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
-------
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
ii
-------
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
111
-------
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 Ill
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 N02-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
iv
-------
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 Di stributi on 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
v
-------
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
vi
-------
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 S02 and NOx
Emissions Under The Transport Rule 370
Appendix D: Integrated Planning Model Runs 377
Appendix E: Allowance Values for Emissions Trading Programs 381
vii
-------
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 tol45 to 1,
or 55 to 130 to 1. The benefits are primarily from 14,000 to 36,000 fewer PM2.5 and ozone-
related 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 $2.8 billion, higher
than the social costs. Consideration of the above benefit cost ratios and analysis of a greater
SO2 control suggests that, if EPA could require additional emission reductions, there could
be greater net benefits. Notably, since the proposed rule expedites installation of pollution
controls in 2012 that were formerly happening by 2014, the benefits of the Transport Rule in
2012 are actually even greater at the outset of the program.
The benefits and costs in 2014 of the preferred remedy (State Budgets/Limited
Trading) in the proposed rule are in Table 1-1. This preferred remedy covers the electric
1
-------
power industry and allows intrastate emissions trading of sulfur dioxide (SO2) and nitrogen
oxides (NOx) and limited interstate trading of these are pollutants in 32 eastern states.1
Table 1-1. Summary of EPA's Estimates of Benefits, Costs, and Net Benefits of the
Preferred Remedy in the Proposed Transport Rule in 2014a (billions of 2006$)
„ . . Estimate Estimate
Description
(3% Discount Rate) (7% Discount Rate)
Social costsb $2.03 $2.23
Social benefits°'d $120 to $290 + B $110 to $270 + B
Health-related benefits: $120 to $290 + B $110 to $260 + B
Visibility benefits6 $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, unqualified 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.
0 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 SAB-
recommended 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 C02-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.
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.
2
-------
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
3
-------
Table 1-2. Proposed Transport Rule: Estimated Reduction in Incidence of Adverse
Health Effects in 2014 for the Proposed Remedya'b
Within transport
Beyond transport
Health Effect
region
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
59
0.3
59
(< 1 year)
(-66—180)b
(-0.3—0.8)
(-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)
4
-------
Ozone-related endpoints
Premature mortality
&
Bell et al. (2004) (all ages)
50
(16—83)
0.6
(0.2-1)
5(1
(1" X4)
V
J s s
Schwartz et al. (2005)
(all ages)
76
(23—130)
1
(0.2—2)
(24 I'D)
Huang et al. (2005)
(all ages)
83
(31—130)
1
(0.3—2)
84
(-1 I4D)
in
<£>
Cfl
13
i
$
Ito et al. (2005)
(all ages)
220
(130—310)
3
(2—4)
: "0
(140 -20)
Bell et al. (2005) (all ages)
160
(76—250)
2
(1-3)
l<>()
("" 250)
<) -001
Hospital admissions—respiratory
causes
(ages > 65)
380
(-18—730)
4
(-0.4—9)
-')o
i-IS "4oi
Hospital admissions—respiratory
causes(ages <2)
290
(130—460)
4
(1-6)
-00
(l-o 4(>0)
Emergency room visits for asthma (all
ages)
230
(-30—730)
2
(-0.4—8)
2-o
<--o "-())
Minor restricted-activity days (ages
18-65)
300,000
(120,000—480,000)
3,700
(1,300—6,100)
-00.000
( I -0.000
4SO.OOO)
School absence days
110,000
(38,000—160,000)
1,300
(380—2,100)
1 lo.ooo
1 -X.()()()
1 (•().()()() i
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.
5
-------
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
region3
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)
SI 10
(SSS SUM)
7% discount rate
PM2 5 & 03
$100
($7.9—$300)
$0.09
($0.07—$2.7)
SUM)
(S" ') S^OO)
Premature Mortality (Laden et al. 2006 PM mortality and Levy et al. 2005 ozone
mortality estimates)
3% discount rate
PM25 & 03
$280
($25—$810)
$2.5
($0.2—$7.3)
S2XO
(S25 sx:<))
7% discount rate
PM2 5 & O3
$250
($22—$300)
$2.3
($0.2—$6.6)
S2<>0
is:: s" Hi)
Chronic Bronchitis
pm25
$4.3
($0.2—$20)
$0.04
($0.002~$0.2)
S4 ^
iso: s:ui
Non-fatal heart attacks
3% discount rate
pm25
$2.5
($0.4—$6)
$0.03
($0.005—$0.07)
s:.5
(SO 4 S(|)
7% discount rate
pm25
$2.4
($0.4—$5.9)
$0.03
($0.005—$0.07)
S2 4
(SO 4 S5 '))
Hospital admissions—
respiratory
PM2 5 & O3
$0.06
($0.03—$0.1)
$0.00006
($0.00003—$0,001)
SO. Oil
(SO.O ^ SO 1 )
Hospital admissions—
pm25
$0.2
$0,002
so:
cardiovascular
($0.1—$0.3)
($0.001—$0,003)
(SO 1 SO })
Emergency room visits for
asthma
PM2 5 & O3
$0,005
($0.002—$0,008)
...
SO 005
(SO 002 SO DOS)
Acute bronchitis
pm25
$0,009
(-$0.0004—$0.03)
...
SO DO1)
(-SO 0004 SIID'I
Lower respiratory
symptoms
pm25
$0,005
($0.002—$0,009)
...
so 005
(SO 002 SO DO1))
Upper respiratory
pm25
$0,006
SO ()()(>
symptoms
($0.001—$0,014)
(SOOOl SOOI4)
Asthma exacerbation
pm25
$0,012
($0.001~$0.046)
...
so 012
(SO OOl-SO 04<>)
Lost work days
pm25
$0.2
($0.19—$0.24)
$0,002
($0.0016~$0.0002)
so 2
(SO 1') so 24)
School loss days
03
$0.01
($0.004—$0,013)
—
SO 0|
(SO.004 so.oni
Minor restricted-activity
PM2 5 8l O3
$0.64
$0,005
SO. (.4
days
($0.34—$0.97)
($0.003—$0,008)
(SOU so')")
Recreational visibility,
Class I areas
pm25
$3.5
$0.03
$3.6
6
-------
Monetized total Benefits
(Pope et al. 2002 PM25 mortality and Bell et al. 2004 ozone mortality estimates)
3% discount rate
PM9 5, O3
$120
($10—$360)
$1.1
($0.09—$3.3)
7% discount rate
PM2 „ O3
$110
($9—$330)
$0.9
($0.08—$2.9)
3% discount rate
PM9 5, O3
7% discount rate
PM? s. O,
$260
($23—$760)
$2.4
($0.2—$6.8)
SI 20
(SIO—S360)
SI 10
(S9—S330)
Monetized total Benefits
(Laden et al. 2006 PM25 mortality and Levy et al. 2005 ozone mortality estimates)
$290 $2.6
($26—$840) ($0.2—$7.5)
S290
(S26—S840)
S2"70
(S24—S^.O)
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.
Q
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.
7
-------
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
$400
and 12 expert functions
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.
8
-------
Figure 1-2: Net Benefits of the Transport Rule According to PM2.5 Epidemiology or
Expert-derived Mortality Risk Estimate4
$400
¦ 3% DR _
B-TS&DR I
$350
$300
Laden eta
.
$250
—
i
r
|
$200
-
$150
Pope etal.
$100
/
PM2 5 Benefits estimates derived from 2 epidemiology functions and 12 expert functions
A Column total equals sum of PM2l5-related mortality and morbidity benefits and ozone-related morbidity and
mortality benefits using the Bell et al. (2004) mortality estimate.
1.1.3 Assessment of More and Less Stringent Scenarios
1.1.3.1 Assessment of Other Alternatives
EPA also analyzed the costs and benefits of the two alternative proposed remedies -
direct control and intrastate trading programs. Finally, the Agency also considered options
that were more and less stringent for the control of SO2 emissions.
Air quality modeling was not conducted for these alternatives; thus we estimated the
benefits of these alternatives by applying the same benefit per-ton approach as done for the
alternative remedy options. The costs of these alternatives are estimated using IPM. Table
1-4 below presents the health-related benefits and social costs, including net social benefit, of
the two scenarios alongside that of the proposed Transport Rule remedy
Table 1-4 provides the benefits of the direct control and intrastate trading remedies.
9
-------
Table 1-4. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the
Proposed Remedy Option in 2014a (billions of 2006$)
Description
Proposed Remedy- State
Budgets/Limited Trading
Direct Control Intrastate Trading
Social costsb
3 % discount rate
7 % discount rate
$2.03
$2.23
$2.68
$2.91
$2.49
$2.70
Health-related benefits0'"1
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)'
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 CCVrelated 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 S02
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
10
-------
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
7 % discount rate
Health-related benefits0'"1
3 % discount rate
7 % discount rate
Net benefits (benefits-costs)6
3 % discount rate
7 % discount rate
$2.03
$2.23
$118 to $288
$108 to $262
$116 to $288
$105 to $260
$1.12*
$1.23*
$82 to 200
$76 to 184
$81 to 200
$74 to 182
$2.21*
$2.43*
$120 to 292
$110 to 267
$117 to 290
$107 to 264
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.
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.
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 CCVrelated 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
PMis-related benefit per ton estimates.
Not all possible benefits or disbenefits are monetized in this analysis. These are listed in Table 1-6.
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.
11
-------
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
estimates'3
Premature mortality based on expert
elicitation estimates
Hospital admissions: respiratory and
cardiovascular
Emergency room visits for asthma
Nonfatal heart attacks (myocardial
PM: health" infarctions)
Lower and upper respiratory illness
Minor restricted activity days
Work loss days
Asthma exacerbations (among asthmatic
populations
Respiratory symptoms (among asthmatic
populations)
Infant mortality
Low birth weight
Pulmonary function
Chronic respiratory diseases other than chronic
bronchitis
Non-asthma respiratory emergency room visits
UVb exposure (+/-)°
Visibility in Class I areas
PM: welfare
Household soiling
Visibility in residential and non-class I areas
UVb exposure (+/-)°
Global climate impacts0
Ozone: health
Premature mortality based on short-term
study estimates
Hospital admissions: respiratory
Emergency room visits for asthma
Minor restricted activity days
School loss days
Chronic respiratory damage
Premature aging of the lungs
Non-asthma respiratory emergency room visits
UVb exposure (+/-)°
Decreased outdoor worker productivity
Ozone: welfare
Yields for:
-Commercial forests
-Fruits and vegetables, and
—Other commercial and noncommercial crops
Damage to urban ornamental plants
Recreational demand from damaged forest
aesthetics
Ecosystem functions
UVb exposure (+/-)"
N02: health
Respiratory hospital admissions
Respiratory emergency department visits
Asthma exacerbation
Acute respiratory symptoms
Premature mortality
Pulmonary function
N02: welfare
Commercial fishing and forestry from acidic
deposition
Commercial fishing, agriculture and forestry
from nutrient deposition
Recreation in terrestrial and estuarine
ecosystems from nutrient deposition
Other ecosystem services and existence values
12
-------
for currcnih lie illhy ecosystems
Coastal eutropluc mon from nitrogen deposition
Respiratory hospn il idmissions
Asthma emergency room visits
, ,,, Asthma exacerbation
S02: health . ,
Acute respiratory symptoms
Premature mortality
Pulmonary function
Commercial fishing and forestry from acidic
deposition
S02: welfare Recreation in terrestrial and aquatic ecosystems
from acid deposition
Increased me rain methylation
Incidence of neurological disorders
Incidence of learning disabilities
Incidences in developmental delays
Mercury: Potential cardiovascular effects including:
health -Altered blood pressure regulation
-Increased heart rate variability
-Incidences of hcari attack
Potential reproductive effects
Mercury: Impact on birds and mammals (e.g. reproductive
environment effects)
Impacts to commercial., subsistence and
Mprmrv*
... • recreational fishing
welfare
" 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
13
-------
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.
14
-------
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.
15
-------
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.
16
-------
• 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 |ig/m3 and
about 97% occur among those initially exposed to annual mean PM2.5 level of 7.5 |ig/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 concentration-
response (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
17
-------
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.
18
-------
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.
19
-------
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
20
-------
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.
21
-------
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.
22
-------
As discussed above, EPA is proposing FIPs to directly regulate EGU S02 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 S02 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 S02
reductions required to eliminate that state's significant contribution and interference with
maintenance. There would be a stringent S02 tier comprising 15 states ("group 1") and a
moderate S02 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 S02 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 S02 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 S02 tier ("group 1"), the 2014 phase would
substantially increase the S02 reduction requirements (i.e., these states would have smaller
S02 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 S02 stringency tiers lead to two exclusive S02 trading groups.
23
-------
the 24-hour PM2 5 NAAQS. For the 13 states in the moderate S02 tier ("group 2") the 2014
S02 emissions budgets would remain the same as the 2012 S02 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.s and 8-Hour Ozone NAAQS
Covered for 24-hour Covered for 8-hour
and/or annual PM2 5 ozone
State
Required to reduce S02 Required to reduce
and NOx 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
24
-------
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.
25
-------
Figure 2-1 - PM2i5 Region (S02 and Annual NOx States) Under the Proposed Transport
Rule
States in both groups are covered for annual NOx
26
-------
Ozone (25 States)
27
-------
2.3 Regulated Entities
This action proposes to directly regulate emissions of NOx and S02,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.
28
-------
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 S02 program, emission requirements in
many areas would revert to the comparatively less stringent requirements of the Title IV
Acid Rain program. As a result, S02 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 S02 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 S02 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.
29
-------
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 state-
specific emissions budgets and allow intrastate trading, but prohibit interstate trading. The
second alternative remedy option, for which EPA also requests comment, would use state-
specific 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.
30
-------
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.
31
-------
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
32
-------
• 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
33
-------
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
34
-------
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.
35
-------
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: avejire
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
PMio 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:
aim 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.
36
-------
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 S02 ("S") ECA (see
http://www.epa.eov/otaa/oceanvessels.htm).
Onroad
California,
NMIM-based, and
MOVES sources
not subject to
temperature
adjustments:
onnoadj
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.
37
-------
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 (PMi0), 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 S02 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 S02 data were
used to create S02-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.
38
-------
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 county-
based 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 S02 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,
onmovesrunpm, and onmovesstartpm sectors). Finally, the Fires column represents the
average-year fire (avefire sector) emissions for wildfires and prescribed burning mentioned
previously.
39
-------
Table 3-2. 2005 Base Year S02 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
40
-------
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
41
-------
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
42
-------
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 (onnoadj, onmovesrunpm, and onmovesstartpm 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.
43
-------
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 S02 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 S02, 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/epa-
ipm/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 S02
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
44
-------
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 (CI 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#2008finaD.
45
-------
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.
46
-------
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
47
-------
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
48
-------
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
49
-------
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.
50
-------
Table 3-8. Summary of Emissions Changes for the Transport Rule in Lower 48 States
Pollutant
Item NOx S02
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
EGU NOx
2014 Controlled
Reduction
EGU NOx
State
2014 Base NOx
NOx
(tons)
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%
51
-------
EGU NOx
2014 Controlled
Reduction
EGU NOx
State
2014 Base NOx
NOx
(tons)
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
52
-------
Table 3-10. State Specific Changes in Annual EGU SO2 for the Lower 48 StatesA
2014 Base
2014
EGU S02
EGU S02
State
so2
Controlled S02
Reduction (tons)
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%
53
-------
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
54
-------
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 (PMio and PM25)-as estimated using a national-
scale 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 PM25 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 PM25 and ozone air quality in the
contiguous U.S. CAMx is a three-dimensional grid-based Eulerian photochemical model
designed to estimate PM25 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
55
-------
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.
11 Given 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.
56
-------
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.
57
-------
58
-------
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 future-
year 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 three-
dimensional 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
59
-------
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.
60
-------
4.2 Results for PM2i5 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:
61
-------
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 |ig/m3) in 2014. The population-weighted average mean
concentration declined by 11 percent (or 1.21 |ig/m3) in 2014. This indicates the rule
generates greater absolute air quality improvements in more populated, urban areas.
62
-------
Table 4-1. Summary of Base Case PM2i5 Air Quality and Changes Due to the
Transport Rule.
2014
Statistic
Base Case
Control
Case
Percent
Change3
PM2.5 (l-ig/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 Meanb
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 |ig/m3. Furthermore, over 43 percent of this
population will benefit from reductions in annual mean PM2.5 concentrations of greater than
1.25 |ig/m3 and almost 88 percent will live in areas with reductions of greater than 0.5 |ig/m3.
63
-------
Table 4-2. Distribution of PM2.s Air Quality Improvements Over Population in 2014
Due to the Transport Rule for the Eastern U.S.
