From: Ed Nam/AA/USEPA/US 

To: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

Cc: Lucie Audette/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, William Charmley/AA/USEPA/US@EPA, 

Kathryn Sargeant/AA/USEPA/US@EPA 

Date: 07/20/2012 11:02 PM 

Subject: Re: Executive Summary 

Chandana. 

Attached is our Executive summary. WE will attach to our Preamble for final. 

-Ed 

Executive Summary 2017-2025 CAFE & GHG to OMB 7-20-2012b.docx 

From: Robin Moran/AA/USEPA/US 

To: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

Cc: Ed Nam/AA/USEPA/US@EPA, William Charmley/AA/USEPA/US@EPA, james.tamm@dot.gov, 

rebecca.yoon@dot.gov 

Date: 07/26/2012 04:51 PM 

Subject: EPA Placeholders on Air Quality Modeling and Cost Sensitivities 

Chandana, 

In our 7/18 package submittal, we had a couple placeholders for our analysis of air quality modeling impacts, the health 

impacts analysis, and the cost sensitivities analysis. The following files contain our EPA RIA and Preamble Section III 

write-ups for these issues. 

Please let me know if you have any questions. 

Robin 

Robin Moran 

Senior Policy Advisor 

Assessment and Standards Division 

U.S. EPA, Office of Transportation and Air Quality 

2000 Traverwood Dr. 

Ann Arbor, MI 48105 

(734) 214-4781 

(734) 214-4821 (fax) 

RIA 3.11_Cost Sensitivities.docx 

 

Preamble III.G and H_ AQ Modeling and Health Impacts.docx 

RIA 6.2.2 and 6.3_AQ Impacts and Health Benefits.docx 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 1 of 7

Executive Summary 

I. Purpose of the Regulatory Action 

a. The Need for the Action and How the Action addresses the Need 

NHTSA, on behalf of the Department of Transportation, and EPA are issuing final rules 

to further reduce greenhouse gas emissions and improve fuel economy for light-duty vehicles for 

model years 2017and beyond. On May 21, 2010, President Obama issued a Presidential 

Memorandum requesting that EPA and NHTSA develop through notice and comment 

rulemaking a coordinated National Program to improve fuel economy and reduce greenhouse gas 

emissions of light-duty vehicles for model years 2017-2025, building on the success of the first 

phase of the National Program for these vehicles for model years 2012-2016. These final rules 

are consistent with the President’s request and respond to the country’s critical need to address 

global climate change and to reduce oil consumption. 

These standards apply to passenger cars, light-duty trucks, and medium-duty passenger 

vehicles (i.e. sport utility vehicles, cross-over utility vehicles, and light trucks), and represent the 

continuation of a harmonized and consistent National Program for these vehicles. Under the 

National Program automobile manufacturers will be able to continue building a single light-duty 

national fleet that satisfies all requirements under both programs. 

The National Program is estimated to save approximately 4 billion barrels of oil and to 

reduce GHG emissions by the equivalent of approximately 2 billion metric tons over the 

lifetimes of those light duty vehicles produced in MYs 2017-2025. The agencies project that fuel 

savings will far outweigh higher vehicle costs, and that the net benefits to society of the MYs 

2017-2025 National Program will be in the range of $326 billion to $451 billion (7 and 3 percent 

discount rates, respectively) over the lifetimes of those light duty vehicles sold in MYs 2017-

2025. 

The National Program is projected to provide significant savings for consumers due to 

reduced fuel use. Although the agencies estimate that technologies used to meet the standards 

will add, on average, about $1,800 to the cost of a new light duty vehicle in MY 2025, 

consumers who drive their MY 2025 vehicle for its entire lifetime will save, on average, $5,500 

to $7,200 (7 and 3 percent discount rates, respectively) in fuel, for a net lifetime savings of 

$3,200 to $4,800. This estimate assumes gasoline prices of $3.87 per gallon in 2025 with small 

increases most years throughout the vehicle’s lifetime. 

b. Legal Authority 

EPA and NHTSA are finalizing separate sets of standards for passenger cars and for light 

trucks, under their respective statutory authority. EPA is setting national CO2 emissions 

standards for passenger cars and light-trucks under section 202 (a) of the Clean Air Act (CAA) 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 2 of 7

((42 USC section 7521 (a)), and under its authority to measure passenger car and passenger car 

fleet fuel economy pursuant to the Energy Policy and Conservation Act (EPCA) 49 USC section 

32904 (c). NHTSA is setting national corporate average fuel economy (CAFE) standards under 

the Energy Policy and Conservation Act (EPCA), as amended by the Energy Independence and 

Security Act (EISA) of 2007 (49 U.S.C. 32902). 

Section 202 (a) of the Clean Air Act requires EPA to establish standards for emissions of 

pollutants from new motor vehicles which emissions cause or contribute to air pollution which 

may reasonably be anticipated to endanger public health or welfare. See Coalition for 

Responsible Regulation v. EPA, No. 09-1322 (D.C. Cir. June 26, 2012) slip op. p. 41 (“’[i]f EPA 

makes a finding of endangerment, the Clean Air Act requires the [a]gency to regulate emissions 

of the deleterious pollutant from new motor vehicles. ‘...Given the non-discretionary duty in 

Section 202 (a)(1) and the limited flexibility available under Section 202 (a)(2), which this court 

has held relates only to the motor-vehicle industry,...EPA had no statutory basis on which it 

could ‘ground [any] reasons for further inaction” (quoting State of Massachusetts v. EPA, 549 

U.S. 497, 533, 535 (2007). In establishing such standards, EPA must consider issues of technical 

feasibility, cost, and available lead time. Standards under section 202 (a) thus take effect only 

“after providing such period as the Administrator finds necessary to permit the development and 

application of the requisite technology, giving appropriate consideration to the cost of 

compliance within such period” (CAA section 202 (a)(2) (42 USC section 7512 (a)(2)). 

EPCA, as amended by EISA, contains a number of provisions regarding how NHTSA 

must set CAFE standards. EPCA requires that NHTSA establish separate passenger car and light 

truck standards (49 U.S.C. 32902(b)(1)) at “the maximum feasible average fuel economy level 

that it decides the manufacturers can achieve in that model year (49 U.S.C. 32902(a)),” based on 

the agency’s consideration of four statutory factors: technological feasibility, economic 

practicability, the effect of other standards of the Government on fuel economy, and the need of 

the nation to conserve energy (49 U.S.C. 32902(f)). EPCA does not define these terms or 

specify what weight to give each concern in balancing them; thus, NHTSA defines them and 

determines the appropriate weighting that leads to the maximum feasible standards given the 

circumstances in each CAFE standard rulemaking. For MYs 2011–2020, EPCA further requires 

that separate standards for passenger cars and for light trucks be set at levels high enough to 

ensure that the CAFE of the industry-wide combined fleet of new passenger cars and light trucks 

reaches at least 35 mpg not later than MY 2020 (49 U.S.C. 32902(b)(2)(A))]. For model years 

after 2020, standards need simply be set at the maximum feasible level [CITE]. 

Section I.D of the preamble contains a detailed discussion of both agencies’ statutory 

authority. 

II. Summary of the Major Provisions of the Final Rule 
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Page 3 of 7

NHTSA and EPA are finalizing rules for light-duty vehicles that the agencies believe 

represent the appropriate levels of fuel economy and GHG emissions standards for model years 

2017 and beyond pursuant to their respective statutory authorities. 

Standards 

EPA is establishing standards that are projected to require, on an average industry fleet 

wide basis, 163 grams/mile of carbon dioxide (CO2) in model year 2025, which is equivalent to 

54.5 mpg if this level were achieved solely through improvements in fuel efficiency.1

 

Consistent with its statutory authority, NHTSA has developed two phases of passenger car and 

light truck standards in this rulemaking action. The first phase, from MYs 2017-2021, includes 

final standards that are projected to require, on an average industry fleet wide basis, a range from 

40.3 – 41.0 mpg in MY 2021. The second phase of the CAFE program, from MYs 2022-2025, 

includes standards that are not final, due to the statutory requirement that NHTSA set average 

fuel economy standards not more than 5 model years at a time. Rather, those standards are 

augural, meaning that they represent NHTSA’s current best estimate, based on the information 

available to the agency today, of what levels of stringency might be maximum feasible in those 

model years. NHTSA projects that those standards could require, on an average industry fleet 

wide basis, a range from 48.7 – 49.7 mpg in model year 2025. 

Both the CO2 and CAFE standards are footprint-based, as are the standards currently in 

effect for these vehicles through model year 2016. The standards will become more stringent on 

average in each model year from 2017 through 2025. Generally, the larger the vehicle footprint, 

the less numerically stringent the corresponding vehicle CO2 emissions and MPG targets. As a 

result of the footprint-based standards, the burden of compliance is distributed across all vehicle 

footprints and across all manufacturers. Manufacturers are not compelled to build vehicles of any 

particular size or type (nor do the rules create an incentive to do so), and each manufacturer will 

have its own fleet-wide standard that reflects the light duty vehicles it chooses to produce. 

Mid-Term Evaluation

The agencies will conduct a comprehensive mid-term evaluation and agency decisionmaking process for the MYs 2022-2025 standards as described in the proposal. The mid-term 

evaluation reflects the rules’ long time frame and, for NHTSA, the agency’s statutory obligation 

to conduct a de novo rulemaking in order to establish final standards for MYs 2022-2025. In 

order to align the agencies’ proceedings for MYs 2022-2025 and to maintain a joint national 

program, EPA and NHTSA will finalize their actions related to MYs 2022-2025 standards 

 

1 Real-world CO2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower than the 

CO2 and CAFE compliance values discussed here. 163g/mi would be equivalent to 54.5 mpg, if the entire fleet were 

to meet this CO2 level through tailpipe CO2 and fuel economy improvements. The agencies expect, however, that a 

portion of these improvements will be made through improvements in air conditioning leakage and through use of 

alternative refrigerants, which would not contribute to fuel economy. 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 4 of 7

concurrently. If the EPA determination is that standards may change, the agencies will issue a 

joint NPRM and joint Final Rules. NHTSA and EPA fully expect to conduct this mid-term 

evaluation in coordination with the California Air Resources Board, given our interest in 

maintaining a National Program to address GHG emissions and fuel economy. Further 

discussion of the mid-term evaluation is found in Sections III.B.3 and IV.A.3.b. 

Compliance Flexibilities

As proposed, the agencies are finalizing several provisions which provide compliance 

flexibility to manufacturers to meet the standards without compromising the program’s overall 

environmental and energy security objectives. Further discussion of compliance flexibilities is in 

Sections I. B.4, II.F, III.B, III.C, IV.I. 

Credit Averaging, Banking and Trading 

The agencies are continuing to allow manufacturers to generate credits for overcompliance with the CO2 and CAFE standards.2

 A manufacturer will generate credits if its car 

and/or truck fleet achieves a fleet average CO2/CAFE level better than its car and/or truck 

standards. Conversely, a manufacturer will incur a debit/shortfall if its fleet average CO2/CAFE 

level does not meet the standard when all credits are taken into account. As in the prior CAFE 

and GHG programs, a manufacturer whose fleet generates credits in a given model year would 

have several options for using those credits, including credit carry-back, credit carry-forward, 

credit transfers, and credit trading. 

Air Conditioning Improvement Credits 

As proposed, EPA is establishing that the maximum total A/C credits available for cars 

will be 18.8 grams/mile CO2-equivalent and 24.4 grams/mile for trucks CO2-equivalent.3 The 

approaches used to calculate these credits for direct and indirect A/C improvement (i.e., 

improvements to A/C leakage (including substitution of low GHG refrigerant) and A/C 

efficiency) are generally consistent with those of the MYs 2012-2016 program, although there 

are several revisions. Most notably, a new test for A/C efficiency, optional under the GHG 

program starting in MY 2014, will be used exclusively in MY 2017 and beyond. Under its 

EPCA authority, EPA proposed and is finalizing provisions to allow manufacturers to generate 

fuel consumption improvement values for purposes of CAFE compliance based on these same 

improvements in air conditioner efficiency. 

Off-Cycle Credits 

 

2

 This credit flexibility is required by EPCA/EISA, see 49 U.S.C. 32903, and is well within EPA’s discretion under 

section 202 (a) of the CAA. 

3

 This is further broken down by 5.0 and 7.2 g/mi respectively for car and truck A/C efficiency credits, and 13.8 and 

17.2 g/mi respectively for car and truck alternative refrigerant credits. 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 5 of 7

EPA proposed and is finalizing provisions allowing manufacturers to continue to 

generate and use off-cycle credits to demonstrate compliance with the GHG standards. These 

credits are for measureable GHG emissions and fuel economy improvements attributable to use 

of technologies whose benefits are not measured by the two-cycle test mandated by EPCA. 

Under its EPCA authority, EPA proposed and is finalizing provisions to allow manufacturers to 

generate fuel consumption improvement values for purposes of CAFE compliance based on the 

use of off-cycle technologies. 

Incentives for Electric Vehicles, Plug-in Hybrid Electric Vehicles, Fuel Cell Vehicles and 

compressed Natural Gas Vehicles 

In order to provide temporary regulatory incentives to promote the penetration of certain 

“game changing” advanced vehicle technologies into the light duty vehicle fleet, EPA is 

finalizing, as proposed, an incentive multiplier for CO2 emissions compliance purposes for all 

electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs) 

sold in MYs 2017 through 2021. The incentives are expected to promote increased application 

of these advanced technologies in the program’s early model years, which could achieve 

economies of scale that will support the wider application of these technologies to help achieve 

the more stringent standards in MYs 2022-2025. In addition, in response to public comments 

persuasively explaining how infrastructure for compressed natural gas (CNG) vehicles could 

serve as a bridge to use of advanced technologies such as hydrogen fuel cells, EPA is finalizing 

an incentive multiplier for CNG vehicles sold in MYs 2017 through 2021. 

NHTSA currently interprets EPCA and EISA as precluding it from offering incentives for 

the alternative fuel operation of EVs, PHEVs, FCVs, and NGVs, except as specified by statute, 

and thus did not propose and is not including incentive multipliers comparable to the EPA 

incentive multipliers described above. 

Incentives for Use of Advanced Technologies including Hybridization for full-Size Pickup Trucks 

The agencies recognize that the standards presented in this final rule for MYs 2017-2025 

will be challenging for large vehicles, including full-size pickup trucks. To help address this 

challenge, the program will, as proposed, contain incentives for the use of hybrid electric and 

other advanced technologies in full-size pickup trucks. 

III. Costs and Benefits of National Program 

It is important to note that NHTSA’s CAFE standards and EPA’s GHG standards will 

both be in effect, and both will lead to increases in average fuel economy and reductions in 

GHGs. The two agencies’ standards together comprise the National Program, and the following 

discussions of the respective costs and benefits of NHTSA’s CAFE standards and EPA’s GHG 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 6 of 7

standards does not change the fact that both the CAFE and GHG standards, jointly, are the 

source of the benefits and costs of the National Program 

The costs and benefits projected by NHTSA to result from the CAFE standards are 

presented first, followed by those projected by EPA to result from the GHG emissions standards. 

For several reasons, the estimates for costs and benefits presented by NHTSA and EPA for their 

respective rules, while consistent, are not directly comparable, and thus should not be expected 

to be identical. See Section I.C of the preamble for further details and discussion. 

NHTSA has analyzed in detail the projected costs and benefits for the 2017-2025 CAFE 

standards for light-duty vehicles. NHTSA estimates that the required fuel economy increases 

would lead to fuel savings totaling a range from 180 billion to 184 billion gallons throughout the 

lives of light duty vehicles sold in MYs 2017-2025. At a 3 percent discount rate, the present 

value of the economic benefits resulting from those fuel savings is between $513 billion and 

$525 billion; at a 7 percent private discount rate, the present value of the economic benefits 

resulting from those fuel savings is between $400 billion and $409 billion. The agency further 

estimates that these new CAFE standards will lead to corresponding reductions in CO2 emissions 

totaling 1.9 billion metric tons during the lives of light duty vehicles sold in MYs 2017-2025. 

The present value of the economic benefits from avoiding those emissions is approximately $53 

billion, based on a global social cost of carbon value of about $26 per metric ton (in 2017, and 

growing thereafter). 

NHTSA’s Estimated MYs 2017-2025 Costs, Benefits, and Net Benefits (Billions of 2010 

dollars)) under the CAFE Standards4

 3% discount rate 7% discount rate 

 

Lifetime 

present value 

Annualized 

value 

Lifetime 

present value 

Annualized 

value 

Costs 2008 

2010 

(150) – 

(143)

(6.4) –

(6.2)

(148) – 

(140)

(10.7) –

(10.4)

Benefits 2008 

2010 

657 – 

641

26.3 –

25.6

525 – 

512

36.7 –

35.8

Net Benefits 2008 

2010 

507 – 

498

19.9 –

19.4

377 – 

372

26.0 –

25.4

EPA has analyzed in detail the projected costs and benefits of the 2017-2025 GHG 

standards for light-duty vehicles. The Table below shows EPA’s estimated lifetime discounted 

cost, fuel savings, and benefits for all such vehicles projected to be sold in model years 2017-

 

4

 “Estimated Required” analysis that does not accounting for the compliance flexibilities and advanced 

technologies that NHTSA is prohibited from considering when determining the maximum feasible level of new 

CAFE standards, since manufacturers’ decisions to use those flexibilities and technologies are voluntary. 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 7 of 7

2025. The benefits include impacts such as climate-related economic benefits from reducing 

emissions of CO2 (but not other GHGs), reductions in energy security externalities caused by 

U.S. petroleum consumption and imports, the value of certain particulate matter-related health 

benefits (including premature mortality), the value of additional driving attributed to the VMT 

rebound effect, the value of reduced refueling time needed to fill up a more fuel efficient vehicle. 

The analysis also includes estimates of economic impacts stemming from additional vehicle use, 

such as the economic damages caused by accidents, congestion and noise (from increased VMT 

rebound driving). 

EPA’s Estimated 2017-2025 Model Year Lifetime Discounted Costs, Benefits, and Net 

Benefits assuming the 3% discount rate SCC Value5 (Billions of 2010 dollars) 

Lifetime Present Valued

– 3% Discount Rate 

Program Costs -$150 

Fuel Savings $475 

Benefits $126 

Net Benefitsd $451 

Annualized Valuef – 3% Discount Rate 

Annualized costs -$6.49 

Annualized fuel savings $20.5 

Annualized benefits $5.46 

Net benefits $19.5 

Lifetime Present Valued - 7% Discount Rate 

Program Costs -$144 

Fuel Savings $364 

Benefits $106 

Net Benefitse $326 

Annualized Valuef – 7% Discount Rate 

Annualized costs -$10.8 

Annualized fuel savings $27.3 

Annualized benefits $7.96 

Net benefits $24.4 

 

5

 Further notes and details concerning these SCC Value are found in Section I.C.2. Table I-17. 
2017 Regulatory Impact Analysis 

1 

Regulatory Impacts Analysis, Chapter 3.11 

3.11 Cost Sensitivities 

3.11.1 Overview 

We have conducted several sensitivity analyses on a variety of input parameters. We

have run the OMEGA model to generate 2025MY results for each of these sensitivities. We 

have looked at different levels of mass reduction costs, battery pack costs, indirect cost 

multipliers, and learning rates. These sensitivities are summarized in 

Table 3.11-1, followed by a discussion of the methods, with the results in Table 3.11-10 . 

Additional sensitivities with regard to benefits are shown in RIA Chapter 4.

Table 3.11-1 Summary of Cost Sensitivities 

Sensitivity parameter Low side sensitivity High side sensitivity 

Mass reduction direct 

manufacturing costs 

40% lower 40% higher 

Battery pack direct manufacturing 

costs 

10% lower for P2 HEVs 

20% lower for PHEV/EV 

10% higher for P2 HEVs 

20% higher for PHEV/EV 

Indirect cost multipliers Low side of 95% confidence 

interval of modified Delphi 

survey results 

High side of 95% confidence 

interval of modified Delphi 

survey results 

Learning ratesa P-value of 30% on steep portion 

of the curve; cost reductions of 

4%/3%/2% per year for each 5 

year increment on the flat portion 

of the learning curve

P-value of 10% on steep portion 

of the curve; cost reductions of 

2%/1%/0% per year for each 5 

year increment on the flat portion 

of the learning curve

a

 Higher learning rates results in lower costs, hence the low side sensitivity uses the higher learning rates 

while the high side sensitivity uses the lower learning rates. 

3.11.2 Mass Sensitivity 

For the mass reduction cost sensitivity, we adjusted the mass reduction DMC cost 

equation by +/-40%. That cost equation is shown in Table 3.11-2 along with the cost equation 

used for each side of the mass reduction cost sensitivity. 

Table 3.11-2 Mass Reduction Cost Sensitivities 

Sensitivity parameter Mass reduction DMC equation used

Low side DMC=$2.60x, where x=% mass reduction

Primary case DMC=$4.33x, where x=% mass reduction

High side DMC=$6.06x, where x=% mass reduction

As mass reduction is a relatively cost effective technology, even with higher costs,

OMEGA still chooses a relatively similar, but somewhat diminished degree of mass 

reduction. By contrast, even with lower costs, mass reduction is still limited by the 

constraints given by the safety analysis.. These impacts would be greater on manufacturers 

that use more mass reduction technology, and less on those that use less. 

Formatted: Indent: First line: 44 px, Space

Before: 16 px, After: 16 px, Don't hyphenate

Deleted: For the analyses presented in and

Formatted: Space Before: 16 px, After: 16

px, Don't hyphenate

Deleted: summarized 

Deleted: ¶

Deleted: 

Deleted: ¶

Deleted: We did not re-rank OMEGA packages for 

the mass reduction cost sensitivities but rather used 

the same input files used for our primary analysis 

with new mass costs. This should have no impact on

the results other than making them conservative 

since re-ranking packages would serve to move the 

modeling to more cost effective technologies, thus 

reducing the cost impact of this sensitivity. Because

Deleted: would 

Deleted: given the stringency of the MY 2025 

standards

Deleted: would 

Deleted: still 

Deleted: be 

Deleted: fatality

Deleted: analysis

Deleted: As a result, the mass reduction sensitivity 

does not have any impact on the relative ranking of

packages or the engine and hybridization 

technologies that would be added in response to our

standards.¶

The high mass cost inputs increased the average 

compliance costs of the reference case by $XX and 

the control case by $XX, for a net average pervehicle cost increase of $102 in MY 2025. The low 

mass cost inputs decreased the average compliance

costs of the reference case by $xx and the control 

case by $xx for a net average cost decrease of $xx 
2017 Regulatory Impact Analysis 

2 

3.11.3 Battery Sensitivity 

For the battery pack cost sensitivities, we decreased/increased the battery pack DMCs 

by the amounts shown in Table 3.11-3. As presented in Chapter 3 of the joint TSD, we have 

developed linear regressions for our battery pack costs. These linear regressions provide 

battery pack DCM as a function of net weight reduction of the vehicle. Table 3.11-3 and 

Table 3.11-5 show the linear regressions used for our low side and high side sensitivity 

analyses, respectively, while Table 3.11-4 presents the linear regressions used for our primary 

analysis (as presented in Chapter 3 of the joint TSD). 

Table 3.11-3 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net 

Weight Reduction used for Low Side Sensitivity (2010$) 

Vehicle 

Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$163x+$653 -$688x+$2,026 -$1,214x+$2,917 -$1,488x+$4,105 -$1,734x+$4,892 -$1,636x+$6,464

Standard 

car

-$216x+$721 -$1,235x+$2,370 -$1,756x+$3,511 -$2,203x+$4,818 -$2,367x+$5,645 -$2,042x+$7,803

Large car -$332x+$843 -$1,505x+$2,987 -$3,760x+$4,808 -$3,485x+$6,180 -$3,718x+$6,904 -$2,272x+$8,896

Small MPV -$202x+$701 -$858x+$2,268 -$1,565x+$3,397 -$1,649x+$4,797 -$2,119x+$5,834 -$15x+$8,087

Large MPV -$272x+$788

Truck -$330x+$909 

Notes: 

“x” in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack 

with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-

$163)x(15%)+$653=$629. 

The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not 

believe these vehicle classes would use the technologies. 

Table 3.11-4 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net 

Weight Reduction used for the Primary Analysis (2010$) 

Vehicle 

Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$181x+$726 -$861x+$2,533 -$1,517x+$3,646 -$1,859x+$5,131 -$2,168x+$6,115 -$2,045x+$8,080

Standard 

car -$240x+$801 -$1,543x+$2,962 -$2,195x+$4,389 -$2,754x+$6,023 -$2,958x+$7,056 -$2,552x+$9,753

Large car -$369x+$937 -$1,881x+$3,734 -$4,700x+$6,010 -$4,356x+$7,725 -$4,647x+$8,630 -$2,840x+$11,120

Small MPV -$224x+$779 -$1,073x+$2,835 -$1,957x+$4,247 -$2,061x+$5,997 -$2,649x+$7,293 -$19x+$10,109

Large MPV -$303x+$876

Truck -$367x+$1,010 

Notes: 

“x” in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack 

with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-

$181)x(15%)+$726=$699. 

The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not 

believe these vehicle classes would use the technologies.

Table 3.11-5 Linear Regressions of Battery Pack Direct Manufacturing Costs vs Net 

Weight Reduction used for the High Side Sensitivity (2010$) 

Vehicle 

Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150 

Small car -$200x+$798 -$1,033x+$3,039 -$1,821x+$4,376 -$2,231x+$6,157 -$2,601x+$7,338 -$2,455x+$9,696

Standard 

car -$264x+$881 -$1,852x+$3,555 -$2,633x+$5,266 -$3,305x+$7,227 -$3,550x+$8,467 -$3,063x+$11,704
2017 Regulatory Impact Analysis 

3 

Large car -$406x+$1,031 -$2,257x+$4,480 -$5,639x+$7,212 -$5,227x+$9,269 -$5,577x+$10,357 -$3,407x+$13,344

Small MPV -$247x+$857 -$1,287x+$3,402 -$2,348x+$5,096 -$2,473x+$7,196 -$3,179x+$8,752 -$23x+$12,131

Large MPV -$333x+$963

Truck -$404x+$1,111 

Notes: 

“x” in the equations represents the net weight reduction as a percentage, so a subcompact P2 HEV battery pack 

with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (-

$200)x(15%)+$798=$768. 

The agencies did not regress PHEV or EV costs for the large MPV and truck vehicle classes since we do not 

believe these vehicle classes would use the technologies.

In the high case, the penetration of EVs decreased slightly, and MHEVs declined 

slightly as companies and MY 2025 TDS 24, start stop and HEV penetrations increased

slightly. In the low cost case, the MY 2025 penetration of EVs increased, while the HEV 

penetration decreased. In general, these shifts were slight, as this rulemaking doesn’t rely 

heavily on strong hybrids, EVs, or other battery technology vehicles. In general, changing 

the battery costs shifted the choice between HEVs and EVs. As both EVs and HEVs are less 

cost effective (in this set of inputs) than conventional technologies, the penetrations of nonbattery dependent technologies was generally little changed. 

3.11.4 ICM Sensitivity 

For the ICM sensitivity, we looked at the 95% confidence intervals of the survey 

responses gathered as part of the modified Delphi process used to generate our low, medium 

and high2 complexity ICMs. We discuss this modified Delphi process in Chapter 3 of the 

joint TSD and provide details in a memorandum to the docket (EPA-HQ-OAR-2010-0799).A

 

In that memorandum, the survey responses from each respondent are presented for each 

element of the ICM along with average responses, standard deviations and other statistical 

measures. Using these, we calculate the ICM elements at the low side of the 95% confidence 

interval and at the high side. Table 3.11-6 and Table 3.11-8 show the ICMs used for the low 

side and high side sensitivity analyses, respectively, while Table 3.11-7 shows the ICMs 

used for our primary analysis. For the High1 ICM, since it was generated using a consensus 

approach rather than blind surveys, we have scaled the ICM elements using the same ratios as 

resulted from the 95% confidence intervals for the High2 ICM. 

Table 3.11-6 ICMs used for the Low Side Sensitivity 

 Near term Long term Summed 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.004 0.113 0.001 0.090 1.118 1.091 

Medium 0.037 0.225 0.025 0.148 1.262 1.174 

High1 0.043 0.361 0.027 0.217 1.404 1.243 

High2 0.048 0.479 0.041 0.272 1.528 1.313 

Table 3.11-7 ICMs used for the Primary Analysis 

 Near term Long term Summed 

 

A

 “Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,” 

Helfand, G., and Sherwood, T., Memorandum dated August 2009. 

Deleted: For the battery pack sensitivities, unlike 

the mass reduction sensitivities, we did re-rank 

OMEGA packages. However, we started with the 

master-sets of packages as described in Chapter 1 of 

this RIA for both the 2016MY and 2025MY and did 

not start with preliminary-sets of packages and 

conduct a full package building/ranking process. 

Using the master-sets of packages, we inserted new 

battery pack costs, re-ranked the master-sets of 

packages to get the proper ordering of packages for

each sensitivity case, then ran OMEGA. As noted 

above, this should have no impact on the results 

other than making them conservative since starting 

with a preliminary-set of packages and re-ranking 

would serve to move the modeling to more cost 

effective technologies, thus reducing the cost impact. 

The high battery cost inputs increased the average 

compliance costs of the reference case by $X and the 

control case by $XX, for a net cost increase of $XX. 

Deleted: from XX% to XX%, 

Deleted: chose more cost effective options

Deleted: .

Deleted: from XX% to XX%

Deleted: The low battery cost inputs decreased the 

average compliance costs of the reference case by 

$X and the control case by $XX for a net decrease 

of $XX. 

