For the sales test, we divided the representative establishment compliance costs reported in Table 6-6 by the representative establishment receipts reported in Tables 6-2 and 6-3. This is known as the cost-to-receipt (i.e., sales) ratio, or the "sales test." The "sales test" is the impact methodology EPA employs in analyzing small entity impacts as opposed to a "profits test," in which annualized compliance costs are calculated as a share of profits.
      Information on annual revenues or sales is more commonly available data for entities normally affected by EPA regulations, and profits data normally made available are often not the true profit earned by firms because of accounting and tax considerations. Revenues as typically published are usually correct figures and are more reliably reported when compared with profit data. The use of a "sales test" for estimating small business impacts for a rulemaking such as this

Figure 6-1.	Population of Firebox Models and Average Models per Establishment by Product Type

one is consistent with guidance offered by EPA on compliance with SBREFA and is consistent with guidance published by the SBA's Office of Advocacy that suggests that cost as a percentage of total revenues is a metric for evaluating cost increases on small entities in relation to increases on large entities (SBA, 2003). The annualized cost per sales for a company represents the maximum price increase in affected product needed for the company to completely recover the annualized costs imposed by the regulation.

Figure 6-2.	Distribution of National Compliance Costs by Product Type in 2014 to 2022

For purposes of this analysis, EPA assumes most small entities in the residential wood heating industry are likely to manufacture fewer than three distinctive firebox models and in many cases they would support only one model. We assume for this analysis that most small entities in this industry will manufacture an average of two distinctive firebox models. Hence, EPA believes that the estimate in Table 6-6 above is the most representative establishment costs to assess impacts on small businesses. If the cost-to-receipt ratio is less than 1%, then we consider the rule to not have a significant impact on the establishment (and, company) in question. Table 6-7 presents the cost-to-receipt ratios for each category of establishments (establishments with ratios that exceed 1% under each case are highlighted) for the proposal option. Table 6-8 present these ratios for the alternative option.

Table 6-7.	Cost-to-Receipt Ratio Results for the Proposal Option by NAICS Code[a]
NAICS
Description
All Establishments
Fewer than 20 Employees
22 to 99 Employees
100 to 499 Employees
332510
Hardware manufacturing
                                     3.12%
                                    19.74%
                                     3.40%
                                     0.99%
333414
Heating equipment manufacturing
                                     1.89%
                                    20.73%
                                     2.34%
                                     0.67%
423720
Plumbing and heating equipment wholesalers
                                     3.50%
                                    10.15%
                                     2.71%
                                     2.43%

NAICS
Description
Total
Less than 100K Receipts
100 to 499K Receipts
500 to 999K Receipts
1,000 to 4,999K Receipts
5,000 to 9,999K Receipts
10,000 to 49,999K Receipts
236115
New single-family general contractors
7.18%
                                    231.81%
                                    43.78%
                                    16.39%
                                     5.62%
                                     1.78%
                                     0.69%
238140
Masonry contractors
11.59%
                                    232.50%
                                    47.70%
                                    16.54%
                                     5.43%
                                     1.72%
                                     0.74%
442299
All other home furnishing stores
7.86%
                                    224.82%
                                    46.00%
                                    17.02%
                                     7.47%
                                     3.84%

[a]	All the cost to receipts results incorporate costs that are primarily R&D activities that firms will engage in to build appliance models that comply with the options analyzed in this RIA. The R&D cycle is estimated at 6 years, while the appliance life for all affected categories is 20 years.
Table 6-8.	Cost-to-Receipt Ratio Results for the Alternative Option by NAICS Code[a]
NAICS
Description
All Establishments
Fewer than 20 Employees
22 to 99 Employees
100 to 499 Employees
332510
Hardware manufacturing
                                     5.22%
                                    33.05%
                                     5.69%
                                     1.66%
333414
Heating equipment manufacturing
                                     3.17%
                                    34.70%
                                     3.92%
                                     1.12%
423720
Plumbing and heating equipment wholesalers
                                     5.87%
                                    17.00%
                                     4.54%
                                     4.07%

NAICS
Description
Total
Less than 100K Receipts
100 to 499K Receipts
500 to 999K Receipts
1,000 to 4,999K Receipts
5,000 to 9,999K Receipts
10,000 to 49,999K Receipts
236115
New single-family general contractors
                                    12.03%
                                    389.44%
                                    73.55%
                                    27.54%
                                     9.44%
                                     2.99%
                                     1.16%
238140
Masonry contractors
                                    19.47%
                                    390.60%
                                    80.14%
                                    27.79%
                                     9.12%
                                     2.89%
                                     1.24%
442299
All other home furnishing stores
                                    13.20%
                                    377.70%
                                    77.28%
                                    27.23%
                                    13.00%
                                     6.45%

[a]	All the cost to receipts results incorporate costs that are primarily R&D activities that firms will engage in to build appliance models that comply with the options analyzed in this RIA. The R&D cycle is estimated at 6 years, while the appliance life for all affected categories is 20 years.

