerns cannot be 
291

adequately addressed using these types of models alone. 

As noted above, the labor supply elasticities in the model have been chosen from the 

CGE literature on labor markets and tax distortions as discussed above. Other important 

assumptions about the production technologies and input substitution possibilities have been 

chosen from the MIT EPPA model. To ensure transparence of the assumptions, 

EMPAX-CGE underwent peer review in 2006, and detailed model documentation and results 

of the peer review can be accessed at the following Web site: 

http://www.epa.gov/ttnecas1/EMPAXCGE.htm. 

8.2 EMPAX-CGE Model Results 

8.2.1 Macroeconomic Variables and Social Costs 

The transport rule will bring about changes in business and household behavior and 

will influence macroeconomic variables (gross domestic product [GDP] and consumption) 

and household economic welfare as estimated by the Hicksian EV method previously 

mentioned. Gross domestic product is the dollar value of all goods and services produced by 

the U.S. economy in a particular year. Consumption is defined in this analysis as the dollar 

value of goods and services consumed in the U.S. in a particular year. In 2015, EMPAX 

estimates that GDP and consumption levels are approximately 0.01% lower ($1.6 billion) 

(Figure 8-3).75 Since the pollution controls vary by region, economic effects also vary by 

region; for example, Northeast GDP falls by 0.04% (Figure 8-4). There are small declines in 

GDP by region except for the Plains and West, where regional GDP increases as productive 

activities shift to these less regulated regions. 

 

75 We use 2015 estimates as a proxy for the impacts of compliance with the proposed rule in 2014. 
292

Figure 8-3. Change in Macroeconomic Variables and Household Welfare (Percent 

change and Change in billion $2006) in 2015 

-0.014%

-0.012%

-0.010%

-0.008%

-0.006%

-0.004%

-0.002%

0.000%

-$2.5 

-$2.0 

-$1.5 

-$1.0 

-$0.5 

$0.0 

Change ($ billion) Percent Change

Percentage Change

Change ($ billion)

GDP Consumption Hicksian EV (Annual)

Note: GDP represents the dollar value of all goods and services produced in the US in 2015. Consumption is the dollar 

value of all goods and services consumed within the US in 2015. Hicksian EV is the change in household economic welfare 

(defined in Section 8.1.3.1). 

Average-annual social costs (as measured by Hicksian equivalent variation) are 

approximately 0.01% lower with the transport rule. Over the model’s time horizon, the total 

present value of the losses is approximately $21.3 billion.76 As noted in section 8.1.3.1 of this 

chapter, EMPAX-CGE does not incorporate any environmental benefits associated with air 

quality improvements. As a result, EMPAX welfare measures only approximate the rule’s 

social cost. Using this interpretation, the annual social cost for 2015 is estimated to be $2.2 

 

76 Values are discounted back to 2010 at the 5% interest rate used in the model. EPA uses a 5% interest rate 

based on the MIT Emissions Prediction and Policy Analysis (EPPA) model and SAB guidance from 2003 as 

discussed in U.S. EPA, Office of Policy Analysis and Review. 2003. “Benefits and Costs of the Clean Air Act 

1990 - 2020: Revised Analytical Plan For EPA's Second Prospective Analysis.” We recognize that this interest 

rate is not one of the interest rates (3 and 7%) that OMB’s Circular A-4 guidance calls for in regulatory 

analyses. Detailed results for this EMPAX run for the proposed remedy can be found in the file 

“EMPAXresults_proposed transport remedy,” that is available in the docket for this rule. 
293

billion. With a 3 percent interest rate, the annual social cost for 2015 is estimated to be $2.0 

billion. 

8.2.2 Industry Effects 

The proposed rule directly influences the electricity sector’s fuel use and private cost 

expenditures. As the electricity sector responds to these changes, other economy-wide 

changes occur. For example, higher electricity prices may encourage electricity-dependent 

sectors to reduce production levels, switch to other energy sources (e.g., oil) and/or seek 

energy efficiency improvements in their production process. Electricity sectors also make 

additional private cost expenditures in order to comply with the transport rule; these 

expenditures lead to other economy-wide changes. For example each dollar spent to comply 

with the program is used to buy environmental protection goods and services.77 As a result, 

the demand for environmental protection goods and services will be higher with the transport 

rule. For sectors supplying environmental protection goods or services, the secondary effect 

may offset higher electricity costs. The following sections report and discuss output changes 

(i.e., changes in physical quantities of the goods/services each industry sector in each region 

produces) associated with the impacts of compliance in the year 2015, which serves as a 

proxy for compliance in 2014. 

 

77 Additional details are described in EMPAX-CGE model documentation (5-2 to 5-5).
294

Figure 8-4. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015 

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

Northeast South Midwest Plains West US

Percentage Change

Note: GDP in each region is the dollar value of goods and services produced in the region in 2015. See Figure 8-2 for a 

presentation of the states in each region 

8.2.2.1 Energy Sectors 

The EMPAX modeling system shows that the electricity sector experiences the most 

significant changes under the transport rule. Electricity output and fuel mix changes used to 

meet the transport rule also influence other energy sectors. For example, U.S. electricity and 

coal output both decline by approximately 0.3%. U.S. natural gas output changes for two 

reasons: 1) natural gas is used in electricity generation and electricity generation declines and 

2) natural gas is a substitute for electricity, so gas use increases when electricity becomes 

more expensive. Overall natural gas output declines because the first effect (reduced 

electricity generation) is greater than the second effect (substitution from electricity use to 

natural gas use)). Crude oil and petroleum output decline, but the changes are small; these 

inputs are less critical to the electricity sector making them less sensitive to changes in 

electricity production (Figure 8-5). 

Given the regional distribution of controls, there are differences in regional output 

quantity changes. For example, electricity production in the Northeast experiences the largest 

decline while the Plains and West electricity sectors see small output increases. Coal output 
295

changes to meet coal demand predictions from the IPM electricity model, and the IPM 

modeling system suggests that the Northeast’s electricity sector uses additional coal inputs to 

meet the rule’s requirements. 

8.2.2.2 Energy-Intensive Sectors 

Energy-intensive manufacturing industries are more sensitive to electricity and other 

energy price changes. Although the net U.S. output change for each energy-intensive 

industry is less than 0.1%, these sectors do show some (but economically small) regional 

variation. The most significant regional differences are seen in the aluminum sector, where 

production shifts from the Northeast, South, and Midwest regions to the Plains and West 

regions. Similar geographic shifts are observed in other energy-intensive industries 

(Figure 8-6). 

8.2.2.3 Nonenergy Sectors 

Although electricity expenditures represent a small fraction of nonenergy-sector 

production costs, higher electricity prices still influence nonenergy-sector production levels. 

However, nonenergy sector output effects are very small. National output levels for four 

broad nonenergy sectors: agriculture, other manufacturing, services, and transportation fall 

by less than one one-hundredth of a percent (0.01%). There is some regional variation as 

production shifts to areas with lower electricity costs (e.g., West, Plains), but the differences 

are not significant (Figure 8-7). 
296

Figure 8-5. Output Changes in 2015: Energy Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Coal Crude Oil Electricity Natural Gas Petroleum

Percentage Change

Note: Outcomes reflect changes in the physical quantities of goods/services each regional sector produces. 
297

Figure 8-6. Output Changes in 2015: Energy-Intensive Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Food and 

Kindred

Paper and 

Allied Chemicals Glass Cement Iron and Steel Aluminum

Percentage Change 

Note: Outcomes reflect changes in the physical quantities of goods/services each regional sector produces. 
298

Figure 8-7. Output Changes in 2015: Nonenergy Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Agriculture

Energy-Intensive 

Manufacturing Other Manufacturing Services

Transportation 

Services

Percentage Change

Note: Outcomes reflect changes in the physical quantities of goods/services each regional sector produces. 
299

8.3 References 

Babiker, M.H., and T.F. Rutherford. 1997. “Input Output and General Equilibrium Estimates 

of Embodied CO2: A Data Set and Static Framework for Assessment.” University of 

Colorado at Boulder, Working Paper 97-2. Available at 

http://www.mpsge.org/mainpage/mpsge.htm. 

Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C. Hyman. 2001. “The 

MIT Emissions Prediction and CO2 Policy Analysis (EPPA) Model: Revisions, 

Sensitivities, and Comparisons of Results.” MIT Joint Program on the Science and 

Policy of Global Change, Report No. 71. Available at 

http://web.mit.edu/globalchange/www/eppa.html. 

Ballard, C. J. Shoven, and J. Whalley. 1985. “General Equilibrium Computations of the 

Marginal Welfare Costs of Taxation in the United States.” American Economic 

Review 75(1):128-138. 

Bovenberg, L.A., and L.H. Goulder. 1996. “Optimal Environmental Taxation in the Presence 

of Other Taxes: General Equilibrium Analysis.” American Economic Review

86(4):985–1000. Available at http://www.aeaweb.org/aer/. 

Brooke, A., D. Kendrick, and A. Meeraus. 1996. GAMS Homepage. Available at 

http://www.gams.com. 

Feenberg, D., and E. Coutts. 1993. “An Introduction to the TAXSIM Model.” Journal of 

Policy Analysis and Management 12(1):189-194. Available at 

http://www.nber.org/~taxsim/. 

Fullerton, D., and D. Rogers. 1993. “Who Bears the Lifetime Tax Burden?” Washington, 

DC: The Brookings Institute. Available at 

http://bookstore.brookings.edu/book_details.asp?product%5Fid=10403. 

Global Insight. 2007. “U.S. State, Metropolitan Area, and County-level Forecasting 

Services.” http://www.globalinsight.com/ProductsServices/ProductDetail1100.htm. 

Goulder, L.H., and R.C. Williams. 2003. “The Substantial Bias from Ignoring General 

Equilibrium Effects in Estimating Excess Burden, and a Practical Solution.” Journal 

of Political Economy 111:898-927. Available at 

http://www.journals.uchicago.edu/JPE/home.html. 

Minnesota IMPLAN Group. 2006. State Level Data for 2004. Available from 

http://www.implan.com/index.html. 
300

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection Industry: A 

Proposed Framework for Assessment. U.S. Environmental Protection Agency, Office 

of Policy, Planning, and Evaluation. EPA 230-R-95-001. Available at 

http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b

8b642ab9371df852564500004b543/$FILE/EE 0217A 1.pdf. 

Paltsev, S., J.M. Reilly, H.D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. 

Asadoorian, and M. Babiker. 2005. “The MIT Emissions Prediction and Policy 

Analysis (EPPA) Model: Version 4.” MIT Joint Program on the Science and Policy 

of Global Change, Report No. 125. Cambridge, MA. Available at 

http://web.mit.edu/globalchange/www/eppa.html. 

Rutherford, T.F. 1999. “Applied General Equilibrium Modeling with MPSGE as a GAMS 

Subsystem: An Overview of the Modeling Framework and Syntax.” Computational 

Economics 14(1):1-46. Available at http://www.gams.com/solvers/mpsge/syntax.htm. 

Rutherford, T.F., and S.V. Paltsev. 2000. “GTAP Energy in GAMS: The Dataset and Static 

Model.” University of Colorado at Boulder, Working Paper 00 2. Available at 

http://www.mpsge.org/mainpage/mpsge.htm. 

U.S. Department of Energy, Energy Information Administration. Undated (a). State Energy 

Data Report. Washington, DC. Available at http://www.eia.doe.gov/emeu/states/ 

_use_multistate.html. 

U.S. Department of Energy, Energy Information Administration. Undated (b). State Energy 

Price and Expenditure Report. Washington, DC. Available at 

http://www.eia.doe.gov/emeu/states/price_multistate.html. 

 U.S. Department of Energy, Energy Information Administration. 2003. Manufacturing 

Energy Consumption Survey 2002. Washington, DC. Available at 

http://www.eia.doe.gov/emeu/mecs/. 

U.S. Department of Energy, Energy Information Administration. January 2007. Annual 

Energy Outlook 2007. DOE/EIA-0383(2007). Washington, DC. Available at 

http://www.eia.doe.gov/oiaf/aeo/index.html. 

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. 

OAQPS Economic Analysis Resource Document. 

http://www.epa.gov/ttn/ecas/analguid.html. 
301

U.S. Environmental Protection Agency. September 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. Washington, DC: EPA. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. Accessed August 

2009. 
302

CHAPTER 9 

STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES 

This chapter presents discussion and analyses relating to relevant Executive Orders 

and statutory requirements relevant for the Transport Rule. We discuss potential impacts to 

affected small entities as required by the Regulatory Flexibility Act (RFA), as amended by 

the Small Business Regulatory Enforcement Fairness Act (SBREFA). We also describe the 

analysis conducted to meet the requirements of the Unfunded Mandates Reform Act of 1995 

(UMRA) that assess the impact of the Transport Rule for state, local and Tribal governments 

and the private sector. Analyses conducted to comply with the Paperwork Reduction Act 

(PRA) are also discussed. In addition, we address the requirements of Executive Order (EO) 

13045: Protection of Children from Environmental Health and Safety Risks; EO 13175: 

Consultation and Coordination with Indian Tribal Governments; and EO 12898: Federal 

Actions to Address Environmental Justice in Minority Populations and Low-Income 

Populations. The Discussion of Executive Order 13211: Actions that Significantly Affect 

Energy Supply, Distribution or Use is provided in Chapter 7 of this RIA. 

9.1 Small Entity Impacts 

 The Regulatory Flexibility Act (5 U.S.C. ' 601 et seq.), as amended by the Small 

Business Regulatory Enforcement Fairness Act (Public Law No. 104-121), provides that 

whenever an agency is required to publish a general notice of proposed rulemaking, it must 

prepare and make available an initial regulatory flexibility analysis, unless it certifies that the 

proposed rule, if promulgated, will not have Aa significant economic impact on a substantial 

number of small entities@ (5 U.S.C. ' 605[b]). Small entities include small businesses, small 

organizations, and small governmental jurisdictions. 

For the purposes of assessing the impacts of the Transport Rule on small entities, a 

small entity is defined as: 

(1) A small business according to the Small Business Administration size 

standards by the North American Industry Classification System (NAICS) 

category of the owning entity. The range of small business size standards for 
303

electric utilities is 4 billion kilowatt-hours of production or less; 

(2) a small government jurisdiction that is a government of a city, county, town, 

district, or special district with a population of less than 50,000; and 

(3) a small organization that is any not-for-profit enterprise that is independently 

owned and operated and is not dominant in its field. 

Table 9-1 lists entities potentially affected by this proposed rule with the applicable 

NAICS code. 

Table 9-1. Potentially Regulated Categories and Entitiesa

Category NAICS 

Codeb Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel-fired electric utility steam generating units.

Federal 

Government

221112c Fossil fuel-fired electric utility steam generating units owned by 

the federal government.

221112c Fossil fuel-fired electric utility steam generating units owned by 

municipalities.

State/Local/

Tribal 

Government

921150 Fossil fuel-fired electric utility steam generating units in Indian 

Country.

a

Include NAICS categories for source categories that own and operate electric generating units only. 

b

North American Industry Classification System. 

c

Federal, state, or local government-owned and operated establishments are classified according to the activity 

in which they are engaged.

EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected entities will implement control 

measures to meet their NOx and SO2 budgets. This analysis does not examine potential 

indirect economic impacts associated with the Transport Rule, such as employment effects in 

industries providing fuel and pollution control equipment, or the potential effects of 
304

electricity price increases on industries and households. 

9.1.1 Identification of Small Entities 

 EPA used Velocity Suite’s Ventyx data as a basis for identifying plant ownership and 

compiling the list of potentially affected small entities.78 The data set contains detailed 

ownership and corporate affiliation information. For plants burning fossil fuel as the primary 

fuel, plant-level boiler and generator capacity, heat input, generation, and emissions data 

were aggregated by owner and then parent company. Entities with more than 4 billion kWh 

of annual electricity generation were removed from the list, as were municipal-owned 

entities serving a population greater than 50,000. Finally, for cooperatives, investor-owned 

utilities, and subdivisions that generate less than 4 billion kWh of electricity annually but 

may be part of a large entity, additional research on power sales, operating revenues, and 

other business activities was performed to make a final determination regarding size. 

Because the rule does not affect units with a generating capacity of less than 25 MW, small 

entities that do not own at least one generating unit with a capacity greater than or equal to 

25 MW were dropped from the data set. According to EPA’s analysis, nearly 600 small 

entities were exempted by this provision. Finally, small entities for which IPM does not 

project generation in 2014 in the base case were omitted from the analysis because they are 

not projected to be operating and thus will not face the costs of compliance with the 

Transport Rule. After omitting entities for the reasons above, EPA identified a total of 81 

potentially affected small entities, out of a possible 760.79 The number of potentially 

affected small entities by ownership type is listed in Table 9-2. 

9.1.2 Overview of Analysis and Results 

 This section presents the methodology and results for estimating the impact on the 

Transport Rule to small entities in 2014 based on the following endpoints: 

 

78 For details, see http://www.ventyx.com/

79 There are 82 entities that are not technically electricity generating utilities so we applied other criteria that 

would apply to financial or industrial companies and found that they did not meet the definitions of small 

entities in this context.
305

 annual economic impacts of the Transport Rule on small entities and 

 ratio of small entity impacts to revenues from electricity generation. 