Deleted: to XX%, 

Deleted: to X%.
2017 Regulatory Impact Analysis 

4 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.012 0.230 0.005 0.187 1.242 1.193 

Medium 0.045 0.343 0.031 0.259 1.387 1.290 

High1 0.065 0.499 0.032 0.314 1.564 1.345 

High2 0.074 0.696 0.049 0.448 1.770 1.497 

Table 3.11-8 ICMs used for the High Side Sensitivity 

 Near term Long term Summed 

Complexity Warranty Non-warranty Warranty Non-warranty Near term Long term 

Low 0.019 0.347 0.010 0.284 1.366 1.294 

Medium 0.052 0.461 0.037 0.369 1.513 1.406 

High1 0.087 0.637 0.037 0.411 1.723 1.447 

High2 0.099 0.914 0.057 0.623 2.012 1.680 

3.11.5 Learning Rate Sensitivity 

For the learning rate sensitivity, we increased the learning effects for the low side case 

and decreased the learning effects for the high side case. This sounds counterintuitive, but we 

have done this because the increased learning rates result in lower technology costs so, 

therefore, are more appropriate for the low side sensitivity. The reverse is true when 

decreasing the learning rates. For our primary analysis, as described in Chapter 3 of the joint 

TSD, we have used a 20% p-value for technologies on the steep portion of the learning curve 

and then have used learning rates of 3% per year for five years, 2% per year for 5 years, then 

1% per year for 5 years for technologies on the flat portion of the learning curve. Table 

3.11-9 shows how we have adjusted these learning rates for both the low and high side 

sensitivities. 

Table 3.11-9 Learning Rates used for our Learning Rate Sensitivity 

Sensitivity Steep learning rate Flat learning rate 

Low side 30% 4%, 3%, 2% 

Primary case 20% 3%, 2%, 1% 

High side 10% 2%, 1%, 0% 

3.11.6 Summary of Sensitivity Impacts 

The average per-vehicle impacts of the sensitivity runs are shown in Table 3.11-10. 

Note that the majority of these impacts are less than $150 relative to the primary analysis 

costs. The ICM impacts are larger. For those sensitivities that change technology costs, 

generally, an increase in the cost of a single technology will provide a smaller incremental 

change in total cost than a equivalent decrease in cost of a single technology. This is due to 

the TARF function in the model which attempts to minimize incremental cost. By contrast, 

learning and ICM changes, because they affect every technology, tend to produce more 

symmetrical increases and decreases.

Table 3.11-10 Summary of Per-vehicle Cost Impacts of Sensitivity Analyses in MY 2025 

relative to Primary Analysis (2010$) 

Deleted: Like done for the battery pack 

sensitivities, we re-ranked OMEGA packages using 

the master-sets of packages as described in Chapter 1 

of this RIA for the 2016MY and 2025MY. We did 

not start with preliminary-sets of packages and 

conduct a full package building/ranking process. 

Using the master-sets of packages, we inserted new 

costs calculated using the low/high IMCs, re-ranked

the master-sets of packages to get the proper 

ordering of packages for each sensitivity case, then 

ran OMEGA. As noted above, this should have no 

impact on the results other than making them 

conservative since starting with a preliminary-set of 

packages and re-ranking would serve to move the 

modeling to more cost effective technologies, thus 

reducing the cost impact. ¶

The high ICM inputs increased the average 

compliance costs of the reference case by $XX and 

the control case by $XX, for a net average pervehicle cost increase of $XX9 in MY 2025. The low 

ICM inputs decreased the average compliance costs 

of the reference case by $XX5 and the control case 

by $XX for a net average cost decrease of $XX. ¶

Deleted: For the learning sensitivity, we re-ranked 

OMEGA packages using the master-sets of packages 

as described in Chapter 1 of this RIA for the 

2016MY and 2025MY. We did not start with 

preliminary-sets of packages and conduct a full 

package building/ranking process. Using the mastersets of packages, we inserted new costs calculated 

using the low/high learning rates, re-ranked the 

master-sets of packages to get the proper ordering of 

packages for each sensitivity case, then ran 

OMEGA. As noted above, this should have no 

impact on the results other than making them 

conservative since starting with a preliminary-set of 

packages and re-ranking would serve to move the 

modeling to more cost effective technologies, thus 

reducing the cost impact. ¶

The high learning inputs increased the average 

compliance costs of the reference case by $48 and 

the control case by $XX, for a net average pervehicle cost increase of $XX in MY 2025. The low 

ICM inputs decreased the average compliance costs

of the reference case by $XX and the control case by 

$XX for a net average cost decrease of $XX. ¶

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2017 Regulatory Impact Analysis 

5 

Sensitivity Title Reference 

Case Change

Control Case

 Change

Impact

Primary Case -- -- --

 Mass Cost High $21 $65 $44

Mass Cost Low -$24 -$87 -$63

Battery Cost High $2 $72 $69

Battery Cost Low -$2 -$74 -$72

ICM High $110 $316 $206

ICM Low -$114 -$317 -$203

Learning Rate High $48 $177 $129

Learning Rate Low -$44 -$159 -$115

3.11.7 NAS report 

As in the proposal, we note that EPA has decided not to base a sensitivity case on the 

2010 National Academy of Science Report “Assessment of Technologies for Improving LightDuty Vehicle Fuel Economy, Assessment for Fuel Economy Technologies for Light-Duty 

Vehicles” (The National Academies Press, June 2010). 

As discussed in detail in Chapter 3 of the Joint Technical Support Document for this 

final rule, EPA and NHTSA have utilized the best available information in order to estimate 

the cost and effectiveness for a large number of technologies which can be used to reduce 

GHG emissions and improve fuel efficiency. 

In 2007, NHTSA commissioned the National Academy of Science to perform an 

assessment of, among other things, the cost and effectiveness of technologies for improving 

the fuel economy of light-duty vehicles. The 2010 NAS Committee published their results of 

their assessment in June of 2010. EPA has reviewed this report in detail and for the reasons 

discussed below, we have not relied upon this report as a primary assessment for our cost and 

effectiveness estimates for this final rule, and we have also not used the report to perform a 

sensitivity assessment based on the 2010 NAS report for the same reasons. 

Our principal reasons are twofold. First, the 2010 NAS Committee focused their 

report on the near-term, specifically the 2010-2015 time frame, and not on the time frame of 

this final rule, which is 2017 to 2025. Second, on a range of topics EPA and NHTSA have 

relied upon newer information for cost and effectiveness estimates. 

With respect to the time frame of interest, in the Summary of the NAS 2010 report 

(pages S-1 and S-2), the NAS Committee discusses that their costs estimates are for the 2010-

2015 time frame. The 2010 NAS Report also discusses that there are longer-term 

technologies which are in the 5 to 15 year time horizon which are not the focus of the NAS 

2010 report. There are a number of specific examples where this difference in time frame is 

relevant to any potential comparison between the 2010 NAS report and the EPA and NHTSA 

assessment for this final rule. For example, there are a number of technologies that EPA and 

NHTSA discuss in Chapter 3 of the Joint TSD which are not a single, discrete piece of 

hardware, but rather a continuum of improvements where the level of improvement can 

change given the potential time horizon. The 2010 NAS Committee considered at least six of 

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2017 Regulatory Impact Analysis 

6 

these technologies: low friction lubricants, engine friction reduction, improved accessories, 

lower rolling resistance tires, aerodynamic drag improvement, and improved internals for 

automatic transmissions. The 2010 NAS report provides cost and effectiveness estimates for 

one increment of improvement for each of these technologies applicable to the 2010-2015 

time frame. This is similar to the approach utilized by NHTSA and EPA for the MYs 2012-

2016 rulemaking. However, for the MYs 2017-2025 final rules, where EPA and NHTSA are 

using a model year baseline set of vehicles, the agencies estimate that for each these 

technologies two increments of improvement can be implemented across the fleet between

between promulgation of the final rule and MY 2025. Using the NAS Report estimates for 

these technologies thus, without basis, would not consider the further projected incremental 

improvements in these technologies. 

A second example of the importance of the time frame is evaluation of the 

effectiveness of gasoline direct injection with turbocharging and downsizing. The 2010 NAS 

Committee considered one level of downsizing in the 2010-2015 time frame, and EPA and 

NHTSA took a similar approach for the MYs 2012-2016 rule. But, for the MYs 2017-2025 

final rule, based on data in the literature, our discussions with the auto companies and 

automotive suppliers, and a 2011 Ricardo study commissioned by EPA, in the longer term 

additional levels of downsizing are achievable, including in some cases with the use of cooled 

exhaust gas recirculation, that provide additional CO2/fuel consumption reductions. Those 

additional levels of downsizing were not considered by the 2010 NAS Committee in their 

assessment of near-term costs and effectiveness. 

In addition to the difference in time frames being considered by the 2010 NAS report 

and this final rule, a second significant difference between the two assessments were the 

additional studies and information available to EPA and NHTSA which were not reviewed by 

2010 NAS Committee. In many cases this was due to the additional two years EPA and 

NHTSA had available (while the NAS Committee’s report was published in 2010, the bulk of 

their assessments occurred between 2007 and 2009), and in other areas this new information 

was the result of the many confidential meetings EPA and NHTSA had with auto companies 

and auto suppliers over the past two years. 

The additional publically available studies which EPA and NHTSA utilized included 

new studies on the costs for mass reduction, lithium-ion battery packs, 8 speed automatic 

transmissions, 8 speed dual-clutch transmissions, hybrid electric vehicle, plug-in hybrid 

electric vehicle, and all electric vehicles. EPA and NHTSA also utilized new reports dealing 

with the use of indirect cost multipliers for estimating indirect manufacturing costs. EPA and 

NHTSA also are using a number of new studies which were not available to the 2010 NAS 

Committee for the estimation of the effectiveness of a large number of the 2017-2025 

technologies; these include peer reviewed papers in the literature as well as the 2011 Ricardo 

study (discussed in detail in Chapter 3 of the Joint TSD). A partial list of the studies and data 

sources regarding technology feasibility, costs, lead time, and effectiveness considered by 

EPA which were not reviewed by the 2010 NAS Committee or were published after they 

completed their work, or was obtained confidentially from automotive suppliers includes: 

- 2011 Ricardo Report “Computer Simulation of Light-duty Vehicle Technologies for 

Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe”1

, this report has 

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2017 Regulatory Impact Analysis 

7 

been peer reviewed and the peer review report and the response to peer review 

comments are available in the EPA docket EPA-HQ-OAR-2010-0799. 

- Argonne National Laboratories 2011 Report “Modeling the Performance and Cost of 

Lithium-Ion Batteries for Electric-Drive Vehicles”2 and the accompanying Battery 

Performance and Cost Model, which estimates lithium-ion battery pack cost for the 

2020 time frame. This report was peer reviewed and revised in 2011, and the model, 

report, and peer review report are available in the EPA docket EPA-HQ-OAR-2010-

0799. 

- 2011 FEV Report “Light-Duty Technology Cost Analysis Power-split and P2 HEV 

Case Studies.”3 This report was peer reviewed, and a copy of the report, the peer 

review report, and the response to peer review comments report are available in the 

EPA docket EPA-HQ-OAR-2010-0799. 

- 2011 FEV Report “Light-Duty Technology Cost Analysis: Advanced 8-speed 

Transmissions”4

. A copy of this report is available in the EPA docket EPA-HQ-OAR2010-0799. 

- 2010 Lotus Engineer Study “An Assessment of Mass Reduction Opportunities for a 

2017 – 2020 Model Year Vehicle Program”5

, this report has been peer reviewed, and a 

copy of the report and the peer review report are available in the EPA docket EPAHQ-OAR-2010-0799. 

- EPA vehicle fuel economy certification data from MY2011 and MY2012 vehicles, 

including for example the MY2011 Ford F-150 with the 3.5L Ecoboost engine, 

MY2011 Sonata Hybrid, MY2012 Infiniti M35h hybrid, and several other advanced 

technology production vehicles. 

- “EBDI - Application of a Fully Flexible High BMEP Downsized Spark Ignited 

Engine.” Society of Autmotive Engineers (SAE) Technical Paper No. 2010-01-0587, 

Cruff, L., Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010.6

- “Water Cooled Exhaust Manifold and Full Load EGR Technology Applied to a 

Downsized Direct Injection Spark Ignition Engine.” SAE Technical Paper Series No. 

2010-01-0356. Taylor, J., Fraser, N., Wieske, P., 2010.7

- “Requirements of External EGR Systems for Dual Cam Phaser Turbo GDI Engines.” 

SAE Technical Paper Series No. 2010-01-0588. Roth, D.B., Keller, P, Becker, M., 

2010.8

- “Doing More with Less - The Fuel Economy Benefits of Cooled EGR on a Direct 

Injected Spark Ignited Boosted Engine,” SAE Technical Paper Series, No. 2010-01-

0589. Kaiser, M., Krueger, U., Harris, R., Cruff, L., 2010.9 EPA-HQ-OAR-2010-

0799 
2017 Regulatory Impact Analysis 

8 

- “Using indirect cost multipliers to estimate the total cost of adding new technology in 

the automobile industry,” International Journal of Production Economics Rogozhin, 

A.,et al., 2009.10

- “Documentation of the Development of Indirect Cost Multipliers for Three 

Automotive Technologies,” EPA Technical Memorandum, Helfand, G., Sherwood, T., 

August 2009.11

- Confidential business information regarding the development status, effectiveness and 

costs for a large number of technologies obtained by EPA in meetings during 2010 

and 2011 with more than a dozen worldwide automotive suppliers involved in the 

development and production of a wide range of technologies, including but not limited 

to fuel injection systems, transmissions, turbochargers, lower mass automotive 

components, tires, and automotive lithium-ion batteries. 

With the exception of the confidential business information and copyrighted 

information, copies of the reports and studies listed above are available in the EPA docket for 

this final rule, EPA-HQ-OAR-2010-0799. Information on how to obtain copies of the SAE 

papers is also available in the EPA docket, or they can be order from SAE on-line at 

http://papers.sae.org/. 

For the reasons described above, EPA has elected not to perform a sensitivity 

assessment based on the 2010 NAS Report, nor have we used the 2010 NAS Report as our 

primary basis for assessing the costs and effectiveness of technologies for the proposal or this 

final rule. 

EPA requested comment on our overall approach for basing our assessment on 

technology feasibility, lead time, costs and effectiveness on the full range of information 

described in the Joint Technical Support Document (which includes consideration of the 

2010 NAS Study), as opposed to an alternative approach in which EPA would base our 

technology feasibility, lead time, costs and effectiveness primarily on the 2010 NAS Study 

and place lower weighting or no weighting on the additional information which has become 

available since the 2010 NAS Study (including those data sources, studies and reports listed 

above). EPA received public comment from the Delphi Corportation recommending that “the 

National Research Council technology cost estimates and implementation cadence data be 

included in the agencies' analyses and be considered a primary source of information.” This 

comment is discussed in the Response to Comment document, section 12.3. 

EPA also requested comment specifically on EPA’s use of the 2011 Ricardo study 

(listed above), and any ways to improve our estimates of technology effectiveness, including 

the use of full vehicle simulation modeling as was used in the 2011 Ricardo study or 

alternative approaches. We also requested comment on the 2011 Ricardo Study and the 

Ricardo response to comments report with respect to the peer review conducted on the 

Ricardo report. We received comments from the ICCT, and several other organizations, these 

comments are discussed in section 12 of the Response to Comments document. These 

documents are all available in the EPA docket for this rulemaking (EPA-HQ-OAR-2010-
2017 Regulatory Impact Analysis 

9 

0799). Significant additional detail regarding the 2011 Ricardo study and how it was used to 

inform EPA’s estimates of technology effectiveness is contained in Chapter 3 of the Joint 

Technical Support Document. 

 
2017 Regulatory Impact Analysis 

10 

 

1

 2011 Ricardo Report “Computer Simulation of Light-duty Vehicle Technologies for 

Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe” 

2

 Argonne National Laboratories 2011 Report “Modeling the Performance and Cost of 

Lithium-Ion Batteries for Electric-Drive Vehicles” 

3

 2011 FEV Report “Light-Duty Technology Cost Analysis Power-split and P2 HEV Case 

Studies” 

4

 2011 FEV Report “Light-Duty Technology Cost Analysis: Advanced 8-speed 

Transmissions” 

5

 2010 Lotus Engineer Study “An Assessment of Mass Reduction Opportunities for a 2017 – 

2020 Model Year Vehicle Program” 

6

 “EBDI - Application of a Fully Flexible High BMEP Downsized Spark Ignited Engine.” 

Society of Autmotive Engineers (SAE) Technical Paper No. 2010-01-0587, Cruff, L., Kaiser, 

M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010 

7

 “Water Cooled Exhaust Manifold and Full Load EGR Technology Applied to a Downsized 

Direct Injection Spark Ignition Engine.” SAE Technical Paper Series No. 2010-01-0356. 

Taylor, J., Fraser, N., Wieske, P., 2010 

8

 “Requirements of External EGR Systems for Dual Cam Phaser Turbo GDI Engines.” SAE 

Technical Paper Series No. 2010-01-0588. Roth, D.B., Keller, P, Becker, M., 2010 

9

 “Doing More with Less - The Fuel Economy Benefits of Cooled EGR on a Direct Injected 

Spark Ignited Boosted Engine,” SAE Technical Paper Series, No. 2010-01-0589. Kaiser, M., 

Krueger, U., Harris, R., Cruff, L., 2010 

10 “Using indirect cost multipliers to estimate the total cost of adding new technology in the 

automobile industry,” International Journal of Production Economics Rogozhin, A.,et al., 

2009 

11 “Documentation of the Development of Indirect Cost Multipliers for Three Automotive 

Technologies,” EPA Technical Memorandum, Helfand, G., Sherwood, T., August 2009 

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Page 1 of 27

Preamble III.G.4 (Air Quality Impacts of Non-GHG 

Pollutants); III.H.7.a (Quantified and Monetized Non-GHG Health 

Impacts); III.10 (Summary of Costs and Benefits) 

Preamble Section III.G.4 

4. Air Quality Impacts of Non-GHG Pollutants 

Air quality modeling was performed to assess the impact of the vehicle standards on 

criteria and air toxic pollutants. In this section, we present information on current levels of 

pollution as well as projections for 2030, with respect to ambient PM2.5, ozone, selected air 

toxics, visibility levels and nitrogen and sulfur deposition. The results are discussed in more 

detail in Section 6.2.2 of the RIA. 

a. Ozone 

i. Current Levels 

Concentrations that exceed the level of the ozone NAAQS occur in many parts of the country. 

The primary and secondary NAAQS for ozone are 8-hour standards with a level of 0.075 ppm. 

The most recent revision to the ozone standards was in 2008; the previous 8-hour ozone 

standards, set in 1997, had a level of 0.08 ppm. In 2004, the U.S. EPA designated nonattainment 

areas for the 1997 8-hour ozone NAAQS.1,2 As of July 20, 2012, there were 43 ozone 

nonattainment areas for the 1997 ozone NAAQS composed of 237 full or partial counties, with a 

total population of over 129 million. Nonattainment designations for the 2008 ozone standards

were finalized on April 30, 2012 and May 31, 2012.

3

 These designations include 46 areas, 

composed of 227 full or partial counties, with a population of over 123 million. As of July 20, 

2012, 140 million people are living in ozone nonattainment areas.

. 

ii. Projected Levels without the Vehicle Standards 

States with ozone nonattainment areas are required to take action to bring those areas into 

attainment. The attainment date assigned to an ozone nonattainment area is based on the area’s 

classification. Most ozone nonattainment areas are required to attain the 1997 8-hour ozone 

NAAQS in the 2007 to 2013 time frame and attainment dates for the 2008 8-hour ozone NAAQS 

 

1

 69 FR 23858 (April 30, 2004). 

2

 A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is 

contributing to a nearby area that is violating the standard. 

3

 77FR 30088 (May 21, 2012). 

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are in the 2015 to 2032 timeframe.4 Once an ozone nonattainment area has attained the NAAQS 

they are then required to maintain it thereafter. 

EPA has already adopted many emission control programs that are expected to reduce 

ambient ozone levels. As a result of these and other federal, state and local programs, 8-hour 

ozone levels are expected to improve in the future. Even so, our air quality modeling projects 

that in 2030, with all current controls but excluding the impacts of the vehicle standards, up to 10

counties with a population of over 30 million would have projected design values above the level 

of the 2008 ozone standard of 0.075 ppm (75 ppb). These numbers do not account for those 

areas that are close to (e.g., within 10 percent of) the 2008 ozone standard. These areas, although 

not above the standards, will also be impacted by changes in ozone concentrations as they work 

to ensure long-term maintenance of the ozone NAAQS.

iii. Projected Levels with the Vehicle Standards 

Our modeling indicates that there will be very small changes in ambient ozone 

concentrations across most of the country. However, there will be small decreases in ozone 

design value concentrations in some areas of the country and small increases in ozone design 

value concentrations in other areas. 5 The increases in ozone design values are likely due mainly 

to the VMT rebound effect in some places and in other places are likely due mainly to increased 

electricity generation. The ozone decreases are likely due mainly to changes in the location of 

EGUs, or power plants, in some places and in other places are likely due mainly to reduced fuel 

production. The average modeled 8-hour ozone design values are projected to increase by 0.01 

ppb in 2030 and the design values for those counties that are projected to be above the 2008 

ozone standard in 2030 will decrease by 0.14 ppb due to the vehicle standards.

b. Particulate Matter 

i. Current Levels 

There are many areas of the country that are currently in nonattainment for the PM2.5

NAAQS. There are two NAAQS for PM2.5: an annual standard (15 micrograms per cubic meter

(μg/m3) and a 24-hour standard (35 μg/m3). The most recent revisions to these standards were in 

1997 and 2006. In June 2012, EPA proposed to revise the PM2.5 NAAQS and is scheduled to 

 

4

 The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air Basin 8-

hour ozone nonattainment area are designated as extreme and will have to attain before June 15, 2024. The 

Sacramento, Coachella Valley, Western Mojave and Houston 8-hour ozone nonattainment areas are designated as 

severe and will have to attain by June 15, 2019. 

5

 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour 

ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 40 

CFR part 50.

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issue final revisions in December 2012 under a court-ordered schedule. The proposed changes 

include revising the annual PM2.5 standard to a level between 12 and 13 μg/m3

, and establishing 

a distinct secondary PM2.5 standard for the protection of visibility, particularly in urban areas. 

In 2005 EPA designated nonattainment areas for the 1997 PM2.5 NAAQS.6 As of July 

20, 2012, over 91 million people lived in the 35 areas that are designated as nonattainment for 

the 1997 PM2.5 NAAQS. These PM2.5 nonattainment areas are comprised of 191 full or partial 

counties. On October 8, 2009, the EPA issued final nonattainment area designations for the 2006 

24-hour PM2.5 NAAQS.7 These designations include 32 areas, composed of 121 full or partial 

counties, with a population of over 70 million. In total, there are 50 PM2.5 nonattainment areas 

with a population of over 105 million people.8

 

ii. Projected Levels without the Vehicle Standards 

States with PM2.5 nonattainment areas will be required to take action to bring those areas 

into attainment in the future. The 1997 PM2.5 nonattainment areas are required to attain the 1997 

PM2.5 NAAQS in the 2010 to 2015 time frame and then maintain it thereafter.9 The 2006 24-

hour PM2.5 nonattainment areas are required to attain the 2006 24-hour PM2.5 NAAQS in the 

2014 to 2019 time frame and then maintain it thereafter.10 

EPA has already adopted many mobile source emission control programs that are 

expected to reduce ambient PM levels. As a result of these and other federal, state and local 

programs, the number of areas that fail to meet the PM2.5 NAAQS in the future is expected to 

decrease. Even so, our air quality modeling projects that in 2030, with all current controls but 

excluding the impacts of the vehicle standards adopted here, at least 4 counties with a population 

of almost 7 million would have projected design values above the level of the 1997 annual PM2.5

standard of 15 μg/m3 and 21 counties with a population of over 31 million would have projected 

design values above the level of the 2006 24-hour PM2.5 standard of 35 μg/m3. These numbers 

do not account for those areas that are close to (e.g., within 10 percent of) the PM2.5 standards. 

These areas, although not above the standards, will also be impacted by any changes in PM2.5

concentrations as they work to ensure long-term maintenance of the PM2.5 NAAQS. 

 

6

 70 FR 19844 (April 14, 2005). 

7

 74 FR 58688 (November 13, 2009). 

8

 Data come from Summary Nonattainment Area Population Exposure Report, current as of July 20, 2012 at: 

http://www.epa.gov/oar/oaqps/greenbk/popexp.html and contained in Docket EPA-HQ-OAR-2010-0799. 

10 U.S. EPA. (2011). PM Standards Revision – 2006: Timeline. Available at 

http://www.epa.gov/PM/naaqsrev2006.html#timeline. Accessed December 31, 2011. 

10 U.S. EPA. (2011). PM Standards Revision – 2006: Timeline. Available at 

http://www.epa.gov/PM/naaqsrev2006.html#timeline. Accessed December 31, 2011. 

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iii. Projected Levels with the Vehicle Standards 

 Our modeling indicates that there will be very small changes in ambient PM2.5

concentrations across most of the country. However, there will be small decreases in PM2.5

design value concentrations in some areas of the country and small increases in PM2.5 design 

value concentrations in other areas. 11 The decreases in PM2.5 design values for some counties 

are likely due to emission reductions related to lower fuel production and the increases are likely 

due to increased emissions from the VMT rebound effect or increased electricity generation. 

 

c. Air Toxics 

i. Current Levels 

The majority of Americans continue to be exposed to ambient concentrations of air toxics 

at levels which have the potential to cause adverse health effects.12 The levels of air toxics to 

which people are exposed vary depending on where people live and work and the kinds of 

activities in which they engage, as discussed in detail in U.S. EPA’s 2007 Mobile Source Air 

Toxics Rule.13 According to the National Air Toxic Assessment (NATA) for 2005,14 mobile 

sources were responsible for 43 percent of outdoor toxic emissions and over 50 percent of the 

cancer risk and noncancer hazard associated with primary emissions. Mobile sources are also 

large contributors to precursor emissions which react to form secondary concentrations of air 

toxics. Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively 

assessed in the 2005 NATA. Mobile sources were responsible for over 40 percent of primary 

emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor 

emissions. Benzene is also a large contributor to cancer risk, and mobile sources account for over 

70 percent of ambient exposure. Over the years, EPA has implemented a number of mobile 

source and fuel controls which have resulted in VOC reductions, which also reduced 

formaldehyde, benzene and other air toxic emissions. 

 

11 An annual PM2.5 design value is the concentration that determines whether a monitoring site meets the annual 

NAAQS for PM2.5. The full details involved in calculating an annual PM2.5 design value are given in appendix N of 

40 CFR part 50. A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site meets 

the 24-hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5 design value are given in 

appendix N of 40 CFR part 50.

12 U..S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

Rule. 72 FR 8434, February 26, 2007. 

13 U.S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

Rule. 72 FR 8434, February 26, 2007. 

14 U.S. EPA. (2011). 2005 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005/. 

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ii. Projected Levels

Our modeling indicates that national average ambient concentrations of the modeled air 

toxics change less than 1 percent across most of the country due to the final standards. 

Additional detail on the air toxics results can be found in Section 6.2.2.3 of the RIA.

d. Nitrogen and Sulfur Deposition 

i. Current Nitrogen and Sulfur Deposition Levels 

Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen 

and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show 

that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years. The 

data show that reductions were more substantial for sulfur compounds than for nitrogen 

compounds. In the eastern U.S., where data are most abundant, total sulfur deposition decreased 

by about 44 percent between 1990 and 2007, while total nitrogen deposition decreased by 25 

percent over the same time frame.15 These numbers are generated by the U.S. national 

monitoring network and they likely underestimate nitrogen deposition because neither ammonia 

nor organic nitrogen is measured. Although total nitrogen and sulfur deposition has decreased 

over time, many areas continue to be negatively impacted by deposition. Deposition of inorganic 

nitrogen and sulfur species routinely measured in the U.S. between 2005 and 2007 were as high 

as 9.6 kilograms of nitrogen per hectare (kg N/ha) averaged over three years and 20.8 kilograms 

of sulfur per hectare (kg S/ha) averaged over three years.

16

 

ii. Projected Nitrogen and Sulfur Deposition Levels 

Our air quality modeling projects increases in nitrogen deposition in some localized areas 

across the U.S. along with a few areas of decreases in nitrogen deposition as a result of the GHG 

standards. The increases in nitrogen deposition are likely due to projected upstream emissions 

increases in NOX from increased electricity generation and increased driving due to the VMT 

rebound effect. The decreases in nitrogen deposition are likely due to projected upstream 

emissions decreases in NOX from changes in the location of electricity generation. The 

remainder of the country will experience only minimal changes in nitrogen deposition, ranging 

from decreases of less than 0.5% to increases of less than 0.5%.

Our air quality modeling also projects both increases and decreases in sulfur deposition 

as a result of the GHG standards. The decreases in sulfur deposition are likely due to projected 

 

15 U.S. EPA. (2012). U.S. EPA’s Report on the Environment. Data accessed online February 15, 2012 at: 

http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listBy

Chapter&r=216610 and contained in Docket EPA-HQ-OAR-2010-0799. 

16 U.S. EPA. (2012). U.S. EPA's Report on the Environment. Data accessed online February 15, 2012 at: 

http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listBy

Chapter&r=216610 and contained in Docket EPA-HQ-OAR-2010-0799. 