Change in Annual Mean PM2 5
Concentrations (jig/m3)
2014 Populationb
Number
(millions)
Percent (%)
0 > A PM2 5 Cone < 0.25
3,332,940
1%
0.25 > A PM2 5 Cone < 0.5
27,405,217
11%
0.5 > A PM2 5 Cone < 0.75
39,549,835
16%
0.75 > APM2 5 Cone < 1.0
34,181,327
14%
1.0 > APM2 5 Cone < 1.25
33,590,794
14%
1.25 > APM2 5 Cone < 1.5
34,097,507
14%
1.5 > APM2 5 Cone < 1.75
21,029,053
9%
APM2 5 Cone > 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.
64
-------
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 full-
season 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.
65
-------
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
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.
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.
66
-------
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
b Percent
Statistic Base Case Change h
Change
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.
0 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
67
-------
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
68
-------
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.
69
-------
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 S02?
2. What is the economic value of these effects?
In this analysis we consider an array of health and welfare impacts attributable to changes
70
-------
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). S02 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.
71
-------
Table 5-1: Estimated monetized benefits of the proposed Transport Rule (billions of
2006$)a
Outside
Within Transport Transport
Benefits Estimate Region8 Region Total
Pope et al. (2002) PM2.5 mortality and Bell et al. (2004) ozone mortality estimates
Using a 3% discount $120 +B $1.1 +B $120 +B
rate ($10—$360) ($0.09—$3.3) ($10—$360)
Using a 7% discount $110 +B $0.9 +B $110 +B
rate ($9—$330) ($0.08—$2.9) ($9—$330)
Laden et al. (2006) PM2.5 mortality and Levy et al. (2005) ozone mortality estimates
Using a 3% discount $290 +B $2.6 +B $290 +B
rate ($26—$840) ($0.2—$7.5) ($26—$840)
Using a 7% discount $260 +B $2.4 +B $270 +B
rate ($23—$760) ($0.2—$6.8) ($24—$760)
AFor notational purposes, unqualified 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 unqualified 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
72
-------
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 PM25-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 PM25-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
|ig/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 |ig/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.
73
-------
Table 5-2: Human Health and Welfare Effects of Pollutants Affected by the Transport
Rule
Pollutant/
Effect
Quantified and monetized in base estimate Unquantified
PM:
health3
Premature mortality based on cohort study
estimates'3 and expert elicitation estimates
Hospital admissions: respiratory and
cardiovascular
Emergency room visits for asthma
Nonfatal heart attacks (myocardial
infarctions)
Lower and upper respiratory illness
Minor restricted activity days
Work loss days
Asthma exacerbations (among asthmatic
populations
Respiratory symptoms (among asthmatic
populations)
Infant mortality
Low birth weight, pre-term birth and other reproductive
outcomes
Pulmonary function
Chronic respiratory diseases other than chronic
bronchitis
Non-asthma respiratory emergency room visits
UVb exposure (+/-)°
PM:
welfare
Visibility in Class I areas in SE, SW, and CA
regions
Household soiling
Visibility in residential areas
Visibility in non-class I areas and class 1 areas in NW,
NE, and Central regions
UVb exposure (+/-)°
Global climate impacts0
Premature mortality based on short-term
study estimates
Ozone: Hospital admissions: respiratory
health Emergency room visits for asthma
Minor restricted activity days
School loss days
Chronic respiratory damage
Premature aging of the lungs
Non-asthma respiratory emergency room visits
UVb exposure (+/-)°
Ozone:
welfare
N02:
health
Decreased outdoor worker productivity
Yields for:
-Commercial forests
-Fruits and vegetables, and
—Other commercial and noncommercial crops
Damage to urban ornamental plants
Recreational demand from damaged forest aesthetics
Ecosystem functions
UVb exposure (+/-)°
Climate impacts
Respiratory hospital admissions
Respiratory emergency department visits
Asthma exacerbation
Acute respiratory symptoms
Premature mortality
Pulmonary function
NOx:
welfare
Commercial fishing and forestry from acidic deposition
effects
Commercial fishing, agriculture and forestry from
74
-------
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
S02: Asthma exacerbation
health Acute respiratory symptoms
Premature mortality
Pulmonary function
Commercial fishing and forestry from acidic deposition
gQ . effects
Recreation in terrestrial and aquatic ecosystems from
welfare ., , rr ,
acid deposition effects
Increased mercury methylation
Incidence of neurological disorders
Incidence of learning disabilities
Incidences in developmental delays
Mercuiy: Potential cardiovascular effects including:
health -Altered blood pressure regulation
-Increased heart rate variability
-Incidences of Myocardial infarction
Potential reproductive effects
Impact on birds and mammals (e.g. reproductive
Mercuiy: 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
75
-------
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 short-
term 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.
76
-------
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.
77
-------
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 CAMx-
generated 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:
Ay = J1!? " — i) ¦ Pop
whereto 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; Ax is the change in air quality; and P 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.
78
-------
Figure 5-1: Illustration of Ben MAP Approach
Air wl'iy Pott-Policy SntMrio AtrQunlty
( X
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
79
-------
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.
80
-------
Figure 5-2: Data inputs and outputs for the BenMAP model
Census
Population
Modeled
Baseline and
Post-Control
2014 Ambient
PM25 and 03
Concentrations
PM25 & 03 Health
Functions
2014
Population
Projections
PM2 5 & o3
Incremental Air
Qualit)
Change
PM25 & o3-
Related Health
Woods &
Poole
Population
Projections
Background
Incidence and
Prevalence Rates
Monetized PM25
and 03-related
Benefits
Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP
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
81
-------
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 SO 2 emission
reductions of the proposed remedy. We first quantified the health impacts and monetized
benefits of total PM2.5 mass formed from the S02 reductions of the proposed remedy,
allowing us to isolate the PM air quality impacts from S02 reductions alone.19 This
procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-related
benefits of incremental changes in S02 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-|ig/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 S02.
The resulting BPT estimates were then multiplied by the projected S02 emission reductions
for the Direct Control and Interstate Trading scenarios to produce an estimate of the PM- and
19
The Transport Rule includes both S02 and NOx emissions reductions. In general S02 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 S02 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 S02 emissions were reduced. We calculated benefits from this air
quality modeling run to generate an S02-only benefit per ton estimate. The results of the S02-only sensitivity run
may be found in the EPA Benefits TSD [Docket No. EPA-HQ-OAR-2010-0491]
82
-------
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
83
-------
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
84
-------
larger uncertainty surrounding the entire analysis.
In 2006 the EPA requested an NAS study to evaluate the extent to which the epidemiological
literature to that point improved the understanding of ozone-related mortality. The NAS found
that short-term ozone exposure was likely to contribute to ozone-related mortality (NRC, 2008)
and issued a series of recommendations to EPA, including that the Agency should:
1. Present multiple short-term ozone mortality estimates, including those based on multi-
city analyses such as the National Morbidity, Mortality and Air Pollution Study
(NMMAPS) as well as meta-analytic studies.
2. Report additional risk metrics, including the percentage of baseline mortality attributable
to short-term exposure.
3. Remove reference to a no-causal relationship between ozone exposure and premature
mortality.
The quantification and presentation of ozone-related premature mortality in this chapter
is responsive to these NRC recommendations 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 PMio 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.
85
-------
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.
86
-------
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 export-
based 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
87
-------
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
88
-------
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.
89
-------
Table 5-4. Criteria Used when Selecting C-R functions
Consideration
Comments
Peer-Reviewed Peer-reviewed research is preferred to research that has not undergone the peer-review
Research process.
Study Type
Study Period
Population Attributes
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.
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.
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
Study Location
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).
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.
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.
For this analysis, impact functions based on PM2 5 are preferred to PMi0 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, PMi0
functions are used as surrogates, recognizing that there will be potential downward
(upward) biases if the fine fraction of PMi0 is more (less) toxic than the coarse fraction.
Economically Some health effects, such as forced expiratory volume and other technical measurements
Valuable Health of lung function, are difficult to value in monetary terms. These health effects are not
Effects quantified in this analysis.
Pollutants Included in
Model
Measure of PM
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.
90
-------
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.
91
-------
Table 5-5. Health Endpoints and Epidemiological Studies Used to Quantify Health
Impacts3
Endpoint
Pollutant
Study
Study
Population
Premature Mortality
Premature mortality—daily
time series
Premature mortality—cohort
study, all-cause
Premature mortality, total
exposures
Premature mortality—all-
cause
03
(8-hour
max)
PM2<
(annual
avg)
PM25
(annual
avg)
pm25
(annual
avg)
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)
Pope et al. (2002)
Laden et al. (2006)
Expert Elicitation (Roman et al., 2008)
Woodruff et al. (2006)
All ages
>29 years
>25 years
>24 years
Infant (<1 year)
Chronic Illness
Chronic bronchitis
Nonfatal heart attacks
pm25
(annual
avg)
PM2,
(24-hour
avg)
Abbey et al. (1995)
Peters et al. (2001)
>26 years
Adults (>18
years)
Hospital Admissions
Respiratory
03
(8-hour
max)
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)
Burnett et al. (2001)
pm25
(24-hour
avg)
PM25
(24-hour
avg)
pm25
(24-hour
avg)
pm25
(24-hour
Sheppard (2003)—ICD 493 (asthma)
>64 years
<2 years
Pooled estimate:
Moolgavkar (2003)—ICD 490-496 (COPD) >64 years
Ito (2003)—ICD 490-496 (COPD)
Moolgavkar (2000)—ICD 490-496 (COPD) 20-64 years
Ito (2003)—ICD 480-486 (pneumonia) >64 years
<65 years
92
-------
Endpoint
Pollutant
Study
Study
Population
avg)
Cardiovascular
Pooled estimate:
>64 years
pm25
(24-hour
avg)
pm25
(24-hour
avg)
Moolgavkar (2003)—ICD 390-429 (all
cardiovascular)
Ito (2003)—ICD 410—414, 427-428 (ischemic
heart disease, dysrhythmia, heart failure)
Moolgavkar (2000)—ICD 390-429 (all
cardiovascular)
20-64 years
Asthma-related ER visits
o3
Pooled estimate:
(8-hour
max)
Peel et al. (2005)
Wilson etal.(2005)
All ages
All ages
pm25
(24-hour
avg)
Norris et al. (1999)
0-18 years
Other Health Endpoints
Acute bronchitis
pm25
(annual
avg)
Dockery et al. (1996)
8-12 years
PMjn
Asthmatics, 9-
Upper respiratory symptoms
(24-nour
avg)
Pope et al. (1991)
11 years
Lower respiratory symptoms
pm2S
(24-hour
avg)
pm25
(24-hour
avg)
Schwartz and Neas (2000)
Pooled estimate:
7-14 years
6-18 yearsb
Asthma exacerbations
Ostro et al. (2001) (cough, wheeze and shortness
of breath)
Vedal et al. (1998) (cough)
Work loss days
pm25
(24-hour
avg)
Ostro (1987)
18-65 years
o3
Pooled estimate:
School absence days
(8-hour
max)
Gilliland et al. (2001)
Chen et al. (2000)
5-17 years0
03
(8-hour
Ostro and Rothschild (1989)
18-65 years
Minor Restricted Activity
max)
Days (MRADs)
pm25
(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-SAB-
COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health
Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.
0 Gilliland et al. (2001) studied children aged 9 and 10. Chenet 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.
93
-------
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 community-
level 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
94
-------
(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
95
-------
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 S02, 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 PM25.
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 follow-
up 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
96
-------
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 S02
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-to-
date 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 "[consistent
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
97
-------
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.
98
-------
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 meta-
analyses" (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.
99
-------
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.
100
-------
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 PMi0 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 PMi0 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 PMi0 rather than PM2.5. We selected the Norris et al. (1999) effect
21
Note 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.
101
-------
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
102
-------
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
22
See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002.
103
-------
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).
104
-------
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 PMi0 and respiratory symptoms for the asthmatic population was only reported for two
23
Estimating 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.
105
-------
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
106
-------
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
107
-------
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.
108
-------
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 county-
specific rate
CDC Wonder (1996-1998)
U.S. Census bureau
Hospitalizations
Daily hospitalization rate
Age-, region-,
and cause-
specific rate
1999 NHDS public use data filesb
Age- and
2000 NHAMCS public use data
Asthma ER Visits
Daily asthma ER visit rate
region- specific
visit rate
files0; 1999 NHDS public use data
filesb
Annual prevalence rate per
Chronic Bronchitis
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 African-
American children
• daily wheeze
0.076 (0.173)
Ostro et al. (2001)
Asthma Exacerbations
• daily cough
• daily dyspnea
0.067 (0.145)
0.037 (0.074)
Prevalence among asthmatic
children
• daily wheeze
0.038
Vedaletal. (1998)
• daily cough
• daily dyspnea
0.086
0.045
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)
1996 HIS (Adams, Hendershot, and
• Aged 18-24
0.00540
Marano, 1999, Table 41); U.S.
• Aged 25-44
• Aged 45-64
0.00678
0.00492
Bureau of the Census (2000)
109
-------
School Loss Days Rate per person per year, National Center for Education
assuming 180 school days per 9.9 Statistics (1996) and 1996 HIS
year (Adams et al., 1999, Table 47);
Minor Restricted- Daily MRAD incidence rate 0.02137 Ostro and Rothschild (1989, p. 243)
Activity Days per person
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/.
0 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 files3
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
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
110
-------
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
111
-------
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.
112
-------
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).
113
-------
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.
114
-------
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.
115
-------
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
116
-------
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 pollution-
related 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
117
-------
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 $74,746 for someone between the ages of 55 and 65. The corresponding age-
specific estimates of lost earnings (in 2006$) 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 year-
2000 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.
118
-------
• 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 =
Wittelsetal. (1990) $144,1113 5
Russell etal. (1998) $30,102b 5
Eisenstein et al. (2001) $64,003b 10
Russell etal. (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 wage-
related 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 Cost Total Cost
0-24
$0
$84,955
$84,955
25-14
$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
$84,955
a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).
b From Cropper and Krupnick (1990), using a 3% discount rate.
119
-------
5.4.4.4 Hospital Admissions Valuation
In the absence of estimates of societal WTP to avoid hospital visits/admissions for
specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost
productivity) typically are used as conservative, or lower bound, estimates. These estimates are
biased downward, because they do not include the willingness-to-pay value of avoiding pain and
suffering.
The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates
used in this analysis consist of estimated hospital charges and the estimated opportunity cost of
time spent in the hospital (based on the average length of a hospital stay for the illness). We
based all estimates of hospital charges and length of stays on statistics provided by the Agency
for Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day
spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized
individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median
weekly wage by five and inflated the result to year 2006$ 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 $127.93 multiplied by n.
Table 5-11: Unit Values for Economic Valuation of Health Endpoints (2006$)
Health Endpoint
Central Estimate of Value Per Statistical
Incidence
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).
120
-------
Chronic Bronchitis (CB)
$340,000
$460,000
The WTP to avoid a
case of pollution-
related CB is
calculated as where x
is the severity of an
average CB case,
WTP13 is the WTP for
a severe case of CB,
and $ is the parameter
relating WTP to
severity, based on the
regression results
reported in Krupnick
and Cropper (1992).
The distribution of
WTP for an average
severity-level case of
CB was generated by
Monte Carlo methods,
drawing from each of
three distributions: (1)
WTP to avoid a severe
case of CB is assigned
a 1/9 probability of
being each of the first
nine deciles of the
distribution of WTP
responses in Viscusi et
al. (1991); (2) the
severity of a pollution-
related case of CB
(relative to the case
described in the
Viscusi study) is
assumed to have a
triangular distribution,
with the most likely
value at severity level
6.5 and endpoints at
1.0 and 12.0; and (3)
the constant in the
elasticity of WTP with
respect to severity is
normally distributed
with mean =0.18 and
standard deviation =
0.0669 (from Krupnick
and Cropper [1992]).
This process and the
rationale for choosing
121
-------
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
No distributional
information available.
Age-specific cost-of-
illness values reflect
lost earnings and direct
medical costs over a 5-
year period following a
nonfatal MI. Lost
earnings estimates are
based on Cropper and
Krupnick (1990).
Direct medical costs
are based on simple
average of estimates
from Russell et al.
(1998) and Wittels et
al. (1990).
Lost earnings:
Cropper and Krupnick
(1990). Present
discounted value of 5
years of lost earnings:
age of onset: at 3
25-44 $8,774 $7,8;
45-54 $12,932 11,5'
55-65 $74,746 66,9:
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)
$79,685
$88,975
$93,897
$167,532
$79,685
$79,685
$88,975
$93,897
$167,532
$79,685
$77,769
$87,126
$91,559
$157,477
$77,769
$77,769
$87,126
$91,559
$157,477
$77,769
Hospital Admissions
Chronic Obstructive Pulmonary Disease
(COPD)
$16,606
$16,606
No distributional
information available.
The COI estimates
122
-------
(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).
123
-------
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
124
-------
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 mid-
range estimates of
WTP (IEc, 1994) to
avoid each symptom in
the cluster and
assuming additivity of
WTPs. The dollar
value for LRS is the
average of the dollar
values for the 11
different types of LRS.