9.1.2.1 Methodology for Estimating Impacts of the Transport Rule on Small Entities 

 An entity can comply with the Transport Rule through some combination of the 

following: installing retrofit technologies, purchasing allowances, switching to a cleaner fuel, 

or reducing emissions through a reduction in generation or improved efficiency. 

Additionally, units with more allowances than needed can sell these allowances in the 

market. The chosen compliance strategy will be primarily a function of the unit’s marginal 

control costs and its position relative to the marginal control costs of other units. 

To attempt to account for each potential control strategy, EPA estimates compliance 

costs as follows: 

CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R 

where C represents a component of cost as labeled, and Δ R represents the value of foregone 

electricity generation, calculated as the difference in revenues between the base case and the 

Transport Rule. 

In reality, compliance choices and market conditions can combine such that an entity 

may actually experience a savings in any of the individual components of cost. Under the 

Transport Rule, some units will forgo some level of electricity generation (and thus 

revenues) to comply and this impact will be lessened on these entities by the projected 

increase in electricity prices under the Transport Rule. On the other hand, those increasing 

generation levels will see an increase in electricity revenues and as a result, lower net 

compliance costs. If entities are able to increase revenue more than an increase in fuel cost 

and other operating costs, ultimately they will have negative net compliance costs (or 

savings). Elsewhere, units burning high or medium sulfur coal might decide to pay relatively 

more for low-sulfur coal under the Transport Rule and sell allowances on the market, in the 

hopes of negating some or all of their compliance cost. Overall, small entities are not 

projected to install relatively costly emissions control retrofits, but may choose to do so in 

some instances. Because this analysis evaluates the total costs along each of the compliance 

strategies laid out above for each entity, it inevitably captures savings or gains such as those 
306

described. As a result, what we describe as cost is really more of a measure of the net 

economic impact of the rule on small entities. 

For this analysis, EPA used IPM-parsed output to estimate costs based on the 

parameters above, at the unit level. These impacts were then summed for each small entity, 

adjusting for ownership share. Net impact estimates were based on the following: operating 

and retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity 

generation revenues under the Transport Rule relative to the base case. These individual 

components of compliance cost were estimated as follows: 

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case 

and the Transport Rule, EPA identified units that install control technology 

under the Transport Rule and the technology installed. The equations for 

calculating retrofit costs were adopted from EPA’s version of IPM. The 

model calculates the capital cost (in $/MW); the fixed operation and 

maintenance (O&M) cost (in $/MW-year); the variable O&M cost (in 

$/MWh); and the total annualized retrofit cost for units projected to install 

FGD, SCR, or SNCR. 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and 

NOx annual and NOx ozone season allowance holdings. For both SO2 and 

NOx, the state emission budgets were assumed to be apportioned to units 

based on their share of the state's total emissions. EPA calculated each unit's 

SO2 and NOx annual and NOx ozone season allowance allocations as the ratio 

of that unit's adjusted emissions (as determined in the state budget calculation 

methodology) to the sum of all units' emissions in the applicable state, times 

the final state budget. Thus each unit's allocation is the unit's proportional 

share of the state budget, based on emissions as determined in the state budget 

calculation. See State Budgets, Unit Allocations, and Unit Emissions Rates 

TSD. 

To estimate the value of allowances holdings, allocated allowances were 

subtracted from projected emissions, and the difference was then multiplied 

by the allowance prices projected by IPM for 2014. Units were assumed to 

purchase or sell allowances to exactly cover their projected emissions under 

the Transport Rule. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by 

region and fuel-type-adjusted fuel prices ($/MMBtu) from IPM. The change 

in fuel expenditures under the Transport Rule was then estimated by taking 
307

the difference in fuel costs between the Transport Rule and the base case. 

(4) Value of electricity generated: EPA estimated electricity generation by first 

estimating unit capacity factor and maximum fuel capacity. Unit capacity 

factor is estimated by dividing fuel input (MMBtu) by maximum fuel capacity 

(MMBtu). The maximum fuel capacity was estimated by multiplying 

capacity (MW) * 8,760 operating hours * heat rate (MMBtu/MWh). The 

value of electricity generated is then estimated by multiplying capacity (MW) 

* capacity factor * 8,760 * regional-adjusted retail electricity price ($/MWh), 

for all entities except those categorized as “Private” in Ventyx. For private 

entities, EPA used wholesale electricity price instead retail electricity price 

because most of the private entities are independent power producers (IPP). 

IPPs sell their electricity to wholesale purchasers and do not own transmission 

facilities and thus their revenue was estimated with wholesale electricity 

prices. 

As discussed later in this analysis, 75 percent of small entities projected to be 

affected by the Transport Rule do not have to operate in a competitive market 

environment and thus should be able to pass compliance costs on to 

consumers. We defined cost of service regions as regions with a deregulation 

percentage of less than 20 percent. The deregulation percentage is defined for 

this analysis as a percentage estimating the degree of competition in 

electricity market, as provided by EIA. The lower this percentage means that 

there are more areas with cost of service market characteristics. We have used 

the estimates published in AEO 2009. 

To somewhat account for this cost pass-through, we incorporated the 

projected regional-adjusted retail electricity price calculated under the 

Transport Rule in our estimation of generation revenue under the Transport 

Rule. 

(5) Administrative costs: Because most affected units are already monitored as 

a result of other regulatory requirements, EPA considered the primary 

administrative cost to be transaction costs related to purchasing or selling 

allowances. EPA assumed that transaction costs were equal to 1.5 percent of 

the total absolute value of a unit’s allowances. This assumption is based on 

market research by ICF International. 

9.1.2.2 Results 

 The potential impacts of the Transport Rule on small entities are summarized in Table 

9-2. All costs are presented in $2006. EPA estimated the annualized net compliance cost to 
308

small entities to be approximately - $35.9 million in 2014 or savings of $35.9 million.80 The 

fact that the net compliance costs for all entities are actually net savings does not mean that 

each small entity would benefit from the Transport Rule. The net savings are driven by a few 

entities that are able to increase their revenues by increasing generation and taking advantage 

of higher electricity prices. 

Table 9-2. Projected Impact of the Transport Rule on Small Entities in 2014 

EGU 

Ownership 

Type

Number of 

Potentially 

Affected 

Entities

Total Net 

Compliance 

Cost ($2006 

millions)

Number of Small 

Entities with 

Compliance Costs 

>1% of Generation 

Revenues

Number of Small 

Entities with 

Compliance Costs 

>3% of Generation 

Revenues

Cooperative 16 -$27.4 5 3

InvestorOwned 

Utility

3 -$4.8 0 0

Municipal 46 -$8.2 19 7

Subdivision 5 -$3.3 1 0

Private 11 $7.7 5 4

Total 81 -$35.9 30 14

Note: The total number of potentially affected entities in this table excludes around 600 entities that have 

been dropped because they will not be affected by the Transport Rule. Also, the total number of entities 

with costs greater than 1 percent or 3 percent of revenues includes only entities experiencing positive 

costs. A negative cost value implies that the group of entities experiences a net savings under the 

Transport Rule. 

Source: IPM analysis

 

80 Neither the costs nor the revenues of units that retire under the Transport Rule are included in the impact 

estimates. Because these units are better off retiring under the Transport Rule than continuing operation, the 

true cost of the rule on these units is not represented by our modeling. The true cost of the Transport Rule for 

these units is the differential between their costs in the base case and the costs of meeting their customers’ 

demand under the rule. 
309

EPA assessed the economic and financial impacts of the rule using the ratio of 

compliance costs to the value of revenues from electricity generation, focusing in particular 

on entities for which this measure is greater than 1 percent. Although this metric is 

commonly used in EPA impact analyses, it makes the most sense when as a general matter an 

analysis is looking at small businesses that operate in competitive environments. However, 

small businesses in the electric power industry often operate in a price-regulated environment 

where they are able to recover expenses through rate increases. Given this, EPA considers 

the 1 percent measure in this case a crude measure of the price increases these small entities 

will be asking of rate commissions or making at publicly owned companies. 

Of the 81 small entities considered in this analysis, 30 entities may experience 

compliance costs greater than 1 percent of generation revenues in 2014. Entities that 

experience negative net costs under the Transport Rule are excluded from these totals. These 

results do not fully account for the reality that about three-quarters of these entities operate in 

cost of service markets and thus should be able to recover all of their costs of complying with 

the Transport Rule. Furthermore, of the approximately 550 units identified by EPA as being 

potentially owned by small entities, approximately two-thirds of the units that have higher 

costs are not expected to make operational changes as a result of this rule (e.g. install control 

equipment or switch fuels). Their increased costs are largely due to increased cost of the fuel 

they would be expected to use whether or not they had to comply with the proposed rule. 

Increased fuel costs are often passed through to rate-payers as common practice in many 

areas of the U.S. due to fuel adder arrangements instituted by state public utility 

commissions. Finally, EPA’s decision to exclude units smaller than 25 MW has already 

significantly reduced the burden on small entities by nearly 600. Hence, EPA has concluded 

that there is no significant economic impact on a substantial number of small entities (No 

SISNOSE) for this rule. The number of entities with compliance costs exceeding 3 percent 

of generation revenues is also included in Table 9-2. 

The distribution across entities of economic impacts as a share of base case revenue is 

summarized in Table 9-3. Although the distributions of economic impacts on each 

ownership type are in general fairly tight, there are a few outliers for which the percentage of 

economic impacts as a share of revenue is either very low or very high relative to the 

capacity-weighted average. In the cases where entities are projected to experience negative 
310

net impacts that are a high percentage of revenues, these entities have units that are able to 

increase generation with the Transport Rule, thus increasing revenues. In the cases where 

entities are projected to experience positive net impacts that are a high percentage of 

revenues, these entities do not find it economic to retrofit and are unable to switch to a lower 

sulfur coal. Thus, another reason for entities incurring impacts is that they are expected to 

reduce their generation under the Transport Rule which reduces revenues collected from 

electricity sales and inflates net costs. 

Table 9-3. Summary of Distribution of Economic Impacts of the Transport Rule on 

Small Entities in 2014 

EGU Ownership 

Type

Capacity-Weighted 

Average Economic 

Impacts as a % of 

Generation Revenues

Min Max

Cooperative -3.1% -29.5% 14.4%

Investor-owned utility -3.5% -7.5% 0.3%

Municipal 2.7% -19.7% 50.5%

Subdivision 0.0% -2.4% 2.5%

Private 14.3% -2.1% 86.1%

All 3.5% -29.5% 86.1%

Source: IPM analysis
311

The separate components of annualized costs to small entities under the Transport 

Rule are summarized in Table 9-4. The most significant components of incremental cost to 

these entities under the Transport Rule are due to lower electricity revenues and increased 

fuel costs. Fuel costs increase over all ownership groups except the ones under the 

ownership type “Private” because an entity with the second largest generation under 

“Private” is projected to cut its generation by 25 percent under the Transport Rule, which 

translates to lower fuel costs for the whole group. Additionally, increases in electricity 

generation revenue, shown as cost savings or negative costs are experienced by cooperative, 

investor-owned utility, municipal and subdivision entities. This is due largely to the 

projected increase in electricity prices under the Transport Rule. Among the private category, 

however, reduced generation by the one entity with a large share of generation leads to 

higher net costs for the entire category. Our data suggests this entity owns a group of 

combined cycle units and which are presumably marginal units in their respective load 

segments under the base case. 

Table 9-4. Incremental Annualized Costs under the Transport Rule Summarized by 

Ownership Group and Cost Category in 2014 ($2006 millions) 

EGU 

Ownership 

Type

Retrofit + 

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Lost 

Electricity 

Revenue

Administrative 

Cost

Cooperative $6.1 $1.8 $11.2 -$46.5 $0.1

InvestorOwned Utility $0.7 $0.0 $4.7 -$10.2 $0.0

Municipal $5.8 -$4.0 $13.0 -$23.0 $0.0

Subdivision $1.6 $0.6 $4.0 -$9.6 $0.0

Private $4.5 $0.1 -$23.6 $26.9 $0.0

Source: IPM analysis. 

Furthermore, 26 MW of total small entity capacity, or 0.04 percent of total small 

entity capacity in the Transport Rule region, is projected to be uneconomic to maintain under 

the Transport Rule relative to the base case. To put these numbers in context, of all affected 
312

capacity under the Transport Rule, about 1.9 GW (0.5 percent) of coal-fired capacity is 

projected to be uneconomic to maintain relative to the base case. This comparison suggests 

that small entities should not be disproportionately affected by the Transport Rule. In 

practice, units projected to be uneconomic to maintain may be mothballed, retired, or kept in 

service to ensure transmission reliability in certain parts of the grid. Our IPM modeling is 

unable to distinguish between these potential outcomes. 

9.1.3 Summary of Small Entity Impacts 

 EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected states will implement control measures 

to meet their emissions. To summarize, of the 81 small entities potentially affected, and the 

760 small entities in the Transport Rule region that are included in EPA’s modeling, 30 may 

experience compliance costs in excess of 1 percent of revenues in 2014, based on 

assumptions of how the affected states implement control measures to meet their emissions 

budgets as set forth in this rulemaking. Potentially affected small entities experiencing 

compliance costs in excess of 1 percent of revenues have some potential for significant 

impact resulting from implementation of the Transport Rule. However, as noted above, it is 

EPA’s position that because very few of the affected entities currently operate in a 

competitive market environment, they should generally be able to pass the costs of 

complying with the Transport Rule on to rate-payers. Furthermore, the decision to include 

only units greater than 25 MW in size exempts around 600 small entities that would 

otherwise be potentially affected by the Transport Rule. 

9.2 Unfunded Mandates Reform Act (UMRA) Analysis 

 Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements 

for federal agencies to assess the effects of their regulatory actions on state, local, and Tribal 

governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, EPA 

generally must prepare a written statement, including a cost-benefit analysis, for any 

proposed or final rule that includes any Federal mandate that may result in the expenditure 

by State, local, and Tribal governments, in the aggregate, or by the private sector, of 

$100,000,000 or more ... in any one year. A Federal mandate is defined under Section 

421(6), 2 U.S.C. 658(6), to include a Federal intergovernmental mandate and a Federal 
313

private sector mandate. A Federal intergovernmental mandate, in turn, is defined to include 

a regulation that would impose an enforceable duty upon State, Local, or Tribal 

governments, Section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a 

duty that is a condition of Federal assistance, Section 421(5)(A)(i)(I). A Federal private 

sector mandate includes a regulation that would impose an enforceable duty upon the private 

sector, with certain exceptions, Section 421(7)(A), 2 U.S.C. 658(7)(A). 

Before promulgating an EPA rule for which a written statement is needed under 

Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires 

EPA to identify and consider a reasonable number of regulatory alternatives and adopt the 

least costly, most cost-effective, or least burdensome alternative that achieves the objectives 

of the rule. EPA included descriptions of three remedy options that it considered when 

developing its proposed rule: (1) the proposed remedy of State Budgets/Limited Trading, (2) 

State Budgets/Intrastate Trading, and (3) Direct Controls. Moreover, section 205 allows EPA 

to adopt an alternative other than the least costly, most cost-effective or least burdensome 

alternative if the Administrator publishes with the final rule an explanation why that 

alternative was not adopted. 

Furthermore, as EPA stated in the proposal, EPA is not directly establishing any 

regulatory requirements that may significantly or uniquely affect small governments, 

including Tribal governments. Thus, under the proposed Transport Rule, EPA is not 

obligated to develop under Section 203 of the UMRA a small government agency plan. 

EPA analyzed the economic impacts of the Transport Rule on government entities. 

This analysis does not examine potential indirect economic impacts associated with the 

Transport Rule, such as employment effects in industries providing fuel and pollution control 

equipment, or the potential effects of electricity price increases on industries and households. 

9.2.1 Identification of Government-Owned Entities 

 Using Ventyx data, EPA identified state- and municipality-owned utilities and 

subdivisions in the Transport Rule region. EPA then used IPM-parsed output to associate 

these plants with individual generating units. Entities that did not own at least one unit with 

a generating capacity of greater than 25 MW were omitted from the analysis because of their 
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exemption from the rule. This exempts 380 entities owned by state or local governments. 

Additionally, government-owned entities for which IPM does not project generation in 2014 

under the base case or the Transport Rule were exempted from this analysis, because they are 

not projected to be operating and thus will not face the costs of compliance with the 

Transport Rule. Twenty-five entities were dropped from the analysis for this reason. Out of 

the 380 and 25 dropped entities, 7 of them are both less than 25 MW and not projected to 

operate in 2014. Thus, EPA identified 84 state and municipality-owned utilities that are 

potentially affected by the Transport Rule, out of a possible 482, which are summarized in 

Table 9-5. 

9.2.2 Overview of Analysis and Results 

 After identifying potentially affected government entities, EPA estimated the impact 

of the Transport Rule in 2014 based on the following: 

 total impacts of compliance on government entities and 

 ratio of government entity impacts to revenues from electricity generation. 

The financial burden to owners of EGUs under the Transport Rule is composed of 

compliance and administrative costs. This section outlines the compliance and 

administrative costs for the 84 potentially affected government-owned units in the Transport 

Rule region. 