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relatively little impact on 
Page 6 of 27

upstream emissions decreases from changes in the location of electricity generation and from 

reduced gasoline production. The increases in sulfur deposition are likely due to projected 

upstream emissions increases from increased electricity generation. The remainder of the 

country will experience only minimal changes in sulfur deposition, ranging from decreases of 

less than 0.5% to increases of less than 0.5%. 

For maps of 2030 deposition impacts and additional information on these impacts see 

Section 6.2.2.4 of the RIA. 

e. Visibility 

i. Current Visibility Levels 

As mentioned in Section III.G.4.i, millions of people live in nonattainment areas for the 

PM2.5 NAAQS. These populations, as well as large numbers of individuals who travel to these 

areas, are likely to experience visibility impairment. In addition, while visibility trends have 

improved in mandatory class I federal areas, the most recent data show that these areas continue 

to suffer from visibility impairment. In summary, visibility impairment is experienced 

throughout the U.S., in multi-state regions, urban areas, and remote mandatory class I federal 

areas. 

ii. Projected Visibility Levels 

Air quality modeling was used to project visibility conditions in 139 mandatory class I 

federal areas across the U.S. The results show that in 2030 all the modeled areas would continue 

to have annual average deciview levels above background.17 Overall the vehicle standards will

have a very small impact on visibility. The average visibility at all modeled mandatory class I 

federal areas on the 20 percent worst days is projected to improve by 0.003 deciviews, or 0.03

percent, in 2030. Section 6.2.2.5 of the RIA contains more detail on the visibility portion of the 

air quality modeling.

5. Other Unquantified Health and Environmental Effects

In the NPRM, EPA sought comment on whether there are any other health and 

environmental impacts associated with advancements in vehicle GHG reduction technologies 

that the agency should consider. In particular, EPA requested information on studies or research 

 

17 The level of visibility impairment in an area is based on the light-extinction coefficient and 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 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.

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underway on vehicle’s life-cycle impacts (e.g., materials usage, manufacturing, end of life 

disposal) beyond issues regarding fuel production and distribution (upstream) discussed in 

Section III.C.18

EPA received a mix of comments on this topic, many highlighting recent or upcoming 

studies including new research from the University of California, Davis and the University of 

Michigan. Some commenters argued that EPA should base future GHG standards on lifecycle 

emissions in order to avoid favoring technologies that have lower emissions during operation or 

the “use phase,” but higher total greenhouse gas emissions when production and other stages of a 

vehicle’s life are considered. For example, several organizations from the steel industry 

recommended that EPA and NHTSA consider incorporating life-cycle assessment into vehicle 

regulations as part of the 2018 mid-term evaluation and outlined one potential framework for 

establishing such life-cycle based standards. 

Other commenters agreed with the agencies’ proposal not to consider life-cycle impacts 

as part of the standards, arguing that life-cycle analysis (LCA) is beyond the intended scope of 

the rulemaking and that regulating emissions from vehicle operation addresses the majority of 

GHG emissions. The American Chemistry Council also noted, “Further, this type of rulemaking 

is not an appropriate place to apply LCA because of the lack of consensus regarding how to 

calculate inputs and outputs in an LCA evaluation at this time.” 

EPA is glad to see the advances in research on this important topic and plans to monitor 

new work in this area. However, the agency continues to believe that, as of the time of this 

rulemaking, there is too much uncertainty about the life-cycle impacts of future advanced 

technologies to conduct the type of detailed, vehicle-specific assessments that would be needed 

in a regulatory context. See the EPA Response to Comments document for a more detailed 

discussion on this topic and a fuller summary of comments received.19 

 

 

18 See 76 FR at 75112. 

19 Also, see Ch. 6 of NHTSA’s Environmental Impact Statement for this rulemaking, “ Literature Synthesis of Lifecycle Environmental Impacts of Certain Vehicle Materials and Technologies,” Docket No. NHTSA-2011-0056. 

The range of different models and approaches utilized in the surveyed LCA studies, and the sensitivity of the results 

to study assumptions, demonstrate the challenge of developing a fair and robust method to evaluate life-cycle 

impacts across a range of different vehicle technologies at this time. 
Page 8 of 27

Preamble III.H.7.a – Quantified and Monetized Non-GHG 

Health Impacts 

7. Non-Greenhouse Gas Health and Environmental Impacts 

This section presents EPA’s analysis of the criteria pollutant-related health and 

environmental impacts that will occur as a result of the final standards. Light-duty vehicles and 

fuels are significant sources of mobile source air pollution such as direct PM, NOX, SOX, VOCs 

and air toxics. The impact that improved fuel economy will have on rebound driving will affect 

exhaust and evaporative emissions of these pollutants from vehicles. In addition, increased fuel 

savings associated with improved fuel economy achieved under the standards will affect 

emissions from upstream sources (see Section III.G for a complete description of emission 

impacts associated with the final standards). Emissions of NOX (a precursor to ozone formation 

and secondarily-formed PM2.5), SOX (a precursor to secondarily-formed PM2.5), VOCs (a 

precursor to ozone formation and, to a lesser degree, secondarily-formed PM2.5) and directlyemitted PM2.5 contribute to ambient concentrations of PM2.5 and ozone. Exposure to ozone and 

PM2.5 is linked to adverse human health impacts such as premature deaths as well as other 

important public health and environmental effects. 

As many commenters noted, it is important to quantify the health and environmental 

impacts associated with the final rule because it allows us to more accurately assess the net costs 

and benefits of the standards. Moreover, co-pollutant impacts tend to accrue in the near term, 

while any effects from reduced climate change mostly accrue over a time frame of several 

decades or longer. 

This section is split into two sub-sections: the first presents the PM- and ozone-related 

health and environmental impacts associated with the final rule in calendar year (CY) 2030; the 

second presents the PM-related dollar-per-ton values used to monetize the PM-related cobenefits associated with the model year (MY) analysis (i.e., over the lifetimes of the MY 2017-

2025 vehicles) of the final rule.20

 

EPA did receive adverse comments regarding the omission of some non-GHG impacts in 

the proposal. In that analysis, we used “dollar-per-ton” estimates to monetize the health-related 

 

20 EPA typically analyzes rule impacts (emissions, air quality, costs and benefits) in the year in which they occur; for 

this analysis, we selected 2030 as a representative future year. We refer to this analysis as the “Calendar Year” 

(CY) analysis. EPA also conducted a separate analysis of the impacts over the model year lifetimes of the 2017 

through 2025 model year vehicles. We refer to this analysis as the “Model Year” (MY) analysis. In contrast to the 

CY analysis, the MY lifetime analysis shows the lifetime impacts of the program on each MY fleet over the course 

of its lifetime. 
Page 9 of 27

impacts of reduced exposure to PM2.5. We continue to apply these values in the MY analysis for 

the final rule. No “dollar-per-ton” method exists for ozone or toxic air pollutants due to 

complexity associated with atmospheric chemistry (for ozone and toxics) and a lack of economic 

valuation data/methods (for air toxics). However, we have conducted full-scale photochemical 

air quality modeling to estimate the change in ambient concentrations of ozone, PM2.5 and air 

toxics for the CY analysis in 2030 and used these modeling results as the basis for estimating the 

human health impacts and their economic value of the rule in 2030. EPA had neither the time 

nor resources to conduct such modeling for the Model Year analysis. 

a. Quantified and Monetized Non-GHG Human Health Benefits of the 2030 

Calendar Year (CY) Analysis 

This analysis reflects the impact of the final light-duty GHG rule in 2030 compared to a 

future-year reference scenario without the rule in place. 

We estimate that the final rule will lead to a small net reduction in PM2.5-related health 

impacts - the reduction in population-weighted national average PM2.5 exposure results in a small 

net reduction in adverse PM-related human health impacts (the reduction in national populationweighted annual average PM2.5 is 0.0065 μg/m3). 

The air quality modeling also projects a very small increase in ozone concentrations in 

many areas (population-weighted maximum 8-hour average ozone increases by 0.0009 ppb). 

While the ozone-related impacts are very small, the increase in population-weighted national 

average ozone exposure results in a very small increase in ozone-related health impacts. 

We base our analysis of the final rule’s impact on human health in 2030 on peerreviewed studies of air quality and human health effects.21,22 These methods are described in 

more detail in the RIA that accompanies this action. Our benefits methods are also consistent 

with recent rulemaking analyses such as the proposed Portland Cement National Emissions 

Standards for Hazardous Air Pollutants (NESHAP) RIA,

23 the final NO2 NAAQS, 24 and the 

 

21 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for the Proposed 

National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of Air and Radiation. 

Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html 

22 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis. Prepared by: 

Office of Air and Radiation, Office of Air Quality Planning and Standards. Retrieved March, 26, 2009 at 

http://www.epa.gov/ttn/ecas/ria.html 

23 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 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf. Accessed March 15, 2010. 

24 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis 

(RIA). Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet 

at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 15, 2010. 

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Page 10 of 27

final Category 3 Marine Engine rule,25 and the final Cross State Air Pollution Rule.26 To model 

the ozone and PM air quality impacts of the final rule, we used the Community Multiscale Air 

Quality (CMAQ) model (see Section III.G.4). The modeled ambient air quality data serves as an 

input to the Environmental Benefits Mapping and Analysis Program (BenMAP).27 BenMAP is a 

computer program developed by the U.S. EPA that integrates a number of the modeling elements 

used in previous analyses (e.g., interpolation functions, population projections, health impact 

functions, valuation functions, analysis and pooling methods) to translate modeled air 

concentration estimates into health effects incidence estimates and monetized benefits estimates. 

The range of total monetized ozone- and PM-related health impacts is presented in Table 

III-1. We present total benefits (the sum of morbidity-related benefits and mortality-related 

benefits) based on the PM- and ozone-related premature mortality function used. The benefits

ranges therefore reflect the addition of each estimate of ozone-related premature mortality 

(across six selected studies, each with its own row in Table III-1) to each estimate of PM-related 

premature mortality (based on either Pope et al., 2002 or Laden et al., 2006), along with all 

morbidity-related benefits. These estimates represent EPA’s preferred approach to 

characterizing a best estimate of monetized impacts. As is the nature of Regulatory Impact 

Analyses (RIAs), the assumptions and methods used to estimate air quality impacts evolve to 

reflect the Agency’s most current interpretation of the scientific and economic literature. 

Table III-1: Estimated 2030 Monetized PM-and Ozone-Related Health Impactsa

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Billions, 2010$, 3% 

Discount Rate)b,c,d 

Total Benefits 

(Billions, 2010$, 7% 

Discount Rate) b,c,d

Multi-city analyses Bell et al., 2004 Total: $1.0 – $2.6

PM: $1.1 - $2.6

Ozone: -$0.006

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006

Huang et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.006

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006

Schwartz, 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.009

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.009

 

25 U.S. Environmental Protection Agency. 2009. Regulatory Impact Analysis: Control of Emissions of Air

Pollution from Category 3 Marine Diesel Engines. EPA-420-R-09-019, December 2009. Prepared by Office of Air 

and Radiation. http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09019.pdf. Accessed February 9, 2010. 

26 U.S. Environmental Protection Agency. 2011. Regulatory Impact Analysis for the Federal Implementation Plans 

to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Approvals for 

22 States. EPA-HQ-OAR-2009-0491, June 2011. Prepared by Office of Air and Radiation. 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf. Accessed May 16, 2012. 

27 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 

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Page 11 of 27

Meta-analyses Bell et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.019

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.019

Ito et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.026

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.026

Levy et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.027

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.027

Notes: 

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was 

developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related 

premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 

2006).

b

 Note that totals presented here do not include a number of unquantified health impact categories. A detailed 

listing of unquantified health and welfare effects is provided in Table III-2. 

c

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for 

Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of 

presentation and computation. 

d

 Negatives indicate a disbenefit, or an increase in health effect incidence. Monetized impacts are rounded to 

two significant digits. Totals may not sum due to rounding. 

The monetized impacts in Table III-1 include all of the human health impacts we are able 

to quantify and monetize at this time. However, the full complement of human health and 

welfare effects associated with PM, ozone and other criteria pollutants remain unquantified 

because of current limitations in methods or available data. We have not quantified a number of 

known or suspected health effects linked with ozone, PM and other criteria pollutants for which 

appropriate health impact functions are not available or which do not provide easily interpretable 

outcomes (e.g., changes in heart rate variability). Additionally, we are unable to quantify a 

number of known welfare effects, including reduced acid and particulate deposition damage to 

cultural monuments and other materials, and environmental benefits due to reductions of impacts 

of eutrophication in coastal areas. These are listed in Table III-2. As a result, the health benefits 

quantified in this section do not reflect the full range of possible impacts attributable to the final

rule. 
Page 12 of 27

Table III-2: Unquantified and Non-Monetized Potential Effects 

Pollutant/Effects Effects Not Included in Analysis - Changes in: 

Ozone Healtha Chronic respiratory damageb

Premature aging of the lungsb 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e

Ozone Welfare Yields for 

-commercial forests 

-some fruits and vegetables 

-non-commercial crops 

Damage to urban ornamental plants 

Impacts on recreational demand from damaged forest aesthetics 

Ecosystem functions 

Exposure to UVb (+/-)e 

PM Healthc Premature mortality - short term exposuresd

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic bronchitis 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e 

PM Welfare Residential and recreational visibility in non-Class I areas 

Soiling and materials damage 

Damage to ecosystem functions 

Exposure to UVb (+/-)e 

Nitrogen and Sulfate 

Deposition Welfare 

Commercial forests due to acidic sulfate and nitrate deposition 

Commercial freshwater fishing due to acidic deposition 

Recreation in terrestrial ecosystems due to acidic deposition 

Existence values for currently healthy ecosystems 

Commercial fishing, agriculture, and forests due to nitrogen deposition 

Recreation in estuarine ecosystems due to nitrogen deposition 

Ecosystem functions 

Passive fertilization 

CO Health Behavioral effects 

HC/Toxics Healthf Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood components (benzene) 

Reduction in the number of blood platelets (benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-butadiene) 

Irritation of eyes and mucus membranes (formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics (formaldehyde) 

Irritation of the eyes, skin, and respiratory tract (acetaldehyde) 

Upper respiratory tract irritation and congestion (acrolein) 

HC/Toxics Welfare Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 

Notes: 

a

 The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation 
Page 13 of 27

in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are 

likely partially represented by our quantified endpoints. 

b

 The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be 

partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of 

other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified. 

c

 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. 

d

 While some of the effects of short-term exposures are likely to be captured in the estimates, there may be 

premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. 

However, the PM mortality results derived from the expert elicitation do take into account premature mortality 

effects of short term exposures. 

e

 May result in benefits or disbenefits. 

f

 Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the CAA. 

While there will be impacts associated with air toxic pollutant emission changes that 

result from the final rule, we do not attempt to monetize those impacts. This is primarily because 

currently available tools and methods to assess air toxics risk from mobile sources at the national 

scale are not adequate for extrapolation to incidence estimations or benefits assessment. The 

best suite of tools and methods currently available for assessment at the national scale are those 

used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory Board 

specifically commented in their review of the 1996 NATA that these tools were not yet ready for 

use in a national-scale benefits analysis, because they did not consider the full distribution of 

exposure and risk, or address sub-chronic health effects.28 While EPA has since improved the 

tools, there remain critical limitations for estimating incidence and assessing benefits of reducing 

mobile source air toxics. EPA continues to work to address these limitations; however, we did 

not have the methods and tools available for national-scale application in time for the analysis of 

the final rule.29 

EPA is also unaware of specific information identifying any effects on listed endangered 

species from the small fluctuations in pollutant concentrations associated with this rule (see 

Section III.G.4). Furthermore, our current modeling tools are not designed to trace fluctuations 

in ambient concentration levels to potential impacts on particular endangered species. 

 

28 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an 

SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html. 

29 In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials. 
Page 14 of 27

i. Quantified Human Health Impacts 

Table III-3 and Table III-4 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the final rule for 2030. For each endpoint presented in 

Table III-3 and Table III-4, we provide both the mean estimate and the 90% confidence interval. 

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and SixCities studies and no threshold assumption in the model of mortality, we estimate that the final 

rule will reduce between 110 and 280 cases of PM2.5-related premature mortality annually in 

2030. For ozone-related premature mortality in 2030, we estimate a range of between 1 to 3

cases of additional premature mortality. 

Table III-3: Estimated PM2.5-Related Health Impactsa

Health Effect 

2030 Annual Reduction in 

Incidence 

(5th% - 95th%ile) 

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002) 

 Adult, age 25+, Six-Cities Study (Laden et al., 2006) 

 Infant, age <1 year (Woodruff et al., 1997) 

110

(30 – 190)

280

(130 – 440)

0

(0 – 1)

Chronic bronchitis (adult, age 26 and over) 76

(1 – 150)

Non-fatal myocardial infarction (adult, age 18 and over) 130

(32 – 230)

Hospital admissions - respiratory (all ages)c20

(8 – 32)

Hospital admissions - cardiovascular (adults, age >18)d 50

(33 – 60)

Emergency room visits for asthma (age 18 years and younger) 72

(34 – 110)

Acute bronchitis, (children, age 8-12) 160

(-42 – 370)

Lower respiratory symptoms (children, age 7-14) 2,100

(770 – 3,400)

Upper respiratory symptoms (asthmatic children, age 9-18) 1,600

(260 – 2,900)

Asthma exacerbation (asthmatic children, age 6-18) 3,500

(-120 – 9,700)

Work loss days 14,000

(12,000 – 16,000)

Minor restricted activity days (adults age 18-65) 81,000

(65,000 – 96,000)

Notes: 

a

 Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous 

United States. 

b

 PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 

2002) and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult 

Deleted: result in

Deleted: XX

Deleted: XX

Deleted: XX 

Deleted: XX
Page 15 of 27

mortality and should not be summed. PM-related infant mortality based upon a study by Woodruff, 

Grillo, and Schoendorf, (1997).30 

c

 Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease 

(COPD), pneumonia and asthma. 

d

 Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic

heart disease, dysrhythmias, and heart failure. 

Table III-4: Estimated Ozone-Related Health Impactsa

Health Effect 

2030 Annual Reduction in 

Incidence 

(5th% - 95th%ile) 

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental 

 Huang et al. (2005) – Cardiopulmonary 

 Schwartz (2005) – Non-accidental 

Meta-analyses: 

 Bell et al. (2005) – All cause 

 Ito et al. (2005) – Non-accidental 

 Levy et al. (2005) – All cause 

-1

(-4 – 3)

-1

(-5 – 4)

-1

(-6 – 4)

-2

(-10 – 6)

-3

(-11 – 6)

-3

(-10 – 4)

Hospital admissions- respiratory causes (adult, 65 and 

older)c 

-6

(-30 – 15)

Hospital admissions -respiratory causes (children, under 2) -3

(-12 – 6)

Emergency room visit for asthma (all ages) -1

(-18 – 15)

Minor restricted activity days (adults, age 18-65) -930

(-18,000 – 16,000)

School absence days -850

(-6,700 – 5,100)

Notes: 

a

 Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two 

significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b

 Estimates of ozone-related premature mortality are based upon incidence estimates derived from several 

alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. 

(2005); Levy et al. (2005). The estimates of ozone-related premature mortality should therefore not be 

summed. 

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and 

subcategories for COPD and pneumonia. 

 

30 Woodruff, T.J., J. Grillo, and K.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. 
Page 16 of 27

ii. Monetized Impacts 

Table III-5 presents the estimated monetary value of changes in the incidence of ozone 

and PM2.5-related health effects. All monetized estimates are stated in 2010$. These estimates 

account for growth in real gross domestic product (GDP) per capita between the present and 

2030. Our estimate of total monetized impacts in 2030 for the final rule, using the ACS and SixCities PM mortality studies and the range of ozone mortality assumptions, is between $1.0 and 

$2.6 billion, assuming a 3 percent discount rate, and between $0.92 and $2.3 billion, assuming a 

7 percent discount rate. As the results below indicate, monetized impacts are driven primarily by 

the change in premature fatalities in 2030. 

Deleted: XX

Deleted: XX

Deleted: m

Deleted: both 

Deleted: and 7 

Deleted: (totals are the same due to a rounding 

convention of two significant digits).

Deleted: ozone-related 

Deleted: each year
Page 17 of 27

Table III-5: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (in millions of 2010$) a,b

2030 

PM2.5-Related Health Effect (5th and 95th %ile) 

Premature Mortality – 

Derived from Epidemiology 

Studiesc,d,

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

$980

($110 - $2,600)

$880

($97 - $2,400)

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

$2,500

($340 - $6,300)

$2,300

($310 - $5,700)

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

$3.8

(-$3.9 - $15)

Chronic bronchitis (adults, 26 and over) $42

($0.4 - $140)

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

$14

($2.3 - $36)

$12

($1.8 - $30)

Hospital admissions for respiratory causes $0.32

($0.13 - $0.51)

Hospital admissions for cardiovascular causes $0.73

($0.07 - $1.4)

Emergency room visits for asthma $0.03

($0.01 - $0.05)

Acute bronchitis (children, age 8–12) $0.08

(-$0.02 - $0.21)

Lower respiratory symptoms (children, 7–14) $0.04

($0.01 - $0.09)

Upper respiratory symptoms (asthma, 9–11) $0.05

($0.009 - $0.12)

Asthma exacerbations $0.20

(-$0.007 - $0.58)

Work loss days $2.2

($1.9 - $2.6)

Minor restricted-activity days (MRADs) $5.6

($3.2 - $8.1)

Ozone-related Health Effect 

Premature Mortality, All ages – 

Derived from Multi-city analyses

Bell et al., 2004 -$5.8

(-$45 - $27)

Huang et al., 2005 -$6.2

(-$60 - $41)
Page 18 of 27

Schwartz, 2005 -$8.7

(-$71 - $44)

Premature Mortality, All ages – 

Derived from Meta-analyses 

Bell et al., 2005 -$19

(-$120 - $38)

Ito et al., 2005 -$26

(-$140 - $58)

Levy et al., 2005 -$27

(-$120 - $38)

Hospital admissions- respiratory causes (adult, 65 and older) -$0.16

(-$0.77 - $0.39)

Hospital admissions- respiratory causes (children, under 2) -$0.03

(-$130 - $0.07)

Emergency room visit for asthma (all ages) -$0.0003

(-$0.007 - $0.006)

Minor restricted activity days (adults, age 18-65) -$0.06

(-$1.3 - $1.1)

School absence days -$0.08

(-$0.65 - $0.49)

Notes: 

a

 Negatives indicate a disbenefit, or an increase in health effect incidence. Monetized impacts are rounded 

to two significant digits for ease of presentation and computation. PM and ozone benefits are nationwide. 

b

 Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis

year (2030). 

c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses. 

iii. What Are the Limitations of the Benefits Analysis? 

Every benefit-cost analysis examining the potential effects of a change in environmental 

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

(such as geographic coverage), and uncertainties in the underlying scientific and economic 

studies used to configure the benefit and cost models. Limitations of the scientific literature 

often result in the inability to estimate quantitative changes in health and environmental effects, 

such as potential increases in premature mortality associated with increased exposure to carbon 

monoxide. Deficiencies in the economics literature often result in the inability to assign 

economic values even to those health and environmental outcomes which can be quantified. 

These general uncertainties in the underlying scientific and economics literature, which can lead 

to valuations that are higher or lower, are discussed in detail in the RIA and its supporting 

references. Key uncertainties that have a bearing on the results of the benefit-cost analysis of the 

final rule include the following: 

• The exclusion of potentially significant and unquantified benefit categories (such as 

health, odor, and ecological benefits of reduction in air toxics, ozone, and PM); 
Page 19 of 27

• Errors in measurement and projection for variables such as population growth; 

• Uncertainties in the estimation of future year emissions inventories and air quality; 

• Uncertainty in the estimated relationships of health and welfare effects to changes in 

pollutant concentrations including the shape of the concentration-response function, the 

size of the effect estimates, and the relative toxicity of the many components of the PM 

mixture; 

• Uncertainties in exposure estimation; and 

• Uncertainties associated with the effect of potential future actions to limit emissions. 

As Table III-5 indicates, total benefits are driven primarily by the reduction in premature 

mortalities each year. Some key assumptions underlying the premature mortality estimates 

include the following, which may also contribute to uncertainty: 

• Inhalation of fine particles is causally associated with premature death at concentrations 

near those experienced by most Americans on a daily basis. Although biological 

mechanisms for this effect have not yet been completely established, the weight of the 

available epidemiological, toxicological, and experimental evidence supports an 

assumption of causality. The impacts of including a probabilistic representation of 

causality were explored in the expert elicitation-based results of the 2006 PM NAAQS 

RIA. 

• All fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality. This is an important assumption, because PM produced via 

transported precursors emitted from stationary sources may differ significantly from PM 

precursors released from mobile sources and other industrial sources. However, no clear 

scientific grounds exist for supporting differential effects estimates by particle type. 

• The C-R function for fine particles is approximately 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 PM, including both regions 

that may be in attainment with PM2.5 standards and those that are at risk of not meeting

the standards. 

• There is uncertainty in the magnitude of the association between ozone and premature 

mortality. The range of ozone impacts associated with the final standards is estimated 

based on the risk of several sources of ozone-related mortality effect estimates. In a 2008 

report on the estimation of ozone-related premature mortality published by the National 

Research Council, a panel of experts and reviewers concluded that short-term exposure to 

ambient ozone is likely to contribute to premature deaths and that ozone-related mortality 

should be included in estimates of the health benefits of reducing ozone exposure.31 EPA 

has requested advice from the National Academy of Sciences on how best to quantify 

uncertainty in the relationship between ozone exposure and premature mortality in the 

context of quantifying benefits. 

Acknowledging the data limitations and uncertainties, we present a best estimate of the 

total monetized health impacts based on our interpretation of the best available scientific 

 

31 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. The National Academies Press: Washington, D.C. 

Deleted: benefits associated with the 

coordinated strategy
Page 20 of 27

literature and methods supported by EPA’s technical peer review panel, the Science Advisory 

Board’s Health Effects Subcommittee (SAB-HES). The National Academies of Science (NRC, 

2002) has also reviewed EPA’s methodology for analyzing the health benefits of measures taken 

to reduce air pollution. EPA addressed many of these comments in the analysis of the final PM 

NAAQS.32,33 The analysis in this final rule incorporates this most recent work to the extent 

possible. 

 

 

32 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution 

Regulations. The National Academies Press: Washington, D.C. 

33 U.S. Environmental Protection Agency. October 2006. Final Regulatory Impact Analysis (RIA) for the Proposed 

National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of Air and Radiation. 

Available at http://www.epa.gov/ttn/ecas/ria.html. 
Page 21 of 27

Preamble III.H.10 Summary of Costs and Benefits 

10. Summary of Costs and Benefits 

In this section, the agencies present a summary of costs, benefits, and net benefits of the 

proposed program. Table Error! No text of specified style in document.-6 shows the estimated 

annual monetized costs of the proposed program for the indicated calendar years. The table also 

shows the net present values of those costs for the calendar years 2012-2050 using both 3 percent 

and 7 percent discount rates.34 Table Error! No text of specified style in document.-7 shows the 

undiscounted annual monetized fuel savings of the proposed program. The table also shows the 

net present values of those fuel savings for the same calendar years using both 3 percent and 7 

percent discount rates. In this table, the aggregate value of fuel savings is calculated using pretax fuel prices since savings in fuel taxes do not represent a reduction in the value of economic 

resources utilized in producing and consuming fuel. Note that the fuel savings shown here result 

from reductions in fleet-wide fuel use. Thus, fuel savings grow over time as an increasing 

fraction of the fleet meets the proposed standards. 

Table Error! No text of specified style in document.-6 Undiscounted Annual Costs & Costs of 

the Proposed Program Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 

2010$)a

 2017 2020 2030 2040 2050 NPV, Years 

2012-2050, 3% 

Discount Rate 

NPV, Years 

2012-2050, 7% 

Discount Rate 

Technology 

Costs 

$2,440 $8,860 $33,700 $37,400 $42,000 $521,000 $231,000 

Maintenance 

Costs 

$37 $330 $2,260 $3,630 $4,540 $39,500 $15,600 

Vehicle 

Program 

Costs 

$2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247,000 

Note:

a

 Technology costs for separate light-duty vehicle segments can be found in Section III.H.2. Annual costs shown are 

undiscounted values. 

Table Error! No text of specified style in document.-7 Undiscounted Annual Fuel Savings & 

Proposed Program Fuel Savings Discounted Back to 2012 at 3% and 7% Discount Rates 

(Millions, 2010$)a

 2017 2020 2030 2040 2050 NPV, Years 

2012-2050, 3% 

NPV, Years 

2012-2050, 7% 

 

34 For the estimation of the stream of costs and benefits, we assume that after implementation of the proposed MY 

2017-2025 standards, the 2025 standards apply to each year thereafter. 
Page 22 of 27

Discount Rate Discount Rate 

Fuel Savings 

(pre-tax) 

$651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 

Note:

a

 Fuel savings for separate light-duty vehicle segments can be found in Section III.H.3. Annual costs shown are 

undiscounted values. 

Table Error! No text of specified style in document.-8 presents estimated annual 

monetized benefits for the indicated calendar years. The table also shows the net present values 

of those benefits for the calendar years 2012-2050 using both 3 percent and 7 percent discount 

rates. The table shows the benefits of reduced CO2 emissions—and consequently the annual 

quantified benefits (i.e., total benefits)—for each of the four social cost of carbon (SCC) values 

estimated by the interagency working group. As discussed in the RIA Chapter 7.2, there are 

some 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. 

In addition, these monetized GHG benefits exclude the value of net reductions in nonCO2 GHG emissions (CH4, N2O, HFC) expected under this action. Although EPA has not 

monetized the benefits of reductions in non-CO2 GHGs, the value of these reductions should not 

be interpreted as zero. Rather, the net reductions in non-CO2 GHGs will contribute to this 

program’s climate benefits, as explained in Section III.H.5. 