In the absence of
information
surrounding the
frequency with which
each of the 11 types of
125
-------
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 mid-
range values
recommended by IEc
(1994) for two
126
-------
symptoms believed to
be associated with
acute bronchitis:
coughing and chest
tightness. The high
daily estimate was
taken to be twice the
value of a minor
respiratory restricted-
activity day, or $110.
Work Loss Days (WLDs)
Variable (U.S.
median = $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.
127
-------
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
128
-------
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).
129
-------
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.
130
-------
Optical Characteristics of Illumination
• Sunlight (Sun Angle)
• Cloud Cower (Overcast, Puffy, etc.)
• skV
• Detection Thresholds
• Psychological Response to
Incoming Light ^
• Value Judgements jKk
Optical Characteristics of
Intervening Atmosphere
Optical Characteristics of
• Light Added to Sight Path by
Particles and Gases
• Image-Forming Light Subtracted
from Sight Path by Scattering
and Absorption
• Color
• Contrast Detail (Texture)
• Form
• Brightness
Figure 5-3: Important factors involved in seeing a scenic vista (Malin, 1999)
Light from clouds
scattered Into
sight path v
Image-forming
light scattered
out of sight path
Sunlight ^
scattered
Light reflected
from ground
scattered into
sight path
Image-forming
light absorbed
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).
131
-------
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).
132
-------
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).
133
-------
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).
134
-------
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
135
-------
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
136
-------
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
-------
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 {apriori 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
138
-------
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 Growth3
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 Kalian, 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.
Benefit Category
Central Elasticity Estimate
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Visibility
0.14
0.45
0.40
0.90
139
-------
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 Growth3
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 N02 and S02 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.
Benefit Category
2014
Minor Health Effect
Severe and Chronic Health Effects
Premature Mortality
Visibility
1.04
1.16
1.14
1.35
140
-------
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
141
-------
Figure 5-5: Linkages between categories of ecosystem services and components of
human well-being from Millennium Ecosystem Assessment (MEA, 2005)
CONSTITUENTS OF WELL-BEING
ECOSYSTEM SERVICES
Provisioning
FOOD
FRESH WATER
WOOD AND FIBER
FUEL
Supporting
NUTRIENT CYCLING
SOIL FORMATION
PRIMARY PRODUCTION
Regulating
CLIMATE REGULATION
FLOOD REGULATION
DISEASE REGULATION
WATER PURIFICATION
Cultural
AESTHETIC
SPIRITUAL
EDUCATIONAL
RECREATIONAL
LIFE ON EARTH - BIODIVERSITY
Security
PERSONAL SAFETY
SECURE RESOURCE ACCESS
SECURITY FROM DISASTERS
Basic material
for good life
Freedom
ADEQUATE LIVEUHOODS
of choice
SUFFICIENT NUTRITIOUS FOOD
and action
SHELTER
ACCESS TO GOODS
OPPORTUNITY TO BE
ABLE TO ACHIEVE
WHAT AN INDIVIDUAL
Health
STRENGTH
VALUES DOING
AND BEING
FEELING WELL
ACCESS TO CLEAN AIR
AND WATER
Good social relations
SOCIAL COHESION
MUTUAL RESPECT
ABILITY TO HELP OTHERS
Source: Millennium Ecosystem Assessment
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.
142
-------
Figure 5-6: Schematic of the benefits assessment process (U.S. EPA, 2006b)
EPA action
Ecosystems
: goods and services
affected by the policy
Planning and problem formulation
Goods and services
identified
Goods and
services not
identified
Ecological analysis
Goods and services
quantified
Identified
goods and
services not
quantified
Economic analysis
Quantified
goods and
services not
monetized
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 S02, and nitrogen can be emitted as
NO, N02. 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.
143
-------
Figure 5-7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition
f
N02 Atmospheric
Fate and Transpo
V
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
144
-------
lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in
N02 and S02 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
145
-------
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 [j,eq/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).
146
-------
Table 5-14: Aquatic Status Categories
Category Label ANC Levels
Expected Ecological Effects
Acute
Concern
<0 micro
equivalent per
Liter (|icq/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 (ieq/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 (ieq/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 (ieq/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 (ieq/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.
147
-------
Figure 5-8: Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 20081")
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.
148
-------
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,
result of the emission reductions achieved by this rule.
37 These estimates reflect the total value of the service,
result of the emission reductions achieved by this rule.
not the marginal change in the value of the service as a
not the marginal change in the value of the service as a
149
-------
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.
150
-------
Figure 5-9: Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008f)
| Area of Higas! Potential Sensitivity
| Top Quartile N
I Top Quartile S
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 (DeFIayes et al., 1999). Figure
5-10 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S.
151
-------
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).
152
-------
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,
153
-------
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 Me thy lation
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.
154
-------
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, sulfur-
reducing 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 mercury-
binding 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.
155
-------
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).
156
-------
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.
157
-------
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 top-
predator 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 mercury-
related 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
158
-------
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.
40
Salonen, 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.
41
Sorensen, 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.
42
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. p. 229.
159
-------
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 mercury-
contaminated 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
160
-------
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 S02 exposures over the growing season. Besides foliar injury,
chronic exposure to low S02 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. S02 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 S02 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 mercury-
contaminated 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
161
-------
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 well-
publicized 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).
162
-------
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.
163
-------
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 (C02) 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.
164
-------
(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
165
-------
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 long-
term 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.
166
-------
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.
167
-------
Figure 5-13: Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002
Degree of injury:
a, b
None
Low
Moderate
High
Severe
Percent of monitoring sites in each category:
Region 1
(54 sites)
Region 2
(42 sites)
Region 3
(111 sites)
Region 4
(227 sites)
Region 5
(180 sites)
Region 6
(59 sites)
Region 7
(63 sites)
Region 8
(72 sites)
Region 9
(80 sites)
Region 10
(57 sites)
68.5
16.7
11.1
61.9
21.4
7.1
7.1
-3.7
2.4
55.9
18.0
14.4
7.2 4.5
75.3
10.1
7.0
T-n.5
4.0
75.6
18.3
6.1
94.9
-5.1
85.7
9.5
^3.2
1.6
100.0
76.3
12.5
8.8
1.3
1.3
100.0
Coverage: 945 monitoring sites,
located in 41 states.
totals may not add to 100% due to
rounding.
Data source: USDA Forest Service,
2006
EPA Regions
® D- 0 ®
o
o .p '
®
© •
0-
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 ozone-
exposure/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
168
-------
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 Samuel son, 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 (Primus 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 sppj 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).
169
-------
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)
i ¦ -
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).
170
-------
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.
171
-------
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
S02 (U.S. EPA, 2008). The immediate effect of S02 on the respiratory system in humans is
bronchoconstriction. Asthmatics are more sensitive to the effects of S02 likely resulting
from preexisting inflammation associated with this disease. A clear concentration-response
relationship has been demonstrated in laboratory studies following exposures to S02 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 S02 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 S02 ISA. The S02 ISA also
concluded that the relationship between short-term S02 exposure and premature mortality
was "suggestive of a causal relationship" because it is difficult to attribute the mortality risk
effects to S02 alone. Although the S02 ISA stated that studies are generally consistent in
reporting a relationship between S02 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 N02 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.
172
-------
EPA, 2008c). The N02 ISA provides a comprehensive review of the current evidence of
health and environmental effects of N02. The N02 ISA concluded that the evidence "is
sufficient to infer a likely causal relationship between short-term N02 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 N02
ISA concluded that the relationship between short-term N02 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 N02 alone. Although the N02 ISA stated
that studies consistently reported a relationship between N02 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 (C02) 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
173
-------
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 C02 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/otaa/climate/regulations.htm
48
The interagency group decided that these estimates apply only to C02 emissions. Given that warming
profiles and impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group
concluded "transforming gases into C02-equivalents using GWP, and then multiplying the carbon-equivalents
by the SCC, would not result in accurate estimates of the social costs of non-C02 gases" (SCC TSD, pg 13).
174
-------
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 non-
catastrophic 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 C02 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.
175
-------
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 C02 emissions will be
discounted at rates that differ from those used to develop the SCC estimates.
176
-------
remedy. The results of this analysis may be found in Appendix A. The benefits of the more
and less stringent S02 sensitivity analyses may be found in the Chapter 10 cost-benefit
comparison chapter.
177
-------
Table 5-17: Estimated Reduction in Incidence of Adverse Health Effects of the
Proposed remedy (95% confidence intervals)A
Beyond transport
Health Effect Within transport region region Total
PM-Related endpoints
Premature Mortality
Pope et al. (2002) (age 14,000 130
>30) (4,000—24,000) (35—220)
14,000
(4,000—25,000)
Laden et al. (2006) (age 36,000 320
>25) (17,000—55,000) (150—500)
36,000
(17,000—56,000)
59 0 3
Infant (< 1 year) (-66-180) (-0.3-0.8)
59
(-66—180)
9 200 89
Chronic Bronchitis (310—18.000) (3-160)
9,200
(320—18,000)
Non-fatal heart attacks (age > 22,000 250
18) (5,700—39,000) (64^140)
23,000
(5,800—39,000)
Hospital admissions— ^ goo 35
(1.400-5.500) (14—56)
3,500
(1,400—5,500)
Hospital admissions— 7,500 76
cardiovascular (age > 18) (5,200—8,800) (51—93)
7,500
(5,200—8,900)
Emergency room visits for 14 000 71
asthma (7,100—21,000) (36—110)
(age <18)
14,000
(7,200—21,000)
Acute bronchitis 21,000 150
(age 8-12) (-4,800—46,000) (33—320)
21,000
(-4,800—-16,000)
Lower respiratory symptoms 250,000 1,700
(age 7-14) ' ' (98,000—400,000) (670—2,800)
250,000
(98,000—400,000)
Upper respiratory symptoms 190,000 1,300
(asthmatics age 9-18) (36,000—350,000) (250—2,400)
190,000
(36,000—350,000)
Asthma exacerbation 230,000 1,700
(asthmatics 6-18) (8,300—800,000) (11—5,700)
240,000
(8,300—800,000)
Lost work days 1,800,000 14,000
(ages 18-65)' (1,500,000—2,000,000) (12,000—17,000)
1,800,000
(1,500,000—2,000,000)
Minor restricted-activity days 10,000,000 86,000
(ages 18-65) ' ' (8,600,000—12,000,000) (71,000—100,000)
11,000,000
(8,600,000—12,000,000)
178
-------
Ozone-related endpoints
Premature mortality
, Bell et al. (2004) (all
a ages)
50
(16—83)
0.6
(0.2-1)
50
(17—84)
5 Schwartz et al. (2005)
^ ^ (all ages)
76
(23—130)
1
(0.2—2)
77
(24—130)
Huang et al. (2005)
(all ages)
83
(31—130)
1
(0.3—2)
84
(31—140)
Ito et al. (2005) (all
8 a§es)
c/a
220
(130—310)
3
(2—4)
230
(140—320)
g Bell et al. (2005) (all
t ages)
160
(76—250)
2
(1-3)
160
(77—250)
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.
179
-------
Table 5-18: Estimated Economic Value of Health and Welfare Benefits (95%
confidence intervals, billions of 2006$)A
Within transport Beyond transport
Health Effect Pollutant region regionB Total
Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality
estimates)
w a¦ ~ ~ da/t * r, $110 $°.!
3% discount rate PM25&03 ($8.8_$330) ($0.08-$3)
$110
($8.8—$340)
•70/ A' t t DA A £ (~\ $100 $009
7/o discount rate PM25&03 ($7.9—$300) ($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 PM25&03 ^^lO) ($0.2-$7.3)
$280
($25—$820)
7% discount rate PM25&03 ($0.2-$6.6)
$260
($22—$310)
$4 3 $0 04
Chrome Bronchitis PM25 ($o.2_$20) ($0,002-^0.2)
$4.3
($0.2—$20)
Non-fatal heart attacks
,0/ j* t t DA, $2.5 $0.03
3% discount rate PM2 5 ($0.4-$6) ($0.005-$0.07)
$2.5
($0.4—$6)
no/ a- t t da/t $2-4 $0 03
7% discount rate PM2 5 ($0.4-$5.9) ($0.005-$0.07)
$2.4
($0.4—$5.9)
Hospital admissions— pM „ „ $0.06 $0.00006
respiratory 2 3 ($0.03—$0.1) ($0.00003—$0,001)
$0.06
($0.03—$0.1)
Hospital admissions— pM $0.2 $0,002
cardiovascular 25 ($0.1—$0.3) ($0.001—$0,003)
$0.2
($0.1—$0.3)
Emergency room visits pM „ „ $0,005
for asthma" 2 S 3 ($0.002—$0,008)
$0,005
($0.002—$0,008)
a . u dat $0,009
Acute bronchitis PM2 5 (.$0.0004-$0.03)c
$0,009
(-$0.0004—$0.03)
Lower respiratory $0,005
symptoms ' 25 ($0.002—$0,009)
$0,005
($0.002—$0,009)
Upper respiratory $0,006
symptoms 25 ($0.001—$0,014)
$0,006
($0.001—$0,014)
$0 012
Asthma exacerbation PM2 5 ($0.001--$0.046)
$0,012
($0.001~$0.046)
$0 2 $0 002
Lost work days PM2 5 ($0.19—$0.24) ($0.0002~$0.002)
$0.2
($0.19—$0.24)
School loss days 03 ($0.004-$0.013)
$0.01
($0.004—$0,013)
Minor restricted-activity pM „ „ $0.64 $0,005
days ' 25 3 ($0.34—$0.97) ($0.003—$0,008)
$0.64
($0.34—$0.97)
$3.6
$0.35
Recreational visibility. pM^ $3 5 $0 03
Class I areas
Social cost of carbon (3%
discount rate, 2014 C02
value)
180
-------
Monetized total Benefits
(Pope et al. 2002 PM2.S 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.S
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
181
-------
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-l 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
182
-------
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.
183
-------
Figure 5-15: Estimated reduction in excess PM2.s-related premature mortalities
estimated to occur in each county in 2014 as a result of the proposed remedy.
184
-------
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
Excess 03-related premature mortalities avoided
Bell et al. (2004) 03 mortality risk estimate
| -0.09
¦ -0.08- -0.01
HI 0.00-0.10
0.11 -0.25
¦ 0 26 - 0.83
185
-------
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
0% 1 1 1 1 1 1 1 1 1 1 1 1 1
0.10% 0.30% 0.50% 0.70% 0.90% 1.10% 1.30% 1.50% 1.70% 1.90% 2.10% 2.30%
Reduction in percentage of all-cause mortality attributable to reductions in PM2.5 and ozone
10 Counties with Largest Populations, Rank Ordered
1—Los Angeles; 2—Chicago; 3—Houston; 4—Phoenix; 5—San Diego; 6—Dallas; 7—San Jose; 8— New York;
9—San Antonio; 10— Philadelphia
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 PM-
related 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 log-
linear model most adequately portrays the PM-mortality concentration-response relationship
186
-------
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 |ig/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
|ig/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.
187
-------
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.
188
-------
Figure 5-18: Estimated PM2.5- related premature mortalities avoided according to
epidemiology or expert-derived PM mortality risk estimateA
8 $150
PMii Benefits estimates derived from 2 epidemiology function! and 12 expert
function!
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.
189
-------
Figure 5-19: Distribution of PM2.s-related mortality impacts by baseline PM2.5 levels,
PM2.5 epidemiology study and lowest measured level (LML) of each study
Laden et al. (2006) mortality estimate Pope et al, (2002) mortality estimate
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
4,000
8,000
Impacts estimated at
or below LML of
Laden et al. (2002) (10
ug/m3)
Impacts estimated at
or below LML of Pope
et al. (2002) (7.5
ug/m3)
190
-------
Figure 5-20: Percentage of total PM-related mortalities avoided by baseline air quality
level
30% -
LML of Pope etal. (2002) study
LM L of Laden et al. (2006) study
¦
_ ¦ ¦ 1
,
I
I 2 3 4 5 6 7 7.5 8 9 10 I I 12 13 14 15 16 17 18 19 20
Baseline annual mean RM2l5 level (ug/m3)
Of the total mortalities avoided:
97% occur among populations exposed to PM levels at or above the LML of the Pope et al. study.
80% occur amongpopulations exposed to PM levels at or above the LML of the Laden et al study.
191
-------
Figure 5-21: Cumulative percentage of total PM-related mortalities avoided by baseline
air quality level
100%
M 90%
1)
5
£ 80%
0
E
r 70%
Q.
¦o
01
2 60%
o
s
"S 50%
0»
w
It
t 40%
0>
u
i.
-------
Figure 5-22: Cumulative percentage of adult population at annual mean PM2.5 levels
(pre- and post-2014 Transport Rule)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
The control strategy lowers PM2s levels
substantially, particularly among highly
exposed populations. In the baseline, 89% of
the population lived in areas where PM2s
levels above the lowest rn easured levels of
the Pope study, increasing our confidence in
the estimated mortality reductions for this
rule.