9.2.2.1 Methodology for Estimating Impacts of the Transport Rule on Government Entities 

 The primary burden on state and municipal governments that operate utilities under 

the Transport Rule is the cost of installing control technology on units to meet SO2 and NOx

emission limits or the cost of purchasing allowances. However, an entity can comply with 

the Transport Rule through any combination of the following: installing retrofit 

technologies, purchasing allowances, switching to a cleaner fuel, or reducing emissions 

through a reduction in generation. Additionally, units with more allowances than needed can 

sell these allowances on the market. The chosen compliance strategy will be primarily a 

function of the unit’s marginal control costs and its position relative to the marginal control 

costs of other units. 
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To attempt to account for each potential control strategy, EPA estimates compliance 

costs as follows: 

CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R 

where C represents a component of cost as labeled, and Δ R represents the retail value of 

foregone electricity generation. 

In reality, compliance choices and market conditions can combine such that an entity 

may actually experience a savings in any of the individual components of cost. Under the 

Transport Rule, for example, some units will forgo some level of electricity generation (and 

thus revenues) to comply, this impact will be lessened on these entities by the projected 

increase in electricity prices under the Transport Rule, while those not reducing generation 

levels will see an increase in electricity revenues. Because this analysis evaluates the total 

costs along each of the four compliance strategies laid out above for each entity, it inevitably 

captures savings or gains such as those described. As a result, what we describe as cost is 

really more of a measure of the net economic impact of the rule on small entities. 

In this analysis, EPA used IPM-parsed output for the base case and the Transport 

Rule to estimate compliance cost at the unit level. These costs were then summed for each 

small entity, adjusting for ownership share. Compliance cost estimates were based on the 

following: operating and retrofit costs, sale or purchase of allowances, and the change in 

fuel costs or electricity generation revenues under the Transport Rule relative to the base 

case. These components of compliance cost were estimated as follows: 

(1) Retrofit and operating costs: Using the IPM-parsed output for the base case 

and the Transport Rule, EPA identified units that install control technology 

under the Transport Rule and the technology installed. The equations for 

calculating retrofit costs for SCR, SNCR, and FGD were adopted from EPA’s 

TRUM. The model calculates the capital cost (in $/MW), the fixed O&M 

cost (in $/MW-year), the variable O&M cost (in $/MWh), and the total 

annualized retrofit and operating cost by unit. 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and 

NOx annual and NOx ozone season allowance holdings. For both SO2 and 

NOx, the state emission budgets were assumed to be apportioned to units 

based on their share of the state's total emissions. EPA calculated each unit's 
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SO2 and NOx annual and NOx ozone season allowance allocations as the ratio 

of that unit's adjusted emissions (as determined in the state budget calculation 

methodology) to the sum of all units' emissions in the applicable state, times 

the final state budget. Thus each unit's allocation is the unit's proportional 

share of the state budget, based on emissions as determined in the state budget 

calculation. See State Budgets, Unit Allocations, and Unit Emissions Rates 

TSD. 

To estimate the value of allowances holdings, allocated allowances were 

subtracted from projected emissions, and the difference was then multiplied 

by the allowance price projected by IPM. Units were assumed to purchase or 

sell allowances to exactly cover their projected emissions under the Transport 

Rule. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by 

region and fuel type-adjusted fuel prices ($/MMBtu) from TRUM. The 

change in fuel expenditures under the Transport Rule was then estimated by 

taking the difference in fuel costs between the Transport Rule and the base 

case. 

(4) Value of electricity generated: EPA estimated electricity generation by first 

estimating the unit capacity factor and maximum fuel capacity. The unit 

capacity factor is estimated by dividing fuel input (MMBtu) by maximum fuel 

capacity (MMBtu). The maximum fuel capacity was estimated by 

multiplying capacity (MW) * 8,760 operating hours * heat rate 

(MMBtu/MWh). The value of electricity generated was then estimated by 

multiplying capacity (MW) * capacity factor * 8,760 * regional-adjusted retail 

electricity price ($/MWh). 

(5) Administrative costs: Because most affected units are already monitored as 

a result of other regulatory requirements, EPA considered the primary 

administrative cost to be transaction costs related to purchasing or selling 

allowances. EPA assumed that transaction costs were equal to 1.5 percent of 

the total absolute value of a unit’s allowances. This assumption is based on 

market research by ICF International. 

9.2.2.2 Results 

 A summary of economic impacts on government-owned entities is presented in 

Table 9-5. According to EPA’s analysis, the total net economic impact on each category of 
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government-owned entity (state- and municipality-owned utilities and subdivisions) is 

expected to be negative in 2014.81 

Table 9-5. Summary of Potential Impacts on Government Entities under the Transport 

Rule in 2014 

EGU Ownership 

Type

Potentially 

Affected Entities

Projected 

Annualized 

Costs ($2006 

millions) 

Number of 

Government 

Entities with 

Compliance 

Costs >1% of 

Generation 

Revenues

Number of 

Government 

Entities with 

Compliance 

Costs >3% of 

Generation 

Revenues

Subdivision 7 $8.1 2 0

State 3 -$12.3 1 1

Municipal 74 -$11.4 24 7

Total 84 -$15.7 27 8

Note: The total number of potentially affected entities in this table excludes the 482 entities that have been 

dropped because they will not be affected by the Transport Rule. Also, the total number of entities with costs 

greater than 1 percent or 3 percent of revenues includes only entities experiencing positive costs. A negative 

cost value implies that the group of entities experiences a net savings under the Transport Rule. 

Source: IPM analysis

As was done for the small entities analysis, EPA further assessed the economic and 

financial impacts of the rule using the ratio of compliance costs to the value of revenues from 

electricity generation in the base case, also focusing specifically on entities for which this 

measure is greater than 1 percent.82 EPA projects that 27 government entities will have 

 

81All costs are reported in 2006 dollars. 

82Neither the costs nor the revenues of units that retire under the Transport Rule are included in this portion of 

the analysis. Because these units are better off retiring under the Transport Rule than continuing operation, the 

true cost of the rule on these units is not represented by our modeling. The true cost of the Transport Rule for 

these units is the differential between their costs in the base case and the costs of meeting their customers’ 

demand under the rule. 
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compliance costs greater than 1 percent of revenues from electricity generation in 2014. 

Also similar to the small entity analysis, the majority of the units that have higher costs are 

not expected to make operational changes as a result of this rule (e.g., install control 

equipment or switch fuels). Their increased costs are largely due to increased cost of the fuel 

they would be expected to use whether or not they had to comply with the proposed rule. 

Further, increased fuel costs are often passed through to rate-payers as common practice in 

many areas of the U.S. due to fuel adder arrangements instituted by state public utility 

commissions. Entities that are projected to experience negative compliance costs under the 

Transport Rule are not included in those totals. This approach is more indicative of a 

significant impact when an analysis is looking at entities operating in a competitive market 

environment. Government-owned entities do not operate in a competitive market 

environment and therefore will be able to recover expenses under the Transport Rule through 

rate increases. Given this, EPA considers the 1 percent measure in this case a crude measure 

of the extent to which rate increases will be made at publicly owned companies. 

The distribution across entities of economic impacts as a share of base case revenue is 

summarized in Table 9-6. For municipality-owned entities and subdivisions, the maximum 

economic impact as a share of base case revenues is approximately 50.5 and 2.5 percent, 

respectively. A few municipality-owned entities experience economic impacts that are 

significantly higher than the capacity-weighted average for this group. In the cases where 

entities are projected to experience positive net costs that are a high percentage of revenues, 

these entities do not find it economic to retrofit and are unable to switch to a lower-sulfur 

coal. Thus, another reason for entities incurring impacts is that they are expected to reduce 

their generation under the Transport Rule which reduces revenues collected from electricity 

sales and inflates net costs. 
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Table 9-6. Distribution of Economic Impacts on Government Entities under the 

Transport Rule in 2014 

EGU Ownership 

Type

Capacity-Weighted 

Average Economic 

Impacts as a % of 

Generation Revenues

Min Max

Sub-division 0.7% -2.4% 2.5%

State -2.7% -4.0% 4.3%

Municipal -0.2% -54.5% 50.5%

All -0.0% -54.5% 50.5%

Source: IPM analysis

Additionally, a few entities are projected to experience negative net costs that are a 

high percentage of base case revenues. These entities have units that are able to increase 

generation levels, thus increasing revenues. Additionally, entities in regions for which we 

project large electricity price increases relative to other regions tend to be among those at the 

lower end of the distribution. 

The various components of annualized incremental cost under the Transport Rule to 

each group of government entities are summarized in Table 9-7. In 2014, subdivisions are a 

net purchaser of allowances, while states and municipalities sell allowances. Additionally, 

each group experiences both an increase in fuel expenditures and an increase in electricity 

revenue under the Transport Rule. Incremental fuel costs are positive because these entities 

are projected to increase generation and face higher fuel prices. Overall, increases in total 

electricity revenue by government entities under the Transport Rule exceed the increases in 

fuel and operating costs. 
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Table 9-7. Incremental Annualized Costs under the Transport Rule Summarized by 

Ownership Group and Cost Category ($2006 millions) in 2014 

EGU 

Ownership 

Type

Retrofit + 

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Lost 

Electricity 

Revenue

Administrative 

Cost

Subdivision $2.9 $3.0 $20.3 -$18.2 $0.1

State $9.0 $0.2 $12.6 -$34.0 $0.0

Municipal $30.2 $6.2 $49.6 -$97.8 $0.4

Source: ICF International analysis based on IPM analysis

IPM modeling of the Transport Rule projects that approximately 60 MW (2 units of 

347 in this analysis) of municipality-owned capacity would be uneconomic to maintain under 

the Transport Rule, beyond what is projected in the base case. This represents about 0.1 

percent of all subdivision, state, and municipality capacity in the Transport Rule region. For 

comparison, overall affected capacity under the Transport Rule, about 1.9 GW, or 0.5 percent 

of all coal-fired capacity is projected to be uneconomic to maintain relative to the base case. 

This comparison suggests that government entities should not face a disproportionate burden 

under the Transport Rule. In practice, units projected to be uneconomic to maintain may be 

“mothballed,” retired, or kept in service to ensure transmission reliability in certain parts of 

the grid. Our IPM modeling is unable to distinguish between these potential outcomes. 

9.2.3 Summary of Government Entity Impacts 

 EPA examined the potential economic impacts on state and municipality-owned 

entities associated with this rulemaking based on assumptions of how the affected states will 

implement control measures to meet their emissions. According to EPA’s analysis, the total 

net economic impact on government-owned entities is expected to be over -$15 million in 

2014 or a net savings of more than $15 million. This does not mean that each government 

entity will experience net savings as the overall net savings is driven by several entities 

garnering large savings. Of the 84 government entities considered in this analysis and the 

482 government entities in the Transport Rule region that are included in EPA’s modeling, 

27 may experience compliance costs in excess of 1 percent of revenues in 2014, based on our 
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assumptions of how the affected states implement control measures to meet their emissions 

budgets as set forth in this rulemaking. 

Government entities projected to experience compliance costs in excess of 1 percent 

of revenues have some potential for significant impact resulting from implementation of the 

Transport Rule. However, as noted above, it is EPA’s position that because these 

government entities can pass on their costs of compliance to rate-payers, they will not be 

significantly affected. Furthermore, the decision to include only units greater than 25 MW in 

size exempts 380 government entities that would otherwise be potentially affected by the 

Transport Rule. 

9.3 Paperwork Reduction Act 

 In compliance with the Paperwork Reduction Act (44 U.S.C. 3501 et seq.), EPA 

submitted a proposed Information Collection Request (ICR) (EPA ICR number 2512.01) to 

the Office of Management and Budget (OMB) for review and approval on July 19, 2004 

(FR 42720-42722). The ICR describes the nature of the information collection and its 

estimated burden and cost associated with the final rule. In cases where information is 

already collected by a related program, the ICR takes into account only the additional 

burden. This situation arises in states that are also subject to requirements of the 

Consolidated Emissions Reporting Rule (EPA ICR number 0916.10; OMB control number 

2060-0088) or for sources that are subject to the Acid Rain Program (EPA ICR number 

1633.13; OMB control number 2060-0258) or NOx SIP Call (EPA ICR number 1857.03; 

OMB number 2060-0445) requirements. 

EPA solicited comments on specific aspects of the information collection. The 

purpose of the ICR is to estimate the anticipated monitoring, reporting, and record-keeping 

burden estimates and associated costs for states, local governments, and sources that are 

expected to result from the Transport Rule. 

The record-keeping and reporting burden to sources resulting from states choosing to 

participate in a regional cap-and-trade program is approximately $28 million annually. This 

estimate includes the annualized cost of installing and operating appropriate SO2 and NOx
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emissions monitoring equipment to measure and report the total emissions of these pollutants 

from affected EGUs (serving generators greater than 25 megawatts capacity). The burden to 

state and local air agencies includes any necessary SIP revisions, performance of monitoring 

certification, and fulfilling of audit responsibilities. More information on the ICR analysis is 

included in the official the Transport Rule docket. 

In accordance with the Paperwork Reduction Act on July 19, 2004, an ICR was made 

available to the public for comment. The 60-day comment period expired September 19, 

2004, with no public comments received specific to the ICR. 

9.4 Environmental Justice 

 Executive Order (EO) 12898 (59 FR 7629 (Feb. 16, 1994)) establishes federal 

executive policy on environmental justice. Its main provision directs federal agencies, to the 

greatest extent practicable and permitted by law, to make environmental justice part of their 

mission by identifying and addressing, as appropriate, disproportionately high and adverse 

human health or environmental effects of their programs, policies, and activities on minority 

populations and low-income populations in the United States. 

9.4.1. Consideration of Environmental Justice Issues in the Rule Development Process 

In the rulemaking process EPA considers whether there are positive or negative 

impacts of the action that appear to affect low-income, minority, or Tribal communities 

disproportionately, and, regardless of whether a disproportionate effect exists, whether there 

is a chance for these communities to meaningfully participate in the rulemaking process. 

EPA expects that this rule “Federal Implementation Plans to Reduce Interstate Transport of 

Fine Particulate Matter and Ozone” will provide significant health and environmental 

benefits to, among others, people with asthma, people with heart disease, and people living in 

ozone or fine particle (PM2.5) nonattainment areas. This rule also has the potential to affect 

the cost structure of the utility industry and could lead to regional shifts in electricity 

generation and/or emissions of various pollutants. Therefore we expect this rule to be of 

interest to many environmental justice communities. EPA’s analysis of the effects of this 

proposed rule, including information on air quality changes and the resulting health benefits, 
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is presented both in section IX of the preamble and in more detail in the air quality modeling 

Technical Support Document and chapters 3 and 4 of this Regulatory Impact Analysis (RIA). 

These documents can be accessed through the rule docket No. EPA-HQ-OAR-2009-0491 

and from the main EPA webpage for the rule www.epa.gov/airtransport. This section 

summarizes the legal basis for this rule, and provides background information on how this 

rule fits into the larger regulatory strategy for controlling pollution from the power sector. A 

summary of the emissions, air quality, and health benefit estimates for this rule then follows. 

This rule is replacing an earlier rule (the 2005 Clean Air Interstate Rule (CAIR)) that 

was first vacated and then remanded to EPA by the U.S. Court of Appeals for the District of 

Columbia Circuit. CAIR was vacated by the U.S. Court of Appeals for the District of 

Columbia Circuit in July 2008 in a case known as North Carolina v EPA. In December 2008, 

the vacatur was altered to a remand based on the likely environmental harms of vacating the 

rule and EPA’s stated intent to replace the rule promptly. At the time of the 2008 court 

ruling, many sources had already begun to install and run emissions control devices or 

otherwise alter their operations and had successfully begun reducing their emissions. The 

court decision has led to significant uncertainty among affected sources as to what emission 

reductions will be required and among states and communities as to what air quality benefits 

will be achieved. By proposing this aggressive replacement rule that meets the legal 

requirements of the Clean Air Act (CAA) as interpreted by the Court in the North Carolina

decision promptly, EPA is both maximizing the likelihood that the goals of the CAA will be 

met, and helping communities receive the air quality benefits they need as quickly as 

possible by minimizing the chance that any emission reductions achieved under CAIR would 

be lost. 

It is important to note that CAA § 110(a)(2)(d), which addresses transport of criteria 

pollutants between states and is the authority for this rule, is only one of many provisions of 

the CAA that provide EPA, states, and local governments with authorities to reduce exposure 

to ozone and PM2.5 in communities. These legal authorities work together to reduce exposure 

to these pollutants in communities, including environmental justice communities, and 

provide substantial health benefits to both the general public and sensitive sub-populations. 
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9.4.2. Potential Environmental and Public Health Impacts to Vulnerable Populations 

There are several considerations to take into account when assessing the effects of 

this proposed rule on minority, low-income, and Tribal populations. These include: amount 

of emissions reductions and where they take place (including any potential for areas of 

increased emissions); the changes in ambient concentrations across the affected area; and the 

health benefits expected from the rules. 