Table Error! No text of specified style in document.-8 Monetized Undiscounted Annual 

Benefits & Benefits of the Proposed Program Discounted Back to 2012 at 3% and 7% 

Discount Rates (Millions, 2010$) 

 2017 2020 2030 2040 2050 NPV, Years 

2012-2050, 

3% Discount 

Ratea

NPV, Years 

2012-2050, 

7% Discount 

Ratea

Reduced CO2 Emissions at each assumed SCC value b

5% (avg SCC) $14 $164 $2,500 $5,510 $8,540 $32,400 $32,400 

3% (avg SCC) $55 $633 $8,410 $17,000 $24,400 $170,000 $170,000 

2.5% (avg SCC) $87 $1,000 $12,900 $25,400 $35,400 $290,000 $290,000 

3% (95th %ile) $167 $1,940 $25,700 $51,800 $74,100 $519,000 $519,000 

Energy Security 

Benefits (macrodisruption costs) 

$33 $371 $4,560 $8,320 $10,400 $84,500 $32,200 

Accidents, 

Congestion, Noise 

Costsg

-$54 -$564 -$5,710 -$9,650 -$12,100 -$101,000 -$39,200 

Increased Travel 

Benefits 

$79 $865 $9,560 $17,000 $14,500 $167,000 $64,800 

Refueling Time 

Savings 

$25 $282 $3,360 $6,350 $8,870 $64,900 $24,500 

Non-GHG Related 

Health Impacts c,d,e

B B $920 -

$1,000

$920 -

$1,000

$920 -

$1,000

$9,190 $3,050
Page 23 of 27

Non-CO2 GHG 

Impactsf 

n/a n/a n/a n/a n/a n/a n/a 

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000

3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000

2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000

3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000

Notes: 

a

 Net present value of reduced CO2 emissions is calculated differently than other benefits. The same discount rate 

used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown are 

undiscounted values. 

b

 Section III.H.5 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as follows: 

for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; and for 

95th percentile SCC at 3%: $70-$140. 

c

 Note that “B” indicates unquantified criteria pollutant benefits in years prior to 2030 (2017-2029). For the final 

rule, EPA only conducted full-scale photochemical air quality modeling to estimate the rule’s PM2.5- and ozonerelated impacts in the calendar year 2030. For the purposes of estimating a stream of future-year criteria pollutant 

benefits associated with the final standards, we assume that the annual benefits out to 2050 are equal to, and no less 

than, those modeled in 2030 as reflected by the stream of estimated future emission reductions. The NPV of criteria 

pollutant-related benefits should therefore be considered a conservative estimate of the potential benefits associated 

with the final rule. 

d

 The PM2.5-related portion of the health benefits presented in this table are based on an estimate of premature 

mortality derived from the ACS study (Pope et al., 2002). However, EPA’s primary method of characterizing PMrelated premature mortality is to use both the ACS and the Six Cities study (Laden et al., 2006) to generate a coequal range of benefits estimates. The decision to present only the ACS-based estimate in this table does not convey 

any preference for one study over the other. We note that this is also the more conservative of the two estimates - 

PM-related benefits would be approximately 245 percent (or nearly two-and-a-half times) larger had we used the 

per-ton benefit values based on the Six Cities study instead. Refer to Section III.H.7 to see the full range of nonGHG related health benefits in Calendar Year 2030. 

e

 The range of calendar year non-GHG benefits presented in this table assume either a 3% discount rate in the 

valuation of PM-related premature mortality ($1,000 million) or a 7% discount rate ($920 million) to account for a 

twenty-year segmented cessation lag. Note that the benefits estimated using a 3% discount rate were used to 

calculate the NPV using a 3% discount rate and the benefits estimated using a 7% discount rate were used to 

calculate the NPV using a 7% discount rate. 

f

 The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this program (See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO2

 GHGs, 

the value of any increases or reductions should not be interpreted as zero. We seek comment on a method of 

quantifying non-CO2 GHG benefits in Section III.H.5. 

g Negative values for Accidents, Congestion, and Noise costs represent disbenefits. 

Table Error! No text of specified style in document.-9 presents estimated annual net 

benefits for the indicated calendar years. The table also shows the net present values of those net 

benefits for the calendar years 2012-2050 using both 3 percent and 7 percent discount rates. The 

table includes the benefits of reduced CO2 emissions (and consequently the annual net benefits) 

for each of the four SCC values considered by EPA. 

Table Error! No text of specified style in document.-9 Undiscounted Annual Monetized Net 

Benefits & Net Benefits of the Proposed Program Discounted Back to 2012 at 3% and 7% 

Discount Rates (Millions, 2009$) 

2017 2020 2030 2040 2050 NPV, 3%a

 NPV, 7%a

Vehicle Program $2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247,000 
Page 24 of 27

Costs 

Fuel Savings $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 

Total Annual Benefits at each assumed SCC value b

5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000

3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000

2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000

3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000

Monetized Net Benefits at each assumed SCC value c

5% (avg SCC) -$1,690 -$316 $68,000 $146,000 $201,000 $1,290,000 $478,000

3% (avg SCC) -$1,650 $153 $73,900 $158,000 $217,000 $1,430,000 $616,000

2.5% (avg SCC) -$1,610 $524 $78,300 $166,000 $228,000 $1,550,000 $736,000

3% (95th %ile) -$1,530 $1,460 $91,200 $192,000 $267,000 $1,780,000 $964,000

Notes: 

a

 Net present value of reduced CO2 emissions is calculated differently than other benefits. The same discount rate 

used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. Annual costs shown are 

undiscounted values. 

b

 Section VIII.H.5 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as 

follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; 

and for 95th percentile SCC at 3%: $70-$140 Section VIII.H.5 also presents these SCC estimates. 

c

 Net Benefits equal Fuel Savings minus Technology Costs plus Benefits. 

EPA also conducted a separate analysis of the total benefits over the model year lifetimes 

of the 2017 through 2025 model year vehicles. In contrast to the calendar year analysis 

presented above in Table Error! No text of specified style in document.-6 through Table Error! 

No text of specified style in document.-9, the model year lifetime analysis below shows the 

impacts of the proposed program on vehicles produced during each of the model years 2017 

through 2025 over the course of their expected lifetimes. The net societal benefits over the full 

lifetimes of vehicles produced during each of the nine model years from 2017 through 2025 are 

shown in Table Error! No text of specified style in document.-10 and Table Error! No text of 

specified style in document.-11 at both 3 percent and 7 percent discount rates, respectively. 

Table Error! No text of specified style in document.-10 Monetized Technology Costs, Fuel 

Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2017-2025 Model Year 

Light-Duty Vehicles (Millions, 2009$; 3% Discount Rate)h

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY 

Sum 

Vehicle 

Program 

Costs 

$2,770 $5,460 $7,720 $10,100 $14,000 $19,900 $25,400 $30,900 $33,600 $150,000 

Fuel Savings 

(pre-tax) 

$7,040 $15,500 $24,300 $34,100 $50,400 $64,900 $78,500 $92,800 $107,000 $475,000 

Energy 

Security 

Benefits 

(macrodisruption 

costs) 

$365 $807 $1,260 $1,780 $2,650 $3,430 $4,170 $4,950 $5,750 $25,200 

Accidents, 

Congestion, 

Noise Costs f

-$548 -$1,150 -$1,770 -$2,440 -$3,480 -$4,420 -$5,270 -$6,160 -$7,040 -$32,300 
Page 25 of 27

Increased 

Travel 

Benefits 

$1,000 $2,180 $3,390 $4,700 $6,840 $8,650 $10,200 $11,900 $13,600 $62,500 

Refueling 

Time Savings 

$273 $604 $945 $1,330 $1,970 $2,550 $3,100 $3,680 $4,280 $18,700 

PM2.5 Related 

Health 

Impactsc,d,e 

$74 $171 $271 $385 $606 $776 $928 $1,090 $1,250 $5,540 

Non-CO2

GHG 

Impactsg

n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Reduced CO2 Emissions at each assumed SCC value a, b

5% (avg 

SCC) 

$152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2,820 $11,800 

3% (avg 

SCC) 

$642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,260 $10,800 $46,600 

2.5% (avg 

SCC) 

$1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100 

3% (95th 

%ile) 

$1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000

Monetized Net Benefits at each assumed SCC value a, b

5% (avg 

SCC) 

$5,590 $13,000 $21,200 $30,500 $46,200 $57,500 $68,100 $79,700 $94,400 $416,000 

3% (avg 

SCC) 

$6,080 $14,100 $22,900 $33,000 $49,900 $62,200 $73,900 $86,600 $102,000 $451,000 

2.5% (avg 

SCC) 

$6,480 $15,000 $24,300 $34,900 $52,800 $66,000 $78,500 $92,000 $109,000 $479,000 

3% (95th 

%ile) 

$7,400 $17,100 $27,600 $39,600 $59,800 $75,200 $89,800 $106,000 $125,000 $547,000 

Notes: 

a

 Net present value of reduced CO2 emissions is calculated differently than other benefits. The same discount rate 

used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. 

b

 Section III.H.5 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as follows: 

for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; and for 

95th percentile SCC at 3%: $70-$140. Section III.H.5 also presents these SCC estimates. 

c

 Note that the non-GHG impacts associated with the standards presented here do not include the full complement of 

endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related impacts. 

Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated 

with reductions in PM2.5 exposure. Ideally, human health and environmental benefits would be based on changes in 

ambient PM2.5 and ozone as determined by full-scale air quality modeling. However, EPA was unable to conduct a 

full-scale air quality modeling for the Model Year analysis. Full scale air quality modeling was conducted for the 

Calendar Year analysis. See Section III.G and III.H.7 for a discussion of that analysis. 

d

 The PM2.5-related health benefits (derived from benefit-per-ton values) presented in this table are based on an

estimate of premature mortality derived from the ACS study (Pope et al., 2002). However, EPA’s primary method 

of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Laden et al., 2006) 

to generate a co-equal range of benefits estimates. The decision to present only the ACS-based estimate in this table 

does not convey any preference for one study over the other. We note that this is also the more conservative of the 

two estimates - PM-related benefits would be approximately 245 percent (or nearly two-and-a-half times) larger had 

we used the per-ton benefit values based on the Six Cities study instead. See Joint TSD 4 for a detailed description 

of the dollar-per-ton values used in this analysis.

e

 The PM2.5-related health benefits (derived from benefit-per-ton values) presented in this table assume a 3% 

discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation lag. If a 7% 

discount rate had been used, the values would be approximately 9% lower. 

f

 Negative values for Accidents, Congestion, and Noise costs represent disbenefits. 
Page 26 of 27

g

 The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this action (See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO2 GHGs, the 

value of any increases or reductions should not be interpreted as zero. We seek comment on a method of quantifying 

non-CO2 GHG benefits in Section III.H.5. 

h

 Model year values are discounted to the first year of each model year; the “Sum” represents those discounted 

values summed across model years. 

Table Error! No text of specified style in document.-11 Monetized Technology Costs, Fuel 

Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2017-2025 Model Year 

Light-Duty Vehicles (Millions, 2009$; 7% Discount Rate) h

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY 

Sum 

Vehicle 

Program 

Costs 

$2,650 $5,220 $7,370 $9,610 $13,300 $19,200 $24,600 $29,900 $32,500 $144,000 

Fuel Savings 

(pre-tax) 

$5,410 $11,900 $18,600 $26,100 $38,600 $49,700 $60,100 $71,100 $82,300 $364,000 

Energy 

Security 

Benefits 

(macrodisruption 

costs) 

$279 $615 $964 $1,360 $2,020 $2,620 $3,180 $3,780 $4,400 $19,200

Accidents, 

Congestion, 

Noise Costs f

-$425 -$893 -$1,370 -$1,890 -$2,690 -$3,410 -$4,070 -$4,760 -$5,440 -$24,900 

Increased 

Travel 

Benefits 

$761 $1,650 $2,550 $3,530 $5,120 $6,470 $7,640 $8,870 $10,100 $46,700 

Refueling 

Time Savings

$209 $461 $721 $1,020 $1,500 $1,940 $2,360 $2,800 $3,260 $14,300 

PM2.5 Related 

Health 

Impactsc,d,e 

$59 $136 $215 $305 $478 $607 $721 $840 $959 $4,320 

Non-CO2

GHG 

Impactsg

n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 

Reduced CO2 Emissions at each assumed SCC value a, b

5% (avg 

SCC) 

$152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2,820 $11,800 

3% (avg 

SCC) 

$642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,260 $10,800 $46,600 

2.5% (avg 

SCC) 

$1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100 

3% (95th 

%ile) 

$1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000 

Monetized Net Benefits at each assumed SCC value a, b

5% (avg 

SCC) 

$3,800 $9,010 $14,900 $21,600 $32,900 $40,300 $47,300 $55,100 $65,800 $291,000 

3% (avg 

SCC) 

$4,290 $10,100 $16,600 $24,100 $36,500 $45,000 $53,100 $62,000 $73,800 $326,000 

2.5% (avg 

SCC) 

$4,690 $11,000 $18,000 $26,000 $39,400 $48,800 $57,600 $67,400 $80,100 $353,000 
Page 27 of 27

3% (95th 

%ile) 

$5,610 $13,100 $21,300 $30,700 $46,500 $58,000 $69,000 $81,000 $96,100 $421,000 

Notes: 

a

 Net present value of reduced CO2 emissions is calculated differently than other benefits. The same discount rate 

used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net 

present value of SCC for internal consistency. Refer to the SCC TSD for more detail. 

b

 Section III.H.5 notes that SCC increases over time. For the years 2012-2050, the SCC estimates range as follows: 

for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; and for 

95th percentile SCC at 3%: $70-$140. Section III.H.5 also presents these SCC estimates. 

c

 Note that the non-GHG impacts associated with the standards presented here do not include the full complement of 

endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related impacts. 

Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated 

with reductions in PM2.5 exposure. Ideally, human health and environmental benefits would be based on changes in 

ambient PM2.5 and ozone as determined by full-scale air quality modeling. However, EPA was unable to conduct a 

full-scale air quality modeling for the Model Year analysis. Full scale air quality modeling was conducted for the 

Calendar Year analysis. See Section III.G and III.H.7 for a discussion of that analysis. 

d

 The PM2.5-related health benefits (derived from benefit-per-ton values) presented in this table are based on an

estimate of premature mortality derived from the ACS study (Pope et al., 2002). However, EPA’s primary method 

of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Laden et al., 2006) 

to generate a co-equal range of benefits estimates. The decision to present only the ACS-based estimate in this table 

does not convey any preference for one study over the other. We note that this is also the more conservative of the 

two estimates - PM-related benefits would be approximately 245 percent (or nearly two-and-a-half times) larger had 

we used the per-ton benefit values based on the Six Cities study instead. See Joint TSD 4 for a detailed description 

of the dollar-per-ton values used in this analysis.

e

 The PM2.5-related health benefits (derived from benefit-per-ton values) presented in this table assume a 3% 

discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation lag. If a 7% 

discount rate had been used, the values would be approximately 9% lower 

.

f

 Negative values for Accidents, Congestion, and Noise costs represent disbenefits. 

g

 The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this action (See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO2 GHGs, the 

value of any increases or reductions should not be interpreted as zero. We seek comment on a method of quantifying 

non-CO2 GHG benefits in Section III.H.5. 

h

 Model year values are discounted to the first year of each model year; the “Sum” represents those discounted 

values summed across model years. 
2017 Regulatory Impact Analysis 

1 

Regulatory Impacts Analysis, Chapter 6.2.2 and 6.3 – Air 

Quality Modeling Results; Quantified and Monetized Non-GHG 

Health Impacts 

6.2.1.3 Modeling Uncertainties and Limitations

All the results presented below must be interpreted with the understanding that there are 

uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of the modeling 

process. While it is beyond the scope of this RIA to include a comprehensive discussion of all 

limitations and uncertainties associated with air quality modeling, several sources of uncertainty 

that impact analyses for this rule are discussed. 

A source of uncertainty is the photochemical mechanisms in CMAQ 4.7.1. Pollutants 

such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3-butadiene can be formed 

secondarily through atmospheric chemical processes. Since secondarily formed pollutants can 

result from many different reaction pathways, there are uncertainties associated with each 

pathway. Simplifications of chemistry must be made in order to handle reactions of thousands of 

chemicals in the atmosphere. Mechanisms for formation of ozone, PM, acetaldehyde and 

peroxyacetyl nitrate (PAN) are discussed in Section Error! Reference source not found.. 

For PM, there are a number of uncertainties associated with SOA formation. As 

mentioned in Section Error! Reference source not found., a large number of VOCs emitted 

into the atmosphere, which have the potential to form SOA, have not yet been studied in detail. 

In addition, the amount of ambient SOA that comes from benzene is uncertain. Simplifications 

to the SOA treatment in CMAQ have also been made in order to preserve computational 

efficiency. These simplifications are described in release notes for CMAQ 4.7 on the 

Community Modeling and Analysis System (CMAS) website.1

6.2.2 Air Quality Modeling Results

6.1.1 Air Quality Modeling Results 

6.1.1.1 Ozone 

As described in Section Error! Reference source not found., exposure to ozone causes 

adverse health effects, and the EPA has set national standards to provide requisite protection 

against those health effects. In this section, we present information on current and modelprojected future ozone levels. 

6.1.1.1.1 Current Levels of Ozone 

Figure Error! No text of specified style in document..2-Error! No text of specified 

style in document.-1 shows a snapshot of measured ozone concentrations in 2010. The highest 

ozone concentrations were located in California. 

Formatted: Right: 0.88"

Deleted: ¶

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Chapter 6 

2 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-1 Ozone Concentrations (average of annual fourth highest daily maximum 8-

hour concentration) in ppm for 20102

The primary and secondary NAAQS for ozone are 8-hour standards set at 0.075 ppm. The most 

recent revision to the ozone standards was in 2008; the previous 8-hour ozone standards, set in 

1997, had been set at 0.08 ppm. In 2004, the U.S. EPA designated nonattainment areas for the 

1997 8-hour ozone NAAQS (69 FR 23858, April 30, 2004).A As of July 20, 2012, there were 43 

8-hour ozone nonattainment areas for the 1997 ozone NAAQS, composed of 237 full or partial 

counties, with a total population of over 129 million. Nonattainment areas for the 1997 8-hour 

ozone NAAQS are pictured in Figure Error! No text of specified style in document..2-Error! 

No text of specified style in document.-2. Nonattainment designations for the 2008 ozone 

standards were finalized on April 30, 2012 and May 31, 2012.

3

 These designations include 46 

areas, composed of 227 full or partial counties, with a population of over 123 million. 

Nonattainment areas for the 2008 ozone NAAQS are pictured in Figure Error! No text of 

specified style in document..2-Error! No text of specified style in document.-8. As of July 

20, 2012, 140 million people are living in ozone nonattainment areas.

 

A

 A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is 

contributing to a nearby area that is violating the standard. 

Deleted: 4

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Deleted: XX 

Deleted: XX

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2017 Regulatory Impact Analysis 

3 

. 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-2 1997 8-hour Ozone Nonattainment Areas

Deleted: In total, there are XX ozone 

nonattainment areas, composed of XX counties, with 

a population of XX million people

Deleted: 5
Chapter 6 

4 

Figure Error! No text of specified style in document.-3 2008 8-hour Ozone Nonattainment 

Areas

States with ozone nonattainment areas are required to take action to bring those areas into 

attainment in the future. The attainment date assigned to an ozone nonattainment area is based 

on the area’s classification. Most ozone nonattainment areas are required to attain the 1997 8-

hour ozone NAAQS in the 2007 to 2013 time frame.B Once an ozone nonattainment area has 

attained the NAAQS they are then required to maintain it thereafter. The attainment dates for 

areas designated nonattainment for the 2008 8-hour ozone NAAQS are in the 2015 to 2032 

timeframe, depending on the severity of the problem in each area. 

6.1.1.1.2 Projected Levels of Ozone

In the following sections, we describe projected ozone levels in the future with and 

without the vehicle standards. Our modeling indicates that there will be very small changes in 

ozone across most of the country. In addition, ozone concentrations in some areas will decrease 

and ozone concentrations in some other areas will increase. The impacts of the standards on 

 

B

 The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air Basin 8-

hour ozone nonattainment area are designated as extreme and will have to attain before June 15, 2024. The 

Sacramento, Coachella Valley, Western Mojave, and Houston 8-hour ozone nonattainment areas are designated as 

severe and will have to attain by June 15, 2019. 

Formatted: Keep with next

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Heading 4,TSD Heading 4,RIA: Heading

4,Preamble Heading 4,ECA Heading 4,Heading

4 - Preamble,Preamble Heading 41,RIA:

Heading 41,Preamble Heading 42,RIA: Heading

42,Preamble Heading 43,RIA: Heading

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after: 68 px + Indent at: 68 px

Deleted: ¶
2017 Regulatory Impact Analysis 

5 

ozone are a function of VMT increases from rebound, upstream reductions in petroleum 

consumption from crude oil production and transport, and gasoline production, distribution and 

transport, and changes in location and amount of electricity generation. Information on the air 

quality modeling methodology is contained in Section Error! Reference source not found. and 

additional detail can be found in the air quality modeling technical support document (AQM 

TSD). 

 Projected Levels of Ozone without this Final Action

 EPA has already adopted many emission control programs that are expected to reduce 

ambient ozone levels. These control programs include the New Marine Compression-Ignition 

Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine 

Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 8, 2008), the 

Locomotive and Marine Rule (73 FR 25098, May 6, 2008), the Clean Air Interstate Rule (70 FR 

25162, May 12, 2005), the Clean Air Nonroad Diesel Rule (69 FR 38957, June 29, 2004), and the 

Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Requirements (66 FR 5002, January 18, 2001). As a result of these and other federal, state and 

local programs, 8-hour ozone levels are expected to improve in the future. However, even with 

the implementation of all current state and federal regulations, there are projected to be counties 

that would have projected design values above the level of the ozone NAAQS well into the 

future.

The air quality modeling projects that in 2030, with all current controls in effect but 

excluding the emissions changes expected to occur as a result of this final action, at least 10 

counties, with a projected population of over 30 million people, would have projected design 

values above the level of the 2008 8-hour ozone standard of 75 ppb. Since the emission changes 

from this final action go into effect during the period when some areas are still working to attain 

the ozone NAAQS, the projected emission changes will impact state and local agencies in their 

effort to attain and maintain the ozone standard. In the following section we discuss the 

projected ozone impacts associated with the vehicle standards. 

 Projected Levels of Ozone with this Final Action

 This section summarizes the results of our modeling of ozone air quality impacts in the 

future with the vehicle standards. Specifically, we compare a 2030 reference scenario, a scenario 

without the vehicle standards, to a 2030 control scenario which includes the vehicle standards. 

Our modeling indicates that there will be very small changes in ambient ozone concentrations 

across most of the country. However, there will be small decreases in ozone design value 

concentrations in some areas of the country and small increases in ozone design value 

concentrations in other areas. C 

 

C

 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour 

ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 40 

CFR part 50.
Chapter 6 

6 

Figure Error! No text of specified style in document.-4 presents the changes in 8-hour 

ozone design value concentrations in 2030 between the reference case and the control case. The 

ozone impacts are related to downstream emissions changes from VMT rebound and upstream 

emissions changes in electrical power generation and fuel production. In some areas the ozone 

impact is a result of a combination of the various emissions changes but in other areas the impact 

is likely mainly the result of one of the types of emissions changes. Some of the ozone increases 

and decreases are related mainly to upstream emissions changes in electricity generation. For 

example, the projected increases in Las Vegas, Dayton, and Little Rock are due mainly to 

increased demand for electricity from electric vehicles and the projected decrease in ozone in 

northeast West Virginia is due mainly to reductions in power plant emissions.D Some of the 

ozone decreases are mainly related to upstream emissions reductions from reduced refinery 

demand as fuel production decreases (e.g. the Gulf Coast) and some of the ozone increases are 

mainly related to increased emissions of NOX from the VMT rebound effect (e.g., Knoxville and 

Atlanta).

Figure Error! No text of specified style in document.-4 Projected Change in 2030 8-hour 

Ozone Design Values Due to the Final Standards

 

D

 Section 4.7.3.1 has more information on the Integrated Planning Model (IPM) analysis which was done to project 

future electricity demand and plant locations. 
2017 Regulatory Impact Analysis 

7 

As can be seen in Figure Error! No text of specified style in document.-4, the majority 

of the ozone design value impacts are between + 0.3 ppb and -0.3 ppb. However, there are two 

counties that will experience 8-hour ozone design value decreases of more than 0.3 ppb: Garrett 

County, Maryland, and Harris County, Texas. The maximum projected decrease in an 8-hour 

ozone design value is 0.47 ppb in Garrett County, Maryland and is likely related to the projected 

reductions in power plant emissions in northeast WV. There is also one county, Pulaski County 

in Arkansas, with a projected design value increase greater than 0.3 ppb. The projected increase 

in Pulaski County is 0.37 ppb. There are 10 counties, most of them in California, that are 

projected to have 8-hour ozone design values above the 2008 NAAQS in 2030 with the vehicle 

standards in place. Table Error! No text of specified style in document.-1 below presents the 

changes in design values for these counties. 

Table Error! No text of specified style in document.-1 Change in Ozone Design Values (ppb) 

for Counties Projected to be Above the 2008 Ozone NAAQS in 2030

County Name Change in 8-hour 

Ozone Design 

Value (ppb)

Population in 

2030a

San Bernardino Co, California -0.20 2,784,490

Riverside Co, California -0.23 2,614,198

Los Angeles Co, California -0.13 10,742,722

Kern Co, California 0.05 981,806

Harris Co, Texas -0.31 5,268,889

Tulare Co, California -0.02 528,663

Orange Co, California -0.12 4,431,071

Fresno Co, California -0.04 1,196,950

Suffolk Co, New York -0.06 1,705,822

Brazoria Co, Texas -0.30 364,257

Note:

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. 

Population by Single Year of Age CD.

Table Error! No text of specified style in document.-2 shows the average change in 2030 

8-hour ozone design values for: (1) all counties with 2005 baseline design values, (2) counties 

with 2005 baseline design values that exceeded the 2008 ozone standard, (3) counties with 2005 

baseline design values that did not exceed the 2008 standard, but were within 10% of it, (4) 

counties with 2030 design values that exceeded the 2008 ozone standard, and (5) counties with 

2030 design values that did not exceed the standard, but were within 10% of it. Counties within 

10% of the standard are intended to reflect counties that although not violating the standards, will 

also be impacted by changes in ozone as they work to ensure long-term maintenance of the ozone 

NAAQS. The average modeled future-year 8-hour ozone design values are projected to increase 

by 0.01 ppb in 2030. Average design values in those counties that are projected to be above the 

2008 ozone standard in 2030 will decrease by 0.14 ppb due to the vehicle standards. 

Formatted: Space Before: 16 px, After: 16

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Chapter 6 

8 

Table Error! No text of specified style in document.-2 Average Change in Projected 8-hour 

Ozone Design Value 

Averagea Number 

of US 

Counties

2030 

Populationb

Change in 

2030 design 

value (ppb)

All

675 261,439,344

0.01

All, population-weighted 0.00

Counties whose 2005 base year is above the 2008 

8-hour ozone standard

393 194,118,748

0.02

Counties whose 2005 base year is above the 2008 

8-hour ozone standard, population-weighted 0.00

Counties whose 2005 base year is within 10 percent 

of the 2008 8-hour ozone standard

201 44,436,103

0.02

Counties whose 2005 base year is within 10 percent 

of the 2008 8-hour ozone standard, populationweighted 0.01

Counties whose 2030 control case is above the 2008 

8-hour ozone standard

10 30,618,868

-0.14

Counties whose 2030 control case is above the 2008 

8-hour ozone standard, population-weighted -0.16

Counties whose 2030 control case is within 10% of 

the 2008 8-hour ozone standard

40 29,661,201

-0.02

Counties whose 2030 control case is within 10% of 

the 2008 8-hour ozone standard, populationweighted 0.00

Notes:

a

 Averages are over counties with 2005 modeled design values 

b

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by 

Single Year of Age CD.

Ground-level ozone pollution is formed by the reaction of VOCs and NOX in the 

atmosphere in the presence of heat and sunlight. The science of ozone formation, transport, and 

accumulation is complex.4 The projected ozone impacts which are seen in the air quality 

modeling for this final action are a result of the emissions changes due to the vehicle standards 

combined with the photochemistry involved, the different background concentrations of VOCs 

and NOX in different areas of the country, and the different meteorological conditions in different 

areas of the country. 

When VOC levels are relatively high, relatively small amounts of NOX enable ozone to 

form rapidly. Under these conditions, VOC reductions have little effect on ozone and while NOX

reductions are highly effective in reducing ozone, conversely NOX increases lead to increases in 

ozone. Such conditions are called “NOX -limited.” Because the contribution of VOC emissions 

from biogenic (natural) sources to local ambient ozone concentrations can be significant, even 

some areas where man-made VOC emissions are relatively low can be NOX -limited. Rural areas 

are usually NOX -limited, due to the relatively large amounts of biogenic VOC emissions in such 

areas. 

When NOX levels are relatively high and VOC levels relatively low, NOX forms inorganic 

nitrates (i.e., particles) but relatively little ozone. Such conditions are called “NOx-saturated.” 
2017 Regulatory Impact Analysis 

9 

Under these conditions, VOC reductions are effective in reducing ozone, but NOX reductions can 

actually increase local ozone under certain circumstances. 