Pope etal. 2002 Laden etal, 2006
7 7.5 8 9
Post-control
10 11 12 13 14 15 16 17 IS 19 20
Baseline
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 non-
mortality 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 source-
level emissions, the projection of baseline morbidity rates, incomes and technological
193
-------
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.
194
-------
5.9 References
Abbey, D.E., N. Nishino, W.F. McDonnell, R.J. Burchette, S.F. Knutsen, W. Lawrence
Beeson, and J.X. Yang. 1999. Long-term inhalable particles and other air pollutants
related to mortality in nonsmokers [see comments], American Journal of Respiratory and
Critical Care Medicine 159(2):373-382.
Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. 1995. Estimated
Long-Term Ambient Concentrations of PM(10) and Development of Respiratory
Symptoms in a Nonsmoking Population. Archives of Environmental Health 50(2): 139-
152.
Abt Associates, Inc. 2005. U.S. EPA. Urban ornamental plants: sensitivity to ozone and
potential economic losses. Memorandum to Bryan Hubbell and Zachary Pekar.
Abt Associates, Inc. April 2003. Proposed Nonroad Land-based Diesel Engine Rule: Air
Quality Estimation, Selected Health and Welfare Benefits Methods, and Benefit Analysis
Results. Prepared for Office of Air Quality Planning and Standards, U.S. EPA.
Abt Associates, Inc. 2008. Environmental Benefits and Mapping Program (Version 3.0).
Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air
Quality Planning and Standards. Research Triangle Park, NC. Available on the Internet
at .
Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health
Interview Survey, 1996. Vital Health Stat 10(200): 1-212.
Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. 1989. A reassessment of the
economic effects of ozone on U.S. agriculture. Journal of the Air Pollution Control
Association, 39, 960-968.
Adams, R. M., Hamilton, S. A., McCarl, B. A. 1986. The benefits of pollution control: the
case of ozone and U.S. agriculture. American Journal of Agricultural Economics, 34, 3-
19.
Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and
Utilization Project.
American Lung Association. 1999. Chronic Bronchitis. Available on the Internet at
.
American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American
195
-------
Lung Association, Best Practices and Program Services, Epidemiology and Statistics
Unit. Available on the Internet at
.
Banzhaf, S., D. Burtraw, D. Evans, and A. Krupnick. 2006. "Valuation of Natural Resource
Improvements in the Adirondack^." Land Economics 82:445-464.
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.
Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing Changes in Health
Risks: A Comparison of Alternative Measures. The Southern Economic Journal 53:977-
984.
Brookshire, D.S., Thayer, M.A., Schulze, W.D. & D'Arge, R.C. 1982. "Valuing Public
Goods: A Comparison of Survey and Hedonic Approaches." The American Economic
Review. 72(1): 165-177.
Brown, L.H. 2002. Profile of the Annual Fall Foliage Tourist in Vermont: Travel Year 2001.
Report prepared for the Vermont Department of Tourism and Marketing and the
Vermont Tourism Data Center in association with the University of Vermont, Burlington,
VT.
Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. 2001.
Association between ozone and hospitalization for acute respiratory diseases in children
less than 2 years of age. Am J Epidemiol 153(5):444-452.
Carnethon, M.R., D. Liao, G.W. Evans, W.E. Cascio, L.E. Chambless, W.D. Rosamond, and
G. Heiss. 2002. Does the Cardiac Autonomic Response to Postural Change Predict
Incident Coronary Heart Disease and Mortality? The Atherosclerosis Risk in
Communities Study. American Journal of Epidemiology 155(l):48-56.
Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic Research (CDC
Wonder) (data from years 1996-1998), Centers for Disease Control and Prevention
(CDC), U.S. Department of Health and Human Services, Available on the Internet at
.
196
-------
Chappelka, A.H., Samuelson, L.J. 1998. Ambient ozone effects on forest trees of the eastern
United States: a review. New Phytologist, 139, 91-108.
Chen L, Jennison BL, Yang W, Omaye ST. 2000. Elementary school absenteeism and air
pollution. Inhal Toxicol 12(11):997-1016.
Chesapeake Bay Program. 2008. Underwater Bay Grasses. Available on the Internet at <
http://www.chesapeakebay.net/baygrasses.aspx>. Last accessed March 24, 2010.
Chestnut, L.G and Mills, D.M. (2005) A fresh look at the benefits and costs of the US Acid
Rain Program. Journal of Environmental Management. Vol 77, No 3, pp 252—266.
Chestnut, L.G., and R.D. Rowe. 1990a. A New National Park Visibility Value Estimates. In
Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty
Conference, C. V. Mathai, ed. Air and Waste Management Association, Pittsburgh.
Chestnut, L.G., and R.D. Rowe. 1990b. Preservation Values for Visibility Protection at the
National Parks: Draft Final Report. Prepared for Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC, and Air
Quality Management Division, National Park Service, Denver, CO.
Chestnut, L.G. and R.D. Rowe. 1990c. Economic Valuation of Changes in Visibility: A
State of the Science Assessment for NAPAP. Section B5 in NAPAP State of Science and
Technology Report 27.
Chestnut, L.G. April 15, 1997. Draft Memorandum: Methodology for Estimating Values for
Changes in Visibility at National Parks.
Cordell, H.K., C.J. Betz, G. Green, and M. Owens. 2005. Off-Highway Vehicle Recreation
in the United States, Regions and States: A National Report from the National Survey on
Recreation and the Environment (NSRE). Prepared for the U.S. Department of
Agriculture Forest Service, Southern Research Station, National OHV Policy and
Implementation Teams, Athens, GA. Available on the Internet at
.
Cordell, K., B. Leeworthy, G.T. Green, C. Betz, B. Stephens. 2008. The National Survey on
Recreation & the Environment. Research Work Unit 4953. Pioneering Research on
Changing Forest Values in the South and Nation, U.S. Department of Agriculture Forest
Service, Southern Research Station, Athens, GA. Available on the Internet at
.
197
-------
Coulston, J.W., Riitters, K.H., Smith, G.C. 2004. A preliminary assessment of the Montreal
process indicators of air pollution for the United States. Environmental Monitoring and
Assessment, 95, 57-74.
Crocker, T.D. and R.L. Horst, Jr. Hours of Work, Labor Productivity, and Environmental
Conditions: A Case Study. The Review of Economics and Statistics, 1981. 63: p. 361-
368.
Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung
Disease. Resources for the Future. Washington, DC. Discussion Paper QE 89-16-REV.
Davidson K, Hallberg A, McCubbin D, Hubbell BJ. 2007. Analysis of PM2.5 Using the
Environmental Benefits Mapping and Analysis Program (BenMAP). J Toxicol Environ
Health 70: 332—346.
Dekker, J.M., R.S. Crow, A.R. Folsom, P.J. Hannan, D. Liao, C.A. Swenne, and E.G.
Schouten. 2000. Low Heart Rate Variability in a 2-Minute Rhythm Strip Predicts Risk of
Coronary Heart Disease and Mortality From Several Causes: The ARIC Study.
Circulation 2000 102:1239-1244.
De Steiguer, J., Pye, J., Love, C. 1990. Air Pollution Damage to U.S. Forests. Journal of
Forestry, 88(8), 17-22.
DeHayes, D.H., P.G. Schaberg, G.J. Hawley, and G.R. Strimbeck. 1999. Acid rain impacts
on calcium nutrition and forest health. Bioscience 49(10):789-800.
Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis,
J.H. Ware, M. Raizenne, and F.E. Speizer. 1996. Health Effects of Acid Aerosols On
North American Children-Respiratory Symptoms. Environmental Health Perspectives
104(5):500-505.
Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and
F.E. Speizer. 1993. An Association between Air Pollution and Mortality in Six U.S.
Cities. New England Journal of Medicine 329(24): 1753-1759.
Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir,
G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and
J.T.Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle
Royale fish. Environmental Science and Technology 41(21):7266-7272.
Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and D.B.
198
-------
Mark. 2001. Assessing the Clinical and Economic Burden of Coronary Artery Disease:
1986-1998. Medical Care 39(8): 824-3 5.
Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington,
DC: The National Academies Press.
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.
Felzer, B., J. Reilly, J. Melillo, D Kicklighter, M. Sarogim, C Wang, R. Prinn, Q. Zhuang.
2005. Future effects of Ozone on Carbon Sequestration and Climate Change Policy
using a Global Biogeochemical Model. Climatic Change (2005) 73: 345-373
Fox, S., Mickler, R. A. (Eds.). 1996. Impact of Air Pollutants on Southern Pine Forests,
Ecological Studies. (Vol. 118, 513 pp.) New York: Springer-Verlag.
Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory
and Methods. Washington, DC: Resources for the Future.
Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 2001.
The effects of ambient air pollution on school absenteeism due to respiratory illnesses.
Epidemiology 12(l):43-54.
Gold, D.R., A. Litonjua, J. Schwartz, E. Lovett, A. Larson, B. Nearing, G. Allen, M. Verrier,
R. Cherry., and R. Verrier. 2000. Ambient Pollution and Heart Rate Variability.
Circulation 101(11): 1267-73.
Grulke, N.E. 2003. The physiological basis of ozone injury assessment attributes in Sierran
conifers. In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in
the Sierra Nevada: Distribution and effects on forests, (pp. 55-81). New York, NY:
Elsevier Science, Ltd.
Guallar, et. al., 2002. "Mercury, Fish Oils, and the Risk of Myocardial Infarction." New
England Journal of Medicine, Vol. 374, No. 22, November.
Hall JV, Brajer V, Lurmann FW. 2003. Economic Valuation of Ozone-related School
Absences in the South Coast Air Basin of California. Contemporary Economic Policy
21(4):407-417.
Hallgren et al. 2001. "Markers of high fish intake are associated with decreased risk of a first
myocardial infarction," British Journal of Nutrition, 86, 397-404.
199
-------
Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health and Safety
Regulation. Journal of Urban Economics 22:101-112.
Heck, W.W. &Cowling E.B. 1997. The need for a long term cumulative secondary ozone
standard - an ecological perspective. Environmental Management, January, 23-33.
Hollman, F.W., T.J. Mulder, and J.E. Kalian. January 2000. Methodology and Assumptions
for the Population Projections of the United States: 1999 to 2100. Population Division
Working Paper No. 38, Population Projections Branch, Population Division, U.S. Census
Bureau, Department of Commerce.
Hrabik, T.R., and C.J. Watras. 2002. Recent declines in mercury concentration in a
freshwater fishery: isolating the effects of de-acidification and decreased atmospheric
mercury deposition in Little Rock Lake. Science of the Total Environment 297:229-237.
Huang Y. Dominici F. Bell M. 2005. Bayesian Hierarchical Distributed Lag Models for
Summer Ozone Exposure and Cardio-Respiratory Mortality Environmetrics, 16, 547-562.
Hubbell BJ, Fann N, Levy JI. 2009. Methodological Considerations in Developing Local-
Scale Health Impact Assessments: Balancing National, Regional and Local Data. Air
Qual Atmos Health doi: 10.1007/sll869-009-0037-z [online 31 March 2009],
Hubbell BJ, Hallberg A, McCubbin D, Post, E. 2005. Health-Related Benefits of Attaining
the 8-Hr Ozone Standard. Environ Health Perspect 113: 73—82.
Hutchison, R., and C.E. Kraft. 1994. Hmong Fishing Activity and Fish Consumption.
Journal of Great Lakes Research 20(2):471-487.
Hutchinson, J., D. Maynard, and L. Geiser. 1996. Air Quality and Lichens —A Literature
Review Emphasizing the Pacific Northwest, USA. USD A Forest Service, Pacific
Northwest Region Air Resource Management Program, U.S. Forest Service, U.S.
Department of Agriculture.
Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the
Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared
for the U.S. EPA, Office of Air Quality Planning and Standards, September. Available
on the Internet at .
Industrial Economics, Incorporated (IEc). 1994. Memorandum to Jim DeMocker, Office of
Air and Radiation, Office of Policy Analysis and Review, U.S. Environmental Protection
Agency. March 31.
200
-------
Intergovernmental Panel on Climate change (IPCC). 2007. Climate Change 2007 - Synthesis
Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of
the IPCC.
Ito, K. 2003. Associations of Particulate Matter Components with Daily Mortality and
Morbidity in Detroit, Michigan. In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Health Effects Institute, Boston, MA.
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.
Jaffe DH, Singer ME, Rimm AA. 2003. Air pollution and emergency department visits for
asthma among Ohio Medicaid recipients, 1991-1996. Environ Res 91(l):21-28.
Jenkins, D.H., J. Sullivan, and G.S. Amacher. 2002. Valuing high altitude spruce-fir forest
improvements: Importance of forest condition and recreation activity. Journal of Forest
Economi c s 8:77-99.
Jerrett M, Burnett RT, Pope CA, III, et al. 2009. Long-Term Ozone Exposure and Mortality.
N Engl J Med 360:1085-95.
Joslin, J.D., Kelly, J.M., van Miegroet, H. 1992. Soil chemistry and nutrition of North
American spruce-fir stands: evidence for recent change. Journal of Environmental
Quality, 21, 12-30.
Kahn, J.R., and W.M. Kemp. 1985. Economic Losses Associated with the Degradation of an
Ecosystem: The Case of Submerged Aquatic Vegetation in Chesapeake Bay." Journal of
Environmental Economics and Management 12:246-263.
Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On
National Park Recreation. Final Report October 2003, under Cooperative Agreement CA
1200-99-009, Project number IMDE-02-0070.
Kleckner, N., and J. Neumann. June 3, 1999. Recommended Approach to Adjusting WTP
Estimates to Reflect Changes in Real Income. Memorandum to Jim Democker, U.S.
EPA/OPAR.
Knowler, D. 2002. A Review of Selected Bioeconomic Models: with Environmental
Influences in Fisheries. Journal of Bioeconomics 4:163-181.
Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining
201
-------
Estimates of the Value of Statistical Life for Environmental Policy Analysis.
Environmental and Resource Economics. 34: 385-406.
Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. 1985. Implications of environmental
policy for U.S. agriculture: the case of ambient ozone standards. Journal of
Environmental Management, 20, 321-331.
Kramer, A., T. Holmes, and M. Haefel. 2003. Contingent valuation of forest ecosystem
protection. Pp. 303-320 in Forests in a Market Economy. Edited by E.O. Sills and K.L.
Abt. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M.
Abrahamowicz, and W.H. White. 2000. Reanalysis of the Harvard Six Cities Study and
the American Cancer Society Study of Particulate Air Pollution and Mortality. Special
Report to the Health Effects Institute. Cambridge MA. July.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up
and spatial analysis of the American Cancer Society study linking particulate air
pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston,
MA.
Krupnick, A.J., and M.L. Cropper. 1992. The Effect of Information on Health Risk
Valuations. Journal of Risk and Uncertainty 5(2):29-48.
Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001.
Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates
Based on Time Series or on Cohort Studies? American Journal of Epidemiology
153(11): 1050-55.
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.
Lave, L.B., and E.P. Seskin. 1977. Air Pollution and Human Health. Baltimore: Johns
Hopkins University Press for Resources for the Future.
Leeworthy, V.R., and P.C. Wiley. 2001. Current Participation Patterns in Marine Recreation.
Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric
Administration, National Ocean Service, Special Projects. November. Available on the
Internet at < http://www.naturetourismplanning.com/pdfs/NSRE_2.pdf>.
202
-------
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]
Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. 2005. Ozone exposure and mortality: an
empiric bayes metaregression analysis. Epidemiology. 16(4): p. 458-68.
Leistritz, F. L., D. A. Bangsund, and N. M. Hodur. 2004. Assessing the Economic Impact of
Invasive Weeds: The Case of Leafy Spurge {Euphorbia esula). Weed Technology
18:1392-1395.
Leitch, J. A, F. L Leistritz, and D. A Bangsund. 1996. Economic effect of leafy spurge in the
upper Great Plains: methods, models, and results. Impact Assessment 14: 419-434.
Liao, D., J. Creason, C. Shy, R. Williams, R. Watts, and R. Zweidinger. 1999. Daily
Variation of Particulate Air Pollution and Poor Cardiac Autonomic Control in the
Elderly. Environ Health Perspect 107:521-5.
Liao, D., J. Cai, W.D. Rosamond, R.W. Barnes, R.G. Hutchinson, E.A. Whitsel, P.
Rautaharju, and G. Heiss. 1997. Cardiac Autonomic Function and Incident Coronary
Heart Disease: A Population-Based Case-Cohort Study. The ARIC Study.
Atherosclerosis Risk in Communities Study. American Journal of Epidemiology
145(8):696-706.
Lipfert, F.W., S.C. Morris, and R.E. Wyzga. 1989. Acid Aerosols—the Next Criteria Air
Pollutant. Environmental Science & Technology 23(11): 1316-1322.