Emission reductions. This proposed rule will reduce exposure to PM2.5 and ozone 

pollution in most eastern states by reducing interstate transport of these pollutants and their 

chemical precursors (sulfur dioxide (SO2) and nitrogen oxides (NOx)). This rule has the 

effect of reducing emissions of these pollutants that affect the most-contaminated areas (i.e. 

areas that are not meeting the 1997 and 2006 ozone and PM2.5 National Ambient Air Quality 

Standards (NAAQS)). This rule separately identifies both nonattainment areas and 

maintenance areas (maintenance areas are those that currently meet the NAAQS but that, 

based on past data, are in danger of exceeding the standards in the future). This approach of 

requiring emission reductions to protect maintenance areas as well as nonattainment areas 

reduces the likelihood that any areas close to the level of the standard will exceed the current

health-based standards in the future. 

Ozone and PM2.5 concentrations in both nonattainment and maintenance areas 

identified in this rule are the result of both local emissions and long-range transport of 

pollution. This rule requires upwind states to reduce or eliminate their significant 

contribution to nonattainment or maintenance problems in downwind states. Even when the 

significant contributions of upwind states are fully eliminated, additional emissions 

reductions within the nonattainment area and/or the downwind state will be needed for some 

areas to attain and maintain the NAAQS. 

The proposed remedy option for this rule would use a limited emissions trading 

mechanism among power plants to achieve significant emission reductions in states covered 

by the rule. EPA recognizes that many environmental justice communities have voiced 

concerns about emissions trading and any resulting potential for any emissions increases in 
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any location. 

The proposed rule uses EPA’s authority in CAA §110(a)(2)(d) to require states to 

eliminate emissions from power plants in their state that contribute significantly to 

downwind PM2.5 or ozone nonattainment or maintenance areas. EPA’s proposed mechanism 

for achieving these emission reductions is to use a tightly constrained trading program that 

requires a strict emission ceiling in each state while allowing a limited ability to shift 

emissions between facilities or states. This approach ensures that emissions in each state that 

significantly contribute to downwind nonattainment or maintenance areas are controlled, 

while allowing power companies to adjust generation based on fluctuations in electricity 

demand, weather, availability of low-emitting power sources (e.g. temporary shut-down of a 

nuclear power plant for maintenance or repairs), or other unanticipated factors affecting the 

interconnected electricity grid. 

Any emissions above the state’s allocated level must be offset by emission reductions 

from another state in the region below that state’s budget or by using extra “banked” 

allowances from earlier years. All sources must hold enough allowances to cover their 

emissions; therefore, if they emit more than their allocation they must buy allowances from 

another source that emitted less than its allocation. PM2.5 and ozone pollution from power 

plants have both local and regional components: part of the pollution in a given location – 

even in locations near emissions sources – is due to emissions from nearby sources and part 

is due to emissions that travel hundreds of miles and mix with emissions from other sources. 

Therefore, in many instances the exact location of the upwind reductions does not affect the 

levels of air pollution downwind. 

It is important to recognize that the section of the Clean Air Act providing authority 

for this rule, 110(a)(2)(D), unlike some other provisions, does not dictate levels of control for 

particular facilities. None of EPA’s alternatives within this proposal can ensure there will be 

no emission increases at any facility. Under the direct control alternative, the emission rate 

for each facility is reduced but each facility could emit more by increasing their power output 

in order to meet electricity reliability or other goals. Under the intrastate trading option, state 

emissions must stay constant but individual facilities within each state could increase their 

emissions as long as another facility in the state had decreased theirs. By strictly setting state 
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budgets to eliminate the portion of significant contributions to non-attainment and 

maintenance areas that EPA has identified in today’s action, by limiting the amount of 

interstate trading possible and by requiring any emissions above the level of the allocations 

to be offset by emission decreases elsewhere in the region, the proposed remedy options 

reduce ambient concentrations where they are most needed. 

EPA’s emissions modeling data indicate that nationwide SO2 emissions from electric 

generating units (EGUs) will be approximately 6.4 million tons (60%) lower in 2014 than 

they were in 2005 (which is the year that the Clean Air Interstate Rule was finalized). 

Emissions would also decrease when compared to the base case (the base case estimates of 

SO2 emissions in 2014 in the absence of this proposed rule or the Clean Air Interstate Rule it 

is replacing). SO2 emissions under this proposed rule are projected to be approximately 4.4 

million tons (50%) lower than they would have been in 2014 in the base case (i.e. without 

this rule). 

EPA’s modeling does project that some states not covered by one or more aspects of 

the program may experience increases of SO2 emissions (i.e., their emissions are greater in 

the control case modeling than in the base case modeling). These emission increases are the 

result of forecasted changes in operation of units outside of the controlled region (due to the 

interconnected nature of the utility grid or influence of the rule on the market for lower sulfur 

coal). As shown in Table IV.D.6 of the preamble, Arkansas, Mississippi, North Dakota, 

South Dakota, and Texas all exhibit 2012 SO2 emissions increases over the base case of more 

than 5,000 tons. Texas is projected to have by far the largest increase (136,000 tons), while 

the other states’ increases range from 6,000 to 32,000 tons. Further analysis with the 

simplified air quality assessment tool indicates that these projected increases in the Texas 

SO2 emissions would increase Texas’s contribution to an amount that would exceed the 0.15 

μg/m3 threshold for annual PM2.5. For this reason, EPA requests comment on whether Texas 

should be included in the program as a group 2 state. For additional details, see section IV.D 

of the preamble for this rule. 

EPA’s emissions modeling data indicates that nationwide ozone season NOx

emissions from EGUs will be approximately 400,000 tons (30%) lower in 2014 than they 

were in 2005 (before implementation of the Clean Air Interstate Rule). Emissions would also 
327

decrease compared to the base case. Ozone season NOx emissions from EGUs under this 

proposed rule are projected to be approximately 150,000 tons (15%) lower than they would 

have been in 2014 in the base case (i.e. without this rule). EPA anticipates that additional 

upcoming actions, and likely additional interstate transport reductions to help states attain the 

proposed 2010 ozone NAAQS, will result in significant additional NOx reductions. 

EPA anticipates that this proposed action will significantly reduce, but not eliminate, 

the number of nonattainment and maintenance areas for the 1997 ozone and PM2.5 and 2006 

PM2.5 NAAQS. Table IX-1 of the preamble lists the changes in number of nonattainment 

sites. Most of these sites are located in urban areas. A single nonattainment area usually 

contains multiple monitoring sites; therefore there are more nonattainment sites than 

nonattainment counties or areas. As discussed in detail in section IV.D of the preamble, 

where this proposal does not fully quantify all of the significant contribution and interference 

with maintenance, EPA intends to address these additional requirements quickly. To the 

extent possible, EPA will supplement this proposed notice with additional information so 

that we can provide downwind states with all the certainty about upwind emission reductions 

they need to address their own local nonattainment concerns. In addition, as stated above, 

elimination of these nonattainment areas may require both local and regional emission 

reductions and this proposed action seeks only to address the regional transport component. 

As a result of these SO2 and NOx reductions, EPA’s air quality modeling indicates 

that concentrations of fine particles will decline throughout the eastern U.S. and in all the 

states affected by this rule. These reductions are largest in the area of the Ohio River valley 

and neighboring states and extend east through New England, west to Texas, south to 

Florida, and north through the Great Lakes states. “Border” states immediately outside the 

transport region are also predicted to see reductions in air concentrations, even though 

emissions increase in some of these states. This is because concentrations of fine particles in 

most locations are composed of both local emissions and those transported over hundreds of 

miles and emission reductions far away can cause significant improvements in local air 

quality. 

The modeling suggests also that there may be some small increases in PM2.5 near 

locations in the western U.S. where SO2 emissions are forecast to increase. These increases 
328

are small compared to the reductions predicted to take place in the eastern U.S. The increases 

are due to the regional nature of this rule (i.e. these states are not covered because sources in 

these states have not been found to contribute significantly to downwind nonattainment or 

maintenance areas) and the national nature of both coal markets and the Acid Rain Program 

allowance market. They are not the result of any particular type of remedy option (e.g. 

trading). EPA anticipates that future rulemakings, such as CAA section 112(d) standards and 

anticipated revisions to the 2006 fine particulate standards, are likely to reduce emissions in 

the areas not covered by this rule. 

EPA’s air quality modeling also indicates that concentrations of ozone will decline in 

much of the eastern U.S. These reductions are largest along much of the Gulf Coast and in 

Florida and in a region encompassing western Wisconsin, Iowa, Kansas, Missouri, Arkansas, 

and northeastern Oklahoma. These areas with the largest reductions are roughly the area 

immediately outside the boundaries of the NOx SIP Call region. States in the SIP Call region 

were required to make significant reductions in NOx beginning in 2003 and these emission 

reductions are included in the baseline modeling for this proposed Transport Rule and 

therefore not captured as additional benefits of this rulemaking. 

As is common when modeling many NOx control strategies, the air quality modeling 

for this proposed rule also suggests there may be a few small, localized areas in the Eastern 

U.S. where there are small increases in ozone concentrations. These generally small 

increases are a result of reductions in NOx emissions in these local areas; they do not appear 

to represent a lack of NOx emissions reductions or be the result of any specific emission 

control strategy (e.g. any type of trading). Rather, this phenomenon can result from complex 

atmospheric chemistry reactions taking place among chemical constituents of air pollution in 

these areas. Due to the complex photochemistry of ozone production, NOx emissions lead to 

both the formation and destruction of ozone, depending on the relative quantities of NOx, 

volatile organic compounds, and ozone formation catalysts. In the 2014 base case, NOx

emissions from sources in a few locations act to “quench” (i.e., lower) ozone compared to 

ozone concentrations in surrounding areas. The application of NOx controls in these areas 

reduces this quenching effect, thereby increasing ozone to levels generally on par with those 

of the surrounding area. In this case it is uncertain whether the structure of the model itself is 

potentially exacerbating the spatial extent or magnitude of any ozone increases which might 
329

actually occur as a result of this rule. It should be noted that these same NOx emission 

reductions that might be causing extremely localized ozone increases are certainly causing 

larger, more widespread improvements in ozone concentrations in downwind areas. Finally, 

as stated in the preamble, it is important to note that EPA intends to promulgate additional 

rules over the next few years that will further reduce concentrations of ozone and PM2.5 and 

that the federal government and the states can and do use many different legal authorities to 

limit exposure to ozone. 

Health benefits. This rule reduces concentrations of PM2.5 and ozone pollution, 

exposure to which can cause, or contribute to, adverse health effects including premature 

mortality and many types of heart and lung diseases that affect many minority and lowincome individuals, and Tribal communities. PM2.5 and ozone are particularly (but not 

exclusively) harmful to children, the elderly, and people with existing heart and lung 

diseases, including asthma. Exposure to these pollutants can cause premature death and 

trigger heart attacks, asthma attacks in those with asthma, chronic and acute bronchitis, 

emergency room visits and hospitalizations, as well as milder illnesses that keep children 

home from school and adults home from work. High rates of both heart disease and asthma 

are a cause for concern in many environmental justice communities, making these 

populations more susceptible to air pollution health impacts. In addition, many individuals in 

these communities also lack access to high quality health care to treat these illnesses. 

We estimate that in 2014 the PM-related annual benefits of the proposed remedy 

option include approximately 14,000 to 36,000 fewer premature mortalities, 9,200 fewer 

cases of chronic bronchitis, 22,000 fewer non-fatal heart attacks, 11,000 fewer 

hospitalizations (for respiratory and cardiovascular disease combined), 10 million fewer days 

of restricted activity due to respiratory illness and approximately 1.8 million fewer lost work 

days. We also estimate substantial health improvements for children in the form of fewer 

cases of upper and lower respiratory illness, acute bronchitis, and asthma attacks. 

Ozone health-related benefits are expected to occur during the summer ozone season 

(usually ranging from May to September in the Eastern U.S.). Based upon modeling for 

2014, annual ozone related health benefits are expected to include between 50 and 230 fewer 

premature mortalities, 690 fewer hospital admissions for respiratory illnesses, 230 fewer 
330

emergency room admissions for asthma, 300,000 fewer days with restricted activity levels, 

and 110,000 fewer days where children are absent from school due to illnesses. When adding 

the PM and ozone-related mortalities together, we find that the proposed remedy option for 

this rule will yield between 14,000 and 36,000 fewer premature mortalities. EPA has also 

estimated the benefits of the alternate remedies in this proposal using a benefit-per-ton 

estimation approach and found they would provide similar benefits. 

It should be noted that, as discussed in the RIA for this action, there are other benefits 

to the emission reductions discussed here, such as improved visibility and, indirectly, 

reduced mercury deposition. Additional benefits of reducing emissions of SO2 include 

reduced acidification of lakes and streams, and reduced mercury methylation; additional 

benefits of NOx reductions include reduced acidification of lakes and streams and reduced 

coastal eutrophication. Conversely, it is possible that the modest increases in emissions 

modeled for this rule in some western areas could result in limited increases of one or more 

of these effects in these locations. 

9.4.3. Meaningful Public Participation 

During the comment period for this proposed rule, EPA intends to reach out 

specifically to environmental justice communities and organizations to notify them of the 

opportunity to provide comments on this rule and to solicit their comments on both this rule 

and the upcoming actions described above and in section III.E of the preamble. EPA will 

hold public hearings on this rule; see the information at the very beginning of the preamble 

for locations, times and dates. Comments can also be submitted in writing or electronically 

by following the instructions at the beginning of the preamble. 
331

9.4.4 Summary 

EPA believes that the vast majority of communities and individuals in areas covered 

by this rule, including numerous low-income, minority, and Tribal communities in both rural 

areas and inner cities in the East, will see significant improvements in air quality and 

resulting improvements in health. EPA also recognizes that there is the potential for a 

number of communities or individuals outside the region covered by this rule to experience 

slightly worse air quality as an indirect result of emission reductions required under this 

proposal. EPA requests comment on the impacts of this proposed action on low income, 

minority, and Tribal communities. EPA will further analyze environmental justice issues 

related to the impacts of the rule on those communities based both on additional data that 

may be developed and on comments on those issues prior to final action on this rule. 
332

CHAPTER 10 

COMPARISON OF BENEFITS AND COSTS 

10.1 Comparison of Benefits and Costs 

The estimated social costs to implement the proposed Transport Rule, as described in 

this document, are approximately $2.03 or $2.23 billion annually for 2014 (2006 dollars, 3 

percent and 7 percent discount rate, respectively). Thus, the net benefits (social benefits 

minus social costs) of the program in 2014 are approximately $120 to 292 + B billion or 

$109 to 264 + B billion annually (2006 dollars, based on a discount rate of 3 percent and 7 

percent, respectively and rounded to three significant figures). (B represents the sum of all 

unquantified benefits and disbenefits of the regulation.) Therefore, implementation of this 

rule is expected, based purely on economic efficiency criteria, to provide society with a 

significant net gain in social welfare, even given the limited set of health and environmental 

effects we were able to quantify. Addition of directly emitted PM2.5-, mercury-, 

acidification-, and eutrophication-related impacts would likely increase the net benefits of the 

rule. Table 10-1 presents a summary of the benefits, costs, and net benefits of the final rule. 

The capital costs spent for pollution controls installed for CAIR were not included in 

the annual social costs reported here since the Transport Rule did not lead to their 

installation. Those CAIR-related capital investments are roughly estimated to have an annual 

social cost less than 1.15 -1.29 billion dollars (under the two discount rates). EPA developed 

this estimate using the annual capital costs for compliance for the original CAIR rule in 2005 

from its RIA modeling with IPM. That modeling estimated capital costs for 2010 and 2015 

in 1999$ that we first converted to 2006$ and then interpolated to a 2012 value ($1.6 billion). 

 This value represents a rough estimate of the cost of the CAIR pollution controls that EPA 

recognizes were in place in this Transport Rule RIA. We then converted this estimate of 

compliance costs to social costs appropriate for comparison to the above costs used in the 

benefit cost analysis. Notably, several states and EPA settlement agreements in recent years 

have actually required the pollution controls that we projected in CAIR by 2014, so the range 

offered above should be viewed as a likely upper bound of the capital costs solely 
333

attributable to the original CAIR rules. 

Air quality modeling was not conducted for the two alternative remedy options (state 

budgets/intrastate trading and direct control). We estimate the benefits of these alternatives 

by applying a benefit per-ton approach described in Chapter 5 of this RIA. While these 

benefit per ton estimates quantify the health impacts and monetized benefits of reductions in 

PM2.5, they omit important welfare benefits including changes in recreational visibility. 

Table 10-2 below presents the social costs and health benefits, including net social benefit, of 

these two alternative remedies alongside that of the proposed Transport Rule remedy. 

EPA also analyzed the costs and benefits of two scenarios that differ from the 

proposed State Budgets/Limited Trading in the stringency of SO2 budgets beginning in 2014. 

 Unlike the proposed Transport Rule, these scenarios are not the result of extensive analysis 

of each state’s significant contribution. Nor do they necessarily represent EPA’s view of 

what emission reductions are required by section 110(a)(2)(D)(i)(I) of the Clean Air Act. 

Rather, they are designed to show the effects of more or less stringent reduction requirements 

in a structure otherwise the same as the proposed State Budgets/Limited Trading remedy. 