6.1.1.2 Particulate Matter 

As described in Section Error! Reference source not found., exposure to PM2.5 causes 

adverse health effects, and the EPA has set national standards to provide requisite protection 

against those health effects. In this section, we present information on current and modelprojected future PM2.5 levels. 

6.1.1.2.1 Current Levels of Particulate Matter 

Figure Error! No text of specified style in document..2-Error! No text of specified 

style in document.-5 and Figure Error! No text of specified style in document..2-Error! No 

text of specified style in document.-6 respectively show a snapshot of annual and 24-hour PM2.5

concentrations in 2010. In 2010, the highest annual average PM2.5 concentrations were in 

California, Indiana, Pennsylvania, and Hawaii and the highest 24-hour PM2.5 concentrations were 

in California and Alaska. 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-5 Annual Average PM2.5 Concentrations in μg/m3 for 20105

Deleted: [placeholder]

Deleted: 67
Chapter 6 

10 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-6 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in 

μg/m3 for 20106

There are two NAAQS for PM2.5: an annual standard (15.0 μg/m3

) and a 24-hour standard 

(35 μg/m3). The most recent revisions to these standards were in 1997 and 2006. In June 2012, 

EPA proposed to revise the PM2.5 NAAQS and is scheduled to issue final revisions in December 

2012 under a court-ordered schedule. The proposed changes include revising the annual PM2.5

standard to a level between 12 and 13 μg/m3, and establishing a distinct secondary PM2.5 standard 

for the protection of visibility, particularly in urban areas. 

In 2005 the U.S. EPA designated nonattainment areas for the 1997 PM2.5 NAAQS (70 FR 

19844, April 14, 2005). As of July 20, 2012, over 91 million people lived in the 35 areas that are 

designated as nonattainment for the 1997 PM2.5 NAAQS. These PM2.5 nonattainment areas are 

comprised of 191 full or partial counties. Nonattainment areas for the 1997 PM2.5 NAAQS are 

pictured in Figure Error! No text of specified style in document..2-Error! No text of specified 

style in document.-7. On October 8, 2009, the EPA issued final nonattainment area designations 

for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13, 2009). These designations 

include 32 areas, composed of 121 full or partial counties, with a population of over 74 million. 

Nonattainment areas for the 2006 PM2.5 NAAQS are pictured in Figure Error! No text of 

specified style in document..2-Error! No text of specified style in document.-8. In total, there 

are 50 PM2.5 nonattainment areas with a population of over 105 million people. 

States with PM2.5 nonattainment areas will be required to take action to bring those areas 

into attainment in the future. The 1997 PM2.5 nonattainment areas are required to attain the 1997 

PM2.5 NAAQS in the 2009 to 2015 time frame and then maintain the 1997 PM2.5 NAAQS 

Deleted: 78

Deleted: March 30
2017 Regulatory Impact Analysis 

11 

thereafter.7 The 2006 24-hour PM2.5 nonattainment areas will be required to attain the 2006 24-

hour PM2.5 NAAQS in the 2014 to 2019 time frame and then maintain the 2006 24-hour PM2.5

NAAQS thereafter.8

 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-7 1997 PM2.5 Nonattainment Areas Deleted: 89
Chapter 6 

12 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-8 2006 PM2.5 Nonattainment Areas 

As of July 20, 2012, over 29 million people live in the 46 areas that are designated as 

nonattainment for the PM10 NAAQS. There are 39 full or partial counties that make up the PM10

nonattainment areas. Nonattainment areas for the PM10 NAAQS are pictured in Figure Error! 

No text of specified style in document..2-Error! No text of specified style in document.-9.

Deleted: 910

Deleted: March 30

Deleted: 28 

Deleted: 45 
2017 Regulatory Impact Analysis 

13 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-9 PM10 Nonattainment Areas 

6.1.1.2.2 Projected Levels of PM2.5

In the following sections we describe projected PM2.5 levels in the future, with and 

without the standards. Our modeling indicates that there will be very small changes in PM2.5

across most of the country. The impacts of the standards on PM2.5 are a function of VMT 

increases from rebound, upstream reductions in petroleum consumption from crude oil 

production and transport, and gasoline production, distribution and transport, and changes in 

location and amount of electricity generation. Information on the air quality modeling 

methodology is contained in Section Error! Reference source not found.. Additional detail can 

be found in the air quality modeling technical support document (AQM TSD). 

 Projected Levels of PM2.5 without this Final Action

EPA has already adopted many mobile source emission control programs that are 

expected to reduce ambient PM levels. These control programs include the New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 

2010), the Marine Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 

Deleted: 1011

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numbered + Level: 5 + Numbering Style: 1, 2,

3, ... + Start at: 1 + Alignment: Left + Aligned

at: 0 px + Tab after: 68 px + Indent at: 68

px

Deleted: ¶
Chapter 6 

14 

8, 2008), the Locomotive and Marine Compression-Ignition Engine Rule (73 FR 25098, May 6, 

2008), the Clean Air Nonroad Diesel (69 FR 38957, June 29, 2004), the Heavy Duty Engine and 

Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, January 

18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control 

Requirements (65 FR 6698, February 10, 2000). As a result of these and other federal, state and 

local programs, the number of areas that fail to meet the PM2.5 NAAQS in the future is expected 

to decrease. However, even with the implementation of all current state and federal regulations, 

there are projected to be counties that would have projected design values above the level of the 

PM2.5 NAAQS well into the future. 

The air quality modeling conducted projects that in 2030, with all current controls in 

effect but excluding the emissions changes expected to occur as a result of this final action, at 

least 4 counties, with a projected population of nearly 7 million people, would have projected 

design values above the level of the annual standard of 15 μg/m3 and at least 21 counties, with a 

projected population of over 31 million people, would have projected design values above the 

level of the 2006 24-hour standard of 35 μg/m3. Since the emission changes from this final 

action go into effect during the period when some areas are still working to attain the PM2.5

NAAQS, the projected emission changes will impact state and local agencies in their effort to 

attain and maintain the PM2.5 standard. In the following section we discuss the PM2.5 impacts 

associated with the vehicle standards. 

Projected Annual Average PM2.5 Design Values with this Final Action

This section summarizes the results of our modeling of annual average PM2.5 air quality 

impacts in the future due to the vehicle standards finalized in this action. Specifically, we 

compare a 2030 reference scenario (a scenario without the vehicle standards) to a 2030 control 

scenario which includes the vehicle standards. Our modeling indicates that the majority of the 

modeled counties will experience small changes of between 0.05 μg/m3 and -0.05 μg/m3 in their 

annual PM2.5 design values due to the vehicle standards. 

Figure Error! No text of specified style in document.-10 presents the changes in annual 

PM2.5 design values in 2030.E 

 

E

 An annual PM2.5 design value is the concentration that determines whether a monitoring site meets the annual 

NAAQS for PM2.5. The full details involved in calculating an annual PM2.5 design value are given in appendix N of 

40 CFR part 50.
2017 Regulatory Impact Analysis 

15 

Figure Error! No text of specified style in document.-10 Projected Change in 2030 Annual 

PM2.5 Design Values Due to the Final Standards 

Figure Error! No text of specified style in document.-10, eight counties will experience 

decreases larger than 0.05 μg/m3. These counties are in the Gulf Coast and in Missouri. The 

maximum projected decrease in an annual PM2.5 design value is 0.16 μg/m3 in West Baton Rouge 

County, Louisiana. The decreases in annual PM2.5 design values in the Gulf Coast are likely due 

to emission reductions related to lower fuel production. Additional information on the emissions 

reductions that are projected with this final action is available in Section 4.7. 

There are 4 counties, all in California, that are projected to have annual PM2.5 design 

values above the NAAQS in 2030 with the vehicle standards in place. Table Error! No text of 

specified style in document.-3 below presents the changes in design values for these counties. 

Formatted: Indent: First line: 48 px
Chapter 6 

16 

Table Error! No text of specified style in document.-3 Change in Annual PM2.5 Design Values 

(μg/m3) for Counties Projected to be Above the Annual PM2.5 NAAQS in 2030

County Name Change in 

Annual 

PM2.5

Design 

Value 

(μg/m3)

Population 

in 2030a

Riverside County, California -0.01 2,614,198

San Bernardino County, California 0 2,784,489

Kern County, California -0.02 981,806

Tulare County, California -0.01 528,662

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by Single 

Year of Age CD.

Average changes in 2030 annual PM2.5 design values for a variety of metrics are all 

between 0.00 and -0.03 μg/m3 illustrating the small decrease in annual PM2.5 design values in 

2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with 

2005 baseline design values that exceeded the annual PM2.5 standard, (3) counties with 2005 

baseline design values that did not exceed the standard, but were within 10% of it, (4) counties 

with 2030 design values that exceeded the annual PM2.5 standard, and (5) counties with 2030 

design values that did not exceed the standard, but were within 10% of it. 

Projected 24-hour Average PM2.5 Design Values with this Final Action

This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts 

in the future due to the vehicle standards. Specifically, we compare a 2030 reference scenario (a 

scenario without the vehicle standards) to a 2030 control scenario which includes the vehicle 

standards. Our modeling indicates that the majority of the modeled counties will experience 

changes of between -0.05 μg/m3 and 0.05 μg/m3 in their 24-hour PM2.5 design values. Figure 

Error! No text of specified style in document.-11 presents the changes in 24-hour PM2.5 design 

values in 2030.F 

 

F

 A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site 

meets the 24-hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5

design value are given in appendix N of 40 CFR part 50.

Formatted Table

Formatted: Subscript

Formatted: Subscript

Formatted: Subscript
2017 Regulatory Impact Analysis 

17 

Figure Error! No text of specified style in document.-11 Projected Change in 2030 24-hour 

PM2.5 Design Values Due to the Final Standards

As shown in Figure Error! No text of specified style in document.-11, design value 

concentrations will increase more than 0.05 μg/m3 in six counties and design value 

concentrations will decrease more than 0.05 μg/m3 in 23 counties. The decreases in 24-hour 

PM2.5 design values in some counties are likely due to emission reductions related to lower fuel 

production. The maximum projected decrease in a 24-hour PM2.5 design value is 0.76 μg/m3 in 

East Baton Rouge County, Louisiana. The increases in 24-hour PM2.5 design values in some 

counties are likely due to increased emissions from the VMT rebound effect or increased 

electricity generation. The maximum projected increase in a 24-hour PM2.5 design value is 0.14 

μg/m3 in El Paso County, Colorado. Additional information on the emissions changes that are 

projected with this final action is available in Section 4.7. 

There are 21 counties, mainly in California, that are projected to have 24-hour PM2.5

design values above the NAAQS in 2030 with the vehicle standards in place. Table Error! No text 

of specified style in document.-4 below presents the changes in design values for these counties. 
Chapter 6 

18 

Table Error! No text of specified style in document.-4 Change in 24-hour PM2.5 Design Values (μg/m3) for 

Counties Projected to be Above the 24-hour PM2.5 NAAQS in 2030

County Name Change in 24-

hour PM2.5

Design Value 

(μg/m3)

Population in 2030a

Kern Co, California 0.02 981,806

Riverside Co, California -0.01 2,614,198

Fresno Co, California -0.02 1,196,950

San Bernardino Co, California -0.27 2,784,490

Sacramento Co, California 0 1,856,971

Kings Co, California -0.03 195,067

Los Angeles Co, California 0.03 10,742,722

Tulare Co, California -0.03 528,663

Lane Co, Oregon 0 460,993

Cache Co, Utah 0.04 141,446

Allegheny Co, Pennsylvania -0.01 1,234,931

Stanislaus Co, California -0.02 688,246

Lake Co, Montana 0 40,126

Orange Co, California 0.05 4,431,071

Klamath Co, Oregon 0 77,200

Salt Lake Co, Utah 0.02 1,431,946

Ravalli Co, Montana 0 63,914

Butte Co, California 0 287,236

Missoula Co, Montana 0.01 141,264

Pierce Co, Washington 0.03 1,082,579

Lincoln Co, Montana 0 20,454

Note:

a

 Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by Single 

Year of Age CD.

Average changes in 2030 24-hour PM2.5 design values for a variety of metrics are all 

between 0.00 and -0.03 μg/m3 illustrating the small decrease in 24-hour PM2.5 design values in 

2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with 

2005 baseline design values that exceeded the 24-hour PM2.5 standard, (3) counties with 2005 

baseline design values that did not exceed the standard, but were within 10% of it, (4) counties 

with 2030 design values that exceeded the 24-hour PM2.5 standard, and (5) counties with 2030 

design values that did not exceed the standard, but were within 10% of it. 

Formatted Table
2017 Regulatory Impact Analysis 

19 

6.1.1.3 Air Toxics 

According to the National Air Toxics Assessment (NATA) for 2005, mobile sources were 

responsible for 43 percent of outdoor toxic emissions and over 50 percent of the cancer risk and 

noncancer hazard attributable to direct emissions from mobile and stationary sources.G,H,9

 

According to the 2005 NATA, about three-fourths of the U.S. population was exposed to an 

average chronic concentration of air toxics that has the potential for adverse noncancer 

respiratory health effects. In 2007 EPA finalized vehicle and fuel controls to reduce mobile 

source air toxics.10 In addition, over the years, EPA has implemented a number of mobile source 

and fuel controls resulting in VOC reductions, which also reduce air toxic emissions. Modeling 

from the Mobile Source Air Toxics (MSAT) rule suggests that the mobile source contribution to 

ambient benzene concentrations is projected to decrease over 40% by 2015, with a decrease in 

ambient benzene concentration from all sources of about 25%. Although benzene is used as an 

example, the downward trend is projected for other air toxics as well. See the RIA for the final 

MSAT rule for more information on ambient air toxics projections.11

6.1.1.3.1 Current Levels of Air Toxics 

The majority of Americans continue to be exposed to ambient concentrations of air toxics 

at levels which have the potential to cause adverse health effects.12 The levels of air toxics to 

which people are exposed vary depending on where people live and work and the kinds of 

activities in which they engage, as discussed in detail in U.S. EPA’s 2007 Mobile Source Air 

Toxics (MSAT) Rule.13 In order to identify and prioritize air toxics, emission source types and 

locations which are of greatest potential concern, U. S. EPA conducts the National-Scale Air 

Toxics Assessment (NATA). The most recent NATA was conducted for calendar year 2005, and 

was released in March 2011.14 NATA for 2005 includes four steps: 

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources 

2) Estimating ambient concentrations of air toxics across the United States 

3) Estimating population exposures across the United States 

4) Characterizing potential public health risk due to inhalation of air toxics including both 

cancer and noncancer effects 

 

G

 NATA also includes estimates of risk attributable to background concentrations, which includes contributions from 

long-range transport, persistent air toxics, and natural sources; as well as secondary concentrations, where toxics are 

formed via secondary formation. Mobile sources substantially contribute to long-range transport and secondarily 

formed air toxics. 

H

 NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to 

estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this final action were 

modeled with CMAQ 4.7.1. 

Formatted: Normal

Deleted: [placeholder]¶
Chapter 6 

20 

Figure Error! No text of specified style in document..2-Error! No text of specified 

style in document.-12 and Figure Error! No text of specified style in document..2-Error! No 

text of specified style in document.-13 depict estimated tract-level carcinogenic risk and 

noncancer respiratory hazard from the assessment. The respiratory hazard is dominated by a 

single pollutant, acrolein. 

According to the NATA for 2005, mobile sources were responsible for 43 percent of 

outdoor toxic emissions and over 50 percent of the cancer risk and noncancer hazard attributable 

to direct emissions from mobile and stationary sources.I,J,15 Mobile sources are also large 

contributors to precursor emissions which react to form secondary concentrations of air toxics. 

Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively assessed 

in the 2005 NATA, and mobile sources were responsible for over 40 percent of primary 

emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor 

emissions. Benzene is also a large contributor to cancer risk, and mobile sources account for 

over 70 percent of ambient exposure. Over the years, EPA has implemented a number of mobile 

source and fuel controls which have resulted in VOC reductions, which also reduced 

formaldehyde, benzene and other air toxic emissions. 

 

I

 NATA also includes estimates of risk attributable to background concentrations, which includes contributions from 

long-range transport, persistent air toxics, and natural sources; as well as secondary concentrations, where toxics are 

formed via secondary formation. Mobile sources substantially contribute to long-range transport and secondarily 

formed air toxics 

J

 NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to 

estimate toxic air pollutant concentrations. Projected air toxics concentrations presented in this final action were 

modeled with CMAQ 4.7.1. 
2017 Regulatory Impact Analysis 

21 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-12 Tract Level Average Carcinogenic Risk, 2005 NATA

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-13 County Level Average Noncancer Hazard Index, 2005 NATA 

6.1.1.3.2 Projected Levels of Air Toxics 

In the following sections, we describe results of our modeling of air toxics levels in the 

future with the finalized standards. Although there are a large number of compounds which are 

considered air toxics, we focused on those which were identified as national and regional-scale 

cancer and noncancer risk drivers in past NATA assessments and were also likely to be 

significantly impacted by the standards. These compounds include benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde, and acrolein. Information on the air quality modeling methodology 

is contained in Section Error! Reference source not found.. Additional detail, including 

seasonal concentration maps, can be found in the air quality modeling technical support 

document (AQM TSD) in the docket for this rule. 

It should be noted that EPA has adopted many mobile source emission control programs 

that are expected to reduce ambient air toxics levels. These control programs include the Heavyduty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine 

Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098, 

Deleted: 1114

Deleted: 1215
Chapter 6 

22 

May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air 

Nonroad Diesel Rule (69 FR 38957, June 29, 2004), Heavy-Duty Engine and Vehicle Standards 

and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier 

2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, 

Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the 

future is expected to decrease. The reference case and control case scenarios include these 

controls. 

Our modeling indicates that national average ambient concentrations of the modeled air 

toxics change less than 1 percent across most of the country due to the final standards. Because 

overall impacts are relatively small in future years, we concluded that assessing exposure to 

ambient concentrations and conducting a quantitative risk assessment of air toxic impacts was not 

warranted. However, we did develop population metrics, including the population living in areas 

with changes in concentrations of various magnitudes. 

Acetaldehyde 

Our air quality modeling results show that this rule does not have substantial impacts on 

ambient concentrations of acetaldehyde. Figure Error! No text of specified style in 

document.-14 shows nationwide changes in ambient acetaldehyde in 2030 are between ± 1 

percent, with decreases up to 10 percent in a few urban areas. Reductions in ambient 

acetaldehyde in 2030 range between 0.001 and 0.01 μg/m3 across much of the country with 

decreases as high as 0.1 μg/m3 in urban areas; these changes are mainly associated with 

reductions from upstream sources including fuel production, refining, storage and transport 

(Figure Error! No text of specified style in document.-14). 

Figure Error! No text of specified style in document.-14 Changes in Acetaldehyde Ambient 

Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute 

Changes in μg/m3 (right)

Deleted: the 2017-2025 LD GHG standards have 

relatively little impact on 

Deleted: Percent Changes 

Deleted: Between the Reference Case and the 

Control Case 
2017 Regulatory Impact Analysis 

23 

Formaldehyde 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of formaldehyde as a result of the final standards. In 2030, annual percent 

changes in ambient concentrations of formaldehyde are less than 1 percent across much of the 

country, with a decrease ranging from 2.5 to 10 percent in Oklahoma (Figure Error! No text of 

specified style in document.-15). Ambient formaldehyde reductions in 2030 generally range 

from 0.001 to 0.1 μg/m3 and are associated with upstream reductions in fuel production, refining, 

storage and transport (Figure Error! No text of specified style in document.-15). Decreases in 

Oklahoma are over 0.3 μg/m3 and due to reductions in emissions from refineries in that area. 

Increases in ambient formaldehyde concentrations range between 0.001 to 0.1 μg/m3 in areas 

associated with increased emissions from power plants. 

Figure Error! No text of specified style in document.-15 Changes in Formaldehyde Ambient 

Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute 

Changes in μg/m3 (right)

Benzene

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of benzene as a result of this rule. In 2030, percent changes in ambient 

concentrations of benzene are ± 1 percent nationwide (Figure Error! No text of specified style in 

document.-16); a few areas, mainly in the Gulf Coast region, are projected to have benzene 

reductions from 1 to 10 percent, likely due to decreases in refinery emissions. Absolute changes 

in ambient benzene in 2030 are generally ± 0.001 μg/m3 in the western half of the U.S. with 

decreases up to 0.01 μg/m3 across the eastern half of the U.S due to upstream reductions in fuel 

Deleted: the 2017-2025 LD GHG standards

Deleted: EGUs

Deleted: Percent 

Deleted: Between the Reference Case and the 

Control Case 

Deleted: %
Chapter 6 

24 

production, refining, storage and transport (Figure Error! No text of specified style in 

document.-16). 

Figure Error! No text of specified style in document.-16 Changes in Benzene Ambient 

Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes 

in μg/m3 (right)

1,3-Butadiene

Our modeling also shows that this rule does not have a significant impact on ambient 1,3-

butadiene concentrations in 2030. Figure Error! No text of specified style in document.-17

shows that ambient concentrations of 1,3-butadiene generally range between ± 1 percent across 

the country in 2030. Some areas have 1,3-butadiene increases on the order of 1 to 2.5 percent; 

however, as shown in the map on the right, all changes in absolute concentrations are between ± 

0.001 μg/m3 nationwide (Figure Error! No text of specified style in document.-17). 

Deleted: Percent 

Deleted: Between the Reference Case and the 

Control Case 

Deleted: ¶

Deleted: the 2017-2025 LD GHG standards

Deleted: are
2017 Regulatory Impact Analysis 

25 

Figure Error! No text of specified style in document.-17 Changes in 1,3-Butadiene Ambient 

Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute 

Changes in μg/m3 (right)

Acrolein 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of acrolein as a result of this rule. In 2030, percent changes in ambient acrolein 

concentrations are generally ± 1 percent nationwide (Figure Error! No text of specified style in 

document.-18). Parts of the Midwest and Texas have decreases in ambient acrolein 

concentrations generally between 1 and 10 percent and increases of similar magnitude in a few 

urban areas; however, all absolute changes in ambient acrolein concentrations are between ± 

0.001 μg/m3 in 2030 (Figure Error! No text of specified style in document.-18).

Deleted: Percent 

Deleted: Between the Reference Case and the 

Control Case 
Chapter 6 

26 

Figure Error! No text of specified style in document.-18 Changes in Acrolein Ambient 

Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute 

Changes in μg/m3 (right)

Population Metrics

To assess the impact of this rule’s projected changes in air quality, we developed 

population metrics that show the population experiencing changes in annual ambient 

concentrations across the modeled air toxics. As shown in Table Error! No text of specified 

style in document.-5, over 98 percent of the U.S. population is projected to experience a less 

than one percent change in formaldehyde and 1,3-butadiene. Over 83 percent of the U.S. 

population is projected to experience a less than one percent change in acetaldehyde, benzene and 

acrolein, and over 12 percent are projected to experience a 1 to 5 percent decrease in these 

pollutants. 

Deleted: Percent 

Deleted: Between the Reference Case and the 

Control Case 

Deleted: with the 2017-2025 LD GHG standards
2017 Regulatory Impact Analysis 

27 

Table Error! No text of specified style in document.-5 Percent of Total Population 

Experiencing Changes in Annual Ambient Concentrations of Toxic Pollutants in 2030 as a 

Result of the Final Standards

Percent Change Acetaldehyde Formaldehyde Benzene 1,3-Butadiene Acrolein

≤ -100

> -100 to ≤ -50

> -50 to ≤ -10

> -10 to ≤ -5 0.0% 0.0% 0.8% 0.2%

> -5 to ≤ -2.5 1.5% 0.1% 1.8% 0.0% 2.0%

> -2.5 to ≤ -1 15.3% 1.2% 13.0% 0.2% 10.3%

> -1 to < 1 83.1% 98.7% 84.4% 99.2% 86.1%

≥ 1 to < 2.5 0.0% 0.6% 0.9%

≥ 2.5 to < 5 0.0% 0.0%

≥ 5 to < 10 0.0%

≥ 10 to < 50

≥ 50 to < 100

≥ 100 

6.1.1.4 Deposition of Nitrogen and Sulfur 

6.1.1.4.1 Current Levels of Nitrogen and Sulfur Deposition 

Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen 

and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show 

that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years. The 

data show that reductions were more substantial for sulfur compounds than for nitrogen 

compounds. In the eastern U.S., where data are most abundant, total sulfur deposition decreased 

by about 44 percent between 1990 and 2007, while total nitrogen deposition decreased by 25 

percent over the same time frame.16 These numbers are generated by the U.S. national 

monitoring network and they likely underestimate nitrogen deposition because neither ammonia 

nor organic nitrogen is measured. Although total nitrogen and sulfur deposition has decreased 

over time, many areas continue to be negatively impacted by deposition. Deposition of inorganic 

nitrogen and sulfur species routinely measured in the U.S. between 2005 and 2007 were as high 

as 9.6 kilograms of nitrogen per hectare (kg N/ha) averaged over three years and 20.8 kilograms 

of sulfur per hectare (kg S/ha) averaged over three years.17

 

Deleted: LD GHG Rule

Deleted: [placeholder]
Chapter 6 

28 

Figure Error! No text of specified style in document..2-Error! No text of specified style in 

document.-19 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2005 -2007 Deleted: 1328
2017 Regulatory Impact Analysis

29 
Chapter 6 

30 

Figure Error! No text of specified style in document..2-Error! No text of specified 

style in document.-20 Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2005-

2007 

6.1.1.4.2 Projected Levels of Nitrogen and Sulfur Deposition 

Our air quality modeling projects increases in nitrogen deposition in some localized areas 

across the U.S. along with a few areas of decreases in nitrogen deposition. Figure Error! No 

text of specified style in document.-21 shows that for nitrogen deposition the vehicle standards 

will result in annual percent increases of more than 2 percent in some areas. The increases in 

nitrogen deposition are likely due to projected upstream emissions increases in NOX from 

increased electricity generation and increased driving due to the rebound effect. Figure Error! 

No text of specified style in document.-21 also shows that for nitrogen deposition the vehicle 

standards will result in annual percent decreases of more than 2 percent in a few areas in West 

Virginia and New Mexico. The decreases in nitrogen deposition are likely due to projected 

upstream emissions decreases in NOX from changes in the location of electricity generation. The 

remainder of the country will experience only minimal changes in nitrogen deposition, ranging 

from decreases of less than 0.5 percent to increases of less than 0.5 percent. 

Figure Error! No text of specified style in document.-21 Percent Change in Annual Total 

Nitrogen Deposition as a Result of the Final Standards

Deleted: 1429
2017 Regulatory Impact Analysis 

31 

Our air quality modeling projects both increases and decreases in sulfur deposition in 

localized areas across the U.S. Figure Error! No text of specified style in document.-22 shows 

that for sulfur deposition the vehicle standards will result in annual percent decreases of more 

than 2% in some areas. The decreases in sulfur deposition are likely due to projected upstream 

emissions decreases from changes in the location of electricity generation and from reduced 

gasoline production. Figure Error! No text of specified style in document.-22 also shows that 

for sulfur deposition the vehicle standards will result in annual percent increases of more than 2% 

in some areas. The increases in sulfur deposition are likely due to projected upstream emissions 

increases from increased electricity generation. The remainder of the country will experience 

only minimal changes in sulfur deposition, ranging from decreases of less than 0.5% to increases 

of less than 0.5%. 

Figure Error! No text of specified style in document.-22 Percent Change in Annual Total 

Sulfur Deposition as a Result of the Final Standards

6.1.1.5 Visibility Degradation 

6.1.1.5.1 Current Visibility Levels 

As of August 30, 2011, approximately 101 million people live in nonattainment areas for 

the PM2.5 NAAQS. Thus, at least these populations would likely be experiencing visibility 

impairment, as well as many thousands of individuals who travel to these areas. While visibility 

trends have improved in most Class I areas, the recent data show that these areas continue to 

Deleted: [placeholder]¶

Deleted: Designated PM2.5 nonattainment areas 

indicate that, a
Chapter 6 

32 

suffer from visibility impairment.18 Calculated from light extinction efficiencies from Trijonis et 

al. (1987, 1988), annual average visual range under natural conditions in the East is estimated to 

be 150 km ± 45 km (i.e., 65 to 120 miles) and 230 km ± 35 km (i.e., 120 to 165 miles) in the 

West.19,20,21 In summary, visibility impairment is experienced throughout the U.S., in multi-state 

regions, urban areas, and remote Mandatory Class I Federal areas. 

6.1.1.5.2 Projected Visibility Levels 

Air quality modeling conducted for the final action was used to project visibility 

conditions in 139 mandatory class I federal areas across the U.S. in 2030. The results show that 

all the modeled areas will continue to have annual average deciview levels above background in 

2030.K The results also indicate that the majority of the modeled mandatory class I federal areas 

will see very little change in their visibility. Some mandatory class I federal areas will see 

improvements in visibility due to the vehicle standards and a few mandatory class I federal areas 

will see visibility decreases. The average visibility at all modeled mandatory class I federal areas 

on the 20% worst days is projected to improve by 0.003 deciviews, or 0.03%, in 2030. The 

greatest projected improvement in visibility, 0.1% improvement (0.02 DV) in 2030 due to the 

vehicle standards, occurs in Sipsey Wilderness in AL, Agua Tibia Wilderness in CA, and Alpine 

Lake Wilderness in WA. The following seven areas will see small degradations in visibility in 

2030 as a result of the heavy-duty standards: Wolf Island GA, 0.03 deciview degradation; Joshua 

Tree National Monument CA, 0.02 deciview degradation, San Gorgonio Wilderness CA, 0.01 

deciview degradation; Upper Buffalo Wilderness AR, 0.01 deciview degradation; San Jacinto 

Wilderness CA, 0.01 deciview degradation; Okefenokee GA, 0.01 deciview degradation; and 

Hells Canyon Wilderness OR, 0.01 deciview degradation. Error! Reference source not found.

contains the full visibility results from 2030 for the 138 analyzed areas.