Lipfert, F.W., H. Mitchell Perry Jr., J. Philip Miller, Jack D. Baty, Ronald E. Wyzg, and
Sharon E. Carmody. 2000. The Washington University-EPRI Veterans' Cohort Mortality
Study: Preliminary Results. Inhalation Toxicology 12:41-74.
Lipfert, F.W.; Perry, H.M., Jr.; Miller, J.P.; Baty, J.D.; Wyzga, R.E.; Carmody, S.E. 2003.
Air Pollution, Blood Pressure, and Their Long-Term Associations with Mortality.
Inhalation Toxicology. 15, 493-512.
203
-------
Lipfert, F.W.; Wyzga, R.E.; Baty, J.D.; Miller, J.P. 2006. Traffic Density as a Surrogate
Measure of Environmental Exposures in Studies of Air Pollution Health Effects: Long-
Term Mortality in a Cohort of US Veterans. Atmospheric Environment 40: 154-169.
Lipton, D. W. 1999. Pfiesteria's economic impact on seafood industry sales and recreational
fishing, p. 35-38. In B. L. Gardner and L. Koch (ed.), Proceedings of the Conference,
Economics of Policy Options for Nutrient Management and Pfiesteria. Center for
Agricultural and Natural Resource Policy, University of Maryland, College Park.
Loehman, E.T., S. Park, and D. Boldt. 1994. "Willingness to Pay for Gains and Losses in
Visibility and Health." Land Economics 70(4): 478-498.
Magari, S.R., R. Hauser, J. Schwartz, P.L. Williams, T.J. Smith, and D.C. Christiani. 2001.
Association of Heart rate Variability with Occupational and Environmental Exposure to
Particulate Air Pollution. Circulation 104(9):986-91.
Malm, WC. 1999. Introduction to visibility. Colorado State University, Fort Collins, CO,
USA, 1983, Revised edition, 1999. Available on the Internet at
.
McBride, J.R., Miller, P.R., Laven, R.D. 1985. Effects of oxidant air pollutants on forest
succession in the mixed conifer forest type of southern California. In: Air Pollutants
Effects on Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.
McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck, and M.
Thayer. September 1993. Valuing Eastern Visibility: A Field Test of the Contingent
Valuation Method. Prepared for Office of Policy, Planning and Evaluation, U.S.
Environmental Protection Agency. Available on the Internet at <
http://yoseinite.epa.gov/ee/epa/eenn.nsf7vwAN/EE-0008-l ,pdf/$file/EE-0008-1 ,pdf>
Millennium Ecosystem Assessment Board (MEA). 2005. Ecosystems and Human Well-
being: Synthesis. Washington, DC: World Resources Institute.
Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western
coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency,
Environmental Research Laboratory (EPA600-D-82-276).
Moolgavkar SH, Luebeck EG, Anderson EL. 1997. Air pollution and hospital admissions for
respiratory causes in Minneapolis St. Paul and Birmingham. Epidemiology. 8(4):364-
370.
204
-------
Moolgavkar, S.H. 2003. Air Pollution and Daily Deaths and Hospital Admissions in Los
Angeles and Cook Counties. In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Boston, MA: Health Effects Institute.
Moolgavkar, S.H. 2000. Air Pollution and Hospital Admissions for Diseases of the
Circulatory System in Three U.S. Metropolitan Areas. Journal of the Air and Waste
Management Association 50:1199-1206.
Mrozek, J.R., and L.O. Taylor. 2002. What Determines the Value of Life? A Meta-
Analysis. Journal of Policy Analysis and Management 21(2):253-270.
Munthe, J., R.A. Bodaly, B.A. Branfireun, C.T. Driscoll, C.C. Gilmour, R. Harris, M.
Horvat, M. Lucotte, and O. Malm. 2007. Recovery of mercury-contaminated fisheries.
AMBIO:A Journal of the Human Environment 36:33-44.
Myers, M.D., M.A. Ayers, J.S. Baron, P.R. Beauchemin, K.T. Gallagher, M.B. Goldhaber,
D.R. Hutchinson, J.W. LaBaugh, R.G. Sayre, and S.E. Schwarzbach. 2007. USGS goals
for the coming decade. Science 318:200-201.
National Agricultural Statistics Service (NASS). 2008. Maple Syrup - June 12, 2008: Maple
Syrup Production Up 30 Percent Nationwide. U.S. Department of Agriculture, National
Agricultural Statistics Service, New England Agricultural Statistics, Concord, NH.
National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996,
Indicator 42: Student Absenteeism and Tardiness. U.S. Department of Education.
Washington, DC.
National Center for Health Statistics (NCHS). 1999a. Health Interview Survey (HIS).
National Vital Statistics Reports. U.S. Department of Health and Human Services,
Centers for Disease Control and Prevention, National Center for Health Statistics.
Washington, DC. Volume 47, Number 19. June 30.
National Center for Health Statistics (NCHS). 1999b. National Hospital Discharge Survey
(NHDS). Available on the internet at
.
National Center for Health Statistics (NCHS). 2000. National Hospital Ambulatory Medical
Care Survey (NHAMCS). Available on the internet at
.
National Oceanic and Atmospheric Administration (NOAA). 2007. Annual Commercial
Landing Statistics. August. Available on the Internet at
-------
stl/commercial/landings/annual_landings.html>.
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. p. 229.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed
Air Pollution Regulations. Washington, DC: The National Academies Press.
National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and
Economic Benefits from Controlling Ozone Air Pollution. National Academies Press.
Washington, DC.
Neumann, J.E., M.T. Dickie, and R.E. Unsworth. March 31, 1994. Linkage Between Health
Effects Estimation and Morbidity Valuation in the Section 812 Analysis—Draft
Valuation Document. Industrial Economics Incorporated (IEc) Memorandum to Jim
DeMocker, U.S. Environmental Protection Agency, Office of Air and Radiation, Office
of Policy Analysis and Review.
Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. 1999.
An Association between Fine Particles and Asthma Emergency Department Visits for
Children in Seattle. Environmental Health Perspectives 107(6):489-493.
Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. Air Pollution and
Exacerbation of Asthma in African-American Children in Los Angeles. Epidemiology
12(2):200-208.
Ostro, B.D. 1987. Air Pollution and Morbidity Revisited: A Specification Test. Journal of
Environmental Economics Management 14:87-98.
Ostro, B.D. and S. Rothschild. 1989. Air Pollution and Acute Respiratory Morbidity: An
Observational Study of Multiple Pollutants. Environmental Research 50:238-247.
Ozkaynak, H., and G.D. Thurston. 1987. Associations between 1980 U.S. Mortality Rates
and Alternative Measures of Airborne Particle Concentration. Risk Analysis 7(4):449-
461.
Peel, J. L., P. E. Tolbert, M. Klein, et al. 2005. Ambient air pollution and respiratory
206
-------
emergency department visits. Epidemiology. Vol. 16 (2): 164-74.
Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman. 2001. Increased Particulate Air
Pollution and the Triggering of Myocardial Infarction. Circulation 103:2810-2815.
Peterson, D.E., M.S. Kanarek, M.A. Kuykendall, J.M. Diedrich, H.A. Anderson, P.L.
Remington, and T.B. Sheffy. 1994. Fish Consumption Patterns and Blood Mercury
Levels in Wisconsin Chippewa Indians. Archives of Environmental Health 49(l):53-58.
Pitchford, M. and W. Malm. 1993. Development and applications of a standard visual index.
Atmospheric Environment 28(5): 1049-1054.
Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. Daily Time
Series for Cardiovascular Hospital Admissions and Previous Day's Air Pollution in
London, UK. Occupational and Environmental Medicine 54(8):535-540.
Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. Respiratory Health
and PMio Pollution: A Daily Time Series Analysis. American Review of Respiratory
Diseases 144:668-674.
Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and
C.W. Heath, Jr. 1995. Particulate Air Pollution as a Predictor of Mortality in a
Prospective Study of U.S. Adults. American Journal of Respiratory Critical Care
Medicine 151:669-674.
Pope, CA III, E Majid, D Dockery. 2009. Fine Particle Air Pollution and Life Expectancy in
the United States. New England Journal of Medicine 360: 376—386.
Pope, C.A., III, R.T. Burnett, G.D. Thurston, M.J. Thun, E.E. Calle, D. Krewski, and J.J.
Godleski. 2004. Cardiovascular Mortality and Long-term Exposure to Particulate Air
Pollution. Circulation 109: 71-77.
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.
Prasad, A.M. and Iverson, L.R. 2003. Little's range and FIA importance value database for
135 eastern U.S. tree species. Northeastern Research Station, USDA Forest Service.
Available on the Internet at
http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html
Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: A review. Journal of
207
-------
Environmental Quality, 17, 347-360.
Rae, D.A. 1983. Benefits of Visual Air Quality in Cincinnati Results of a Contingent
Ranking Survey. Prepared for Electric Power Research Institute. May.
Ransom, Michael, and C. Arden Pope. 1992. M.R. Ransom and C.A. Pope, III, Elementary
school absences and PMi0 pollution in Utah Valley. Environ. Res. 58, pp. 204-219.
Rissanen, T., S Voutilainen, T.A. Lakka, and J.T. Salonen. 2000. "Fish oil-derived fatty
acids, docasahexaenoic acid and docosapentainoic acid, and the risk of acute coronary
events: The Kuopio ischaemic heart disease risk factor study", Circulation. 102(22),
pp2677-2679.
Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond,
Bryan J. Hubbell, and Patrick L. Kinney. 2008. Expert Judgment Assessment of the
Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. Environ.
Sci. Technol., 42(7):2268-2274.
Rowe, R.D., and L.G. Chestnut. 1986. Oxidants and Asthmatics in Los Angeles: A Benefits
Analysis- Executive Summary. Prepared by Energy and Resource Consultants, Inc.
Report to the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA-
230-09-86-018. Washington, DC.
Rowlatt, P., Spackman, M., Jones, S., Jones-Lee, M., Loomes, G. 1998. Valuation of deaths
from air pollution. A Report for the Department of Environment, Transport and the
Regions and the Department of Trade and Industry. National Economic Research
Associates (NERA), London.
Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. Direct Medical
Costs of Coronary Artery Disease in the United States. American Journal of Cardiology
81 (9): 111 0-1115.
Samet, J.M., S.L. Zeger, F. Dominici, F. Curriero, I. Coursac, D.W. Dockery, J. Schwartz,
and A. Zanobetti. 2000. The National Morbidity, Mortality and Air Pollution Study: Part
II: Morbidity, Mortality and Air Pollution in the United States. Research Report No. 94,
Part II. Health Effects Institute, Cambridge MA. June.
Salonen, 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.
208
-------
Salonen, J.T., Seppanen,T.A. Lakka, R. Salonen and G.A. Kaplan. 2000. "Mercury
accumulation and accelerated progression of carotid atherosclerosis: A population-based
prospective 4-year follow-up study in men in Eastern Finland. Atherosclerosis. 148:
pp265-273.
Schwartz J. 1995. Short term fluctuations in air pollution and hospital admissions of the
elderly for respiratory disease. Thorax. 50(5):531-538.
Schwartz J. 1994b. Air Pollution and Hospital Admissions For the Elderly in Detroit,
Michigan. Am J Respir Crit Care Med. 150(3):648-655.
Schwartz J. 1994a. PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis
St Paul, Minnesota. Arch Environ Health. 49(5):366-374.
Schwartz, J., and L.M. Neas. 2000. Fine Particles are More Strongly Associated than
Coarse Particles with Acute Respiratory Health Effects in Schoolchildren. Epidemiology
11:6-10.
Schwartz, J. 2005. How sensitive is the association between ozone and daily deaths to
control for temperature? Am J Respir Crit Care Med. Vol. 171 (6): 627-31.
Schwartz, J. 1993. Particulate Air Pollution and Chronic Respiratory Disease.
EnvironmentResearch 62:7-13.
Scudder, B.C., Chasar, L.C., Wentz, D.A., Bauch, N.J., Brigham, M.E., Moran, P.W., and
Krabbenhoft, D.P. 2009. Mercury in fish, bed sediment, and water from streams across
the United States, 1998-2005: U.S. Geological Survey Scientific Investigations Report
2009-5109, 74 p.
Sheppard, L. 2003. Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in
Seattle, Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Boston, MA: Health Effects Institute.
Shogren, J., and T. Stamland. 2002. Skill and the Value of Life. Journal of Political Economy
110:1168-1197.
Sisler, J.F. 1996. Spatial and seasonal patterns and long-term variability of the composition
209
-------
of the haze in the United States: an analysis of data from the IMPROVE network. CIRA
Report, ISSN 0737-5352-32, Colorado State University.
Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders.
1997. A National Estimate of the Economic Costs of Asthma. American Journal of
Respiratory and Critical Care Medicine 156(3 Pt l):787-793.
Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. Benefit Transfer via Preference
Calibration. Land Economics 78:132-152.
Smith, G., Coulston, J., Jepsen, E., Prichard, T. 2003. A national ozone biomonitoring
program—results from field surveys of ozone sensitive plants in Northeastern forests
(1994-2000). Environmental Monitoring and Assessment, 87, 271-291.
Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. Benefit Transfer via Preference
Calibration. Land Economics 78:132-152.
N. Sorensen, 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.
Spencer, D.M., and D.F. Holecek. 2007. Basic characteristics of the fall tourism market.
Tourism Management 28:491-504.
Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. The cost of asthma in the emergency
department and hospital. Am J Respir Crit Care Med. Vol. 160 (1): 211-5.
Stern, A. 2005, "A review of the studies of the cardiovascular health effects of
methylmercury with consideration of their suitability for risk assessment," Environmental
Research, 98 133-142.
Stieb, D. M.; Judek, S.; Burnett, R. T. 2002. Meta-analysis of time-series studies of air
pollution and mortality: effects of gases and particles and the influence of cause of death,
age, and season. J. Air Waste Manage. Assoc. 52: 470-484.
Stieb, D. M.; Judek, S.; Burnett, R. T. 2003. Meta-analysis of time-series studies of air
pollution and mortality: update in relation to the use of generalized additive models. J.
Air Waste Manage. 53: 258-261.
Tagaris E, Liao KJ, Delucia AJ, et al. 2009. Potential impact of climate change on air-
pollution related human health effects. Environ. Sci. Technol. 43: 4979—4988.
Taylor R. 1994. Deterministic versus stochastic evaluation of the aggregate economic effects
210
-------
of price support programs. Agricultural Systems 44: 461-473.
Tingey, D.T., and Taylor, G.E. 1982 Variation in plant response to ozone: a conceptual
model of physiological events. In M.H. Unsworth & D.P. Omrod (Eds.), Effects of
Gaseous Air Pollution in Agriculture and Horticulture, (pp.113-138). London, UK:
Butterworth Scientific.
Tolley, G.S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks.
University of Chicago. Final Report for the U.S. Environmental Protection Agency.
January
Tolley, G., A. Randall, G. Blomquist, M. Brien, R. Fabian, G. Fishelson, A. Frankel, M.
Grenchik, J. Hoehn, A. Kelly, R. Krumm, E. Mensah, and T. Smith. 1986. Establishing
and Valuing the Effects of Improved Visibility in Eastern United States. Prepared for
U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation.
U.S.Environmental Protection Agency Grant #807768-01-0.
Tsuji, H., M.G. Larson, F.J. Venditti, Jr., E.S. Manders, J.C. Evans, C.L. Feldman, D. Levy.
1996. Impact of Reduced Heart Rate Variability on Risk for Cardiac Events. The
Framingham Heart Study. Circulation 94(11):2850-2855.
U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race,
Hispanic Origin and Nativity: 1999 to 2100. Population Projections Program, Population
Division, U.S. Census Bureau, Washington, DC. Available on the Internet at
.
U.S. Bureau of the Census. 2001. Statistical Abstract of the United States, 2001, Section 12:
Labor Force, Employment, and Earnings, Table No. 521. Washington, DC.
U.S. Department of Agriculture - Forest Service (USDA Forest Service). 2006. Ozone
bioindicator data. Accessed 2006. Available on the Internet at
.
U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of
Commerce, U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and
Wildlife-Associated Recreation (NSRE). Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 1999a. Regional Haze Regulations, July
1, 1999. (64 FR 35714, July 1, 1999).
U.S. Environmental Protection Agency (U.S. EPA). 1999b. The Benefits and Costs of the
211
-------
Clean Air Act 1990 to 2010: EPA Report to Congress. Office of Air and Radiation,
Office of Policy, Washington, DC. November. EPA report no. EPA410-R-99-001.
Available on the Internet at .
U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing
Economic Analyses. EPA 240-R-00-003. National Center for Environmental
Economics, Office of Policy Economics and Innovation. Washington, DC. September.
Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2001. Water Quality Criterion for the
Protection of Human Health: Methylmercury (Final). Office of Science and Technology.