Both scenarios represent the same type of remedy as in the proposed FIPs and have 

the same 2012 requirements. In addition to requirements beginning in 2012 for annual NOx, 

ozone-season NOx, and SO2, the proposed Transport Rule requires greater SO2 reductions in 

15 states (Group 1) beginning in 2014. The less stringent of the two scenarios considered 

here removes these 2014 requirements; instead, the 15 states maintain their 2012 

requirements in all subsequent years. This results in budgets that, relative to the proposed 

Transport Rule, allow about 1.4 million tons more SO2 to be emitted annually. 

The more stringent of the two scenarios changes the requirement for Group 1 SO2

emissions reductions beginning in 2014 and moves 8 additional states to Group 1 from 

Group 2. Connecticut, Florida, Kansas, Maryland, Massachusetts, Minnesota, Nebraska, and 

New Jersey join the 15 Group 1 states of the proposed rule, making 23 states in all and 

leaving 4 states and the District of Columbia in Group 2. Also, an additional 200,000 tons of 

SO2 reduction beyond that required in the proposed rule is required in the 23 Group 1 states. 

Air quality modeling was not conducted for these alternatives; thus we estimated the 
334

benefits of these alternatives by applying the same benefit per-ton approach as done for the 

alternative remedy options. The compliance costs of these alternatives are estimated using 

IPM. The social costs of these alternatives are estimated using EMPAX.83 Table 10-3 

presents the social costs and health benefits, including net social benefit, of the two scenarios 

alongside that of the proposed Transport Rule remedy.

 

83 Detailed results for this EMPAX run for these two alternatives can be found in the files 

“EMPAXresults_more stringent SO2 option” and “EMPAXresults_less stringent SO2 option,” respectively, and 

these files are available in the docket for this rule. 
335

Table 10-1. Summary of Annual Benefits, Costs, and Net Benefits of the Proposed 

Transport Rule in 2014a

 (billions of 2006 dollars) 

Description Proposed Remedy 

Social costsb

3 percent discount rate $2.03 

7 percent discount rate $2.23 

Social benefitsc,d,e

3 percent discount rate 

7 percent discount rate 

$122 to $294 + B 

$111 to $266 + B 

Health-related benefits: 

3 percent discount rate 

7 percent discount rate 

$119 to $290 + B 

$108 to $262 + B 

Visibility benefits $3.56 

Net benefits (benefits-costs)

e,f

3 percent discount rate 

7 percent discount rate 

$120 to $292 

$109 to $264 

a

 When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In 

this case we have rounded to three significant digits to facilitate comparison of the benefits and costs among 

the preferred remedy, less and more stringent scenarios. 

b

 Note that costs are the annualized total social costs of reducing pollutants including NOx and SO2 for the EGU 

source category in the proposed Transport Rule region in 2014. The social costs are the loss of household 

utility as measured in Hicksian equivalent variation. More information on the social costs can be found in 

Chapter 8 of this RIA. c Total benefits are comprised primarily of monetized PM-related health benefits. The reduction in premature 

fatalities each year accounts for over 90 percent of total monetized benefits. Benefits in this table are 

nationwide (with the exception of ozone and visibility) and are associated with NOx and SO2 reductions. 

Ozone benefits represent benefits nationwide. Visibility benefits represent benefits in Class I areas in the 

southeast, southwest and California. The estimate of social benefits also includes CO2-related benefits 

calculated using the social cost of carbon, discussed further in chapter 5. 

d

 Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and 

monetized are listed in Table 1-4. e

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

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

economic analyses (EPA, 2000; OMB, 2003). f

 Net benefits are rounded to three significant figures. Columnar totals may not sum due to rounding. 
336

Table 10-2. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the 

Proposed Remedy Option in 2014a

 (billions of 2006 dollars) 

Description Proposed Remedy Less Stringent 

Scenario

More Stringent 

Scenario

Social costsb 

3 percent discount rate $2.03 $1.12* $2.21*

7 percent discount rate $2.23 $1.23* $2.43*

Health-related benefitsc,d 

3 percent discount rate

7 percent discount rate

$118 to $288 + B

$107 to $260 + B

$82 to 200 + B

$76 to 184 + B

$120 to 292 + B

$110 to 267 + B

Net benefits (benefits-costs)

e,f 

3 percent discount rate

7 percent discount rate

$116 to $286

$105 to $258

$81 to 200

$74 to 182

$118 to 290

$107 to 265

a When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In 

this case we have rounded to three significant digits to facilitate comparison of the benefits and costs among 

the proposed remedy and the less and more stringent scenarios. 

b

 Note that costs are the annualized total social costs of reducing pollutants including NOx and SO2 for the EGU 

source category in the proposed Transport Rule region in 2014. The social costs are the loss of household 

utility as measured in Hicksian equivalent variation. More information on the social costs can be found in 

Chapter 8 of this RIA. c Due to methodological limitations, the health benefits of the direct control and intrastate trading remedies 

include PM2.5 –related benefits but omit visibility, ozone, and CO2-related benefits. We present the PM2.5 –

related benefits of the proposed remedy, omitting these other important benefits, so that readers may compare 

directly the benefits of the proposed and alternate remedies. Total benefits are comprised primarily of the 

value of PM-related avoided premature mortalities. The reduction in these premature mortalities in each year 

account for over 90 percent of total PM2.5-related monetized benefits. Benefits in this table are nationwide 

and are associated with NOx and SO2 reductions. To ensure that the benefits of the proposed remedy and the 

more and less stringent scenarios are directly comparable, we exclude the visibility-related benefits of the 

proposed remedy from this table; these visibility-related benefits are approximately $3.6 billion (2006$). 

d

 Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and 

monetized are listed in Table 1-6. e

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

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

economic analyses (EPA, 2000; OMB, 2003). f

 Net benefits are rounded to three significant figures. Columnar totals may not sum due to rounding. 

* The 2014 compliance costs (incremental to the base case) for the proposed remedy, less stringent scenario, 

and more stringent scenario are approximately $2.76, $1.19, and $2.81 billion 2006 dollars. 
337

Table 10-3. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the 

Proposed Remedy Option in 2014a

 (billions of 2006 dollars) 

Description Proposed Remedy Direct Control Intrastate Trading

Social costsb 

3 percent discount rate $2.03 $2.68 $2.49

7 percent discount rate $2.23 $2.91 $2.70

Health-related benefitsc,d 

3 percent discount rate

7 percent discount rate

$118 to $288 + B

$107 to $260 + B

$117 to $286 + B

$108 to $262 + B

$113 to $276 + B

$104 to $252 + B

Net benefits (benefits-costs)

e,f 

3 percent discount rate

7 percent discount rate

$116 to $286

$105 to $258

$115 to $283

$105 to $259

$110 to $273

$101 to $249

a When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In 

this case we have rounded to three significant digits to facilitate comparison of the benefits and costs among 

the proposed remedy and the less and more stringent scenarios. 

b

 Note that costs are the annualized total social costs of reducing pollutants including NOx and SO2 for the EGU 

source category in the proposed Transport Rule region in 2014. The social costs are the loss of household 

utility as measured in Hicksian equivalent variation. More information on the social costs can be found in 

Chapter 8 of this RIA. c Due to methodological limitations, the health benefits of the direct control and intrastate trading remedies 

include PM2.5 –related benefits but omit visibility, ozone, and CO2-related benefits. We present the PM2.5 –

related benefits of the proposed remedy, omitting these other important benefits, so that readers may compare 

directly the benefits of the proposed and alternate remedies. Total benefits are comprised primarily of the 

value of PM-related avoided premature mortalities. The reduction in these premature mortalities in each year 

account for over 90 percent of total PM2.5-related monetized benefits. Benefits in this table are nationwide 

and are associated with NOx and SO2 reductions. To ensure that the benefits of the proposed remedy and the 

more and less stringent scenarios are directly comparable, we exclude the visibility-related benefits of the 

proposed remedy from this table; these visibility-related benefits are approximately $3.6 billion (2006$). 

d

 Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and 

monetized are listed in Table 1-6. e

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

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

economic analyses (EPA, 2000; OMB, 2003). f

 Net benefits are rounded to three significant figures. Columnar totals may not sum due to rounding. 

* The 2014 compliance costs (incremental to the base case) for the proposed remedy, less stringent scenario, 

and more stringent scenario are approximately $2.76, $1.19, and $2.81 billion 2006 dollars. 
338

 As with any complex analysis of this scope, there are several uncertainties inherent in 

the final estimate of benefits and costs that are described fully in Chapters 5 and 7. In 

addition to the uncertainty characterization provided in these chapters, we also present two 

types of probabilistic approaches to characterize uncertainty in the benefit estimate of the 

proposed Transport Rule. The first approach generates a distribution of benefits based on the 

classical statistical error expressed in the underlying health and economic valuation studies 

used in the benefits modeling framework. The second approach uses the results from a pilot 

expert elicitation project designed to characterize key aspects of uncertainty in the ambient 

PM2.5/mortality relationship, and augments the uncertainties in the mortality estimate with 

the statistical error reported for other endpoints in the benefit analysis. 
339

10.2 References 

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

Air Pollution and Mortality. American Journal of Respiratory and Critical Care 

Medicine 173:667-672. 

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

2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine 

Particulate Air Pollution.” Journal of the American Medical Association

287:1132-1141. 

U.S. Environmental Protection Agency (EPA). September 2000. Guidelines for Preparing 

Economic Analyses. EPA 240-R-00-003. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal 

Agencies on Preparation of Regulatory Analysis. 

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

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and Particulate 

Air Pollution in the United States.” Environmental Health Perspectives 105(6):608-

612. 
340

APPENDIX A 

HUMAN HEALTH BENEFITS OF DIRECT CONTROL AND INTRASTATE 

TRADING REMEDIES AND PRESENTATION OF STATE-LEVEL BENEFITS OF 

PROPOSED REMEDY 
341

Synopsis 

This appendix summarizes the results of the human health benefits assessment of the 

direct control and intrastate trading remedies using PM2.5 benefit per ton estimates. The 

PM2.5-related benefits of the direct control remedy are between $120 and $290 billion 

(2006$) discounted at 3% and between $110 and $260 billion (2006$) discounted at 7%. The 

benefits of the intrastate trading remedy are between $110 and $280 billion (2006$) 

discounted at 3% and between $100 and $250 billion (2006$) discounted at 7%. In addition, 

this appendix includes state-level estimates of benefits of the proposed remedy. Due to 

methodological limitations, these estimates omit important benefits categories, including 

benefits from reduced ozone exposure, visibility improvement and ecological improvements. 

 

A.1 Methods 

In section 5.2.3 of the Benefits chapter we describe our approach to estimating a 

PM2.5 benefit per ton metric. In the interest of completeness, we repeat this discussion here. 

Benefit per-ton (BPT) estimates quantify the health impacts and monetized human health 

benefits of an incremental change in air pollution precursor emissions. In situations when we 

are unable to perform air quality modeling because of resource or time constraints, this 

approach can provide a reliable estimate of the benefits of emission reduction scenarios. EPA 

has used the benefit per-ton technique in previous RIAs, including the recent Ozone NAAQS 

RIA (U.S. EPA, 2008) and NO2 NAAQS RIA (U.S. EPA, 2010). Time constraints prevented 

the Agency from modeling the air quality changes resulting from either the intrastate and 

direct control remedies or the more and less stringent SO2 caps and so we estimate a subset 

of these health benefits using PM2.5 benefit per-ton estimates. The assessment of the alternate 

scenarios omits ozone-related benefits for two reasons. First, the overall level of ozonerelated benefits in the modeled case is relatively small compared to those associated with 

PM2.5 reductions (see table 5-17 in the Benefits chapter), due in part to the fairly modest 

summer time NOx emission reductions under this scenario. The level of summertime NOx

emission reductions of the alternate scenarios are very similar to the modeled scenario, 

suggesting that the omission of ozone-related impacts would not greatly influence the overall 

level of benefits. Second, the complex non-linear chemistry of ozone formation introduces 
342

uncertainty to the development and application of a benefit per ton estimate. Taken together, 

these factors argued against developing an ozone benefit per ton estimate for this RIA. 

For this analysis, EPA applies PM2.5 BPT estimates that are methodologically consistent 

with those reported in Fann et al. (2009), but have been adjusted for this analysis to better 

match the spatial distribution of air quality changes projected for the Transport Rule. To 

derive the BPT estimates for this analysis, we: 

1. Quantified the PM2.5 -related human and monetized health benefits of the SO2

emission reductions of the proposed remedy. We first quantified the health impacts and 

monetized benefits of total PM2.5 mass formed from the SO2 reductions of the proposed 

remedy, allowing us to isolate the PM air quality impacts from SO2 reductions alone.84 

This procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-

related benefits of incremental changes in SO2 emissions. Because reductions in NOx

emissions are relatively small in each scenario, and previous EPA modeling indicates 

that PM2.5 formation is less sensitive to NOx emission reductions on a per-μg/m3 basis 

(U.S. EPA, 2006d), we did not quantify the NOx -related PM2.5 changes. 

2. Divided the health impacts and monetized benefits by the emission reduction. This 

calculation yields BPT estimates for PM-related SO2. 

The resulting BPT estimates were then multiplied by the projected SO2 emission 

reductions for the Direct Control and Intrastate Trading remedy options to produce an 

estimate of the PM- and ozone-related health impacts and monetized benefits. There is no 

analogous approach for estimating a BPT for visibility, and so the benefits of the alternative 

 

84 The Transport Rule includes both SO2 and NOx emissions reductions. In general SO2 is a precursor to 

particulate sulfate and NOx is a precursor to particulate nitrate. However, there are also several interactions 

between the PM2.5 precursors which cannot be easily quantified. For example, under conditions in which SO2

levels are reduced by a substantial margin, "nitrate replacement" may occur. This occurs when particulate 

ammonium sulfate concentrations are reduced, thereby freeing up excess gaseous ammonia. The excess 

ammonia is then available to react with gaseous nitric acid to form particulate nitrate. The impact of nitrate 

replacement is also affected by concurrent NOx reductions. NOx reductions can lead to decreases in nitrate, 

which competes with the process of nitrate replacement. NOx reductions can also lead to reductions in 

photochemical by-products which can reduce both particulate sulfate and secondary organic carbon PM 

concentrations. Due to the complex nature of these interactions, EPA performed a sensitivity modeling analysis 

in which only SO2 emissions were reduced. We calculated benefits from this air quality modeling run to 

generate an SO2-only benefit per ton estimate. The results of the SO2-only sensitivity run may be found in the 

EPA Benefits TSD [Docket No. EPA-HQ-OAR-2009-0491]
343

remedies omit this important monetized benefit. The PM-related benefits of the two 

alternative remedies are roughly commensurate with the total benefits of the proposed 

remedy; this is due in large part to the roughly similar emission reductions achieved under 

each of the preferred and alternate remedies and the fact that EPA used air quality modeling 

for the proposed remedy to generate as the basis for the benefit per ton estimates 

subsequently used to quantify the benefits of the two alternate remedies not modeled. 

A.2 Results

Following the procedure described above, we calculated PM2.5 benefit per-ton 

estimates summarized in Table A-1. We then calculated the reduction in incidence of PM2.5 

adverse health effects for each remedy option and present the results in Table A-2. Finally, 

we present the benefits associated with the reduction in incidence of these adverse health 

effects for each remedy option and present the results in Table A-3. For comparison 

purposes these tables also includes the PM-related benefits of the proposed remedy. 

Table A-1: The Benefit per-ton of Reducing a Ton of SO2 from EGU’s in the Transport 

Rule Trading Region in 2014 

Discount rate 

PM2.5 mortality estimateA 3% 7% 

Pope et al. (2002) $26,400 $65,000 

Laden et al. (2006) $24,000 $59,000 

A Values represent the sum of PM-related mortality using each mortality estimate and the value of all avoided 

morbidities. Estimates rounded to two significant figures. 

 
344

Table A-2: Estimated Reduction in Incidence of PM2.5-related Adverse Health Effects 

of Direct Control and Intrastate Trading Remedy Options 

Health Effect Direct controlA

Intrastate 

tradingA Proposed RemedyB

PM-Related endpoints 

Premature Mortality 

Pope et al. (2002) 

(age >30) 14,000 14,000 14,000 (4,000—25,000) 

Laden et al. (2006) 

(age >25) 36,000 35,000 36,000 (17,000—56,000) 

Infant (< 1 year) 58 56 59 

(-66—180)c 

Chronic Bronchitis 9,100 8,800 9,200 

(320—18,000) 

Non-fatal heart attacks (age 

> 18) 22,000 22,000 22,000 (5,800—39,000) 

Hospital admissions—

respiratory 

(all ages) 

3,500 3,300 3,500 

(1,400—5,500) 

Hospital admissions—

cardiovascular (age > 18) 7,400 7,200 7,500 (5,200—8,900) 

Emergency room visits for 

asthma 

(age < 18) 

14,000 13,000 14,000 

(7,200—21,000) 

Acute bronchitis (age 8-12) 21,000 20,000 21,000 

(-4,800—46,000)c 

Lower respiratory 

symptoms (age 7-14) 250,000 240,000 250,000 (98,000—400,000) 

Upper respiratory 

symptoms 

(asthmatics age 9-18) 

190,000 180,000 190,000 

(36,000—350,000) 

Asthma exacerbation 

(asthmatics 6-18) 230,000 230,000 240,000 (8,300—800,000) 

Lost work days (ages 18-

65) 1,800,000 1,700,000 

1,800,000 

(1,500,000—

2,000,000) 

Minor restricted-activity 

days 

(ages 18-65) 

11,000,000 10,000,000 

10,000,000 

(8,600,000—

12,000,000) 
345

A Estimates rounded to two significant figures; column values will not sum to total value. Confidence intervals 

unavailable for estimate calculated using benefit per ton estimates. 