Table Error! No text of specified style in document.-6 Visibility Levels (in Deciviews) for Class 

I Areas on the 20% Worst Days 

Class 1 Area

(20% worst days) State 2005 Base

2030 

LDGHG 

Reference

2030 

LDGHG 

Control

Natural 

Background

Sipsey Wilderness AL 29.88 20.54 20.52 11.39

Caney Creek Wilderness AR 26.69 19.84 19.84 11.33

Upper Buffalo Wilderness AR 26.97 20.17 20.18 11.28

Chiricahua NM AZ 12.89 12.08 12.07 6.92

Chiricahua Wilderness AZ 12.89 12.08 12.07 6.91

Galiuro Wilderness AZ 12.89 12.09 12.09 6.88

Grand Canyon NP AZ 11.86 10.92 10.91 6.95

Mazatzal Wilderness AZ 13.95 12.46 12.45 6.91

 

K

 The level of visibility impairment in an area is based on the light-extinction coefficient and 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 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.

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Formatted Table
2017 Regulatory Impact Analysis 

33 

Mount Baldy Wilderness AZ 11.32 10.74 10.74 6.95

Petrified Forest NP AZ 13.56 12.65 12.65 6.97

Pine Mountain Wilderness AZ 13.95 12.42 12.41 6.92

Saguaro NM AZ 14.39 13.43 13.43 6.84

Sierra Ancha Wilderness AZ 14.45 13.28 13.28 6.92

Superstition Wilderness AZ 14.15 12.85 12.85 6.88

Sycamore Canyon Wilderness AZ 15.45 14.67 14.67 6.96

Agua Tibia Wilderness CA 22.36 18.41 18.39 7.17

Ansel Adams Wilderness (Minarets) CA 15.24 14.39 14.39 7.12

Caribou Wilderness CA 13.65 12.68 12.67 7.29

Cucamonga Wilderness CA 18.44 15.64 15.64 7.17

Desolation Wilderness CA 12.87 12.10 12.09 7.13

Emigrant Wilderness CA 16.87 15.94 15.94 7.14

Hoover Wilderness CA 11.61 11.07 11.06 7.12

John Muir Wilderness CA 15.24 14.34 14.34 7.14

Joshua Tree NM CA 18.90 16.39 16.41 7.08

Kaiser Wilderness CA 15.24 14.11 14.10 7.13

Kings Canyon NP CA 23.73 22.19 22.19 7.13

Lassen Volcanic NP CA 13.65 12.66 12.66 7.31

Lava Beds NM CA 14.13 13.19 13.19 7.49

Mokelumne Wilderness CA 12.87 12.08 12.07 7.14

Pinnacles NM CA 17.90 15.42 15.42 7.34

Point Reyes NS CA 22.40 21.00 21.00 7.39

Redwood NP CA 18.55 17.66 17.66 7.81

San Gabriel Wilderness CA 18.44 15.54 15.53 7.17

San Gorgonio Wilderness CA 21.43 19.27 19.28 7.10

San Jacinto Wilderness CA 21.43 18.10 18.11 7.12

San Rafael Wilderness CA 19.43 17.40 17.39 7.28

Sequoia NP CA 23.73 21.68 21.67 7.13

South Warner Wilderness CA 14.13 13.31 13.31 7.32

Thousand Lakes Wilderness CA 13.65 12.65 12.64 7.32

Ventana Wilderness CA 17.90 16.37 16.37 7.32

Yosemite NP CA 16.87 15.95 15.95 7.14

Black Canyon of the Gunnison NM CO 10.00 9.21 9.21 7.06

Eagles Nest Wilderness CO 8.82 8.05 8.05 7.08

Flat Tops Wilderness CO 8.82 8.32 8.31 7.07

Great Sand Dunes NM CO 11.82 11.20 11.20 7.10

La Garita Wilderness CO 10.00 9.49 9.49 7.06

Maroon Bells-Snowmass Wilderness CO 8.82 8.27 8.26 7.07

Mesa Verde NP CO 12.14 11.31 11.31 7.09

Mount Zirkel Wilderness CO 9.72 9.20 9.19 7.08

Rawah Wilderness CO 9.72 9.15 9.14 7.08

Rocky Mountain NP CO 12.85 12.15 12.15 7.05

Weminuche Wilderness CO 10.00 9.46 9.46 7.06

West Elk Wilderness CO 8.82 8.21 8.21 7.07

Everglades NP FL 22.48 18.43 18.43 11.15

Okefenokee GA 27.21 20.28 20.29 11.45

Wolf Island GA 27.21 20.12 20.15 11.42
Chapter 6 

34 

Craters of the Moon NM ID 14.06 12.94 12.94 7.13

Sawtooth Wilderness ID 14.97 14.70 14.70 7.15

Mammoth Cave NP KY 32.00 22.29 22.29 11.53

Acadia NP ME 22.75 18.34 18.33 11.45

Moosehorn ME 21.19 17.58 17.58 11.36

Roosevelt Campobello International Park ME 21.19 17.57 17.56 11.36

Isle Royale NP MI 21.31 18.19 18.19 11.22

Seney MI 25.05 20.80 20.80 11.37

Boundary Waters Canoe Area MN 20.20 16.56 16.56 11.21

Voyageurs NP MN 19.62 16.61 16.61 11.09

Hercules-Glades Wilderness MO 26.95 21.00 21.00 11.27

Anaconda-Pintler Wilderness MT 17.11 16.69 16.68 7.28

Bob Marshall Wilderness MT 16.13 15.63 15.63 7.36

Cabinet Mountains Wilderness MT 14.31 13.65 13.65 7.43

Gates of the Mountains Wilderness MT 11.94 11.48 11.47 7.22

Glacier NP MT 19.62 18.73 18.73 7.56

Medicine Lake MT 18.21 17.17 17.17 7.30

Mission Mountains Wilderness MT 16.13 15.50 15.49 7.39

Red Rock Lakes MT 11.19 10.62 10.62 7.14

Scapegoat Wilderness MT 16.13 15.59 15.59 7.29

Selway-Bitterroot Wilderness MT 17.11 16.74 16.74 7.32

UL Bend MT 15.49 15.00 15.00 7.18

Linville Gorge Wilderness NC 29.66 20.08 20.07 11.43

Shining Rock Wilderness NC 28.54 19.49 19.48 11.45

Lostwood ND 19.61 17.64 17.64 7.33

Theodore Roosevelt NP ND 17.88 16.02 16.02 7.31

Great Gulf Wilderness NH 21.43 16.46 16.46 11.31

Presidential Range-Dry River Wilderness NH 21.43 16.39 16.39 11.33

Brigantine NJ 28.68 20.96 20.95 11.28

Bandelier NM NM 11.97 10.51 10.51 7.02

Bosque del Apache NM 13.81 12.40 12.40 6.97

Carlsbad Caverns NP NM 16.51 14.48 14.47 7.02

Gila Wilderness NM 13.12 12.41 12.40 6.95

Pecos Wilderness NM 9.60 8.85 8.85 7.04

Salt Creek NM 18.27 16.19 16.18 6.99

San Pedro Parks Wilderness NM 10.42 9.63 9.62 7.03

Wheeler Peak Wilderness NM 9.60 8.66 8.65 7.07

White Mountain Wilderness NM 13.01 12.05 12.05 6.98

Jarbidge Wilderness NV 12.26 11.92 11.92 7.10

Wichita Mountains OK 23.63 18.27 18.26 11.07

Crater Lake NP OR 13.21 12.49 12.49 7.71

Diamond Peak Wilderness OR 13.21 12.39 12.39 7.77

Eagle Cap Wilderness OR 17.34 16.31 16.31 7.34

Gearhart Mountain Wilderness OR 13.21 12.61 12.61 7.46

Hells Canyon Wilderness OR 19.00 17.57 17.58 7.32

Kalmiopsis Wilderness OR 16.38 15.36 15.36 7.71

Mount Hood Wilderness OR 14.68 13.03 13.03 7.77

Mount Jefferson Wilderness OR 15.80 14.78 14.78 7.81
2017 Regulatory Impact Analysis 

35 

Mount Washington Wilderness OR 15.80 14.78 14.77 7.89

Mountain Lakes Wilderness OR 13.21 12.42 12.42 7.57

Strawberry Mountain Wilderness OR 17.34 16.37 16.37 7.49

Three Sisters Wilderness OR 15.80 14.87 14.87 7.87

Cape Romain SC 27.43 19.70 19.70 11.36

Badlands NP SD 16.82 14.91 14.90 7.30

Wind Cave NP SD 15.95 14.21 14.21 7.24

Great Smoky Mountains NP TN 30.56 21.28 21.27 11.44

Joyce-Kilmer-Slickrock Wilderness TN 30.56 20.97 20.97 11.45

Big Bend NP TX 17.21 15.35 15.34 6.93

Guadalupe Mountains NP TX 16.51 14.47 14.46 7.03

Arches NP UT 10.77 9.98 9.97 6.99

Bryce Canyon NP UT 11.62 10.95 10.95 6.99

Canyonlands NP UT 10.77 10.12 10.11 7.01

Capitol Reef NP UT 10.86 10.39 10.39 7.03

James River Face Wilderness VA 28.93 19.62 19.62 11.24

Shenandoah NP VA 29.42 19.58 19.58 11.25

Lye Brook Wilderness VT 24.11 16.87 16.86 11.25

Alpine Lake Wilderness WA 16.99 15.06 15.04 7.86

Glacier Peak Wilderness WA 13.29 12.18 12.17 7.80

Goat Rocks Wilderness WA 12.67 11.35 11.34 7.82

Mount Adams Wilderness WA 12.67 11.39 11.39 7.78

Mount Rainier NP WA 17.07 15.36 15.35 7.90

North Cascades NP WA 13.29 12.15 12.14 7.78

Olympic NP WA 15.83 14.31 14.31 7.88

Pasayten Wilderness WA 15.35 14.36 14.36 7.77

Dolly Sods Wilderness WV 29.94 19.65 19.64 11.32

Otter Creek Wilderness WV 29.94 19.73 19.72 11.33

Bridger Wilderness WY 10.73 10.29 10.29 7.08

Fitzpatrick Wilderness WY 10.73 10.29 10.28 7.09

Grand Teton NP WY 11.19 10.57 10.56 7.09

North Absaroka Wilderness WY 11.30 10.90 10.90 7.09

Teton Wilderness WY 11.19 10.68 10.68 7.09

Washakie Wilderness WY 11.30 10.90 10.90 7.09

Yellowstone NP WY 11.19 10.61 10.61 7.12

 
Chapter 6 

36 

6.3 Quantified and Monetized Non-GHG Health and Environmental Impacts 

This section presents EPA’s analysis of the non-GHG, or co-pollutant, health and 

environmental impacts that can be expected to occur as a result of the final light-duty vehicle 

GHG rule. GHG emissions are predominantly the byproduct of fossil fuel combustion processes 

that also produce criteria and hazardous air pollutants. The vehicles that are subject to the final 

standards are also significant sources of mobile source air pollution such as direct PM, NOx, 

VOCs and air toxics. The standards will affect exhaust emissions of these pollutants from 

vehicles. They will also affect emissions from upstream sources related to changes in fuel 

consumption and electricity generation. Changes in ambient ozone, PM2.5, and air toxics that will 

result from the standards are expected to affect human health in the form of premature deaths and 

other serious human health effects, as well as other important public health and welfare effects. 

It is important to quantify the health and environmental impacts associated with the final 

standards because it allows us to more accurately assess the net costs and benefits of the 

standards. Moreover, co-pollutant impacts tend to accrue in the near term, while effects from 

reduced climate change mostly accrue over a time frame of several decades or longer. 

This section is split into two sub-sections: the first presents the PM- and ozone-related 

health and environmental impacts associated with final rule in calendar year (CY) 2030; the 

second presents the PM-related benefits-per-ton values used to monetize the PM-related cobenefits associated with the model year (MY) analysis (i.e., over the lifetimes of the MY 2017-

2025 vehicles) of the final rule.L 

Though EPA is characterizing the changes in emissions associated with toxic pollutants, 

we were not able to quantify or monetize the human health effects associated with air toxic 

pollutants for this final rule analysis due to data and methodological limitations. Please refer to 

Chapter 4 of this RIA for more information about the air toxics emissions impacts associated 

with the final standards. 

6.3.1 Quantified and Monetized Non-GHG Human Health Benefits of the 2030 Calendar Year 

(CY) Analysis 

This section presents EPA’s analysis of the criteria pollutant-related health and 

environmental impacts that will occur as a result of the final standards. Light-duty vehicles and 

fuels are significant sources of mobile source air pollution such as direct PM, NOX, SOX, VOCs 

and air toxics. The impact that improved fuel economy will have on rebound driving will affect 

 

L

 EPA typically analyzes rule impacts (emissions, air quality, costs and benefits) in the year in which they occur; for 

this analysis, we selected 2030 as a representative future year. We refer to this analysis as the “Calendar Year” (CY) 

analysis. EPA also conducted a separate analysis of the impacts over the model year lifetimes of the 2017 through 

2025 model year vehicles. We refer to this analysis as the “Model Year” (MY) analysis. In contrast to the CY 

analysis, the MY lifetime analysis shows the lifetime impacts of the program on each MY fleet over the course of its 

lifetime. 

Deleted: [placeholder]
2017 Regulatory Impact Analysis 

37 

exhaust and evaporative emissions of these pollutants from vehicles. In addition, increased fuel 

savings associated with improved fuel economy achieved under the standards will affect 

emissions from upstream sources (see Chapter 4 for a complete description of emission impacts 

associated with the final standards). Emissions of NOX (a precursor to ozone formation and 

secondarily-formed PM2.5), SOX (a precursor to secondarily-formed PM2.5), VOCs (a precursor to 

ozone formation and, to a lesser degree, secondarily-formed PM2.5) and directly-emitted PM2.5

contribute to ambient concentrations of PM2.5 and ozone. Exposure to ozone and PM2.5 is linked 

to adverse human health impacts such as premature deaths as well as other important public 

health and environmental effects. 

The analysis in this section aims to characterize the benefits of the final standards by 

answering two key questions: 

1. What are the health and welfare effects of changes in ambient particulate matter 

(PM2.5) and ozone air quality resulting from reductions in precursors including NOX and SO2? 

2. What is the economic value of these effects? 

For the final rule, we have quantified and monetized the health and environmental 

impacts in 2030, representing impacts associated with a year when the standards are fully 

implemented and reflects a limited degree of fleet turnover. Overall, we estimate that the final 

standards will lead to a net decrease in PM2.5-relatedhealth impacts in 2030. The decrease in 

population-weighted national average PM2.5 exposure results in a net decrease in adverse PMrelated human health impacts (the decrease in national population-weighted annual average PM2.5

is 0.0065 μg/m3

 in 2030).M We estimate that there is a very small increase in populationweighted national average ozone exposure, which results in a very small net increase in ozonerelated health impacts (population-weighted maximum 8-hour average ozone increases by 0.0009

ppb in 2030). 

Using the most conservative premature mortality estimates (Pope et al., 2002 for PM2.5

and Bell et al., 2004 for ozone),N,O we estimate that by 2030, implementation of the final

standards will reduce approximately 110 premature mortalities annually and yield approximately 

$0.95 billion in total annual benefits. The upper end of the range of avoided premature mortality 

estimates associated with the standards (based on Laden et al., 2006 for PM2.5 and Levy et al., 

2005 for ozone)P,Q results in approximately 280 premature mortalities avoided in 2030 and yields 

 

M

 Note that the national, population-weighted PM2.5 and ozone air quality metrics presented in this Chapter represent 

an average for the entire, gridded U.S. CMAQ domain. These are different than the population-weighted PM2.5 and 

ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air quality 

monitor. 

N

 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. 

O

 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), 2372-2378. 

P

 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. 

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Chapter 6 

38 

approximately $2.6 billion in total benefits. Thus, even taking the most conservative premature 

mortality assumptions, the health impacts of the standards presented in this rule are substantial. 

6.3.1.1 Overview 

This analysis reflects the impacts of the final MY 2017-2025 standards in 2030 compared 

to a future-year reference scenario without the standards in place. Overall, we estimate that the 

final rule will lead to a net decrease in population-weighted national average PM2.5 exposure, 

which results in a net decrease in adverse PM-related human health and environmental impacts 

(the decrease in national population weighted annual average PM2.5 is 0.0065 μg/m3 in 2030). 

The air quality modeling also projects a very small net increase in ozone concentrations 

(population weighted maximum 8-hour average ozone increases by 0.0009 ppb in 2030). The 

small increase in population-weighted national average ozone exposure results in a very small 

increase in net ozone-related health and environmental impacts. 

We base our analysis of the final rule’s impact on human health and the environment on 

peer-reviewed studies of air quality and human health effects.22,23 Our benefits methods are also 

consistent with recent rulemaking analyses such as the final Transport Rule,24 the final 2012-

2016 MY Light-Duty Vehicle Rule, 25 and the final Portland Cement National Emissions 

Standards for Hazardous Air Pollutants (NESHAP) RIA.

26 To model the ozone and PM air 

quality impacts of the final standard, we used the Community Multiscale Air Quality (CMAQ) 

model (see Section 6.2). The modeled ambient air quality data serves as an input to the 

Environmental Benefits Mapping and Analysis Program version 4.0 (BenMAP).R

 BenMAP is a 

computer program developed by the U.S. EPA that integrates a number of the modeling elements 

used in previous analyses (e.g., interpolation functions, population projections, health impact 

functions, valuation functions, analysis and pooling methods) to translate modeled air 

concentration estimates into health effects incidence estimates and monetized benefits estimates. 

The range of total monetized ozone- and PM-related health impacts in 2030 is presented 

in Table 6.3-Error! No text of specified style in document.-7. We present total benefits (the 

sum of morbidity-related benefits and mortality-related benefits) based on the PM- and ozonerelated premature mortality function used. The benefits ranges therefore reflect the addition of 

each estimate of ozone-related premature mortality (across six selected studies, each with its own 

row) to each estimate of PM-related premature mortality (based on either Pope et al., 2002 or 

Laden et al., 2006), along with all morbidity-related benefits. These estimates represent EPA’s 

preferred approach to characterizing a best estimate of benefits. As is the nature of RIAs, the 

assumptions and methods used to estimate air quality benefits evolve to reflect the Agency’s 

most current interpretation of the scientific and economic literature. 

 

Q

 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005). Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology. 16(4), 458-68. 

R

 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. 

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impacts (see Section 6.2.2 for more information 

about the air quality modeling results). The decrease 

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2017 Regulatory Impact Analysis 

39 

Table 6.3-Error! No text of specified style in document.-7: Estimated 2030 Monetized PMand Ozone-Related Health Benefitsa

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Billions, 2010$, 3% 

Discount Rate)b,c 

Total Benefits 

(Billions, 2010$, 7% 

Discount Rate) b,c

Multi-city analyses Bell et al., 2004 Total: $1.0 – $2.6

PM: $1.1 - $2.6

Ozone: -$0.006

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006

Huang et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.006

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.006

Schwartz, 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.009

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.009

Meta-analyses Bell et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.019

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.019

Ito et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.026

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.026

Levy et al., 2005 Total: $1.0 - $2.6

PM: $1.1 - $2.6

Ozone: -$0.027

Total: $0.92 - $2.3

PM: $0.95 - $2.3

Ozone: -$0.027

Notes: 

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was developed 

by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature 

mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).

b

 Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health and welfare effects is provided in Table 6.3-Error! No text of specified style in 

document.-8. 

c

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing 

Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of presentation 

and computation. Totals may not sum due to rounding. 

The benefits in Table 6.3-Error! No text of specified style in document.-7 include all of 

the human health impacts we are able to quantify and monetize at this time. However, the full 

complement of human health and welfare effects associated with PM, ozone, and other criteria 

pollutants remain unquantified because of current limitations in methods or available data. We 

have not quantified a number of known or suspected health effects linked with ozone, PM, and 

other criteria pollutants for which appropriate health impact functions are not available or which 

do not provide easily interpretable outcomes (e.g., changes in heart rate variability). 

Additionally, we are unable to quantify a number of known welfare effects, including reduced 

acid and particulate deposition damage to cultural monuments and other materials, and 

environmental benefits due to reductions of impacts of eutrophication in coastal areas. These are 

listed in Table 6.3-Error! No text of specified style in document.-8. As a result, the health 

benefits quantified in this section are likely underestimates of the total benefits attributable to the 

final standards. 

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Chapter 6 

40 

Table 6.3-Error! No text of specified style in document.-8: Human Health and Welfare 

Effects of Pollutants Affected by the Final Standards 

Pollutant/ 

Effect 

Quantified and monetized in primary estimate Unquantified 

PM: healtha

Premature mortality based on cohort study 

estimatesb and expert elicitation estimates 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial 

infarctions) 

Lower and upper respiratory illness 

Minor restricted activity days 

Work loss days 

Asthma exacerbations (among asthmatic 

populations 

Respiratory symptoms (among asthmatic 

populations) 

Infant mortality 

Low birth weight, pre-term birth and other 

reproductive outcomes 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

UVb exposure (+/-)c

PM: welfare Visibility in Class I areas in SE, SW, and CA 

regions 

Household soiling 

Visibility in residential areas 

Visibility in non-class I areas and class 1 areas in 

NW, NE, and Central regions 

UVb exposure (+/-)c

Global climate impactsc

Ozone: health Premature mortality based on short-term 

study estimates 

Hospital admissions: respiratory 

Emergency room visits for asthma 

Minor restricted activity days 

School loss days 

Chronic respiratory damage 

Premature aging of the lungs 

Non-asthma respiratory emergency room visits 

UVb exposure (+/-)c

Ozone: welfare Decreased outdoor worker productivity 

Yields for: 

--Commercial forests 

--Fruits and vegetables, and 

--Other commercial and noncommercial crops 

Damage to urban ornamental plants 

Recreational demand from damaged forest 

aesthetics 

Ecosystem functions 

UVb exposure (+/-)c 

Climate impacts 

CO: health Behavioral effects 

Nitrate 

Deposition: 

welfare 

Commercial fishing and forestry from acidic 

deposition effects 

Commercial fishing, agriculture and forestry 

from nutrient deposition effects 

Recreation in terrestrial and estuarine 

ecosystems from nutrient deposition effects 

Other ecosystem services and existence values 

for currently healthy ecosystems 

Coastal eutrophication from nitrogen deposition 

effects 

Deleted: 7
2017 Regulatory Impact Analysis 

41 

Pollutant/ 

Effect 

Quantified and monetized in primary estimate Unquantified 

Sulfate 

Deposition: 

welfare 

Commercial fishing and forestry from acidic 

deposition effects 

Recreation in terrestrial and aquatic ecosystems 

from acid deposition effects 

Increased mercury methylation 

HC/Toxics: 

healthd 

Cancer (benzene, 1,3-butadiene, formaldehyde, 

acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood components 

(benzene) 

Reduction in the number of blood platelets 

(benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-

butadiene) 

Irritation of eyes and mucus membranes 

(formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics 

(formaldehyde) 

Irritation of the eyes, skin, and respiratory tract

(acetaldehyde) 

Upper respiratory tract irritation and congestion 

(acrolein) 

HC/Toxics: 

welfare 

Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 

Notes: 

a

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

with PM health effects including morphological changes and altered host defense mechanisms. The public health 

impact of these biological responses may be partly represented by our quantified endpoints. 

b

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

estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of 

this issue).27 While some of the effects of short term exposure are likely to be captured by the cohort estimates, there 

may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in 

the primary analysis. 

c

 May result in benefits or disbenefits. 

d

 Many of the key hydrocarbons related to this action are also hazardous air pollutants listed in the CAA. 

While there will be impacts associated with air toxic pollutant emission changes that 

result from the final standards, we do not attempt to monetize those impacts. This is primarily 

because currently available tools and methods to assess air toxics risk from mobile sources at the 

national scale are not adequate for extrapolation to incidence estimations or benefits assessment. 

The best suite of tools and methods currently available for assessment at the national scale are 

those used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory 

Board specifically commented in their review of the 1996 NATA that these tools were not yet 

ready for use in a national-scale benefits analysis, because they did not consider the full 

distribution of exposure and risk, or address sub-chronic health effects.28 While EPA has since 
Chapter 6 

42 

improved these tools, there remain critical limitations for estimating incidence and assessing 

benefits of reducing mobile source air toxics. 

As part of the second prospective analysis of the benefits and costs of the Clean Air Act,29

EPA conducted a case study analysis of the health effects associated with reducing exposure to 

benzene in Houston from implementation of the Clean Air Act. While reviewing the draft report, 

EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the challenges for 

assessing progress in health improvement as a result of reductions in emissions of hazardous air 

pollutants (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in 

emissions inventories and background levels, the difficulty of extrapolating risk estimates to low 

doses and the challenges of tracking health progress for diseases, such as cancer, that have long 

latency periods.”30 EPA continues to work to address these limitations; however, we did not 

have the methods and tools available for national-scale application in time for the analysis of the 

final standards.S

 

Human Health Impacts 

Table 6.3-Error! No text of specified style in document.-9 and Table 6.3-Error! No text 

of specified style in document.-10 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the final standards. For each endpoint presented in Table 

6.3-Error! No text of specified style in document.-9 and Table 6.3-Error! No text of specified 

style in document.-10, we provide both the point estimate and the 90 percent confidence 

interval. 

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and SixCities studies and no threshold assumption in the model of mortality, we estimate that the final 

standards will result in between 110 and 280 cases of avoided PM2.5-related premature deaths 

annually in 2030. As a sensitivity analysis, when the range of expert opinion is used, we estimate 

between 36 and 370 fewer premature mortalities in 2030. 

The range of ozone impacts is based on changes in risk estimated using several sources of 

ozone-related mortality effect estimates. This analysis presents six alternative estimates for the 

association based upon different functions reported in the scientific literature, derived from both 

the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (Bell et al., 2004; Huang 

et al., 2005; Schwartz, 2005) and from a series of recent meta-analyses (Bell et al., 2005, Ito et 

al., 2005, and Levy et al., 2005). This approach is not inconsistent with recommendations 

provided by the NRC in their recent report (NRC, 2008) on the estimation of ozone-related 

mortality risk reductions, “The committee recommends that the greatest emphasis be placed on 

estimates from new systematic multicity analyses that use national databases of air pollution and 

 

S

 In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was reached 

on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials. 

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2017 Regulatory Impact Analysis 

43 

mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of 

previously published studies.”31 For ozone-related premature mortality in 2030, we estimate a 

range of between 1 to 3 additional premature mortalities. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies and 

expert elicitation. 

Table 6.3-Error! No text of specified style in document.-11 presents the distributions of 

the reduction in PM2.5-related premature mortality based on the C-R distributions provided by 

each expert, as well as that from the data-derived health impact functions, based on the statistical 

error associated with the ACS study (Pope et al., 2002) and the Six-Cities study (Laden et al., 

2006). The 90 percent confidence interval for each separate estimate of PM-related mortality is 

also provided. 

In 2030, the effect estimates of nine of the twelve experts included in the elicitation panel 

fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one 

expert falls below this range, while two of the experts are above this range. Although the overall 

range across experts is summarized in these tables, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the 

likely mean effect estimate are not evenly distributed across the range illustrated by arraying the 

highest and lowest expert means. 

Table 6.3-Error! No text of specified style in document.-9: Estimated PM2.5-Related Health 

Impactsa

Health Effect 2030 Annual Reduction in 

Incidence (5th - 95th percentile) 

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002) 

 Adult, age 25+, Six-Cities Study (Laden et al., 2006) 

 Infant, age <1 year (Woodruff et al., 1997) 

110

(30 – 190)

280

(130 – 440)

0

(0 – 1)

Chronic bronchitis (adult, age 26 and over) 76

(1 – 150)

Non-fatal myocardial infarction (adult, age 18 and over) 130

(32 – 230)

Hospital admissions - respiratory (all ages)c20

(8 – 32)

Hospital admissions - cardiovascular (adults, age >18)d 50

(33 – 60)

Emergency room visits for asthma (age 18 years and younger) 72

(34 – 110)

Acute bronchitis, (children, age 8-12) 160

(-42 – 370)

Lower respiratory symptoms (children, age 7-14) 2,100

(770 – 3,400)

Upper respiratory symptoms (asthmatic children, age 9-18) 1,600

(260 – 2,900)

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Chapter 6 

44 

Asthma exacerbation (asthmatic children, age 6-18) 3,500

(-120 – 9,700)

Work loss days 14,000

(12,000 – 16,000)

Minor restricted activity days (adults age 18-65) 81,000

(65,000 – 96,000)

Notes: 

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United 

States. 

b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) 

and the Six-Cities Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and 

should not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, 

(1997).T

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), 

pneumonia and asthma. 

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure. 

Table 6.3-Error! No text of specified style in document.-10: Estimated Ozone-Related Health 

Impactsa 

Health Effect 2030 Annual Reduction in Incidence 

(5th - 95th percentile) 

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental 

 Huang et al. (2005) – Cardiopulmonary 

 Schwartz (2005) – Non-accidental 

Meta-analyses: 

 Bell et al. (2005) – All cause 

 Ito et al. (2005) – Non-accidental 

 Levy et al. (2005) – All cause 

-1

(-4 – 3)

-1

(-5 – 4)

-1

(-6 – 4)

-2

(-10 – 6)

-3

(-11 – 6)

-3

(-10 – 4)

Hospital admissions- respiratory causes (adult, 65 and older)c -6

(-30 – 15)

Hospital admissions -respiratory causes (children, under 2) -3

(-12 – 6)

Emergency room visit for asthma (all ages) -1

(-18 – 15)

Minor restricted activity days (adults, age 18-65) -930

(-18,000 – 16,000)

School absence days -850

(-6,700 – 5,100)

 

T

 Woodruff, T.J., J. Grillo, and K.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. 