Office of Water. January. Washington, DC. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2002. Letter to Recipients of Third
Draft EPA PM Criteria Document. National Center for Environmental Assessment,
Research Triangle Park, NC. June 5, 2002. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2004. Air Quality Criteria for
Particulate Matter Volume II of II. National Center for Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC EPA/600/P-99/002bF. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2005. Regulatory Impact Analysis for
the Final Clean Air Interstate Rule. Office of Air and Radiation. March. Available on
the Internet at < http://www.epa.gov/cair/pdfs/finaltech08.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2006a. Air Quality Criteria for Ozone
and Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington,
DC: U.S. EPA. February. Available on the Internet at
http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.
U.S. Environmental Protection Agency (U.S. EPA). 2006b. Ecological Benefits Assessment
Strategic Plan. EPA-240-R-06-001. Office of the Administrator. Washington, DC.
October. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2006c. Willingness to Pay for
Environmental Health Risk Reductions when there are Varying Degrees of Life
212
-------
Expectancy: A White Paper. August. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2006d. Regulatory Impact Analysis,
2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office
of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available
on the Internet at .
U.S. Environmental Protection Agency (U.S. EPA). 2007a. 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. EPA-454/B-
07-002. April. Available on the Internet at
http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf
U.S. Environmental Protection Agency (U.S. EPA). 2007b. Review of the National Ambient
Air Quality Standards for Ozone: Policy assessment of scientific and technical
information. Staff paper. Office of Air Quality Planning and Standards. EPA-452/R-07-
007a. July. Available on the Internet at
http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_ozone_staff_paper.pdf
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 .
U.S. Environmental Protection Agency (U.S. EPA). 2008b. Technical Support Document:
Calculating Benefit Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2007-
0225-0284. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
March. Available on the Internet at .
U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria (Final Report). National Center for
Environmental Assessment, Research Triangle Park, NC. July. Available on the Internet
at http: //cfpub. epa. gov/ncea/cfm / recordi spl ay. cfm? dei d= 194645
U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment
for Sulfur Oxides - Health Criteria (Final Report). National Center for Environmental
Assessment, Research Triangle Park, NC. September. Available on the Internet at <
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.
U.S. Environmental Protection Agency (U.S. EPA). 2008e. Guidelines for Preparing
213
-------
Economic Analyses: External Review Draft. National Center for Environmental
Economics, Office of Policy Economics and Innovation. Washington, DC. September.
Available on the Internet at .
U.S. Environmental Protection Agency (U.S. EPA). 2008f. Integrated Science Assessment
for Oxides of Nitrogen and Sulfur -Ecological Criteria National (Final Report). National
Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139.
December. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis:
National Emission Standards for Hazardous Air Pollutants from the Portland Cement
Manufacturing Industry. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. April. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2009b. The NOx Budget Trading
Program: 2008 Environmental Results. Clean Air Markets Division. September.
Available on the Internet at
http://www.epa.gov/airmarkt/progress/NBP_3/NBP_2008_Environmental_Results.pdf
U.S. Environmental Protection Agency (U.S. EPA). 2009c. Risk and Exposure Assessment
for Review of the Secondary National Ambient Air Quality Standards for Oxides of
Nitrogen and Oxides of Sulfur (Final). EPA-452/R-09-008a. Office of Air Quality
Planning and Standards, Research Triangle Park, NC. September. Available on the
Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2009d. Integrated Science Assessment
for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for
Environmental Assessment - RTP Division. December. Available on the Internet at
.
U.S. Environmental Protection Agency (U.S. EPA). 2009e. 2008 National Listing of Fish
Advisories. Fish Advisory Program. EPA-823-F-09-007. September. Available on the
Internet at .
U.S. Environmental Protection Agency (U.S. EPA) 2010a. Regulatory Impact Analysis,
National Ambient Air Quality Standards for Ozone, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. October. Available on the Internet at <
214
-------
http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact
Analysis (RIA) for the N02 National Ambient Air Quality Standards (NAAQS). Office
of Air Quality Planning and Standards, Research Triangle Park, NC. January. Available
on the Internet at
.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 1999. An
SAB Advisory: The Clean Air Act Section 812 Prospective Study Health and Ecological
Initial Studies. Prepared by the Health and Ecological Effects Subcommittee (HEES) of
the Advisory Council on the Clean Air Compliance Analysis, Science Advisory Board,
U.S. Environmental Protection Agency. Washington DC. EPA-SAB-COUNCIL-ADV-
99-005.
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2000.
An SAB Report on EPA's White Paper Valuing the Benefits of Fatal Cancer Risk
Reduction. EPA-SAB-EEAC-00-013.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2004.
Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA's Second
Prospective Analysis - Benefits and Costs of the Clean Air Act, 1990-2020. EPA-SAB-
COUNCIL-ADV-04- 002 March.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2006.
Clean Air Scientific Advisory Committee's (CASAC) Peer Review of the Agency's 2nd
Draft Ozone Staff Paper. EPA-CASAC-07-001. October. Available on the Internet at
.
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2007a.
SAB Advisory on EPA's Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-
001. October. Available on the Internet at
.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2007b.
Clean Air Scientific Advisory Committee's (CAS AC) Review of the Agency's Final
Ozone Staff Paper. EPA-CASAC-07-002. Available on the Internet at
http://yosemite.epa.gov/sab/sabproduct.nsf/FE915E916333D776852572AC007397B5/$F
ile/casac-07-002.pdf
215
-------
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2008.
Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA-
COUNCIL-08-002. July. Available on the Internet at <
http://yosemite.epa.gOv/sab%5CSABPRODUCT.NSF/43B91173651AED9E8525748700
4EA6CB/$File/EPA-COUNCIL-08-002-unsigned.pdf>.
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009a.
Review of EPA's Integrated Science Assessment for Particulate Matter (First External
Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the
Internet at
.
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009b.
Consultation on EPA's Particulate Matter National Ambient Air Quality Standards:
Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-
09-009. May. Available on the Internet at
.
U.S. Environmental Protection Agency and U.S. Food and Drug Administration (U.S.
EPA/FDA). 2004. What You Need to Know About Mercury in Fish and Shellfish:
Advice for Women Who Might Become Pregnant Women, Who are Pregnant, Nursing
Mothers, Young Children. EPA-823-F-04-009. Available on the Internet at
.
U.S. Fish and Wildlife Service and U.S. Census Bureau (FWS and Census). 2007. 2006
National Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR).
FHW/06-NAT. U.S. Department of the Interior, U.S. Fish and Wildlife Service,
Washington, DC, and U.S. Department of Commerce, U.S. Census Bureau, Washington,
DC.
U.S. Food and Drug Administration (U.S. FDA). 2001. Fish and Fisheries Products Hazards
and Controls Guidance: Chapter 10: Methyl Mercury. Third Edition. June. Available on
the Internet at <
http://www.fda.gov/Food/GuidanceComplianceRegulatoryInformation/GuidanceDocume
nts/Seafood/ucm092041 ,htm>.
U.S. Forest Service (USFS). 2006. Forest Inventory and Analysis National Program: Forest
Inventory Data Online. Online database. U.S. Department of Agriculture Forest Service,
Forest Inventory and Analysis, Arlington, VA. Available on the Internet at
216
-------
.
U.S. Geological Survey (USGS). 2006. Investigations and Monitoring of Mercury in Indiana
by the U.S. Geological Survey. U.S. Department of the Interior, U.S. Geological Survey,
Indiana Water Science Center, Indianapolis, IN. Available
athttp://in. water.usgs.gov/mercury.
Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E.
Turner. 2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric Perspective.
Washington, DC: American Geophysical Union
Van Sickle, J., Baker, J.P., Simonin, H.A., Baldigo, B.P., Kretser, W.A., Sharpe, W.E. 1996.
Episodic acidification of small streams in the northeastern United States: Fish mortality
in field bioassays. Ecological Applications, 6, 408-421.
Vedal, S., J. Petkau, R. White, and J. Blair. 1998. Acute Effects of Ambient Inhalable
Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and
Critical Care Medicine 157(4):1034-1043.
Virtanen JK, S Voutilainen, T.H. Rissanen, J Mursu, T.P.Tuomainen, M.J. Korhonen,
V.P.Walkonen, K. Seppanen,J.A. Laukkanen, J.T.Salonen.Grandjean, P., K. Murata, E.
Budtz-Jorgensen, and P.Weihe. 2004. "Autonomic Activity in Methylmercury
Neurotoxicity: 14-Year Follow-Up of a Faroese Birth Cohort", The Journal of Pediatrics,
February, pp 169-176.
Viscusi, V.K., and J.E. Aldy. 2003. The Value of a Statistical Life: A Critical Review of
Market Estimates throughout the World. Journal of Risk and Uncertainty 27(l):5-76.
Viscusi, W.K., W.A. Magat, and J. Huber. 1991. Pricing Environmental Health Risks:
Survey Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis.
Journal of Environmental Economics and Management 21:32-51.
Wilson, A. M., C. P. Wake, T. Kelly, et al. 2005. Air pollution, weather, and respiratory
emergency room visits in two northern New England cities: an ecological time-series
study. Environ Res. Vol. 97 (3): 312-21.
Winner, W.E. 1994. Mechanistic analysis of plant responses to air pollution. Ecological
Applications, 4(4), 651-661.
217
-------
Winner, W.E., and C.J. Atkinson. 1986. Absorption of air pollution by plants, and
consequences for growth. Trends in Ecology and Evolution 1:15-18.
Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. Medical Costs of Coronary Artery
Disease in the United States. American Journal of Cardiology 65(7):432-440.
Woodruff, T.J., J. Grillo, andK.C. Schoendorf. 1997. The Relationship Between Selected
Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United
States. Environmental Health Perspectives 105(6):608-612.
Woodruff TJ, Parker JD, Schoendorf KC. 2006. Fine particulate matter (PM2.5) air pollution
and selected causes of postneonatal infant mortality in California. Environmental Health
Perspectives 114(5):786-90.
Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM.
Woods & Poole Economics, Inc. Washington, D.C.
Yoshizawa, K et. al. 2002. "Mercury and the Risk of Coronary Heart Disease in Men," New
England Journal of Medicine, Vol. 374, No. 22, November.
218
-------
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).
219
-------
Table 6-1. Existing Electricity Generating Capacity by Energy Source, 2008
Number of
Generators
Generator
Generator Net
Energy Source
Nameplate
Summer
Capacity (MW)
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
Share of Total End
(Billion kWh)
Use
Residential
1,380
35%
Retail Sales
Commercial
1,336
34%
Industrial
1,009
26%
Transportation
8
0.2%
Direct Use
173
4%
Total End Use
3,906
100%
Source: EIA Electric Power Annual 2008, Table 7.2
220
-------
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 Fuel Source
(Billion kWh) 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.
221
-------
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.
222
-------
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
223
-------
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/restracturing/restracture_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 S02 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 S02 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 S02
emissions and 18 percent of total nationwide NOx emissions (see Figure 6-2).
224
-------
Figure 6-2. Emissions of S02 and NOx from the Power Sector (2008)
Sulfur Dioxide
Nitrogen Oxides
Power Sector (18%)
Source: EPA http://www.epa.gov/ttn/cliief/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
225
-------
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
226
-------
N0X 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 oil-
and 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 S02 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 large-
diameter, 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.
227
-------
Federal regulation of S02 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 S02 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
S02, 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 S02 and NOx emissions. The S02 program sets a permanent cap
on the total amount of S02 that can be emitted by electric power plants in the contiguous
United States at about one-half of the amount of S02 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 S02 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 S02 and NOx. Phase I applied
primarily to the largest coal-fired electric generating sources from 1995 through 1999 for
S02 and from 1996 through 1999 for NOx. Phase II for both pollutants began in 2000. For
S02, 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-
228
-------
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 PM25. 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.
229
-------
Figure 6-3. States Covered under the Clean Air Interstate Rule
wa
WIT
OR
I ND 1
\ | i MN i
|D ~ j SD W
—r—J WV I 1
NV
CA
UT
AZ
mn
IA
-r— . I ME \
j CO
|
\ 1
KS
1
MO
OK
AR /
MM
TX
¦ '
1. LA I
IL IN
MS AL \ GA
FL
~ ozone and particles
~ ozone only
~ particles only
~ not covered by CAIR
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.
230
-------
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
231
-------
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 N0X 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
232
-------
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
233
-------
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 S02 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, S02 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, S02, 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.
234
-------
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 S02 emissions controls.
235
-------
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 S02 States
6,439,067
2,267,008
Nationwide (Contiguous 48 States)
7,620,588
3,098,267
Emissions of Transport Rule States as
84%
73%
Percentage of Nationwide Emissions
Source: EPA emissions data from all reporting units.
Note: Transport Rule annual NOx and S02 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 S02 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 S02 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 S02 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.
236
-------
Figure 7-1. Transport Rule Annual NOx and SO2 States
S02 group 1 (15 States)
S02 group 2 (12 States + DC)
in both groups are covered for annual NOx
237
-------
Figure 7-2. Transport Rule Ozone-season NOx States
I Ozone (25 States)
238
-------
Table 7-2. Transport Rule Annual NOx and SO2 State Emission Budgets (tons)
State
S02, 2012 and
S02, 2014 and
NOx Annual,
2013
Later
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 S02 States
3,117,288
1,723,421
Group 2 S02 States
776,582
776,582
Total
3,893,870
2,500,003
1,376,312
239
-------
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/.
240
-------
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.
241
-------
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.
242
-------
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 N0X 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
243
-------
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 Case3 (No Further
Controls) and with Transport Rule Options (Million Tons)
S02
(annual)
NOx
(annual)
NOx
(summer)
Coverage
Contiguous
48 States
Transport
Rule States
Contiguous
48 States
Transport
Rule States
Contiguous
48 States
Transport
Rule States
Base Case
2012 2014
C3
o
C3
m
-------
Figure 7-3. SO2 Emissions from the Power Sector in 2012 and 2014 with and without
the Transport Rule (State Budgets/Limited Trading)
States with the white backgrouds are
within the Transport Region.
| Base Case 2012
~1 State Budgets/Limited Trading 2012
I^\ Base Case 2014
| State Budgets/Limited Trading 2014
967,093 tons in Pennsylvania, Base Case 2012
Source: EPA, IPM, 2010.
245
-------
Figure 7-4. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and
without the Transport Rule (State Budgets/Limited Trading)
States with the white backgrouds are
within the Transport Region.
| Base Case 2012
H State Budgets/Limited Trading 2012
I^\ Base Case 2014
State Budgets/Limited Trading 2014
211,836 tons in Florida, Base Case 2012
Source: EPA, IPM, 2010.
246
-------
Figure 7-5. Ozone-season NOx Emissions from the Power Sector in 2012 and 2014 with
and without State Budgets/Limited Trading
States with the white backgrouds are
within the Transport Region.
| Base Case 2012
State Budgets/Limited Trading 2012
Base Case 2014
| State Budgets/Limited Trading 2014
Scale: I 101,367 tons in Florida, Base Case 2012
Source: EPA, IPM, 2010.
247
-------
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.
248
-------
Figure 7-7. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and
without State Budgets/Intrastate Trading
States with the white backgrouds are
within the Transport Region.
| Base Case 2012
State Budgets/Intrastate Trading 2012
Base Case 2014
| State Budgets/Intrastate Trading 2014
Scale: | 211,836 tons in Florida, Base Case 2012
Source: EPA, IPM, 2010.
249
-------
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.
250
-------
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.
251
-------
Figure 7-10. Annual NOx Emissions from the Power Sector in 2012 and 2014 with and
without Direct Control
States with the white backgrouds are
within the Transport Region.
| Base Case 2012
Direct Control 2012
|^| Base Case 2014
| I Direct Control 2014
211,836 tons in Florida, Base Case 2012
Source: EPA. IPM, 2010.
252
-------
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).
253
-------
Table 7-6. Fossil-fuel Generation Nationwide and in the Transport Region
2008 Generation (Thousand GWh)
Contiguous
Transport
Transport
48 States
Rule Fine
Rule Ozone
Particle Area
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
Transport
Transport
48 States
Rule Fine
Rule Ozone
Particle Area
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.
254
-------
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 $2.8 billion in
2014 (see Table 7-8 below). Another measure of this impact is the change in electricity
prices (discussed in section 7.9). Costs generally are higher in 2012 than in 2014 because of
reduced compliance flexibility in 2012, which is too soon for sources to retrofit new FGD
and SCR that were not already planned.
Table 7-8. Annualized Compliance Cost of the Transport Rule
State
Budgets/Limited
Trading
State
Budgets/Intrastate
Trading
Direct Control
Annualized Cost
(billions of 2006$)
2012
2014
2012
2014
2012
2014
$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
255
-------
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 next-
lowest 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 lower-
emitting 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 C AIR 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
256
-------
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)
Subbituminous
High sulfur
> 1.2
Low sulfur
0.7 to 1.2
Very low sulfur
<0.7
Bituminous
High sulfur
>2
High-medium sulfur
1.4 to 2
Low-medium sulfur
0.9 to 1.4
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
257
-------
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)
Lignite
Subbituminous
Bituminous
Total
High
sulfur
Low
sulfur
Very
low
sulfur
High
sulfur
High-
medium
sulfur
Low-
medium
sulfur
Low
sulfur
2012
Base case
3,911
3,405
125,460
176,479
285,511
106,575
72,952
4,507
778,800
Preferred
approach
3,911
2,143
63,858
246,828
258,847
101,383
89,826
10,518
777,315
2014
Base case
3,883
6,664
110,357
193,885
331,913
69,060
85,248
5,143
806,153
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 S02 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.