B

 PM2.5-related health impacts only. Excludes ozone impacts. 

C The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.
346

Table A-3: Estimated PM2.5-related Monetized Benefits of Direct Control and 

Intrastate Trading Options 

Health Effect Direct controlA

Intrastate 

tradingA Proposed RemedyB 

Premature mortality (Pope et al. 2002 mortality estimate) 

 3% discount rate $110 $110 $110 

($8.8—$340) 

7% discount rate $99 $96 $99 

($7.9—$300) 

Premature mortality (Laden et al. 2006 mortality estimate) 

 3% discount rate $280 $270 $280 

($25—$820) 

7% discount rate $250 $240 $250 

($22—$310) 

Chronic Bronchitis $4.2 $4.1 $4.3 

($0.2—$20) 

Non-fatal heart attacks 

 3% discount rate $2.4 $2.4 $2.5 

($0.4—$6) 

7% discount rate $2.4 $2.3 $2.4 

($0.4—$5.9) 

Hospital admissions—

respiratory $0.05 $0.05 $0.06 ($0.03—$0.1) 

Hospital admissions—

cardiovascular $0.2 $0.2 $0.2 ($0.1—$0.3) 

Emergency room visits 

for asthma $0.005 $0.005 $0.005 ($0.002—$0.008) 

Acute bronchitis $0.009 $0.009 $0.009 

(-$0.0004—$0.03)C 

Lower respiratory 

symptoms $0.005 $0.004 $0.005 ($0.002—$0.009) 

Upper respiratory 

symptoms $0.005 $0.005 $0.006 ($0.001—$0.014) 

Asthma exacerbation $0.01 $0.01 $0.012 

($0.001--$0.046) 

Lost work days $0.2 $0.2 $0.2 

($0.19—$0.24) 

Minor restricted-activity 

days $0.6 $0.6 $0.64 ($0.34—$0.97) 
347

Monetized total PM2.5-related benefits (Pope et al. 2002 mortality estimate) 

3% discount rate $120 $110 $120 

($10—$360) 

7% discount rate $110 $100 $110 

($9.2—$330) 

Monetized total PM2.5-related benefits (Laden et al. 2006 mortality benefits) 

3% discount rate $290 $280 $290 

($26—$840) 

7% discount rate $260 $250 $260 

($23—$760) 

A Estimates rounded to two significant figures. Confidence intervals unavailable for estimate calculated 

using benefit per ton estimates. 

B

 PM2.5-related health impacts only. Excludes ozone and visibility-related benefits. 

C

 The negative 5th percentile estimates for certain endpoints are the result of the weak statistical power of 

the study used to calculate these health impacts and do not suggest that increases in air pollution exposure 

result in decreased health impacts.
348

Table A-4: Avoided PM2.5-Related Premature Mortalities for the Proposed Remedy in 

2014 Summarized at the State Level (90 percent confidence intervals) 

State Pope et al. (2002) mortality 

estimate 

Laden et al. (2006) mortality 

estimate 

Alabama

360

(140--580)

930

(510--1300)

Arizona

1.5

(0.57--2.3)

3.8

(2--5.5)

Arkansas

220

(87--360)

570

(310--830)

California

-0.81

(-0.32---1.3)

-2.1

(-1.1---3)

Colorado

-7.6

(-3---12)

-20

(-11---29)

Connecticut

170

(67--280)

440

(240--640)

Delaware

64

(25--100)

160

(90--240)

District of Columbia

38

(15--62)

98

(54--140)

Florida

600

(230--960)

1,500

(840--2200)

Georgia

560

(220--900)

1,400

(780--2100)

Idaho

-0.61

(-0.24---0.98)

-1.6

(-0.85---2.3)

Illinois

720

(280--1100)

1,800

(1000--2700)

Indiana

640

(250--1000)

1600

(890--2400)

Iowa

100

(40--160)

260

(140--380)

Kansas

74

(29--120)

190

(100--280)

Kentucky

610

(240--970)

1,500

(850--2200)

Louisiana

210

(81--330)

530

(290--770)

Maine

26

(10--42)

67

(36--97)

Maryland

490

(190--790)

1,300

(690--1800)

Massachusetts

190

(74--300)

480

(260--700)
349

Michigan

600

(240--960)

1,500

(840--2200)

Minnesota

80

(31--130)

200

(110--300)

Mississippi

230

(90--370)

590

(320--850)

Missouri

370

(150--600)

950

(520--1400)

Montana

-0.33

(-0.13---0.53)

-0.85

(-0.46---1.2)

Nebraska

33

(13--53)

84

(46--120)

Nevada

-0.0003

(-0.0001---0.0005)

-0.0003

(-0.0002---0.0004)

New Hampshire

37

(15--60)

95

(52--140)

New Jersey

550

(220--880)

1,400

(770--2000)

New Mexico

5.9

(2.3--9.5)

15

(8.3--22)

New York

950

(370--1500)

2,400

(1300--3500)

North Carolina

720

(280--1200)

1,800

(1000--2700)

North Dakota

2.7

(1.1--4.4)

7

(3.8--10)

Ohio

1,300

(520--2100)

3,300

(1800--4800)

Oklahoma

120

(48--200)

320

(170--460)

Oregon

-1

(-0.4---1.7)

-2.6

(-1.4---3.8)

Pennsylvania

1,400

(560--2300)

3,600

(2000--5200)

Rhode Island

38

(15--61)

97

(53--140)

South Carolina

330

(130--530)

850

(460--1200)

South Dakota

8.4

(3.3--13)

21

(12--31)

Tennessee

710

(280--1100)

1,800

(990--2600)

Texas

520

(200--840)

1,300

(730--1900)
350

Utah

-1.4

(-0.56---2.3)

-3.7

(-2---5.4)

Vermont

22

(8.5--35)

56

(30--81)

Virginia

700

(270--1100)

1,800

(970--2600)

Washington

-1.7

(-0.68---2.8)

-4.5

(-2.4---6.5)

West Virginia

280

(110--450)

720

(390--1000)

Wisconsin

190

(74--300)

480

(260--700)

Wyoming

-0.12

(-0.048---0.2)

-0.31

(-0.17---0.46)
351

Table A-5: Avoided Ozone-Related Premature Mortalities for the Proposed Remedy in 

2014 Summarized at the State Level (90 percent confidence intervals) 

State Bell et al. (2004) mortality 

estimate 

Levy et al. (2005) mortality 

estimate 

Alabama

5.8

(3.3--8.4)

8.2

(6--10)

Arizona

0.31

(0.17--0.44)

0.43

(0.32--0.55)

Arkansas

5.7

(3.2--8.2)

8

(5.9--10)

California

0.12

(0.056--0.18)

0.17

(0.11--0.22)

Colorado

0.03

(-0.042--0.1)

0.042

(-0.019--0.1)

Connecticut

0.44

(0.25--0.63)

0.62

(0.46--0.78)

Delaware

0.19

(0.1--0.27)

0.26

(0.19--0.33)

District of Columbia

0.11

(0.062--0.16)

0.15

(0.11--0.2)

Florida

22

(12--32)

31

(22--39)

Georgia

6.7

(3.7--9.6)

9.4

(6.9--12)

Idaho

0.007

(0.0039--0.01)

0.0099

(0.0073--0.013)

Illinois

7.7

(4.3--11)

11

(8--14)

Indiana

4.3

(2.4--6.2)

6.1

(4.5--7.7)

Iowa

4.5

(2.5--6.5)

6.3

(4.7--8)

Kansas

6

(3.4--8.6)

8.4

(6.2--11)

Kentucky

3

(1.7--4.3)

4.2

(3.1--5.3)

Louisiana

5.4

(3--7.8)

7.6

(5.6--9.7)

Maine

0.067

(0.038--0.097)

0.095

(0.07--0.12)

Maryland

1.6

(0.87--2.2)

2.2

(1.6--2.8)

Massachusetts

0.37

(0.21--0.54)

0.53

(0.39--0.67)
352

Michigan

5.1

(2.8--7.3)

7.1

(5.3--9)

Minnesota

4.2

(2.4--6.1)

6

(4.4--7.5)

Mississippi

4.2

(2.4--6.1)

5.9

(4.4--7.5)

Missouri

12

(6.7--17)

17

(12--21)

Montana

0.012

(0.007--0.018)

0.018

(0.013--0.022)

Nebraska

2

(1.1--2.8)

2.8

(2--3.5)

Nevada

0.038

(0.021--0.054)

0.053

(0.039--0.067)

New Hampshire

0.11

(0.063--0.17)

0.16

(0.12--0.21)

New Jersey

1.6

(0.91--2.3)

2.3

(1.7--2.9)

New Mexico

0.37

(0.21--0.54)

0.53

(0.39--0.67)

New York

4.2

(2.4--6.1)

6

(4.4--7.6)

North Carolina

3.6

(2--5.2)

5.1

(3.7--6.4)

North Dakota

0.18

(0.1--0.26)

0.26

(0.19--0.33)

Ohio

7.9

(4.4--11)

11

(8.2--14)

Oklahoma

7.2

(4--10)

10

(7.5--13)

Oregon

0.0032

(0.0009--0.0054)

0.0045

(0.0025--0.0064)

Pennsylvania

8

(4.5--11)

11

(8.3--14)

Rhode Island

0.087

(0.049--0.13)

0.12

(0.09--0.15)

South Carolina

2.9

(1.6--4.2)

4.1

(3--5.2)

South Dakota

0.48

(0.27--0.69)

0.68

(0.5--0.85)

Tennessee

4.2

(2.3--6)

5.9

(4.3--7.4)

Texas

11

(6.3--16)

16

(12--20)
353

Utah

0.02

(0.011--0.029)

0.028

(0.021--0.036)

Vermont

0.092

(0.052--0.13)

0.13

(0.095--0.16)

Virginia

2.3

(1.3--3.3)

3.3

(2.4--4.1)

Washington

0.0018

(0.0003--0.0033)

0.0025

(0.0013--0.0038)

West Virginia

2.1

(1.2--3)

3

(2.2--3.7)

Wisconsin

4.8

(2.6--6.9)

6.7

(4.9--8.5)

Wyoming

0.0088

(0.0042--0.014)

0.013

(0.0085--0.016)
354

References 

Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The influence of location, source, and emission 

type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual 

Atmos Health 2:169–176. 

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 

2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. 

Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010. Final Regulatory Impact Analysis 

(RIA) for the NO2 National Ambient Air Quality Standards (NAAQS). Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. January. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf>. 
355

 APPENDIX B 

ECONOMIC IMPACT ANALYSES OUTSIDE OF ELECTRIC POWER SECTOR 

AND SOCIAL COSTS -– ALTERNATE REMEDIES 
356

B.1 Direct Control Remedy Option 

B.1.1 Macroeconomic Variables and Social Costs 

The transport rule will bring about changes in business and household behavior and 

will influence macroeconomic variables (gross domestic product [GDP] and consumption) 

and household economic welfare. In 2015, EMPAX estimates that GDP and consumption 

levels are approximately 0.02% lower ($2.7 billion) (Figure B-1) 85 Since the pollution 

controls vary by region, economic effects also vary by region; for example, Northeast GDP 

falls by 0.04% (Figure B-2). 

Figure B-1. Change in Macroeconomic Variables and Household Welfare (Percent 

change and Change in billion $2006) in 2015

-0.020%

-0.018%

-0.016%

-0.014%

-0.012%

-0.010%

-0.008%

-0.006%

-0.004%

-0.002%

0.000%

-$3.5 

-$3.0 

-$2.5 

-$2.0 

-$1.5 

-$1.0 

-$0.5 

$0.0 

Change ($ billion) Percent Change

Percentage Change

Change ($ billion)

GDP Consumption Hicksian EV (Annual)

Note: GDP represents the dollar value of all goods and services produced in the US in 2015. Consumption is 

the dollar value of all goods and services consumed within the US in 2015. Hicksian EV is the change in 

household economic welfare (defined in Section 8.1.3.1). 

 

85 We use 2015 estimates as a proxy for the impacts of compliance with the proposed rule in 2014. 
357

Figure B-2. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015 

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

Northeast South Midwest Plains West US

Percentage Change

Note: GDP in each region is the dollar value of goods and services produced in the region in 2015. See Figure 8-2 for a 

presentation of the states in each region. 

Average-annual social costs (as measured by Hicksian equivalent variation) are 

approximately 0.01% lower with the transport rule.86 As noted in Chapter 8, EMPAX-CGE 

does not incorporate any environmental benefits associated with air quality improvements. 

As a result, EMPAX welfare measures only approximate the rule’s social cost. Using this 

interpretation, the annual social cost for 2015 is estimated to be $2.9 billion. 

 

86 Values are discounted back to 2010 at the 5% interest rate used in the model. EPA uses a 5% interest rate 

based on the MIT Emissions Prediction and Policy Analysis (EPPA) model and SAB guidance from 2003 as 

discussed in U.S. EPA, Office of Policy Analysis and Review. 2003. “Benefits and Costs of the Clean Air Act 

1990 - 2020: Revised Analytical Plan for EPA's Second Prospective Analysis.” We recognize that this interest 

rate is not one of the interest rates (3 and 7%) that OMB’s Circular A-4 guidance calls for in regulatory 

analyses. . Detailed results for this EMPAX run for the direct control remedy can be found in the file 

“EMPAXresults_direct control remedy,” that is available in the docket for this rule. 
358

B.1.2 Industry Effects 

The proposed rule directly influences the electricity sector’s fuel use and private cost 

expenditures. As the electricity sector responds to these changes, other economy-wide 

changes occur. For example, higher electricity prices may encourage electricity-dependent 

sectors to reduce production levels, switch to other energy sources (e.g., oil) and/or seek 

energy efficiency improvements in their production process. Electricity sectors also make 

additional private cost expenditures in order to comply with the transport rule; these 

expenditures lead to other economy-wide changes. For example each dollar spent to comply 

with the program is used to buy environmental protection goods and services.87 As a result, 

the demand for environmental protection goods and services will be higher with the transport 

rule. For sectors supplying environmental protection goods or services, the secondary effect 

may offset higher electricity costs. The following sections report and discuss output changes 

associated with the impacts of compliance in the year 2015, which serves as a proxy for 

compliance in 2014. 

B.1.2.1 Energy Sectors 

The EMPAX modeling system shows that the electricity sector experiences the most 

significant changes under the transport rule. Electricity output and fuel mix changes used to 

meet the transport rule also influence other energy sectors. For example, U.S. electricity 

output declines by approximately 0.5%, while coal output declines by 0.2%. Similarly, U.S. 

natural gas output declines. Crude oil and petroleum output decline but the changes are 

small; these inputs are less critical to the electricity sector, making them less sensitive to 

changes in electricity production (Figure B-3). 

Given the regional distribution of controls, there are differences in regional output 

changes. For example, electricity production in the Northeast experiences the largest decline 

while the Plains and West electricity sectors see small output increases. Very few States in 

the Plains and no Western States are included in the Transport Rule region, and lack of 

emission controls applied in these regions may mean lower electricity generation and 

dispatch costs relative to such costs to States within the Transport Rule region. This is an 

 

87 Additional details are described in EMPAX-CGE model documentation (5-2 to 5-5).
359

explanation for the small output increases in the Plains and West. Coal output changes to 

meet coal demand predictions from the IPM electricity model and the IPM modeling system 

suggest that the Northeast’s electricity sector uses additional coal inputs to meet the rule’s 

requirements. 

B.1.2.2 Energy-Intensive Sectors 

Energy-intensive manufacturing industries are more sensitive to electricity and other 

energy price changes. Although the net U.S. output change for each energy-intensive 

industry is less than 0.2%, these sectors do show some (economically small) regional 

variation. The most significant regional differences are seen in the aluminum sector, where 

production shifts from the Northeast, South, and Midwest regions to the Plains and West 

regions. Similar geographic shifts are observed in other energy-intensive industries 

(Figure B-4). 

B.1.2.3 Nonenergy Sectors 

Although electricity expenditures represent a small fraction of non-energy sector 

production costs, higher electricity prices still influence non-energy sector production levels. 