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2017 Regulatory Impact Analysis 

45 

Notes: 

a Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two significant 

digits. Estimates represent incidence within the 48 contiguous U.S. 

b

 Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative 

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). 

The estimates of ozone-related premature mortality should therefore not be summed. 

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

Table 6.3-Error! No text of specified style in document.-11: Results of Application of Expert 

Elicitation: Annual Reductions in Premature Mortality in 2030 Associated with the Final 

Standards 

Source of Mortality 

Estimate 

2030 Incidence 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 30 110 190

Laden et al. (2006) 130 280 440

Expert A 6 300 590

Expert B -23 220 530

Expert C -4 230 530

Expert D 23 160 280

Expert E 150 370 60

Expert F 120 200 290

Expert G 0 130 260

Expert H -38 170 430

Expert I 19 220 430

Expert J 19 180 440

Expert K 0 36 190

Expert L 8 140 330

Monetized Estimates of Human Health and Environmental Impacts 

Table 6.3-Error! No text of specified style in document.-12 presents the estimated 

monetary value of changes in the incidence of ozone and PM2.5-related health and environmental 

effects. Total aggregate monetized benefits are presented in Table 6.3-Error! No text of 

specified style in document.-13. All monetized estimates are presented in 2010$. Where 

appropriate, estimates account for growth in real gross domestic product (GDP) per capita 

between 2000 and 2030.U The monetized value of PM2.5-related mortality also accounts for a 

 

U

 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. Benefits are therefore adjusted by 

multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. 

For growth between 2000 and 2030, this factor is 1.23 for long-term mortality, 1.27 for chronic health impacts, and 

1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the 

reader to the PM NAAQS regulatory impact analysis.9 Note that similar adjustments do not exist for cost-of-illnessbased unit values. For these, we apply the same unit value regardless of the future year of analysis.

Deleted: 10
Chapter 6 

46 

twenty-year segmented cessation lag.V To discount the value of premature mortality that occurs 

at different points in the future, we apply both a 3 and 7 percent discount rate. We also use both 

a 3 and 7 percent discount rate to value PM-related nonfatal heart attacks (myocardial 

infarctions).W 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the final standards is thus equal to 

the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus the 

sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits in 

2030 for the final standards, using the ACS and Six-Cities PM mortality studies and the range of 

ozone mortality assumptions, is between $1.0 and $2.6 billion, assuming a 3 percent discount 

rate, or between $0.92 and $2.3 billion, assuming a 7 percent discount rate. As the results 

indicate, total benefits are driven primarily by the reduction in PM2.5-related premature fatalities 

each year and represent the benefits of the final standards anticipated to occur annually when the 

program is fully implemented. 

The next largest benefit is for reductions in chronic illness (chronic bronchitis and 

nonfatal heart attacks), although this value is more than an order of magnitude lower than for 

premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor 

restricted activity days, and work loss days account for the majority of the remaining benefits. 

The remaining categories each account for a small percentage of total benefit; however, they 

represent a large number of avoided incidences affecting many individuals. A comparison of the 

incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the monetary 

value associated with that endpoint. For example, there are many more work loss days than PMrelated premature mortalities, yet work loss days account for only a very small fraction of total 

monetized benefits. This reflects the fact that many of the less severe health effects, while more 

common, are valued at a lower level than the more severe health effects. Also, some effects, 

such as hospital admissions, are valued using a proxy measure of willingness-to-pay (e.g., costof-illness). As such, the true value of these effects may be higher than that reported here. 

Table 6.3-Error! No text of specified style in document.-12: Estimated Monetary Value of 

Changes in Incidence of Health and Welfare Effects (millions of 2010$) a,b

2030 

PM2.5-Related Health Effect (5th and 95th Percentile) 

 

V

 Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in pollution 

exposures and the total realization of changes in health effects. Within the context of benefits analyses, this term is 

often referred to as “cessation lag”. The existence of such a lag is important for the valuation of premature mortality 

incidence because economic theory suggests that benefits occurring in the future should be discounted. In this 

analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is consistent with the most 

recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science Advisory Board, 2004. 

Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. Letter from 

the Health Effects Subcommittee to the U.S. Environmental Protection Agency Administrator, December. 

W Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost earnings 

and direct medical costs over a 5-year period following a nonfatal MI. 

Deleted: XX

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Deleted: XX

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Deleted: and ozoneDeleted: 11
2017 Regulatory Impact Analysis 

47 

Premature Mortality – 

Derived from Epidemiology 

Studiesc,d

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

 3% discount rate 

 7% discount rate 

$980

($110 - $2,600)

$880

($97 - $2,400)

Adult, age 25+ - Six-Cities study 

(Laden et al., 2006) 

 3% discount rate 

 7% discount rate 

$2,500

($340 - $6,300)

$2,300

($310 - $5,700)

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

$3.8

(-$3.9 - $15)

Chronic bronchitis (adults, 26 and over) $42

($0.4 - $140)

Non-fatal acute myocardial infarctions 

 3% discount rate 

 7% discount rate 

$14

($2.3 - $36)

$12

($1.8 - $30)

Hospital admissions for respiratory causes $0.32

($0.13 - $0.51)

Hospital admissions for cardiovascular causes $0.73

($0.07 - $1.4)

Emergency room visits for asthma $0.03

($0.01 - $0.05)

Acute bronchitis (children, age 8–12) $0.08

(-$0.02 - $0.21)

Lower respiratory symptoms (children, 7–14) $0.04

($0.01 - $0.09)

Upper respiratory symptoms (asthma, 9–11) $0.05

($0.009 - $0.12)

Asthma exacerbations $0.20

(-$0.007 - $0.58)

Work loss days $2.2

($1.9 - $2.6)

Minor restricted-activity days (MRADs) $5.6

($3.2 - $8.1)

Premature Mortality, All ages – 

Derived from Multi-city analyses

Bell et al., 2004 -$5.8

(-$45 - $27)

Huang et al., 2005 -$6.2

(-$60 - $41)

Schwartz, 2005 -$8.7

(-$71 - $44)

Premature Mortality, All ages – 

Derived from Meta-analyses 

Bell et al., 2005 -$19

(-$120 - $38)

Ito et al., 2005 -$26

(-$140 - $58)

Levy et al., 2005 -$27

(-$120 - $38)

Hospital admissions- respiratory causes (adult, 65 and older) -$0.16

(-$0.77 - $0.39)

Hospital admissions- respiratory causes (children, under 2) -$0.03
Chapter 6 

48 

(-$130 - $0.07)

Emergency room visit for asthma (all ages) -$0.0003

(-$0.007 - $0.006)

Minor restricted activity days (adults, age 18-65) -$0.06

(-$1.3 - $1.1)

School absence days -$0.08

(-$0.65 - $0.49)

Notes: 

a Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary benefits are rounded to 

two significant digits for ease of presentation and computation. PM and ozone benefits are nationwide. 

b

 Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis

year (2030). 

c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses. 

Table 6.3-Error! No text of specified style in document.-13: Total Monetized Ozone and PMrelated Benefits Associated with the Final Standards in 2030 

Total Ozone and PM Benefits (billions, 2010$) – 

PM Mortality Derived from the ACS and Six-Cities Studies 

3% Discount Rate 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Multi-city Bell et al., 

2004 

$1.0 - $2.6 Multi-city Bell et al., 

2004 

$0.95 - $2.3

Huang et al., 

2005 

$1.0 - $2.6 Huang et al., 

2005 

$0.94 - $2.3

Schwartz, 

2005 

$1.0 - $2.6 Schwartz, 

2005 

$0.94 - $2.3

Meta-analysis Bell et al., 

2005 

$1.0 - $2.6 Meta-analysis Bell et al., 

2005 

$0.93 - $2.3

Ito et al., 

2005 

$1.0 - $2.6 Ito et al., 

2005 

$0.92 - $2.3

Levy et al., 

2005

$1.0 - $2.6 Levy et al., 

2005

$0.92 - $2.3

Total Ozone and PM Benefits (billions, 2010$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

3% Discount Rate 7% Discount Rate 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Ozone 

Mortality 

Function 

Reference Mean Total 

Benefits 

Multi-city Bell et al., 

2004 

$0.39 - $3.4 Multi-city Bell et al., 

2004 

$0.35 - $3.1

Huang et al., 

2005 

$0.39 - $3.4 Huang et al., 

2005 

$0.35 - $3.1

Schwartz, 

2005 

$0.39 - $3.4 Schwartz, 

2005 

$0.35 - $3.1

Meta-analysis Bell et al., 

2005 

$0.38 - $3.4 Meta-analysis Bell et al., 

2005 

$0.34 - $3.1

Ito et al., 

2005 

$0.37 - $3.4 Ito et al., 

2005 

$0.33 - $3.0

Levy et al., $0.37 - $3.4 Levy et al., $0.33 - $3.0

Deleted: 12
2017 Regulatory Impact Analysis 

49 

2005 2005 

Methodology 

Human Health Impact Functions 

Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 

components: (1) an effect estimate from a particular study; (2) a baseline incidence rate for the 

health effect (obtained from either the epidemiology study or a source of public health statistics 

such as the Centers for Disease Control); (3) the size of the potentially affected population; and 

(4) the estimated change in the relevant ozone or PM summary measures. 

A typical health impact function might look like: 

 ( 1) ∆ = 0

⋅ −

∆⋅ x

y y e

β

, 

where y0 is the baseline incidence (the product of the baseline incidence rate times the 

potentially affected population), β is the effect estimate, and ∆x is the estimated change in the 

summary pollutant measure. There are other functional forms, but the basic elements remain the 

same. The following subsections describe the sources for each of the first three elements: size of 

the potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence 

rates. We also describe the treatment of potential thresholds in PM-related health impact 

functions. Section 8.2 describes the ozone and PM air quality inputs to the health impact 

functions. 

Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 2000 

U.S. Census block level dataset.32 Benefits Modeling and Analysis Program (BenMAP) 

incorporates 250 age/gender/race categories to match specific populations potentially affected by 

ozone and other air pollutants. The software constructs specific populations matching the 

populations in each epidemiological study by accessing the appropriate age-specific populations 

from the overall population database. BenMAP projects populations to 2030 using growth 

factors based on economic projections.33

Effect Estimate Sources 

The most significant quantifiable benefits of reducing ambient concentrations of ozone 

and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents34,35 and the World Health Organization’s 2003 and 200436,37 reports outline numerous 

human health effects known or suspected to be linked to exposure to ambient ozone and PM. 

EPA recently evaluated the ozone and PM literature for use in the benefits analysis for the final 

2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this 
Chapter 6 

50 

analysis; for more information on the studies that underlie the health impacts quantified in this 

RIA, please refer to those documents. 

It is important to note that we are unable to separately quantify all of the possible PM and 

ozone health effects that have been reported in the literature for three reasons: (1) the possibility 

of double counting (such as hospital admissions for specific respiratory diseases versus hospital 

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect 

relationships that are based on clinical studies to the potentially affected population; or (3) the 

lack of an established concentration-response (CR) relationship. Table 6.3-Error! No text of 

specified style in document.-14 lists the health endpoints included in this analysis. 

Table 6.3-Error! No text of specified style in document.-14: Health Impact Functions Used in 

BenMAP to Estimate Impacts of PM2.5 and Ozone Reductions 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

Premature Mortality 

Premature mortality – 

daily time series 

O3 Multi-city

Bell et al (2004) (NMMAPS study)38 – Nonaccidental 

Huang et al (2005)39 - Cardiopulmonary 

Schwartz (2005)40 – Non-accidental 

Meta-analyses:

Bell et al (2005)41 – All cause 

Ito et al (2005)42 – Non-accidental 

Levy et al (2005)43 – All cause 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)44

Laden et al. (2006)45

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)46 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)47 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)48 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)49 Adults (>18 years) 

Hospital Admissions 

Respiratory 

O3 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)50

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)51,52

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)53

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

Burnett et al. (2001)54 <2 years 

Deleted: 13
2017 Regulatory Impact Analysis 

51 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

PM2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)55

Ito (2003)—ICD 490-496 (COPD)56

>64 years 

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)57 20–64 years 

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years 

PM2.5 Sheppard (2003)—ICD 493 (asthma)58 <65 years 

Cardiovascular PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure) 

>64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

Asthma-related ER 

visits 

O3 Pooled estimate:

Peel et al (2005)59

Wilson et al (2005)60

All ages 

All ages 

Asthma-related ER 

visits (cont’d) 

PM2.5 Norris et al. (1999)61 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)62 8–12 years 

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)63 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)64 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)65 (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998)66 (cough) 

6–18 yearsa

Work loss days PM2.5 Ostro (1987)67 18–65 years 

School absence days 

O3 

Pooled estimate:

Gilliland et al. (2001)68

Chen et al. (2000)69

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs) 

O3 Ostro and Rothschild (1989)70 18–65 years 

PM2.5 Ostro and Rothschild (1989) 18–65 years 

Notes: 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) 

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended 

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan 

for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits 

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

b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent 

advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school

absences for all school-aged children based on the biological similarity between children aged 5 to 17. 

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 
Chapter 6 

52 

U.S. To account for the potential impacts of different health care systems or underlying health 

status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due 

to the potential for confounding by co-pollutants, we give preference to effect estimates from 

models including both ozone and PM over effect estimates from single-pollutant models.71,72 

Baseline Incidence Rates 

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 100 ppb decrease in daily ozone levels might, in turn, decrease 

hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to 

convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per 100,000 people, that number must be 

multiplied by the number of 100,000s in the population. 

Table 6.3-Error! No text of specified style in document.-15 summarizes the sources of 

baseline incidence rates and provides average incidence rates for the endpoints included in the 

analysis. Table 6.3-Error! No text of specified style in document.-16 presents the asthma 

prevalence rates used in this analysis. For both baseline incidence and prevalence data, we used 

age-specific rates where available. We applied concentration-response functions to individual 

age groups and then summed over the relevant age range to provide an estimate of total 

population benefits. In most cases, we used a single national incidence rate, due to a lack of 

more spatially disaggregated data. Whenever possible, the national rates used are national 

averages, because these data are most applicable to a national assessment of benefits. For some 

studies, however, the only available incidence information comes from the studies themselves; in 

these cases, incidence in the study population is assumed to represent typical incidence at the 

national level. Regional incidence rates are available for hospital admissions, and county-level 

data are available for premature mortality. We have projected mortality rates such that future 

mortality rates are consistent with our projections of population growth.73

Table 6.3-Error! No text of specified style in document.-15: Baseline Incidence Rates and 

Population Prevalence Rates for Use in Impact Functions, General Population 

Endpoint Parameter Rates 

Value Source 

Mortality Daily or annual mortality 

rate projected to 2020 

Age-, cause-, and 

county-specific rate 

CDC Wonder (2006–2008)

74

U.S. Census bureau 

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate 

2007 HCUP data filesa,75

Asthma ER Visits Daily asthma ER visit rate Age-, region-, state-, 

county- and causespecific rate 

2007 HCUP data filesa

Chronic Bronchitis Annual prevalence rate per 

person 

0.0367 

1999 NHIS (American Lung 

Association, 2002, Table 4)76

 

Deleted: 14
2017 Regulatory Impact Analysis 

53 

Endpoint Parameter Rates 

Value Source 

Aged 18–44 

Aged 45–64 

Aged 65 and older 

0.0505 

0.0587 

 Annual incidence rate per 

person 

0.00378 Abbey et al. (1993, Table 3) 

Nonfatal Myocardial 

Infarction (heart 

attacks) 

Daily nonfatal myocardial 

infarction incidence rate 

per person, 18+ 

Age-, region-, state-, 

and county- specific 

rate 

2007 HCUP data filesa; adjusted by 

0.93 for probability of surviving after 

28 days (Rosamond et al., 1999) 

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily dyspnea

0.076

0.067

0.037 

Ostro et al. (2001)

Acute Bronchitis Annual bronchitis 

incidence rate, children 

0.043 American Lung Association (2002, 

Table 11)77

Lower Respiratory 

Symptoms 

Daily lower respiratory 

symptom incidence among 

childrenb

0.0012 Schwartz et al. (1994, Table 2) 

Upper Respiratory 

Symptoms 

Daily upper respiratory 

symptom incidence among 

asthmatic children 

0.3419 Pope et al. (1991, Table 2) 

Work Loss Days Daily WLD incidence rate 

per person (18–65) 

Aged 18–24 

Aged 25–44 

Aged 45–64 

0.00540 

0.00678 

0.00492 

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41);78 U.S. 

Bureau of the Census (2000)79

School Loss Days Rate per person per year, 

assuming 180 school days 

per year 

9.9 National Center for Education 

Statistics (1996)80 and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity Days Daily MRAD incidence 

rate per person 

0.02137 Ostro and Rothschild (1989, p. 243) 

Notes: 

a

 Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes. 

b

 Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze. 

Table 6.3-Error! No text of specified style in document.-16: Asthma Prevalence Rates Used 

for this Analysis 

Population Group Asthma Prevalence Rates 

Value Source 

All Ages 0.0780 

American Lung Association (2010, Table 7) 

< 18 0.0941

5–17 0.1070 

18–44 0.0719 

45–64 0.0745 

65+ 0.0716 

African American, 5 to 17 0.1776 American Lung Association (2010, Table 9) 

African American, <18 0.1553 American Lung Associationb

Notes: 

a

 See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. 

b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).81

Deleted: 15
Chapter 6 

54 

Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993).82 Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk. For example, suppose a 

pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 

to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). 

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but not 

the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987).83,84 We 

provide unit values for health endpoints (along with information on the distribution of the unit 

value) in Table 6.3-Error! No text of specified style in document.-17. All values are in constant 

year 2010 dollars, adjusted for growth in real income out to 2030 using projections provided by 

Standard and Poor’s. Economic theory argues that WTP for most goods (such as environmental 

protection) will increase if real income increases. Many of the valuation studies used in this 

analysis were conducted in the late 1980s and early 1990s. Because real income has grown since 

the studies were conducted, people’s willingness to pay for reductions in the risk of premature 

death and disease likely has grown as well. We did not adjust cost of illness-based values 

because they are based on current costs. Similarly, we did not adjust the value of school 

absences, because that value is based on current wage rates. For details on valuation estimates 

for PM-related endpoints, see the 2006 PM NAAQS RIA.

85 For details on valuation estimates 

for ozone-related endpoints, see the 2008 Ozone NAAQS RIA.86

Table 6.3-Error! No text of specified style in document.-17: Unit Values for Economic 

Valuation of Health Endpoints (2010$) 

Health Endpoint Central Estimate of 

Value Per Statistical 

Incidence 

2000 

Income 

Level 

2030 

Income 

Level 

Derivation of Distributions of Estimates 

Premature Mortality 

(Value of a Statistical 

Life) 

$8,000,000 $9,900,000 EPA currently recommends a central VSL of $6.3m (2000$) based on a 

Weibull distribution fitted to 26 published VSL estimates (5 contingent 

valuation and 21 labor market studies). The underlying studies, the 

distribution parameters, and other useful information are available in 

Appendix B of EPA's current Guidelines for Preparing Economic 

Analyses (U.S. EPA, 2000). 

Deleted: 16
2017 Regulatory Impact Analysis 

55 

Chronic Bronchitis 

(CB) 

$450,000 $550,000 The WTP to avoid a case of pollution-related CB is calculated as where 

x is the severity of an average CB case, WTP13 is the WTP for a severe 

case of CB, and $ is the parameter relating WTP to severity, based on 

the regression results reported in Krupnick and Cropper (1992). The 

distribution of WTP for an average severity-level case of CB was 

generated by Monte Carlo methods, drawing from each of three 

distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 

probability of being each of the first nine deciles of the distribution of 

WTP responses in Viscusi et al. (1991); (2) the severity of a pollutionrelated case of CB (relative to the case described in the Viscusi study) 

is assumed to have a triangular distribution, with the most likely value 

at severity level 6.5 and endpoints at 1.0 and 12.0; and (3) the constant 

in the elasticity of WTP with respect to severity is normally distributed 

with mean = 0.18 and standard deviation = 0.0669 (from Krupnick and 

Cropper [1992]). This process and the rationale for choosing it is 

described in detail in the Costs and Benefits of the Clean Air Act, 1990 

to 2010 (U.S. EPA, 1999). 

Nonfatal Myocardial 

Infarction (heart 

attack) 

3% discount rate

 Age 0–24 

 Age 25–44 

 

 Age 45–54 

 Age 55–65 

 Age 66 and over 

7% discount rate

 Age 0–24

 Age 25–44

 Age 45–54

 Age 55–65

 Age 66 and over

$89,373

$100,690$1

06,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548

$89,373

$100,690

$106,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548

No distributional information available. Age-specific cost-of-illness 

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs are based on 

simple average of estimates from Russell et al. (1998) and Wittels et al. 

(1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of 

lost earnings:

age of onset: at 3% at 7%

25–44 $8,774 $7,855

45–54 $12,932 $11,578

55–65 $74,746 $66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no discounting)

2. Russell et al. (1998), 5-year period ($22,331 at 3% discount rate; 

$21,113 at 7% discount rate)

Hospital Admissions

Chronic Obstructive 

Pulmonary Disease 

(COPD) 

$17,996 $17,996 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and Quality (2000) 

(www.ahrq.gov). 

Asthma Admissions $11,957 $11,957 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of hospital 

stay, and weighted share of total asthma category illnesses) reported in 

Agency for Healthcare Research and Quality (2000) (www.ahrq.gov). 

All Cardiovascular $30,256 $30,256 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of hospital 

stay, and weighted share of total cardiovascular category illnesses) 

reported in Agency for Healthcare Research and Quality (2000) 

(www.ahrq.gov). 

All respiratory (ages 

65+) 

$25,413 $25,413 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 
Chapter 6 

56 

weighted share of total COPD category illnesses) reported in Agency 

for Healthcare Research and Quality, 2000 (www.ahrq.gov). 

All respiratory (ages 

0–2) 

$10,943 $10,943 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) reported in Agency 

for Healthcare Research and Quality, 2000 (www.ahrq.gov). 

Emergency Room 

Visits for Asthma 

$405 $405 No distributional information available. Simple average of two unit 

COI values: 

(1) $311.55, from Smith et al. (1997) and 

(2) $260.67, from Stanford et al. (1999). 

Respiratory Ailments Not Requiring Hospitalization 

Upper Respiratory 

Symptoms (URS) 

$32 $34 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and 

assuming additivity of WTPs. In the absence of information 

surrounding the frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we assumed a uniform 

distribution between $9.2 and $43.1. 

Lower Respiratory 

Symptoms (LRS) 

$20 $21 Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 11 

different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and 

assuming additivity of WTPs. The dollar value for LRS is the average 

of the dollar values for the 11 different types of LRS. In the absence of 

information surrounding the frequency with which each of the 11 types 

of LRS occurs within the LRS symptom complex, we assumed a 

uniform distribution between $6.9 and $24.46. 

Asthma 

Exacerbations 

$55 $57 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986). This study 

surveyed asthmatics to estimate WTP for avoidance of a “bad asthma 

day,” as defined by the subjects. For purposes of valuation, an asthma 

exacerbation is assumed to be equivalent to a day in which asthma is 

moderate or worse as reported in the Rowe and Chestnut (1986) study. 

The value is assumed have a uniform distribution between $15.6 and 

$70.8. 

Acute Bronchitis $452 $494 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc 1994 for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110. 

Work Loss Days 

(WLDs) 

Variable 

(U.S. 

median = 

$137) 

Variable 

(U.S. 

median = 

$137) 

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 50 (assuming 2 weeks of vacation) 

and then by 5—to get median daily wage. U.S. Year 2000 Census, 

compiled by Geolytics, Inc. 
2017 Regulatory Impact Analysis 

57 

Minor Restricted 

Activity Days 

(MRADs) 

$64 $69 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $22 and a 

maximum of $83, with a most likely value of $52. Range is based on 

assumption that value should exceed WTP for a single mild symptom 

(the highest estimate for a single symptom—for eye irritation—is 

$16.00) and be less than that for a WLD. The triangular distribution 

acknowledges that the actual value is likely to be closer to the point 

estimate than either extreme. 

School Absence Days $95 $95 No distribution available

Processing Air Quality Modeling Data for Health Impacts Analysis 

In Section 6.2, we summarized the methods for and results of estimating air quality for 

the standards. These air quality results are in turn associated with human populations to estimate 

changes in health effects. For the purposes of this analysis, we focus on the health effects that 

have been linked to ambient changes in ozone and PM2.5 related to emission reductions estimated 

to occur due to the implementation of the standards. We estimate ambient PM2.5 and ozone 

concentrations using the Community Multiscale Air Quality model (CMAQ). This section 

describes how we converted the CMAQ modeling output into full-season profiles suitable for the 

health impacts analysis. 

General Methodology 

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell 

from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.X,Y The 

predicted changes in ozone concentrations from the future-year base case to future-year control 

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., 

BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we 

generated full-season ozone profiles for each location in two steps: (1) we combined monitored 

observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid 

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily 8-hour 

maximum.Z,AA 

 

X

 The ozone season for this analysis is defined as the 5-month period from May to September. 

Y

 Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at least 

nine hourly observations per day [8 am to 8 pm] during the ozone season). 

Z

 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

AA This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap. 

Deleted: Manipulating 
Chapter 6 

58 

For PM2.5, we also use the model predictions in conjunction with observed monitor data. 

CMAQ generates predictions of hourly PM species concentrations for every grid. The species 

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a 

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several 

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the 

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5

were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating 

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5 

species concentrations were created by interpolating EPA speciation network (ESPN) ambient 

data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance for 

modeling the PM2.5 standard (EPA, 2001).87 The guidance recommends that model predictions 

be used in a relative sense to estimate changes expected to occur in each major PM2.5 species. 

The procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled 

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the final 

standards. 

Table 6.3-Error! No text of specified style in document.-18 provides those ozone and 

PM2.5 metrics for grid cells in the modeled domain that enter the health impact functions for 

health benefits endpoints. The population-weighted average reflects the baseline levels and 

predicted changes for more populated areas of the nation. This measure better reflects the 

potential benefits through exposure changes to these populations. 

Table 6.3-Error! No text of specified style in document.-18: Summary of CMAQ-Derived 

Population-Weighted Ozone and PM2.5 Air Quality Metrics for Health Benefits Endpoints 

Associated with the Final Standards

2030 

Statistica Baseline Changeb

Ozone Metric: National Population-Weighted Average (ppb)c

Daily Maximum 8-Hour Average 

Concentration 

42.3735 0.0009

PM2.5 Metric: National Population-Weighted Average (μg/m3) 

Annual Average Concentration 8.1135 -0.0065

Notes: 

a

 Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects 

estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of the 

“ozone season” (i.e., May through September). Note that the national, population-weighted PM2.5

and ozone air quality metrics presented in this chapter represent an average for the entire, gridded 

U.S. CMAQ domain. These are different than the population-weighted PM2.5 and ozone design 

value metrics presented in Chapter 7, which represent the average for areas with a current air 

quality monitor. 

b

 The change is defined as the control-case value minus the base-case value; a negative value 

therefore indicates a reduction and a positive value an increase. 

c

 Calculated by summing the product of the projected CMAQ grid-cell population and the 

estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

Deleted: 17

Deleted: minus the control-case value
2017 Regulatory Impact Analysis 

59 

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used in the air quality and benefits modeling are 

slightly different than the final emission inventories estimated for the final standards. Please 

refer to Section 6.2.1 for more information about the inventories used in the air quality modeling 

that supports the health impacts analysis. 

Methods for Describing Uncertainty 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty and this analysis is no exception. As outlined 

both in this and preceding chapters, many inputs were used to derive the estimate of benefits for 

the final standards, 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)88,89 highlighted the need for EPA to 

conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In general, the NRC concluded that EPA’s general methodology for calculating the 

benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. 

Since the publication of these reports, EPA’s Office of Air and Radiation (OAR) continues to 

make progress toward the goal of characterizing the aggregate impact of uncertainty in key 

modeling elements on both health incidence and benefits estimates in two key ways: Monte Carlo 

analysis and expert-derived concentration-response functions. In this analysis, we use both of 

these two methods to assess uncertainty quantitatively, as well as provide a qualitative 

assessment for those aspects that we are unable to address quantitatively. 

First, we used Monte Carlo methods for characterizing random sampling error associated 

with the concentration response functions from epidemiological studies and random effects 

modeling to characterize both sampling error and variability across the economic valuation 

functions. Monte Carlo simulation uses random sampling from distributions of parameters to 

characterize the effects of uncertainty on output variables, such as incidence of premature 

mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around 

the estimated health impact and dollar benefits. The reported standard errors in the 

epidemiological studies determined the distributions for individual effect estimates. 

Second, because characterization of random statistical error omits important sources of 

uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may 

exist), we also incorporate the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008).90 Use of the expert 

elicitation and incorporation of the standard errors approaches provide insights into the likelihood 
Chapter 6 

60 

of different outcomes and about the state of knowledge regarding the benefits estimates. 