258
-------
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)
State
State Budgets/Limited
Budgets/Intrastate
Base Case
Trading
Trading
Direct Control
2012
2014
2012
2014
2012
2014
2012
2014
Total
Capacity
(GW)
Capacity Controlled Year
-round (GW)
&
_o
1
o
H
g
C/3
C/3
C/3
C/3
C/3
C/3
C/3
C/3
C/3
CO
00
S3
o
U
"3
Pi
«
o
a
is
CO
00
S3
o
U
"3
Pi
«
o
a
is
CO
00
S3
o
•5b
"3
Pi
«
o
a
CO
00
S3
o
•5b
"3
Pi
«
o
a
is
CO
00
S3
o
•5b
"3
Pi
«
o
a
is
CO
00
S3
o
•5b
"3
Pi
«
o
a
is
CO
00
S3
o
•5b
"3
Pi
«
o
a
is
CO
00
S3
o
•5b
"3
Pi
«
o
a
a
CO
00
S3
o
•5b
"3
Pi
«
o
a
=3
O
a
=3
O
a
=3
O
a
=3
O
a
=3
O
a
=3
o
a
=3
o
a
=3
o
a
=3
o
a
U
o
O
s
o
o
i-H
H
s
o
o
i-H
H
S3
o
O
i-H
H
S3
o
O
S-H
H
S3
o
O
i-H
H
s
o
o
S-H
H
S3
o
U
i-H
H
s
o
o
i-H
H
s
o
u
i-H
H
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. Coal-
fired 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.
259
-------
Table 7-12. Generation Mix with the Base Case (No Further Controls) and with
Transport Rule Options (Thousand GWh)
2008
1,967
21
798
1,171
3,957
O
-------
Figure 7-12. Generation Mix with the Base Case (No Further Controls) and with
Transport Rule Options
5,000
4,500
4,000
^ 3,500
0 3,000
1 2,500
Co '
U)
= 2,000
i— 1,500
1,000
500
0
03
O
0
(/)
03
CO
llllllll
"O
0
CD
03
0 ^ g5 & o>
03 ^ T3 -i-- C • —
~ ro ro — -a
3 D)
"O
o
O
o
0
03
O
CD
(/)
03
CO
"O
0
0 •-j
-i—* —I
-2 "co
to -ft
a)
"D
0
H—'
CO
0
H—'
CO
H—'
T3
CO
O
O
o
0
CO
CO
CO
0 I
O)
T3
CO
2012
2014
¦ 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
261
-------
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)
State
State
2008
Base Case
Budgets/Limited
Trading
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.
262
-------
Table 7-14. Coal Production for the Electric Power Sector with the Base Case (No
Further Controls) and with Transport Rule Options (Million Tons)
State State
Budgets/Limited Budgets/Intrastate
Historical
Base Case
Trading
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/tbl 1 .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)
State
Change
State
Change
Base
Budgets/Limited
from
Budgets/Intrastate
Percent
Direct
from
Year
Case
Trading
Base
Trading
Change
Control
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).
263
-------
Table 7-16. Retail Electricity Prices by NERC Region with the Base Case (No Further
Controls) and with Transport Rule Options (2006 Mills/kWh)
R
CJ
o
s
u
05
¦o
>3
2 M
«s2
05 H
¦o
a
feX)
2 s
55 03
feX)
=
-fl
U ^
feX) 55
.9 09
"S fl
C? S
>- p
H
feX)
& « 6X5
« ts .a
3 2 "2
c? 5 g
55 h
¦2 S
« ©
feJ3 C
¦O «
5 a bX)
« ts .a
3 2 "2
5 - C5
hS H CQ
fl
o
U
I a
§=§
u «
-M M
« § «
•S= X a
pun
Power
Region
ECAR
(1)
ERCOT
(2)
MAAC
(3)
MAIN
(4)
MAPP
(5)
NY
(6)
NE
(7)
Primary
States
included
OH, MI,
IN, KY,
WV, PA
TX
PA, NJ,
MD, DC,
DE
IL, MO,
WI
MN, IA,
SD, ND,
NE
NY
VT, NH,
ME, MA,
CT,RI
FL
FRCC
(8)
STV (9) VA, NC,
SC, GA,
AL, MS,
TN, AR,
LA
SPP KS, OK,
(10) MO
Regionwide
o
-------
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 lower-
sulfur 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)
265
-------
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)
State Budgets/
Percentage Change
State Budgets/
Percentage Change
Direct
Percentage Change
Base Case
Limited Trading
from Base
Intrastate Trading
from Base
Control
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)
State Budgets/
Percentage Change
State Budgets/
Percentage Change
Direct
Percentage Change
Base Case
Limited Trading
from Base
Intrastate Trading
from Base
Control
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
266
-------
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
267
-------
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 C02 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.
268
-------
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
269
-------
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
270
-------
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.
CO
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).
271
-------
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 S02, 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.
272
-------
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
273
-------
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.
274
-------
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/tbll.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
275
-------
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.
276
-------
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.
277
-------
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).
278
-------
Figure 8-1. General Production and Consumption Nesting Structure in EMPAX-CGE
Utility Household utility is a Constant
Consumption is a Cobb-
Douglas composite of
transportation plus goods.
Consumption
Elasticity of Substitution (CES)
function of consumption and leisure.
Leisure
Transportation is a
CES composite of
personal vehicle
transport and
Diirchascd transport.
Transportation
Consumpt an Goods
Personal vehicles
use fuel and
goods/services.
Petroleum
Personal
Transport
Consumption of
energy is a CES
composite of
energy and other
consumption
goods.
Services
Manufactured
Goods
Domestic
Domestic goods are a CES
composite of local goods and
goods from other U.S. regions.
Local
Output
Regional
Output
Each consumption good is
a CES composite of
foreign and domestically
produced goods.
Imports are a CES
composite across
foreign supply sources.
Most producer goods use
fixed proportions of
intermediate inputs and KLE.
KLE is a CES
composite of
energy (E) and
value-added (K -
capital and L -
labor).
Energy (E) is a
CES composite of 5
energy types. The
structure of this
function varies across
industries.
KLE
Intermediates Intermediate inputs are the
30 types of non-energy
goods, in fixed proportion
for each industry.
Energy
/f\
Energy (5
Types)
Value Added
Value added is a Cobb-
Douglas composite of
capital and labor (KL).
Capital
Labor
279
-------
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
6 5
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/ProductDetailllOO.htm for a description of the Global
Insight U.S. State Forecasting Service.
280
-------
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.
281
-------
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.
282
-------
Table 8-1. Industries in Dynamic EMPAX-CGE
EMPAX Industry
NAICS Classifications
Energy
Coal
2121
Crude oil3
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
Transportation0
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
283
-------
' Although NAICS 211111 covers crude oil and gas extraction, the gas component of this sector is moved to the
natural gas industry.
"Transportation does not include NAICS 4862 (natural gas distribution), which is part of the natural gas
industry.
°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
284
-------
$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 Cobb-
Douglas 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 p° and income m°) 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.
285
-------
EV = u(p°; p',m') - u(p°; p°, m°) = u(p°; p',m') - m°
where
p° = the baseline prices
m° = 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
286
-------
[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
7 3 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).
287
-------
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
S02, NOx, and mercury are not necessarily proportional to fuel use. These types of emissions
7 4 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).
288
-------
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 $/MMBTU)
289
-------
• capital costs ($ 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, recursive-
dynamic 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
290
-------
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/ttnecasl/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.
291
-------
Figure 8-3. Change in Macroeconomic Variables and Household Welfare (Percent
change and Change in billion $2006) in 2015
Change ($ billion)
Percent Change
$0.0
-$2.5
0.000%
-0.002%
-0.004% §>
-C
-0.006% °
O
Ui
-0.008% -j?
O
o
-0.010% S
Q_
-0.012%
0.014%
¦ GDP ^Consumption
n 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.
292
-------
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.
7 7 Additional details are described in EMPAX-CGE model documentation (5-2 to 5-5).
293
-------
Figure 8-4. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015
Northeast South Midwest Plains West US
0.10% -i
0.08%
g 0.06%
g 0.04%
g, 0.02% __
f 0.00% ,, ' ' ||
Q) I 1 I 1
g -0.02% H ~
^ -0.04%
-0.06%
-0.08%
-0.10%
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
294
-------
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).
295
-------
Figure 8-5. Output Changes in 2015: Energy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
South
1
¦
ro Midwest
o
° Plains
West
US
I
Northeast
_ South
® Midwest
5 Plains
O
West
US
Northeast
South
>>
^2 Midwest
8 Plains
LU
West
US
¦
r
Northeast
(o South
to
w Midwest
ro
^ Plains
to
z West
US
!
Northeast
South
5 Midwest
o
Plains
^ West
US
:
Note: Outcomes reflect changes in the physical quantities of goods/services each regional sector produces.
296
-------
Figure 8-6. Output Changes in 2015: Energy-Intensive Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
"g ^ South
^ Midwest
-a "a .
o .E Plains
£ West
US
1
1
Northeast
"g South
2 "§ Midwest
Plains
£_ West
US
¦
¦
1
¦
1
Northeast
| South
.2 Midwest
§ Plains
o West
US
1
1
¦
1
1
Northeast
South
$ Midwest
0 Plains
West
US
¦
1
¦
1
Northeast
^ South
S Midwest
a; Plains
° West
US
1
"33 Northeast
~ South
Midwest
ro Plains
g West
- US
¦
¦
1
¦
Northeast
i
E South
¦
.!= Midwest
i
| Plains
< West
US
¦
Note: Outcomes reflect changes in the physical quantities of goods/services each regional sector produces.
297
-------
Figure 8-7. Output Changes in 2015: Nonenergy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
oj South
^ Midwest
r,. ¦
= Plains
O)
< West
US
Northeast
CD
~ .E South
-2 Midwest
— ®
>^3 Plains
11 West
LU
US
1
1
ra Northeast
3 South
o
>2 Midwest
c
™ Plains
o3 West
o US
Northeast
South
w
0 Midwest
1 Plains
CO
West
US
Northeast
o South
¦e o Midwest
° E
w
-------
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/ProductDetailllOO.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.
299
-------
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/l If680ff78df42f585256b45007e6235/41b
8b642ab9371df852564500004b543/$FILE/EE 0217A l.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/
usemulti state, 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/pricemulti state, 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.
300
-------
U.S. Environmental Protection Agency. September 2000. Guidelines for Preparing
Economic Analyses. EPA 240-R-00-003. Washington, DC: EPA.
http://vosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html. Accessed August
2009.
301
-------
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 "a 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
302
-------
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 Entities3
Category
NAICS
Code"
Examples of Potentially Regulated Entities
Industry
221112
Fossil fuel-fired electric utility steam generating units.
Federal
Government
221112°
Fossil fuel-fired electric utility steam generating units owned by
the federal government.
State/Local/
Tribal
Government
221112°
Fossil fuel-fired electric utility steam generating units owned by
municipalities.
921150
Fossil fuel-fired electric utility steam generating units in Indian
Country.
include NAICS categories for source categories that own and operate electric generating units only.
bNorth American Industry Classification System.
°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 S02 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
303
-------
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:
7 8 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.
304
-------
• 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 A Coperating+Retrofit A Cf"Uel A CAllowances A (^Transaction A R
where C represents a component of cost as labeled, and A 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
305
-------
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
306
-------
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
307
-------
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
Investor-
Owned
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.
308
-------
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
309
-------
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
310
-------
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
Investor-
Owned 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
311
-------
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
312
-------
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
313
-------
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.
314
-------
To attempt to account for each potential control strategy, EPA estimates compliance
costs as follows:
Ccompliance A Coperating+ Retrofit A Cfuei ~l~ A CAllowances A (^Transaction A R
where C represents a component of cost as labeled, and A 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 $/MWh), and the total
annualized retrofit and operating cost by unit.
(2) Sale or purchase of allowances: EPA estimated the value of initial S02 and
NOx annual and NOx ozone season allowance holdings. For both SO2 and
NOx, the state emission budgets were assumed to be apportioned to units
based on their share of the state's total emissions. EPA calculated each unit's
315
-------
S02 and N0X annual and N0X ozone season allowance allocations as the ratio
of that unit's adjusted emissions (as determined in the state budget calculation
methodology) to the sum of all units' emissions in the applicable state, times
the final state budget. Thus each unit's allocation is the unit's proportional
share of the state budget, based on emissions as determined in the state budget
calculation. See State Budgets, Unit Allocations, and Unit Emissions Rates
TSD.
To estimate the value of allowances holdings, allocated allowances were
subtracted from projected emissions, and the difference was then multiplied
by the allowance price projected by IPM. Units were assumed to purchase or
sell allowances to exactly cover their projected emissions under the Transport
Rule.
(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by
region and fuel type-adjusted fuel prices ($/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
316
-------
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
81
All 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.
317
-------
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.
318
-------
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.
319
-------
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
320
-------
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) orNOx 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
321
-------
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,
322
-------
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.
323
-------
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
324
-------
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
325
-------
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
[j,g/m3 threshold for annual PM25. 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
326
-------
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
327
-------
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
328
-------
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 low-
income 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
329
-------
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.
330
-------
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.
331
-------
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
332
-------
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
333
-------
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 S02 option" and "EMPAXresultsless stringent S02 option," respectively, and
these files are available in the docket for this rule.
334
-------
Table 10-1. Summary of Annual Benefits, Costs, and Net Benefits of the Proposed
Transport Rule in 2014" (billions of 2006 dollars)
Description
Social costsb
3 percent discount rate
7 percent discount rate
Social benefits0'"1'6
3 percent discount rate
7 percent discount rate
Health-related benefits:
3 percent discount rate
7 percent discount rate
Visibility benefits
Net benefits (benefits-costs) '
3 percent discount rate $120 to $292
7 percent discount rate $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 S02for 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.
0 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 S02 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 C02-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
unqualified 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.
Proposed Remedy
$2.03
$2.23
$122 to $294+ B
$111 to $266+ B
$119 to $290+ B
$108 to $262+ B
$3.56
335
-------
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
7 percent discount rate
Health-related benefitsc'd
3 percent discount rate
7 percent discount rate
Net benefits (benefits-costs)6
3 percent discount rate
7 percent discount rate
$2.03
$2.23
$118 to $288 + B
$107 to $260+ B
$116 to $286
$105 to $258
$1.12*
$1.23*
$82 to 200 + B
$76 to 184+ B
$81 to 200
$74 to 182
$2.21*
$2.43*
$120 to 292+ B
$110 to 267 + B
$118 to 290
$107 to 265
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.
1 Note that costs are the annualized total social costs of reducing pollutants including NOx and S02for 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.
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 C02-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 S02 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 $3.6 billion (2006$).
1 Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all
unqualified benefits and disbenefits. Potential benefit categories that have not been quantified and
monetized are listed in Table 1-6.
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).
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.
336
-------
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 $118to$288 + B $117to$286 + B $113to$276 + B
7 percent discount rate $107 to $260 + B $108 to $262 + B $104 to $252 + B
Net benefits (benefits-costs)6 f
3 percent discount rate $116 to $286 $115 to $283 $110 to $273
7 percent discount rate $105 to $258 $105 to $259 $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 S02for 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.
0 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 C02-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 S02 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
unqualified 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
-------
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.
338
-------
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.
339
-------
APPENDIX A
HUMAN HEALTH BENEFITS OF DIRECT CONTROL AND INTRASTATE
TRADING REMEDIES AND PRESENTATION OF STATE-LEVEL BENEFITS OF
PROPOSED REMEDY
340
-------
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.l 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 N02 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 ozone-
related 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
341
-------
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 SO 2
emission reductions of the proposed remedy. We first quantified the health impacts and
monetized benefits of total PM2.5 mass formed from the S02 reductions of the proposed
remedy, allowing us to isolate the PM air quality impacts from S02 reductions alone.84
This procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-
related benefits of incremental changes in S02 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-|ig/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 S02.
The resulting BPT estimates were then multiplied by the projected S02 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 S02 and NOx emissions reductions. In general S02 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 S02
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 S02 emissions were reduced. We calculated benefits from this air quality modeling run to
generate an S02-only benefit per ton estimate. The results of the S02-only sensitivity run may be found in the
EPA Benefits TSD [Docket No. EPA-HQ-OAR-2009-0491]
342
-------
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-l. 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-l: 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
$24,000
$65,000
Laden et al. (2006)
$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.
343
-------
Table A-2: Estimated Reduction in Incidence of PM2.s-related Adverse Health Effects
of Direct Control and Intrastate Trading Remedy Options
Intrastate
Health Effect Direct control trading 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)
344
-------
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.
g
PM2 5-related health impacts only. Excludes ozone impacts.