However, non-energy sector output effects are very small. National output levels for four 

broad non-energy sectors: agriculture, other manufacturing, services, and transportation fall 

by less than one one-hundredth of a percent (0.01%). There is some regional variation as 

production shifts to areas with lower electricity costs (e.g., West, Plains), but the differences 

are not significant (Figure B-5). 
360

Figure B-3. Output Changes in 2015: Energy Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Coal Crude Oil Electricity Natural Gas Petroleum

Percentage Change

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector 

produces. 
361

Figure B-4. Output Changes in 2015: Energy-Intensive Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Food and 

Kindred

Paper and 

Allied Chemicals Glass Cement Iron and Steel Aluminum

Percentage Change 

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector 

produces. 
362

Figure B-5. Output Changes in 2015: Nonenergy Sectors (Percent)

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Agriculture

Energy-Intensive 

Manufacturing Other Manufacturing Services

Transportation 

Services

Percentage Change

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector produces. 
363

B.2 Intrastate Trading Remedy Option 

B.2.1 Macroeconomic Variables and Social Costs 

The transport rule will bring about changes in business and household behavior and 

will influence macroeconomic variables (gross domestic product [GDP] and consumption) 

and household economic welfare. In 2015, EMPAX estimates that GDP and consumption 

levels are approximately 0.01% lower ($1.1 billion change in GDP and $1.2 billion change in 

consumption) (Figure B-6).88 Since the pollution controls vary by region, economic effects 

also vary by region; for example, Northeast GDP falls by 0.04% (Figure B-7). 

Figure B-6. Change in Macroeconomic Variables and Household Welfare (Percent 

change and Change in billion $2006) in 2015

-0.012%

-0.010%

-0.008%

-0.006%

-0.004%

-0.002%

0.000%

-$1.8 

-$1.6 

-$1.4 

-$1.2 

-$1.0 

-$0.8 

-$0.6 

-$0.4 

-$0.2 

$0.0 

Change ($ billion) Percent Change

Percentage Change

Change ($ billion)

GDP Consumption Hicksian EV (Annual)

Note: GDP represents the dollar value of all goods and services produced in the US in 2015. Consumption is 

the dollar value of all goods and services consumed within the US in 2015. Hicksian EV is the change in 

household economic welfare (defined in Section 8.1.3.1 of this RIA). 

 

88 We use 2015 estimates as a proxy for the impacts of compliance with the proposed rule in 2014. 
364

Figure B-7. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

Northeast South Midwest Plains West US

Percentage Change

Note: GDP in each region is the dollar value of goods and services produced in the region in 2015. See Figure 8-2 for a 

presentation of the states in each region. 

Average-annual social costs (as measured by Hicksian equivalent variation) are 

approximately 0.01% lower with the transport rule.89 As noted in Chapter 8, EMPAX-CGE 

does not incorporate any environmental benefits associated with air quality improvements. 

As a result, EMPAX welfare measures only approximate the rule’s social cost. Using this 

interpretation, the annual social cost for 2015 is estimated to be $2.7 billion. 

 

89 Values are discounted back to 2010 at the 5% interest rate used in the model. EPA uses a 5% interest rate 

based on the MIT Emissions Prediction and Policy Analysis (EPPA) model and SAB guidance from 2003 as 

discussed in U.S. EPA, Office of Policy Analysis and Review. 2003. “Benefits and Costs of the Clean Air Act 

1990 - 2020: Revised Analytical Plan for EPA's Second Prospective Analysis.” We recognize that this interest 

rate is not one of the interest rates (3 and 7%) that OMB’s Circular A-4 guidance calls for in regulatory 

analyses. Detailed results for this EMPAX run for the intrastate trading remedy can be found in the file 

“EMPAXresults_intrastate trading remedy,” that is available in the docket for this rule. 
365

B.2.2 Industry Effects 

The proposed rule directly influences the electricity sector’s fuel use and private cost 

expenditures. As the electricity sector responds to these changes, other economy-wide 

changes occur. For example, higher electricity prices may encourage electricity-dependent 

sectors to reduce production levels, switch to other energy sources (e.g., oil) and/or seek 

energy efficiency improvements in their production process. Electricity sectors also make 

additional private cost expenditures in order to comply with the transport rule; these 

expenditures lead to other economy-wide changes. For example each dollar spent to comply 

with the program is used to buy environmental protection goods and services.90 As a result, 

the demand for environmental protection goods and services will be higher with the transport 

rule. For sectors supplying environmental protection goods or services, the secondary effect 

may offset higher electricity costs. The following sections report and discuss output changes 

associated with the impacts of compliance in the year 2015, which serves as a proxy for 

compliance in 2014. 

B.2.2.1 Energy Sectors 

The EMPAX modeling system shows that the electricity sector experiences the most 

significant changes under the transport rule. Electricity output and fuel mix changes used to 

meet the transport rule also influence other energy sectors. For example, U.S. electricity 

output declines by approximately 0.3%, while coal output declines by 0.4%. Similarly, U.S. 

natural gas output declines. Crude oil and petroleum output decline but the changes are 

small; these inputs are less critical to the electricity sector, making them less sensitive to 

changes in electricity production (Figure B-8). 

Given the regional distribution of controls, there are differences in regional output 

changes. For example, electricity production in the Northeast experiences the largest decline 

while the Plains and West electricity sectors see small output increases. Very few States in 

the Plains and no Western States are included in the Transport Rule region, and lack of 

emission controls applied in these regions may mean lower electricity generation and 

 

90 Additional details are described in EMPAX-CGE model documentation (5-2 to 5-5).
366

dispatch costs relative to such costs to States within the Transport Rule region. This is an 

explanation for the small output increases in the Plains and West. Coal output changes to 

meet coal demand predictions from the IPM electricity model and the IPM modeling system 

suggest that the Northeast’s electricity sector uses additional coal inputs to meet the rule’s 

requirements. 

B.2.2.2 Energy-Intensive Sectors 

Energy-intensive manufacturing industries are more sensitive to electricity and other 

energy price changes. Although the net U.S. output change for each energy-intensive 

industry is less than 0.2%, these sectors do show some (economically small) regional 

variation. The most significant regional differences are seen in the aluminum sector, where 

production shifts from the Northeast, South, and Midwest regions to the Plains and West 

regions. Similar geographic shifts are observed in other energy-intensive industries 

(Figure B-9). 

B.2.2.3 Nonenergy Sectors 

Although electricity expenditures represent a small fraction of non-energy sector 

production costs, higher electricity prices still influence non-energy sector production levels. 

However, non-energy sector output effects are very small. National output levels for four 

broad non-energy sectors: agriculture, other manufacturing, services, and transportation fall 

by less than one one-hundredth of a percent (0.01%). There is some regional variation as 

production shifts to areas with lower electricity costs (e.g., West, Plains), but the differences 

are not significant (Figure B-10). 
367

Figure B-8. Output Changes in 2015: Energy Sectors (Percent) 

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Coal Crude Oil Electricity Natural Gas Petroleum

Percentage Change

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector 

produces. 
368

Figure B-9. Output Changes in 2015: Energy-Intensive Sectors (Percent) 

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Food and 

Kindred

Paper and 

Allied Chemicals Glass Cement Iron and Steel Aluminum

Percentage Change 

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector 

produces. 
369

Figure B-10. Output Changes in 2015: Nonenergy Sectors (Percent) 

-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Northeast

South

Midwest

Plains

West

US

Agriculture

Energy-Intensive 

Manufacturing Other Manufacturing Services

Transportation 

Services

Percentage Change

Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector 

produces. 
370

APPENDIX C 

COMPARISON OF STATE LEVEL ELECTRICAL GENERATING UNIT 

EMISSIONS UNDER 

VARIOUS REGULATORY ALTERNATIVES TO 

REDUCE SO2 AND NOx EMISSIONS 

UNDER THE TRANSPORT RULE
371

ELECTRICAL GENERATING UNIT SO2 Emissions for Base Case and Regulatory 

Alternatives (tons)

Base Case 

State Budgets/ 

Limited Trading 

State Budgets/ 

Intrastate Trading Direct Control 

State 2012 2014 2012 2014 2012 2014 2012 2014 

Alabama AL 335,734 322,130 185,518 172,197 161,871 161,870 172,198 172,198 

Connecticut CT 5,493 5,512 2,560 2,586 2,713 2,560 2,713 2,560 

Delaware DE 6,918 6,883 7,979 7,996 7,784 7,784 7,991 7,920 

District of Columbia DC 0 0 0 0 0 0 0 0 

Florida FL 228,360 192,903 136,373 137,985 161,739 157,300 120,257 133,546 

Georgia GA 551,326 172,529 267,644 91,648 226,736 88,979 247,709 91,648 

Illinois IL 721,155 197,265 251,084 160,197 208,957 151,530 222,003 159,939 

Indiana IN 806,825 786,593 296,182 214,022 281,693 240,855 393,403 214,022 

Iowa IA 158,443 149,271 91,536 86,892 92,045 87,092 100,464 92,040 

Kansas KS 59,492 65,035 44,527 51,150 57,275 52,403 49,089 53,185 

Kentucky KY 716,807 737,962 222,246 121,399 195,480 125,879 232,552 121,398 

Louisiana LA 98,110 92,695 93,169 92,763 90,477 90,477 93,579 94,220 

Maryland MD 49,078 42,636 39,566 42,756 34,451 42,604 38,138 43,051 

Massachusetts MA 16,300 16,300 8,987 9,340 7,902 7,630 8,064 7,943 

Michigan MI 283,616 268,916 200,547 165,644 200,848 180,919 239,676 165,645 

Minnesota MN 47,090 53,910 40,142 41,103 41,471 41,525 46,979 50,802 

Missouri MO 428,394 481,531 171,496 168,911 168,282 167,587 185,948 168,911 

Nebraska NE 119,258 114,163 73,937 73,473 71,598 71,598 75,466 73,529 

New Jersey NJ 36,116 36,038 13,031 12,925 11,291 11,291 12,925 12,925 

New York NY 131,360 129,757 82,450 45,450 65,968 40,368 71,366 45,450 

North Carolina NC 117,264 131,291 96,166 87,567 75,825 99,689 108,931 87,567 

Ohio OH 936,919 802,942 270,343 189,582 275,549 226,395 440,986 189,583 

Pennsylvania PA 963,947 970,705 270,570 150,855 281,189 179,277 302,447 150,855 

South Carolina SC 145,171 149,157 130,457 124,190 116,483 116,483 124,189 124,189 

Tennessee TN 596,987 600,066 127,175 106,762 100,007 100,007 106,762 94,073 

Virginia VA 132,093 122,393 93,143 44,136 72,595 37,439 77,768 44,137 

West Virginia WV 587,667 495,573 125,333 126,869 119,546 146,804 209,488 126,869 

Wisconsin WI 99,464 105,230 72,392 71,514 75,933 73,438 88,716 71,514 

Arizona AZ 22,773 20,944 24,927 23,477 26,072 23,477 25,457 23,477 

Arkansas AR 85,068 88,187 117,046 119,945 123,920 120,427 116,494 115,389 

California CA 3,307 3,307 3,307 3,307 3,307 3,307 3,307 3,307 

Colorado CO 69,273 69,184 82,964 84,835 82,950 85,042 82,964 88,779 

Idaho ID 0 1 0 0 0 0 0 0 

Maine ME 15,375 11,650 15,921 11,669 16,658 11,669 16,244 11,675 

Mississippi MS 41,304 43,020 59,568 57,228 59,550 57,228 57,147 54,307 

Montana MT 15,892 16,863 18,128 19,093 18,128 19,093 18,097 18,274 

Nevada NV 13,323 20,155 13,288 20,531 13,288 20,531 13,288 21,416 

New Hampshire NH 7,290 6,608 7,290 7,290 7,290 7,290 7,290 7,290 

New Mexico NM 12,684 13,210 12,391 12,529 12,684 12,754 12,617 11,845 

North Dakota ND 77,383 80,320 88,321 88,321 88,321 85,649 84,835 85,649 

Oklahoma OK 156,032 165,773 159,773 165,994 159,773 165,994 159,773 165,905 
372

Oregon OR 14,381 13,366 20,306 20,187 20,028 20,187 14,381 20,187 

Rhode Island RI 0 0 0 0 0 0 0 0 

South Dakota SD 12,121 12,127 18,377 27,565 18,377 27,565 12,121 27,565 

Texas TX 327,726 373,803 463,908 467,617 463,538 514,641 442,319 481,621 

Utah UT 24,972 25,414 26,124 29,117 26,476 29,266 26,476 27,807 

Vermont VT 0 0 0 0 0 0 0 0 

Washington WA 19,663 19,155 19,663 18,863 19,663 18,863 19,663 18,793 

Wyoming WY 52,774 51,254 57,169 56,276 57,229 57,711 57,223 56,276 

Nationwide total 9,350,726 8,283,726 4,623,022 3,833,752 4,422,958 3,990,475 4,949,497 3,839,283 

This table shows the SO2 emissions for each state in the contiguous US that result from the base case and the 

main control options. Notably, states adjacent to the Transport region states can have emissions increase to 

some degree due to change in relative dispatch economics from where the border exists for the Transport region. 

 Note that in the West the increased emissions result from the Court’s decision to not allow the use of Title IV 

allowances in this program and the resulting collapse of the ARP trading market. This occurs in both direct 

control and trading cases. 

Emissions are for fossil EGUs with capacity greater than 25 MW.
373

Electrical Generating Unit Annual NOx Emissions for Base Case and Regulatory 

Alternatives (tons)A

Base Case 

State Budgets/ 

Limited Trading 

State Budgets/ 

Intrastate Trading Direct Control 

State 2012 2014 2012 2014 2012 2014 2012 2014 

Alabama AL 121,772 118,376 68,589 61,214 68,785 61,039 68,814 61,286 

Connecticut CT 2,753 2,793 2,722 2,805 2,750 2,787 2,725 2,805 

Delaware DE 4,277 4,151 4,464 4,572 4,557 4,517 4,470 4,464 

District of Columbia DC 2 1 2 1 2 1 2 1 

Florida FL 194,872 179,796 112,954 109,578 110,610 109,489 81,614 78,069 

Georgia GA 77,815 47,897 74,486 44,092 73,586 44,093 75,646 44,242 

Illinois IL 77,545 79,795 53,669 56,905 48,404 54,090 49,128 57,294 

Indiana IN 201,042 198,802 109,788 110,015 112,544 110,234 113,942 106,203 

Iowa IA 62,241 63,426 48,330 48,522 46,068 45,567 47,395 47,612 

Kansas KS 70,779 78,672 36,291 39,660 38,907 39,715 36,521 39,212 

Kentucky KY 149,032 148,360 73,614 71,148 74,046 71,469 75,800 69,673 

Louisiana LA 43,892 44,607 35,068 36,310 34,989 35,649 35,078 36,219 

Maryland MD 17,063 19,772 17,065 19,849 16,967 17,268 17,064 20,022 

Massachusetts MA 6,201 6,431 6,616 6,797 5,960 5,960 6,624 6,811 

Michigan MI 95,021 95,073 62,226 60,614 62,257 61,163 64,728 63,565 

Minnesota MN 48,892 49,444 32,589 32,970 33,546 33,115 31,868 32,214 

Missouri MO 74,492 79,515 69,168 63,475 57,681 57,682 63,898 63,898 

Nebraska NE 51,597 51,711 32,612 33,718 31,842 32,722 33,591 34,140 

New Jersey NJ 15,285 15,548 11,816 12,002 11,525 11,956 11,780 11,914 

New York NY 22,456 24,850 22,659 24,961 22,031 22,917 22,171 24,963 

North Carolina NC 59,714 59,781 59,688 57,678 51,800 51,800 57,678 57,678 

Ohio OH 156,728 161,040 95,594 94,882 96,662 91,566 102,774 96,927 

Pennsylvania PA 191,749 194,916 112,909 113,620 113,455 113,016 115,684 113,381 

South Carolina SC 46,560 46,310 34,121 33,302 32,703 32,349 33,202 33,184 

Tennessee TN 68,543 68,890 28,460 28,188 27,655 26,850 28,482 28,849 

Virginia VA 32,571 28,705 31,415 26,999 27,423 26,932 29,664 28,191 

West Virginia WV 102,251 99,623 52,587 47,683 51,990 48,489 57,878 50,057 

Wisconsin WI 45,904 49,489 35,559 35,946 36,384 36,576 37,095 36,940 

Arizona AZ 80,943 73,100 80,943 73,053 80,943 73,058 80,943 73,065 

Arkansas AR 43,134 44,703 25,255 26,173 26,536 26,308 25,162 25,812 

California CA 17,539 15,872 17,535 15,905 17,535 15,903 17,535 15,902 

Colorado CO 59,357 59,454 59,754 59,755 59,789 59,779 59,754 59,686 

Idaho ID 397 398 397 397 397 397 397 397 

Maine ME 3,036 2,534 3,104 2,530 3,246 2,594 3,081 2,552 

Mississippi MS 37,016 36,634 22,510 22,598 22,540 22,599 22,510 22,596 

Montana MT 36,761 36,800 36,764 36,789 36,764 36,789 36,762 36,790 

Nevada NV 19,893 29,115 19,891 29,117 19,891 29,117 19,891 29,117 

New Hampshire NH 2,515 2,515 2,444 2,456 2,265 2,456 2,144 2,378 

New Mexico NM 51,134 51,160 51,134 51,178 51,134 51,178 51,134 51,171 

North Dakota ND 59,551 59,559 59,551 59,551 59,551 59,551 59,551 59,551 
374

Oklahoma OK 86,661 80,886 54,584 50,151 54,494 50,170 54,584 50,105 

Oregon OR 13,780 13,889 13,782 13,889 13,782 13,889 13,782 13,889 

Rhode Island RI 220 280 208 276 254 305 222 262 

South Dakota SD 15,116 15,137 15,116 15,132 15,116 15,132 15,116 15,132 

Texas TX 159,170 165,765 140,046 147,556 140,225 147,654 139,281 145,248 

Utah UT 64,074 64,088 64,074 64,070 64,074 64,070 64,074 64,070 

Vermont VT 0 0 0 0 0 0 0 0 

Washington WA 18,214 18,374 18,213 18,359 18,213 18,362 18,213 18,350 

Wyoming WY 72,956 72,963 72,956 72,953 72,965 72,953 72,955 72,953 

Nationwide total 2,882,511 2,860,999 2,083,320 2,039,392 2,054,845 2,011,274 2,062,406 2,008,837 