However, there are significant unquantified uncertainties present in upstream inputs including 

emission and air quality. Both approaches have different strengths and weaknesses, which are 

fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85 to 95 percent of total monetized benefits. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated with 

reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies. In our results, we report credible intervals based on these standard errors, reflecting the 

uncertainty in the estimated change in incidence of avoided premature deaths. We also provide 

multiple estimates, to reflect model uncertainty between alternative study designs. 

For premature mortality associated with exposure to PM, we follow the same approach 

used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation 

study. Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. Furthermore, 

the approach presented here does not yet include methods for addressing correlation between 

input parameters and the identification of reasonable upper and lower bounds for input 

distributions characterizing uncertainty in additional model elements. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the estimates. This information should be interpreted within the context of the 

larger uncertainty surrounding the entire analysis.

In 2006 the EPA requested an NAS study to evaluate the extent to which the 

epidemiological literature to that point improved the understanding of ozone-related mortality. 

The NAS found that short-term ozone exposure was likely to contribute to ozone-related 

mortality (NRC, 2008) and issued a series of recommendations to EPA, including that the 

Agency should: 

1. Present multiple short-term ozone mortality estimates, including those based on multi-city 

analyses such as the National Morbidity, Mortality and Air Pollution Study (NMMAPS) 

as well as meta-analytic studies. 

2. Report additional risk metrics, including the percentage of baseline mortality attributable 

to short-term exposure. 

3. Remove reference to a no-causal relationship between ozone exposure and premature 

mortality. 

The quantification and presentation of ozone-related premature mortality in this chapter is 

responsive to these NRC recommendations. 
2017 Regulatory Impact Analysis 

61 

Some key sources of uncertainty in each stage of both the PM and ozone health impact 

assessment are the following: 

• gaps in scientific data and inquiry; 

• variability in estimated relationships, such as epidemiological effect estimates, 

introduced through differences in study design and statistical modeling; 

• errors in measurement and projection for variables such as population growth rates; 

• errors due to misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; and 

• biases due to omissions or other research limitations. 

In Table 6.3-Error! No text of specified style in document.-19 we summarize some of 

the key uncertainties in the benefits analysis. 

Table 6.3-Error! No text of specified style in document.-19: Primary Sources of Uncertainty 

in the Benefits Analysis 

1. Uncertainties Associated with Impact Functions 

- The value of the ozone or PM effect estimate in each impact function. 

- Application of a single impact function to pollutant changes and populations in all locations. 

- Similarity of future-year impact functions to current impact functions. 

- Correct functional form of each impact function. 

- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source 

epidemiological study. 

- Application of impact functions only to those subpopulations matching the original study population. 

2. Uncertainties Associated with CMAQ-Modeled Ozone and PM Concentrations 

- Responsiveness of the models to changes in precursor emissions from the control policy. 

- Projections of future levels of precursor emissions, especially ammonia and crustal materials. 

- Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from urban to 

rural areas. 

3. Uncertainties Associated with PM Mortality Risk

- Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence. 

- Direct causal agents within the complex mixture of PM have not been identified. 

- The extent to which adverse health effects are associated with low-level exposures that occur many times in the 

year versus peak exposures. 

- The extent to which effects reported in the long-term exposure studies are associated with historically higher 

levels of PM rather than the levels occurring during the period of study. 

- Reliability of the PM2.5 monitoring data in reflecting actual PM2.5 exposures. 

4. Uncertainties Associated with Possible Lagged Effects 

- The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels 

that would occur in a single year is uncertain as well as the portion that might occur in subsequent years. 

5. Uncertainties Associated with Baseline Incidence Rates 

- Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not 

accurately represent the actual location-specific rates. 

- Current baseline incidence rates may not approximate well baseline incidence rates in 2030. 

- Projected population and demographics may not represent well future-year population and demographics. 

6. Uncertainties Associated with Economic Valuation 

Deleted: 18
Chapter 6 

62 

- 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. 

6.3.2 PM-related Monetized Benefits of the Model Year (MY) Analysis 

As described in Chapter 4, the final standards will reduce emissions of several criteria and 

toxic pollutants and precursors. In the MY analysis, EPA estimates the economic value of the 

human health benefits associated with reducing PM2.5 exposure. Due to analytical limitations, 

this analysis does not estimate benefits related to other criteria pollutants (such as ozone, NO2 or 

SO2) or toxics pollutants, nor does it monetize all of the potential health and welfare effects 

associated with PM2.5.

The MY analysis uses a “benefit-per-ton” method to estimate a selected suite of PM2.5-

related health benefits described below. These PM2.5-related benefit-per-ton estimates provide 

the total monetized human health benefits (the sum of premature mortality and premature 

morbidity) of reducing one ton of directly emitted PM2.5, or one ton of a pollutant that contributes 

to secondarily-formed PM2.5 (such as NOx and SOx) from a specified source. Ideally, the human 

health benefits would be estimated based on changes in ambient PM2.5 concentrations and 

population exposure, as determined by complete air quality and exposure modeling. However, 

conducting such detailed modeling for the model year analysis was not possible within the 

timeframe for the final rule. Note that EPA conducted full-scale photochemical air quality 

modeling for the calendar year analysis. Please refer to Chapter 6.2 for a description of EPA’s 

air quality modeling results and to Chapter 6.3.1 for a description of the quantified and monetized 

PM- and ozone-related health impacts of the FRM. 

Due to analytical limitations, the estimated benefit-per-ton values do not include 

comparable benefits related to reductions in other ambient concentrations of criteria pollutants 

(such as ozone, NO2 or SO2) or toxic air pollutants, nor do they monetize all of the potential 

health and welfare effects associated with PM2.5 or the other criteria pollutants. As a result, 

monetizing PM-related health impacts alone underestimates the benefits associated with 

reductions of the suite of non-GHG pollutants that would be reduced by the final standards. 

The dollar-per-ton estimates used to monetize reductions in emissions that contribute to 

ambient concentrations of PM2.5 are provided in Table 6.3-Error! No text of specified style in 

document.-20. 

Table 6.3-Error! No text of specified style in document.-20 PM2.5-related Benefits-per-ton Values (2010$)a 

Year All Sourcesd Upstream (Non-EGU) 

Sourcesd 

Mobile Sources 

SO2 NOX Direct PM2.5 NOX Direct PM2.5

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated 

Using a 3 Percent Discount Ratec

2015 $30,000 $4,900 $230,000 $5,100 $280,000 

Deleted: 19
2017 Regulatory Impact Analysis 

63 

2020 $33,000 $5,400 $250,000 $5,600 $310,000 

2030 $38,000 $6,400 $290,000 $6,700 $370,000 

2040 $45,000 $7,600 $340,000 $8,000 $440,000 

Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated 

Using a 7 Percent Discount Ratec 

2015 $27,000 $4,500 $210,000 $4,600 $250,000 

2020 $30,000 $4,900 $230,000 $5,100 $280,000 

2030 $35,000 $5,800 $270,000 $6,100 $330,000 

2040 $41,000 $6,900 $310,000 $7,300 $400,000 

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 3 

Percent Discount Ratec 

2015 $73,000 $12,000 $560,000 $12,000 $680,000 

2020 $80,000 $13,000 $620,000 $14,000 $750,000 

2030 $94,000 $16,000 $720,000 $16,000 $900,000 

2040 $110,000 $19,000 $840,000 $20,000 $1,100,000 

Dollar-per-ton Derived from Six Cities Analysis (Laden et al., 2006) Estimated Using a 7 

Percent Discount Ratec 

2015 $66,000 $11,000 $510,000 $11,000 $620,000 

2020 $72,000 $12,000 $560,000 $12,000 $680,000 

2030 $84,000 $14,000 $650,000 $15,000 $810,000

2040 $99,000 $17,000 $760,000 $18,000 $960,000

a Total dollar-per-ton estimates include monetized PM2.5-related premature mortality and morbidity endpoints. 

Range of estimates are a function of the estimate of PM2.5-related premature mortality derived from either the ACS 

study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006). 

b

 Dollar-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA extrapolated 

exponentially based on the growth between 2020 and 2030. 

c

 The dollar-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the 

valuation of premature mortality to account for a twenty-year segmented cessation lag. 

d

 Note that the dollar-per-ton value for SO2 is based on the value for Stationary (Non-EGU) sources; no SO2 value 

was estimated for mobile sources. 

As Table 6.3-Error! No text of specified style in document.-20 indicates, EPA projects 

that the per-ton values for reducing emissions of criteria pollutants from both vehicle use and 

stationary sources such as fuel refineries and storage facilities will increase over time.BB These 

projected increases reflect rising income levels, which are assumed to increase affected 

individuals’ willingness to pay for reduced exposure to health threats from air pollution. They 

also reflect future population growth and increased life expectancy, which expands the size of the 

population exposed to air pollution in both urban and rural areas, especially in older age groups 

with the highest mortality risk.91,CC 

For certain PM2.5-related pollutants (such as direct PM2.5 and NOx), EPA estimates 

different per-ton values for reducing mobile source emissions than for reductions in emissions of 

the same pollutant from stationary sources such as fuel refineries and storage facilities. These 

reflect differences in the typical geographic distributions of emissions of each pollutant by 

 

BB As we discuss in the emissions chapter of EPA’s DRIA (Chapter 4), the rule would yield emission reductions 

from upstream refining and fuel distribution due to decreased petroleum consumption. 

CC For more information about EPA’s population projections, please refer to the following: 

http://www.epa.gov/air/benmap/models/BenMAPManualAppendicesAugust2010.pdf (See Appendix K) 
Chapter 6 

64 

different sources, their contributions to ambient levels of PM2.5, and resulting changes in 

population exposure. EPA applies these separate values to its estimates of changes in emissions 

from vehicle use and from fuel production and distribution to determine the net change in total 

economic damages from emissions of those pollutants. 

The benefit per-ton technique has been used in previous analyses, including the 2012-

2016 Light-Duty Greenhouse Gas Rule,92 the Ozone National Ambient Air Quality Standards 

(NAAQS) RIA,93 the Portland Cement National Emissions Standards for Hazardous Air 

Pollutants (NESHAP) RIA,94 and the final NO2 NAAQS.95 Table 6.3-Error! No text of specified 

style in document.-21 shows the quantified and monetized PM2.5-related co-benefits that are 

captured in these benefit-per-ton estimates, and also lists other effects that remain un-quantified 

and are thus excluded from the estimates. 

Table 6.3-Error! No text of specified style in document.-21 Human Health and Welfare Effects of PM2.5 

Quantified and Monetized 

in Primary Estimates

Un-quantified Effects 

Changes in:

Adult premature mortality 

Bronchitis: chronic and acute

Hospital admissions: respiratory and 

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial 

infarction)

Lower and upper respiratory illness

Minor restricted-activity days

Work loss days

Asthma exacerbations (asthmatic 

population)

Infant mortality

Subchronic bronchitis cases

Low birth weight

Pulmonary function

Chronic respiratory diseases other than 

chronic bronchitis

Non-asthma respiratory emergency room 

visits

Visibility

Household soiling

Consistent with the NO2 NAAQS,DD the benefits estimates utilize concentration-response 

functions as reported in the epidemiology literature. Readers interested in reviewing the 

complete methodology for creating the benefit-per-ton estimates used in this analysis can consult 

the Technical Support Document (TSD)96 accompanying the final ozone NAAQS RIA. Readers 

can also refer to Fann et al. (2009)97 for a detailed description of the benefit-per-ton 

methodology.EE

 

DD Although we summarize the main issues in this chapter, we encourage interested readers to see benefits chapter of 

the NO2 NAAQS for a more detailed description of recent changes to the PM benefits presentation and preference 

for the no-threshold model. 

EE The values included in this report are different from those presented in the article cited above. Benefits methods 

change to reflect new information and evaluation of the science. Since publication of the June 2009 article, EPA has 

made two significant changes to its benefits methods: (1) We no longer assume that a threshold exists in PM-related 

models of health impacts, which is consistent with the findings reported in published research; and (2) We have 

revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an estimate of $5.5 million (year 

2000$) used in the June 2009 report. Please refer to the following website for updates to the dollar-per-ton 

estimates: http://www.epa.gov/air/benmap/bpt.html 

Deleted: 20

Formatted Table
2017 Regulatory Impact Analysis 

65 

As described above, national per-ton estimates were developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., NO2 emitted from mobile 

sources; direct PM emitted from stationary sources). Our estimate of total PM2.5 benefits is 

therefore based on the total direct PM2.5 and PM2.5–related precursor emissions (NOx, SOx, and 

VOCs) controlled from each source and multiplied by the respective per-ton values of reducing 

emissions from that source. 

The benefit-per-ton coefficients in this analysis were derived using modified versions of 

the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, 

this analysis uses the benefit-per-ton estimates first applied in the Portland Cement NESHAP 

RIA, which incorporated concentration-response functions directly from the epidemiology 

studies, without any adjustment for an assumed threshold. Removing the threshold assumption is 

a key difference between the method used in this analysis to estimate PM co-benefits and the 

methods used in analyses prior to EPA’s Portland Cement NESHAP.FF As a consequence, the 

benefit-per-ton estimates used in this analysis include incremental benefits of reductions in PM2.5 

concentrations down to their lowest modeled levels. This approach is also consistent with EPA’s 

analysis of the 2012-2016 Light-Duty Vehicle Greenhouse Gas rule. 

Reductions in PM-related mortality provide the majority of the monetized value in each 

benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients that 

underlie the benefits-per-ton estimates are drawn from epidemiology studies that examine two 

large population cohorts: the American Cancer Society cohort (Pope et al., 2002)98 and the 

Harvard Six Cities cohort (Laden et al., 2006).99 The concentration-response (C-R) function 

developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in 

Pope et al. (2002), has previously been used by EPA to generate its primary benefits estimate. 

The extended analysis of the Harvard Six Cities cohort, as reported by Laden et al (2006), was 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS and has been used 

as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 co-benefits estimates in analyses 

completed since the PM2.5 NAAQS. 

These studies provide logical choices for co-equal anchor points when presenting PMrelated benefits because, while both studies are well designed and peer-reviewed, there are 

strengths and weaknesses inherent in each. Although EPA’s primary method of characterizing 

 

FF Based on a review of the current body of scientific literature, EPA estimates PM-related mortality without 

applying an assumed concentration threshold. EPA‘s Integrated Science Assessment for Particulate Matter (U.S. 

Environmental Protection Agency. 2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA600-R-08-139F. National Center for Environmental Assessment – RTP Division. December), which was reviewed 

by EPA‘s Clean Air Scientific Advisory Committee (U.S. Environmental Protection Agency - Science Advisory 

Board. 2009. Review of EPA‘s Integrated Science Assessment for Particulate Matter (First External Review Draft, 

December 2008). EPA-COUNCIL-09-008. May.; U.S. Environmental Protection Agency Science Advisory Board . 

2009. 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), concluded that the scientific literature 

consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentrationresponse relationship while recognizing potential uncertainty about the exact shape of the concentration-response 

function. This assumption is incorporated into the calculation of the PM-related benefits-per-ton values. 
Chapter 6 

66 

PM-related premature mortality is to use both studies to generate a co-equal range of benefits 

estimates, EPA has chosen to present only the benefit-per-ton value derived from the ACS study 

in its summary tables of total Model Year costs and benefits (See RIA Chapter 7). This decision 

was made to provide the reader with summary tables that are easier to understand and interpret 

and does not convey any preference for one study over the other. We note that this is also the 

more conservative of the two estimates - PM-related benefits would be approximately 245 

percent (or nearly two-and-a-half times) larger had we used the per-ton benefit values based on 

the Harvard Six Cities study instead. See RIA Chapter 7.3 for the monetized PM-related health 

impacts of the Model Year analysis. 

As is the nature of benefits analyses, assumptions and methods evolve over time to reflect 

the most current interpretation of the scientific and economic literature. For a period of time 

(2004-2008), EPA’s 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)100 meta-analysis of 33 studies. The $10 million value represented the upper 

end of the interquartile range from the Viscusi and Aldy (2003)101 meta-analysis of 43 studies. 

The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 

million estimated in the Kochi et al. (2006)102 meta-analysis. However, the Agency neither 

changed its official guidance on the use of VSL in rulemakings nor subjected the interim estimate 

to a scientific peer-review process through the Science Advisory Board (SAB) or other peerreview group. 

Until updated guidance is available, EPA determined that a single, peer-reviewed estimate 

applied consistently best reflects the Science Advisory Board Environmental Economics 

Advisory Committee (SAB-EEAC) advice it has received. Therefore, EPA 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)103 while they continue efforts to update their guidance on this 

issue.GG 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 $6.3 million (2000$). The dollar-per-ton estimates used in this analysis are 

based on this revised VSL.HH

The benefit-per-ton estimates are subject to a number of assumptions and uncertainties. 

 

GG In the update of the Economic Guidelines (U.S. EPA, 2011), 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. 

The update of the Economic Guidelines is available on the Internet at 

http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf. 

HH This value differs from the Department of Transportation’s most recent estimate of the value of preventing 

transportation-related fatalities, which is $6.1 million when expressed in today’s (2011) dollars. 
2017 Regulatory Impact Analysis 

67 

• They do not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an overestimate or 

underestimate of the actual benefits of controlling fine particulates in specific locations. 

Please refer to Chapter 6.3.1 for the description of the agency’s quantification and 

monetization of PM- and ozone-related health impacts for the final standards. 

• This analysis assumes 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 stationary sources may differ 

significantly from direct PM2.5 released from engines and other industrial sources. At the 

present time, however, no clear scientific grounds exist for supporting differential effects 

estimates by particle type. 

• This analysis assumes 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 initial 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. 

• There are several health benefits categories that EPA was unable to quantify due to 

limitations associated with using benefits-per-ton estimates, several of which could be 

substantial. Because NOX and VOC emissions are also precursors to ozone, changes in 

NOX and VOC would also impact ozone formation and the health effects associated with 

ozone exposure. Benefits-per-ton estimates for ozone do not exist due to issues 

associated with the complexity of the atmospheric air chemistry and nonlinearities 

associated with ozone formation. The PM-related benefits-per-ton estimates also do not 

include any human welfare or ecological benefits. Please refer to Chapter 6.3.1 for a 

description of the unquantified co-pollutant benefits associated with this rulemaking. 

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as the localized impacts 

associated with the rulemaking may vary significantly. Additionally, the atmospheric chemistry 

related to ambient concentrations of PM2.5, ozone and air toxics is very complex. Full-scale

photochemical modeling is therefore necessary to provide the needed spatial and temporal detail 

to more completely and accurately estimate the changes in ambient levels of these pollutants and 

their associated health and welfare impacts. For this final rule, EPA conducted a national-scale 

air quality modeling analysis for 2030 to analyze the impacts of the standards on PM2.5, ozone, 

and selected air toxics. 

1

 CMAS 2008. Release Notes for CMAQ v4.7. 

http://www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt Docket EPA-HQ-OAR2010-0162.

2

 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

Available at: http://www.epa.gov/airtrends/2011/. 

3

 77FR 30088 (May 21, 2012)

4

 U.S. EPA. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). U.S. EPA, 

Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docket EPA-HQ-OAR-2010-0162.

5

 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

Available at: http://www.epa.gov/airtrends/2011/. 

Deleted: .
Chapter 6 

68 

6

 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 2012. 

Available at: http://www.epa.gov/airtrends/2011/. 

7 U.S. EPA. (2007). PM2.5 National Ambient Air Quality Standard Implementation Rule (Final). Washington, DC: 

U.S. EPA. 72 FR 20586, April 25, 2007. Docket EPA-HQ-OAR-2011-0135. 

8 PM Standards Revision – 2006: Timeline. Docket EPA-HQ-OAR-2011-0135. 

http://www.epa.gov/PM/naaqsrev2006.html#timeline Docket EPA-HQ-OAR-2011-0135.

9

 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment. 

http://www.epa.gov/ttn/atw/nata2005/05pdf/sum_results.pdf. 

10 Control of Hazardous Air Pollutants From Mobile Sources (72 FR 8428; February 26, 2007) 

11 US EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis. EPA

document number 420-R-07-002, February 2007. 

12 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ-OAR-2011-0135. 

13 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final 

Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ-OAR-2011-0135. 

14 U.S. EPA. (2011) 2005 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005/. Docket 

EPA-HQ-OAR-2011-0135 

15 U.S. EPA. (2011) Summary of Results for the 2005 National-Scale Assessment. 

http://www.epa.gov/ttn/atw/nata2005/05pdf/sum_results.pdf. Docket EPA-HQ-OAR-2011-0135. 

16 U.S. EPA. (2012). U.S. EPA’s Report on the Environment. Data accessed online February 15, 2012 at: 

http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listByC

hapter&r=216610 and contained in Docket EPA-HQ-OAR-2011-0135. 

17 U.S. EPA. (2012). U.S. EPA's Report on the Environment. Data accessed online February 15, 2012 at: 

http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listByC

hapter&r=216610 and contained in Docket EPA-HQ-OAR-2011-0135. 

18 US EPA (2009) 

19 Trijonis et al. 1987 

20 Trijonis et al. 1988 

21 Irving, 1991 

22 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for 

the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: 

Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html. 

EPA-HQ-OAR-2009-0472-0240 

23 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact 

Analysis. Prepared by: Office of Air and Radiation, Office of Air Quality Planning and 

Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html. EPA-HQ-OAR2009-0472-0238 

24 U.S. EPA, (2011), Final Transport Rule. Final Rule signed on July 6, 2011. Available online at: 

http://www.epa.gov/airtransport/ 

25 U.S. Environmental Protection Agency. (2010). Final Rulemaking to Establish Light-Duty 

Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards: 

Regulatory Impact Analysis, Assessment and Standards Division, Office of Transportation and 
2017 Regulatory Impact Analysis 

69 

Air Quality, EPA-420-R-10-009, April 2010. Available on the internet: 

http://www.epa.gov/otaq/climate/regulations/420r10009.pdf 

26 U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: 

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Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. Augues. Available on the Internet at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf >. EPA-HQ-OAR-2009-

0472-0241 

27 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. 

28 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics 

Assessment for 1996 – an SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabrev.html. 

29 U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean 

Air Act from 1990 to 2020. Office of Air and Radiation, Washington, DC. March. Available on 

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30 U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. 

Benefits of Reducing Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. July. 

Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/

EPA-COUNCIL-08-001-unsigned.pdf>. 

31 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic 

Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 

32 GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 

1.0. GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 

September 2004] 

33 Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM. 

Woods & Poole Economics, Inc. Washington, D.C. EPA-HQ-OAR-2009-0472-0011 

34 U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related 

photochemical oxidants (second external review draft). Research Triangle Park, NC: National 

Center for Environmental Assessment; report no. EPA/600R-05/004aB-cB, 3v. Available: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006] EPA-HQ-OAR2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, EPA-HQ-OAR-2009-0472-0101 

35 U.S. Environmental Protection Agency, 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/P99/002bF. EPA-HQ-OAR-2009-0472-0097 
Chapter 6 

70 

36 World Health Organization (WHO). (2003). Health Aspects of Air Pollution with Particulate 

Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. World Health 

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37 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K. (2004). Meta-analysis 

of time-series studies and panel studies of Particulate Matter (PM) and Ozone (O3): Report of a 

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38 Bell, M.L., et al. (2004). Ozone and short-term mortality in 95 U.S. urban communities, 1987-

2000. JAMA, 2004. 292(19): p. 2372-8. EPA-HQ-OAR-2009-0472-1662 

39 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical distributed lag models for 

summer ozone exposure and cardio-respiratory mortality. Environmetrics. 16: 547-562. EPAHQ-OAR-2009-0472-0233 

40 Schwartz, J. (2005) How sensitive is the association between ozone and daily deaths to control 

for temperature? Am. J. Respir. Crit. Care Med. 171: 627-631. EPA-HQ-OAR-2009-0472-1678 

41 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. EPA-HQ-OAR-2009-0472-0222 

42 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. EPA-HQ-OAR-2009-

0472-0231 

43 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. EPA-HQ-OAR-2009-

0472-0236 

44 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. EPA-HQ-OAR2009-0472-0263 

45 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. EPA-HQ-OAR-2009-0472-

1661

46 Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of 

the Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Peer Review 

Draft. Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. August. EPA-HQ-OAR-2009-0472-0242 

47 Woodruff, T.J., J. Grillo, and K.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. EPA-HQ-OAR-2009-0472-0382 
2017 Regulatory Impact Analysis 

71 

48 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. EPA-HQ-OAR-2009-0472-0432 

49 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. EPA-HQ-OAR-2009-0472-0239 

50 Schwartz J. (1995). Short term fluctuations in air pollution and hospital admissions of the 

elderly for respiratory disease. Thorax. 50(5):531-538. 

51 Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis 

St Paul, Minnesota. Arch Environ Health. 49(5):366-374. EPA-HQ-OAR-2009-0472-1673 

52 Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, 

Michigan. Am J Respir Crit Care Med. 150(3):648-655. EPA-HQ-OAR-2009-0472-1674 

53 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. EPAHQ-OAR-2009-0472-1673 

54 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. EPA-HQ-OAR-2009-0472-0223 

55 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. 

56 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. EPA-HQ-OAR-2009-0472-1674 

57 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. EPA-HQ-OAR-2009-0472-1664 

58 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. EPA-HQ-OAR-2009-0472-0318 

59 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air pollution and respiratory 

emergency department visits. Epidemiology. Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472-

1663 

60 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. EPA-HQ-OAR-2009-0472-0246 

61 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. EPA-HQ-OAR-2009-0472-0318 
Chapter 6 

72 

62 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. EPA-HQ-OAR-2009-0472-0225 

63 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health 

and PM10 Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases

144:668-674. EPA-HQ-OAR-2009-0472-1672 

64 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. 

65 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. 

66 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. EPAHQ-OAR-2009-0472-1671 

67 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental 

Economics Management 14:87-98. EPA-HQ-OAR-2009-0472-1670 

68 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(1):43-54. EPA-HQ-OAR-2009-0472-1675 

69 Chen L, Jennison BL, Yang W, Omaye ST. (2000). Elementary school absenteeism and air 

pollution. Inhal Toxicol 12(11):997-1016. EPA-HQ-OAR-2009-0472-0224 

70 Ostro, B.D. and S. Rothschild. (1989). Air Pollution and Acute Respiratory Morbidity: An Observational Study 

of Multiple Pollutants. Environmental Research 50:238-247. EPA-HQ-OAR-2009-0472-0364 

71 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. EPA-HQ-OAR-2009-0472-4664 

72 National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed 

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

73 Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate Projections. Memorandum to 

Bryan Hubbell and Zachary Pekar, U.S. EPA. 

74 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 <http://wonder.cdc.gov>. 

75 Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and 

Utilization Project. 

76 American Lung Association. 1999. Chronic Bronchitis. Available on the Internet at 

<http://www.lungusa.org/diseases/lungchronic.html>.
2017 Regulatory Impact Analysis 

73 

77 American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American 

Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit. 

Available on the Internet at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>. 

78 Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health 

Interview Survey, 1996. Vital Health Stat 10(200):1-212. 

79 U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race, 

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Division, U.S. Census Bureau, Washington, DC. Available on the Internet at 

<http://www.census.gov/population/projections/nation/summary/np-t.txt>. 

80 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. 

81 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 <http://wonder.cdc.gov>. 

82 Freeman(III), AM. 1993. The Measurement of Environmental and Resource Values: Theory 

and Methods. Washington, DC: Resources for the Future. 

83 Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health and Safety Regulation. 

Journal of Urban Economics 22:101-112. 

84 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. 

85 U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 

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Planning and Standards, Research Triangle Park, NC. October. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf>. 

86 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 

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Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. 

87 U.S. EPA, (2001), “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 

and Regional Haze”, http://www.epa.gov/ttn/scram/guidance_sip.htm, Modeling Guidance, 

DRAFT-PM 

88 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed 

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

89 National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic 

Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. 
Chapter 6 

74 

90 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. 

91 The issue is discussed in more detail in the PM NAAQS RIA from 2006. See U.S. 

Environmental Protection Agency. 2006. Final Regulatory Impact Analysis (RIA) for the 

Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of 

Air and Radiation. October 2006. Available at http://www.epa.gov/ttn/ecas/ria.html. 

92 U.S. Environmental Protection Agency (U.S. EPA), 2010. Regulatory Impact Analysis, Final 

Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate 

Average Fuel Economy Standards. Office of Transportation and Air Quality. April. Available 

at http://www.epa.gov/otaq/climate/regulations/420r10009.pdf. EPA-420-R-10-009 

93 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. EPA-HQ-OAR-2009-

0472-0238 

94 U.S. Environmental Protection Agency (U.S. EPA). 2010. 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. Augues. Available on the Internet at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf >. EPA-HQ-OAR-2009-

0472-0241 

95 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory 

Impact Analysis (RIA). Office of Air Quality Planning and Standards, Research Triangle Park, 

NC. April. Available on the Internet at 

http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 15, 

2010. EPA-HQ-OAR-2009-0472-0237 

96 U.S. Environmental Protection Agency (U.S. EPA). 2008. 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 <http://www.regulations.gov>. EPA-HQ-OAR-2009-0472-0228 

97 Fann, N. et al. (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. Published 

online: 09 June, 2009. EPA-HQ-OAR-2009-0472-0229 

98 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. EPA-HQ-OAR2009-0472-0263 

99 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 
2017 Regulatory Impact Analysis 

75 

173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, DC: The National Academies Press. EPA-HQ-OAR-2009-0472-1661 

100 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A MetaAnalysis.” Journal of Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-

0472-1677 

101 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(1):5-76. EPA-HQOAR-2009-0472-0245 

102 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis. Environmental and 

Resource Economics. 34: 385-406. EPA-HQ-OAR-2009-0472-0235 

103 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 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. EPA-HQOAR-2009-0472-0226 
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