Q
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.
345
-------
Table A-3: Estimated PM2.s-related Monetized Benefits of Direct Control and
Intrastate Trading Options
Health Effect
Direct control
Intrastate
tradingA
Proposed Remedy8
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)
346
-------
Monetized total PM2.s-related benefits (Pope et al. 2002 mortality estimate)
«i 20
3% discount rate $120 $110
(&1U—Moll)
ci i n
7% discount rate $110 $100 ($9 2 $330)
Monetized total PM2.s-related benefits (Laden et al. 2006 mortality benefits)
$290
3% discount rate $290 $280
($26—Jo4U)
7% discount rate $260 $250 ,nv
(3>23—J76U)
a
Estimates rounded to two significant figures. Confidence intervals unavailable for estimate calculated
using benefit per ton estimates.
g
PM2 5-related health impacts only. Excludes ozone and visibility-related benefits.
Q
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.
347
-------
Table A-4: Avoided PM2.s-Related Premature Mortalities for the Proposed Remedy in
2014 Summarized at the State Level (90 percent confidence intervals)
State
Pope et al. (2002) mortality
Laden et al. (2006) mortality
estimate
estimate
360
930
Alabama
(140-580)
(510-1300)
1.5
3.8
Arizona
(0.57-2.3)
(2-5.5)
220
570
Arkansas
(87-360)
(310-830)
-0.81
-2.1
California
(-0.32—1.3)
(-1.1-3)
-7.6
-20
Colorado
(-3-12)
(-11—29)
170
440
Connecticut
(67-280)
(240-640)
64
160
Delaware
(25-100)
(90-240)
38
98
District of Columbia
(15-62)
(54-140)
600
1,500
Florida
(230-960)
(840-2200)
560
1,400
Georgia
(220-900)
(780-2100)
-0.61
-1.6
Idaho
(-0.24—0.98)
(-0.85—2.3)
720
1,800
Illinois
(280-1100)
(1000-2700)
640
1600
Indiana
(250-1000)
(890-2400)
100
260
Iowa
(40-160)
(140-380)
74
190
Kansas
(29-120)
(100-280)
610
1,500
Kentucky
(240-970)
(850-2200)
210
530
Louisiana
(81-330)
(290-770)
26
67
Maine
(10-42)
(36-97)
490
1,300
Maryland
(190-790)
(690-1800)
190
480
Massachusetts
(74-300)
(260-700)
348
-------
600
1,500
Michigan
(240—960)
(840-2200)
80
200
Minnesota
(31-130)
(110-300)
230
590
Mississippi
(90-370)
(320-850)
370
950
Missouri
(150-600)
(520-1400)
-0.33
-0.85
Montana
(-0.13—0.53)
(-0.46—1.2)
33
84
Nebraska
(13-53)
(46-120)
-0.0003
-0.0003
Nevada
(-0.0001—0.0005)
(-0.0002—0.0004)
37
95
New Hampshire
(15-60)
(52-140)
550
1,400
New Jersey
(220-880)
(770-2000)
5.9
15
New Mexico
(2.3-9.5)
(8.3-22)
950
2,400
New York
(370-1500)
(1300-3500)
720
1,800
North Carolina
(280-1200)
(1000-2700)
2.7
7
North Dakota
(1.1-4.4)
(3.8-10)
1,300
3,300
Ohio
(520-2100)
(1800-4800)
120
320
Oklahoma
(48-200)
(170-460)
-1
-2.6
Oregon
(-0.4—1.7)
(-1.4—3.8)
1,400
3,600
Pennsylvania
(560-2300)
(2000-5200)
38
97
Rhode Island
(15-61)
(53-140)
330
850
South Carolina
(130-530)
(460-1200)
8.4
21
South Dakota
(3.3-13)
(12-31)
710
1,800
Tennessee
(280-1100)
(990-2600)
520
1,300
Texas
(200-840)
(730-1900)
349
-------
-1.4
-3.7
Utah
(-0.56—2.3)
(-2-5.4)
22
56
Vermont
(8.5-35)
(30-81)
700
1,800
Virginia
(270-1100)
(970-2600)
-1.7
-4.5
Washington
(-0.68—2.8)
(-2.4—6.5)
280
720
West Virginia
(110-450)
(390-1000)
190
480
Wisconsin
(74-300)
(260-700)
-0.12
-0.31
Wyoming
(-0.048—0.2)
(-0.17—0.46)
350
-------
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
Levy et al. (2005) mortality
estimate
estimate
5.8
8.2
Alabama
(3.3—8.4)
(6-10)
0.31
0.43
Arizona
(0.17-0.44)
(0.32-0.55)
5.7
8
Arkansas
(3.2-8.2)
(5.9-10)
0.12
0.17
California
(0.056-0.18)
(0.11-0.22)
0.03
0.042
Colorado
(-0.042-0.1)
(-0.019-0.1)
0.44
0.62
Connecticut
(0.25-0.63)
(0.46-0.78)
0.19
0.26
Delaware
(0.1-0.27)
(0.19-0.33)
0.11
0.15
District of Columbia
(0.062-0.16)
(0.11-0.2)
22
31
Florida
(12-32)
(22-39)
6.7
9.4
Georgia
(3.7-9.6)
(6.9-12)
0.007
0.0099
Idaho
(0.0039-0.01)
(0.0073-0.013)
7.7
11
Illinois
(4.3-11)
(8-14)
4.3
6.1
Indiana
(2.4-6.2)
(4.5-7.7)
4.5
6.3
Iowa
(2.5-6.5)
(4.7-8)
6
8.4
Kansas
(3.4-8.6)
(6.2-11)
3
4.2
Kentucky
(1.7-4.3)
(3.1-5.3)
5.4
7.6
Louisiana
(3-7.8)
(5.6-9.7)
0.067
0.095
Maine
(0.038-0.097)
(0.07-0.12)
1.6
2.2
Maryland
(0.87-2.2)
(1.6-2.8)
0.37
0.53
Massachusetts
(0.21-0.54)
(0.39-0.67)
351
-------
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)
352
-------
0.02
0.028
Utah
(0.011—0.029)
(0.021-0.036)
0.092
0.13
Vermont
(0.052-0.13)
(0.095-0.16)
2.3
3.3
Virginia
(1.3-3.3)
(2.4-4.1)
0.0018
0.0025
Washington
(0.0003-0.0033)
(0.0013-0.0038)
2.1
3
West Virginia
(1.2—3)
(2.2-3.7)
4.8
6.7
Wisconsin
(2.6-6.9)
(4.9-8.5)
0.0088
0.013
Wyoming
(0.0042-0.014)
(0.0085-0.016)
353
-------
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 .
U.S. Environmental Protection Agency (U.S. EPA). 2010. Final Regulatory Impact Analysis
(RIA) for the N02 National Ambient Air Quality Standards (NAAQS). Office of Air
Quality Planning and Standards, Research Triangle Park, NC. January. Available on the
Internet at .
354
-------
APPENDIX B
ECONOMIC IMPACT ANALYSES OUTSIDE OF ELECTRIC POWER SECTOR
AND SOCIAL COSTS — ALTERNATE REMEDIES
355
-------
B.l 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-l)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-l. Change in Macroeconomic Variables and Household Welfare (Percent
change and Change in billion $2006) in 2015
Change ($ billion) Percent Change
¦ GDP
~ Consumption
~ Hicksian EV (Annual)
0.000%
-0.002%
-0.004%
-0.006%
-0.008%
-0.010%
-0.012%
-0.014%
-0.016%
-0.018%
-0.020%
a)
u>
c
GJ
-C
O
o
Ui
GJ
¦*-«
c
0)
o
5
Q_
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.
356
-------
Figure B-2. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015
Northeast South Midwest Plains West US
0.10% -i
0.08%
g' 0.06%
g 0.04%
g, 0.02%
c II || ||
1 -0.02% I I
-0.04%
-0.06%
-0.08%
-0.10%
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.
357
-------
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).
358
-------
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.l.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).
359
-------
Figure B-3. Output Changes in 2015: Energy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector
produces.
360
-------
Figure B-4. Output Changes in 2015: Energy-Intensive Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
"g ^ South
^ Midwest
-a "a .
o .E Plains
£ West
US
1
1
1
1
Northeast
"g South
2 "§ Midwest
Plains
£_ West
US
1
¦
¦
1
¦
Northeast
| South
.2 Midwest
§ Plains
o West
US
1
¦
¦
1
1
Northeast
South
$ Midwest
0 Plains
West
US
¦
¦
~
¦
1
Northeast
^ South
S Midwest
a; Plains
° West
US
1
1
"53 Northeast
~ South
Midwest
ro Plains
g West
- US
1
¦
IZZI
¦
Northeast
¦
E South
1
.!= Midwest
| Plains
< West
US
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector
produces.
361
-------
Figure B-5. Output Changes in 2015: Nonenergy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
a, South
~ Midwest
=3
O __. .
¦5= Plains
m
< West
us
1
Northeast
CD
~ I5 South
-2 -§ Midwest
"T
>^3 Plains
O) c
£ | West
LU
US
¦
¦
1
:
Northeast
B South
o
>2 Midwest
£=
™ Plains
a] West
o us
1
Northeast
South
(/>
0 Midwest
1 Plains
CO
West
US
Northeast
o South
"m w
¦e o Midwest
° E
a; Plains
2 West
US
1
1
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector produces.
362
-------
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 $2006) in 2015
Change ($ billion) Percent Change
® -$1.0
0.000%
-0.002%
-0.004%
-0.006%
-0.008% «
a)
u>
c
ra
¦E
O
(1)
U>
$
C
(1)
O
(1)
Q.
-0.010%
-0.012%
¦ GDP
ci Consumption
CHicksian 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.
363
-------
Figure B-7. Change in Regional Gross Domestic Product (GDP) (Percent) in 2015
Northeast South Midwest Plains West US
0.10% -i
0.08%
g' 0.06%
g 0.04%
O) 0 02% i—|
c 0.00% |i ' ' i i
CD 1 1 1 1
5 -0 02%
^ -0.04%
-0.06%
-0.08%
-0.10%
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.
364
-------
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).
365
-------
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).
366
-------
Figure B-8. Output Changes in 2015: Energy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
CO
o
O
Northeast
South
Midwest
Plains
West
US
O
0
T3
o
Northeast
South
Midwest
Plains
West
US
Northeast
-t—'
O
O
_CD
LU
South
Midwest
Plains
West
US
03
CD
=3
-i—¦
03
Northeast
South
Midwest
Plains
West
US
O
0
CL
Northeast
South
Midwest
Plains
West
US
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector
produces.
367
-------
Figure B-9. Output Changes in 2015: Energy-Intensive Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
"g South
^ Midwest
73 73 ni-
CS c Plains
£ West
US
1
1
1
Northeast
"g South
" "S Midwest
2.^ Plains
a. West
US
¦
1
I
Northeast
South
03
Midwest
0 Plains
o West
US
1
¦
¦
¦
Northeast
South
So Midwest
<0 m, ¦
(5 Plains
West
US
¦
¦
¦
1
Northeast
^ South
S Midwest
0 Plains
° West
US
1
0 Northeast
~ South
-q Midwest
ro Plains
§ West
- US
¦
¦
¦
1
1
Northeast
I
E South
.g Midwest
~
¦
¦
| Plains
< West
US
¦
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector
produces.
368
-------
Figure B-10. Output Changes in 2015: Nonenergy Sectors (Percent)
Percentage Change
-1.5% -1.2% -0.9% -0.6% -0.3% 0.0% 0.3% 0.6% 0.9% 1.2% 1.5%
Northeast
m South
^ Midwest
0
^ Plains
o
< West
US
Northeast
a)
11 South
¦21 Midwest
T
> ~ Plains
0) c —
£ | West
LU
US
1
1
1
g Northeast
B South
0
| Midwest
c
| Plains
as West
O US
Northeast
South
0)
o Midwest
a Plains
m
West
US
Northeast
o South
m M
t o Midwest
°'E
% a) Plains
2 West
US
1
1
1
1
1
Note: Outcomes reflect percent changes in the physical quantities of goods/services that each regional sector
produces.
369
-------
APPENDIX C
COMPARISON OF STATE LEVEL ELECTRICAL GENERATING UNIT
EMISSIONS UNDER
VARIOUS REGULATORY ALTERNATIVES TO
REDUCE S02 AND NOx EMISSIONS
UNDER THE TRANSPORT RULE
370
-------
ELECTRICAL GENERATING UNIT S02 Emissions for Base Case and Regulatory
Alternatives (tons)
State
Base Case
State Budgets/
Limited Trading
State Budgets/
Intrastate Trading
Direct Control
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
371
-------
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 S02 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.
372
-------
Electrical Generating Unit Annual NOx Emissions for Base Case and Regulatory
Alternatives (tons)A
State
Base Case
State Budgets/
Limited Trading
State Budgets/
Intrastate Trading
Direct Control
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
373
-------
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.
374
-------
Electrical Generating Unit Ozone Season NOx Emissions for Base Case and Regulatory
Alternatives (tons)A
State
Base Case
State Budgets/
Limited Trading
State Budgets/
Intrastate Trading
Direct Control
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
375
-------
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.
376
-------
APPENDIX D
INTEGRATED PLANNING MODEL RUNS
377
-------
Table A-l 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-HQ-
OAR-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.
378
-------
Table A-l. IPM Runs Used in Transport Rule Analyses
Run Name
Run Description
Iranspori Rule Rase ('ase
TRBaseCase
Base Case model run, which includes the
national Title IV SO2 cap-and-trade program;
NOx SIP Call regional ozone season cap-
and-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).
Iranspori Rule remedy options* •'
TRSBLimitedTrading
This run models the State Budgets/Limited
Trading proposed remedy described in the
Transport Rule preamble.
TRSBIntrastateTrading
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.
. Uhhlionul runs used for analysis oj scenarios in Regulatory Impact
. Ina/vsts*1'
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.
379
-------
Table A-2. IPM Parsed Files Used in Transport Rule Analyses
Run with Parsed Results
Years Parsed
TRBaseCase
2012, 2014
TRSBLimitedTrading
2012, 2014
TRSBIntrastateT rading
2012, 2014
TR Direct Control
2012, 2014
380
-------
APPENDIX E
ALLOWANCE VALUES FOR EMISSIONS TRADING PROGRAMS
381
-------
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-l 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 $500/ton.
382
-------
indicates that compliance can be achieved through year-round operation of existing advanced
NOx controls without the use of more expensive compliance options, such as incrementally
installing new post-combustion controls. Rules for determining whether NOx controls in the
model operate year-round can be found in the Chapter 3 Appendix of the Documentation for
EPA Base Case 2004 Using IPM (http://www.epa.gov/airmarkets/progsregs/epa-
ipm/docs/bc3appendix.pdf).
Table E-l. Projected Regional Allowance Prices for State Budgets/Limited Trading
Preferred Approach
(2006$)
2012
2014
Annual NOx
$500
$500
Ozone-season NOx
$500
$500
Annual
S02
Group 1
$1,000
$1,100
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
$500/ton in 2012 because EPA state budgets were based on the performance of units in 2009
(which reflects improved performance of units complying with Phase I of the CAIR NOx
program and banking substantially for the CAIR SO2 program), while modeling in IPM did
not reflect all of those recent improvements. Actual improved performance that was used to
develop the budgets is therefore not always reflected in the model (e.g. better performance of
low NOx burners and scrubbers and access to lower sulfur coals). In its final modeling, EPA
383
-------
will update IPM to reflect the better unit performance seen in 2009. This will likely result in
lower allowance prices.
Table E-2. Projected Annual NOx and SO2 Allowance Prices for State
Budgets/Intrasta
e Trading Alternative (2006$)
Annua
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 S02
budget for Connecticut.
384
-------
Annua
NOx
Annual S02
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 $500/ton, a number of states
do see higher allowance prices. This is because the budgets were based on the lower of IPM
emission projections or projections of emissions using actual unit performance data (as
explained in the Technical Support Document - "State Budgets, Unit Allocations and Unit
Emission Rates"). Because IPM has not been updated to reflect actual NOx emission rates
seen in 2009, IPM projects that it will be harder to meet those budgets than the most recent
real world data shows it will be (this is why EPA considered the most current data as well as
the IPM projections in setting the budgets). Between proposal and final rulemaking, EPA
will be updating IPM to reflect these lower emission rates. This also occurs in some of the
SO2 budgets. It is most prevalent in either smaller states or states with a larger percentage of
well-controlled units, where the marginal cost curve for the remaining uncontrolled units is
steeper and allowance prices are most sensitive to small changes in budgets. EPA will be
revising IPM to reflect the most recent real world data between proposal and final.
385
-------
Table E-3. Projected Ozone-season NOx Allowance Prices for State Budgets/Intrastate
Trading Alternative (2006$)
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
386
------- |