AEmissions are for fossil EGUs with capacity greater than 25 MW.
375

Electrical Generating Unit Ozone Season NOx Emissions for Base Case and Regulatory 

Alternatives (tons)A

Base Case 

State Budgets/ 

Limited Trading 

State Budgets/ 

Intrastate Trading Direct Control 

State 2012 2014 2012 2014 2012 2014 2012 2014 

Alabama AL 29,676 26,730 29,428 26,461 29,655 26,287 29,552 26,557 

Arkansas AR 20,420 21,529 11,715 11,943 11,772 11,957 11,626 11,939 

Connecticut CT 1,198 1,203 1,169 1,210 1,177 1,203 1,172 1,203 

Delaware DE 1,767 1,675 1,876 1,991 1,973 1,991 1,881 1,932 

District of Columbia DC 1 1 1 1 1 1 1 1 

Florida FL 94,007 87,324 59,509 54,860 56,939 54,675 46,142 40,808 

Georgia GA 35,036 21,789 32,615 19,529 32,144 19,530 33,131 19,591 

Illinois IL 24,085 23,881 22,393 24,644 19,796 22,547 20,754 24,415 

Indiana IN 49,967 48,053 46,204 46,482 47,827 46,817 48,035 44,594 

Kansas KS 30,535 34,284 15,477 17,218 16,672 17,337 15,485 17,029 

Kentucky KY 30,907 29,843 30,161 29,286 30,697 29,581 31,189 29,023 

Louisiana LA 21,188 20,980 16,617 16,924 16,693 16,643 16,622 16,844 

Maryland MD 7,219 8,310 7,186 8,391 7,089 7,198 7,186 8,435 

Michigan MI 28,038 28,119 25,917 25,498 25,819 25,773 28,013 27,600 

Mississippi MS 16,482 16,547 8,080 8,116 8,110 8,118 8,080 8,114 

New Jersey NJ 5,254 5,501 5,209 5,441 5,007 5,400 5,257 5,363 

New York NY 10,622 11,859 10,686 12,012 10,399 11,049 10,457 12,010 

North Carolina NC 25,831 25,765 25,800 24,852 22,498 22,194 24,799 24,739 

Ohio OH 40,641 43,099 40,631 39,337 40,490 38,263 43,592 41,113 

Oklahoma OK 42,871 38,249 27,503 24,175 27,503 24,174 27,503 24,129 

Pennsylvania PA 47,841 48,900 48,531 48,745 48,271 48,271 49,934 48,219 

South Carolina SC 15,223 15,111 14,584 14,199 14,251 14,047 14,196 14,134 

Tennessee TN 11,623 12,010 11,612 11,858 11,272 10,945 11,634 11,997 

Texas TX 78,315 79,118 66,442 68,314 66,437 68,420 65,676 67,377 

Virginia VA 13,861 12,494 13,648 11,601 12,607 11,491 13,417 12,294 

West Virginia WV 23,803 24,149 22,948 20,034 22,234 21,251 24,112 21,285 

Arizona AZ 35,296 32,672 35,296 32,625 35,296 32,630 35,296 32,637 

California CA 8,679 7,087 8,676 7,105 8,676 7,103 8,676 7,102 

Colorado CO 25,852 25,861 25,949 26,087 25,949 26,111 25,949 26,019 

Idaho ID 135 135 135 135 135 135 135 135 

Iowa IA 26,663 27,523 20,712 20,829 19,649 19,648 20,417 20,452 

Maine ME 964 963 1,022 958 1,152 994 1,043 964 

Massachusetts MA 2,489 2,646 2,836 2,825 2,472 2,547 2,844 2,829 

Minnesota MN 21,153 21,544 13,993 14,082 14,662 14,224 13,865 14,054 

Missouri MO 32,584 34,641 30,760 27,881 27,691 25,439 30,189 28,516 

Nebraska NE 22,551 22,715 14,071 14,537 13,709 14,216 14,381 14,960 

Montana MT 16,077 16,109 16,078 16,104 16,078 16,104 16,078 16,105 

Nevada NV 9,216 13,226 9,215 13,223 9,215 13,223 9,215 13,223 

New Hampshire NH 1,134 1,134 1,063 1,068 893 1,068 772 1,008 

New Mexico NM 22,561 22,438 22,561 22,446 22,561 22,446 22,561 22,443 
376

North Dakota ND 26,029 26,033 26,029 26,029 26,029 26,029 26,029 26,029 

Oregon OR 5,398 5,537 5,401 5,537 5,401 5,537 5,401 5,537 

Rhode Island RI 93 103 89 103 111 105 89 103 

South Dakota SD 6,626 6,644 6,626 6,642 6,626 6,642 6,626 6,642 

Utah UT 28,076 28,084 28,076 28,076 28,076 28,076 28,076 28,076 

Vermont VT 0 0 0 0 0 0 0 0 

Washington WA 7,152 7,437 7,152 7,424 7,152 7,428 7,152 7,417 

Wisconsin WI 19,422 21,329 15,465 15,484 15,445 15,736 15,754 16,074 

Wyoming WY 31,848 31,851 31,848 31,847 31,854 31,847 31,848 31,847 

Nationwide total 1,056,410 1,042,235 918,994 894,168 906,160 882,453 911,841 882,917 

AEmissions are for fossil EGUs with capacity greater than 25 MW.
377

APPENDIX D 

INTEGRATED PLANNING MODEL RUNS 
378

Table A-1 lists the Integrated Planning Model (IPM) runs used in analyses presented 

in Chapters 7 and 10. Table A-2 lists the IPM parsed files used in air quality and health 

benefits analyses.91 Chapters 7 and 10 describe the IPM runs in greater detail. The IPM runs 

and parsed files can be found in the docket for this rulemaking (Docket ID No. EPA-HQOAR-2009-0491). 

 

91 Whereas IPM output files report aggregated results for "model" plants (i.e., aggregates of generating units 

with similar operating characteristics), parsed files show IPM results at the generating unit level.
379

Table A-1. IPM Runs Used in Transport Rule Analyses 

Run Name Run Description 

Transport Rule Base Case 

TR_Base_Case Base Case model run, which includes the 

national Title IV SO2 cap-and-trade program; 

NOx SIP Call regional ozone season capand-trade program; and settlements and state 

rules through February 3, 2009. This run 

represents conditions without the proposed 

Transport Rule and without the rule it would 

replace (CAIR). 

Transport Rule remedy options*† 

TR_SB_Limited_Trading This run models the State Budgets/Limited 

Trading proposed remedy described in the 

Transport Rule preamble. 

TR_SB_Intrastate_Trading This run models the State Budgets/Intrastate 

Trading alternative remedy described in the 

Transport Rule preamble. 

TR_Direct_Control This run models the Direct Control 

alternative remedy described in the Transport 

Rule preamble. 

Additional runs used for analysis of scenarios in Regulatory Impact 

Analysis*† 

TR_A-4_less_stringent This run models the less stringent scenario 

described in Chapter 10 of the proposed 

Transport Rule RIA. 

TR_A-4_more_stringent This run models the more stringent scenario 

described in Chapter 10 of the proposed 

Transport Rule RIA. *

In addition to base case assumptions, these runs include additional control options for units between 25 and 

100 MW capacity. See IPM documentation for more details. 

† In addition to base case assumptions, these runs include unit-specific adjustments based on recent emissions 

data for 33 units. See IPM documentation for more details. 
380

Table A-2. IPM Parsed Files Used in Transport Rule Analyses

Run with Parsed Results Years Parsed 

TR_Base_Case 2012, 2014 

TR_SB_Limited_Trading 2012, 2014 

TR_SB_Intrastate_Trading 2012, 2014 

TR_Direct_Control 2012, 2014 
381

APPENDIX E 

ALLOWANCE VALUES FOR EMISSIONS TRADING PROGRAMS 
382

Allowance Values for Emissions Trading Programs 

As discussed in Chapter 7 above, the proposed State Budgets/Limited Trading 

remedy and alternative State Budgets/Intrastate Trading remedy both include emissions 

trading programs. State Budgets/Limited Trading features programs for annual NOx,

92

ozone-season NOx,

93 annual SO2 for the 15 Group 1 states,94 and annual SO2 for the 13 

Group 2 states.95 In contrast, State Budgets/Intrastate Trading includes separate emissions 

trading programs for each pollutant in each affected state, a total of 82 programs. 

Tables E-1 through E-3 below show the projected allowance values resulting from 

modeling of these remedy options using the Integrated Planning Model (IPM). Values for 

SO2 reflect the marginal cost of reducing SO2, including the operation of dispatchable flue 

gas desulfurization controls (FGD). Section 6 of the TSD “Updates to EPA Base Case v3.02 

EISA Using the Integrated Planning Model” contains details on the definition and 

determination of dispatchable controls. 

Similarly, NOx allowance values reflect the marginal cost of reducing NOx, including 

the variable operation and maintenance costs of existing controls.96 For example, an 

allowance price of $500 per ton (reflecting variable cost of operating SCRs) for annual NOx

 

92 The 28 states in this program are Alabama, Connecticut, Delaware, District of Columbia, Florida, Georgia, 

Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, 

Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, 

Virginia, West Virginia, and Wisconsin.

93 The 26 states in this program are Alabama, Arkansas, Connecticut, Delaware, District of Columbia, Florida, 

Georgia, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, New Jersey, New 

York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, and West 

Virginia.

94 Group 1 states are Georgia, Illinois, Indiana, Iowa, Kentucky, Michigan, Missouri, New York, North 

Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and Wisconsin.

95 Group 2 states are Alabama, Connecticut, Delaware, District of Columbia, Florida, Kansas, Louisiana, 

Maryland, Massachusetts, Minnesota, Nebraska, New Jersey, and South Carolina.

96 Because the variable operating cost of an SCR is very similar for most SCRs (about $500/ton), EPA’s 

modeling of dispatchable SCRs assumes that those SCRs are operated under a cap, rather than modeling each 

economic decision independently. Because these costs are not then factored into IPM’s marginal cost 

calculation, EPA exogenously assigns an allowance price of $500/ton if the projected NOx allowance price in 

IPM is less than $500/ton.
383

indicates that compliance can be achieved through year-round operation of existing advanced 

NOx controls without the use of more expensive compliance options, such as incrementally 

installing new post-combustion controls. Rules for determining whether NOx controls in the 

model operate year-round can be found in the Chapter 3 Appendix of the Documentation for 

EPA Base Case 2004 Using IPM (http://www.epa.gov/airmarkets/progsregs/epaipm/docs/bc3appendix.pdf). 

Table E-1. Projected Regional Allowance Prices for State Budgets/Limited Trading 

Preferred Approach 

(2006$) 

2012 2014

Annual NOx $500 $500

Ozone-season NOx $500 $500

Annual Group 1 $1,000 $1,100

SO2 Group 2 $800 $300

Source: EPA 2010 

For SO2 allowances, in group 1 states, allowance prices are lower than $2,000/ton in 

2014 for two reasons (note that all allowance prices are across all states in the applicable 

trading region). First, because of banking allowances in 2012 and 2013, less reduction is 

necessary in 2014. Second, because of the interstate trading companies have more flexibility 

to take advantage of the lowest cost reduction opportunities. Group 2 allowances are lower 

than $500/ton in 2014, because in some states, additional SO2 controls are installed between 

2012 and 2014 due to requirements outside of this rule. Allowance prices are higher than 

$500/ton in 2012 because EPA state budgets were based on the performance of units in 2009 

(which reflects improved performance of units complying with Phase I of the CAIR NOx

program and banking substantially for the CAIR SO2 program), while modeling in IPM did 

not reflect all of those recent improvements. Actual improved performance that was used to 

develop the budgets is therefore not always reflected in the model (e.g. better performance of 

low NOx burners and scrubbers and access to lower sulfur coals). In its final modeling, EPA 
384

will update IPM to reflect the better unit performance seen in 2009. This will likely result in 

lower allowance prices. 

Table E-2. Projected Annual NOx and SO2 Allowance Prices for State 

Budgets/Intrastate Trading Alternative (2006$) 

 Annual NOx Annual SO2

 2012 2014 2012 2014 

Alabama AL $500 $500 $1,400 $1,100 

Connecticut CT $2,900 $3,400 $097 $0 

Delaware DE $500 $500 $4,400 $3,100 

Florida FL $500 $500 $700 $300 

Georgia GA $500 $500 $1,700 $2,000 

Illinois IL $1,500 $1,800 $2,500 $1,500 

Indiana IN $500 $500 $1,300 $1,500 

Iowa IA $500 $600 $900 $1,000 

Kansas KS $500 $500 $100 $100 

Kentucky KY $500 $500 $1,500 $1,700 

Louisiana LA $500 $500 $1,900 $1,900 

Maryland MD $6,800 $8,000 $1,400 $1,700 

Massachusetts MA $17,700 $5,600 $3,200 $2,200 

Michigan MI $500 $500 $1,500 $1,800 

Minnesota MN $500 $500 $200 $200 

Missouri MO $1,700 $1,200 $1,000 $1,200 

Nebraska NE $500 $500 $1,200 $800 

New Jersey NJ $500 $500 $6,000 $2,200 

New York NY $500 $500 $2,200 $2,600 

North Carolina NC $7,100 $2,700 $1,200 $1,400 

Ohio OH $600 $700 $1,100 $1,300 

Pennsylvania PA $500 $600 $1,100 $1,300 

South Carolina SC $700 $800 $1,700 $1,300 

Tennessee TN $1,600 $1,800 $1,800 $1,700 

 

97 In Connecticut IPM’s original modeling (which was used in the determination of significant contribution) did 

not account for the fact that Connecticut’s largest coal unit uses a significantly lower sulfur coal than normally 

projected by IPM. In the final cost modeling, EPA adjusted the coals assigned to this unit to reflect the sulfur 

content of the coal actually used. This results in Connecticut meeting its budget with an allowance cost of 

$0/ton (e.g. without making any emission reductions). In modeling Connecticut in the final rule, EPA will 

account for the lower sulfur content coal throughout the entire analytic process. This could result in a lower SO2

budget for Connecticut. 
385

 Annual NOx Annual SO2

 2012 2014 2012 2014 

Virginia VA $1,000 $1,200 $2,600 $1,800 

West Virginia WV $1,300 $600 $1,100 $1,300 

Wisconsin WI $500 $500 $1,300 $1,500 

Source: EPA, 2010 

 While most of the allowance prices seen in the state-by-state modeling for the 

Intrastate Trading alternative are consistent with the allowance prices that would be expected 

based on EPA’s significant contribution modeling, there are some exceptions. For example, 

while two-thirds of the states have NOx allowance prices near $500/ton, a number of states 

do see higher allowance prices. This is because the budgets were based on the lower of IPM 

emission projections or projections of emissions using actual unit performance data (as 

explained in the Technical Support Document – “State Budgets, Unit Allocations and Unit 

Emission Rates”). Because IPM has not been updated to reflect actual NOx emission rates 

seen in 2009, IPM projects that it will be harder to meet those budgets than the most recent 

real world data shows it will be (this is why EPA considered the most current data as well as 

the IPM projections in setting the budgets). Between proposal and final rulemaking, EPA 

will be updating IPM to reflect these lower emission rates. This also occurs in some of the 

SO2 budgets. It is most prevalent in either smaller states or states with a larger percentage of 

well-controlled units, where the marginal cost curve for the remaining uncontrolled units is 

steeper and allowance prices are most sensitive to small changes in budgets. EPA will be 

revising IPM to reflect the most recent real world data between proposal and final. 
386

Table E-3. Projected Ozone-season NOx Allowance Prices for State Budgets/Intrastate 

Trading Alternative (2006$)

 2012 2014 

Alabama AL $500 $500

Arkansas AR $500 $500

Connecticut CT $500 $500

Delaware DE $500 $500

Florida FL $500 $500

Georgia GA $700 $500

Illinois IL $1,000 $1,200

Indiana IN $500 $500

Kansas KS $500 $500

Kentucky KY $500 $500

Louisiana LA $500 $500

Maryland MD $500 $500

Michigan MI $500 $500

Mississippi MS $500 $500

New Jersey NJ $500 $500

New York NY $1,100 $1,300

North Carolina NC $500 $500

Ohio OH $500 $500

Oklahoma OK $500 $500

Pennsylvania PA $1,300 $700

South Carolina SC $500 $500

Tennessee TN $1,100 $1,300

Texas TX $500 $500

Virginia VA $500 $500

West Virginia WV $500 $500

Source: EPA 2010