Analysis Results
In our small entity analysis for the proposed option, using an annual compliance cost of $127,000 as the estimated cost borne by affected small entities, establishments in NAICS 332510, 333414, and 423720 with fewer than 500 employees have cost-to-receipt ratios higher than 1%. Establishments in NAICS 236115, 238140, and 442299 with receipts less than $10 million have cost-to-receipt ratios higher than 1%. For the alternative option, the impacts are higher than 1% for all establishments in any size class.
After considering the economic impacts of this proposed rule on small entities, we cannot certify that this action will not have a significant economic impact on a substantial number of small entities. This certification is based on the economic impact of this action to all affected small entities across all industries affected. Using the estimate of impacts presented earlier in this chapter, we estimate that all small entities will have annualized costs of greater than 1% of their sales in all industries with fewer than 20 employees and NAICS 236115, 238140, and 442299 with receipts less than $10 million. Those establishments in NAICS 332510, 333414, and 423720 with cost-to-receipt ratios higher than 1% account for more than 80% of small entities. Establishments in NAICS 236115, 238140, and 442299 with cost-to-receipt ratios higher than 1% account for more than 99% of small entities. Small entity impacts are higher than this for the alternative option. We thus conclude that we cannot certify that there is not a significant economic impact on a substantial number of small entities (SISNOSE) for this rule.
It should be noted that the cost to receipts analysis included in this RIA reflect the large majority of annualized costs that are composed of research and development (R&D) activities (nearly 90%, based on the manufacturers' cost memorandum) that have a shorter life than the total life of affected appliances -- six years for an R&D cycle as mentioned in section 5 of this RIA compared to 20 years for the life of affected appliances. The impacts on small entities should be understood in the context that a large share of the estimated annualized costs reflect expenses in the early years of the appliance life, and only a small share recurs each year over the entire appliance life of 20 years. In addition, the application of these costs for the options analyzed in this RIA will lead to a conservative (or, over-stated) cost estimate as stated previously in section 5.1.3. One example of this is that all hydronic heaters will undergo R&D beginning in 2013 to comply with either option analyzed; there is a small percentage of hydronic heaters that already meet the emission limits under each option considered in the RIA. Similar assumptions are also made in the cost estimates for single burn rate stoves and forced air furnaces. Given these two considerations, the costs that are included in the cost to sales analyses presented in this RIA are conservative in nature, and the cost to receipts impacts shown above should be understood in that context.
A sensitivity analysis showing the effect of the R&D cycle lifespan on the cost to sales estimates for each option is below.   Table 6-9 shows how the model firebox costs that are input to the cost to sales (and small business) analyses will change with changes to the R&D cycle lifespan.
    	Table 6-9.  Total Annual Cost (TAC) per Appliance Model  -  for Varying 	Annualized R&D Cycle Lifespans

Annualized R&D Cycle Lifespan (Years)
TAC/Appliance Model (2010$)
TAC per Establishment
6*
$63,850
$127,700
10
40,022
80,044
20
22,418
44,836
*As mentioned in Section 5 of this RIA, six years represents the annualized R&D cycle lifespan incorporated in the cost estimates for the Proposed RWH NSPS rule options.

	The total annual cost per appliance model is the value of costs included in the cost to sales estimates to calculate these values.  Each small entity is expected to modify 2 appliance models on average in order to comply with the proposal, an assumption we estimated earlier in this section of the RIA.  We assume each small entity owns only one establishment (or place of business).  Hence, the total annual cost to each small entity is twice the cost per appliance model.  The annual cost to sales in the RIA will change proportionately to a change in the TAC since the sales estimates in the analysis will remain constant.
As shown in Table 6-9, with an increase in the R&D cycle lifespan from 6 to 10 years, the TAC/appliance model estimate falls to $40,022 from $63,850.  The new TAC/appliance model estimate is 37% less than before.  Thus, the cost to sales estimates will fall by 37% from the previous values. Finally, if the R&D cycle lifespan is reduced to 4 years, the TAC/appliance model estimate increases to $93,755 from $63,850.  The new TAC/appliance model estimate is now 65% lower than before.  Thus, the cost to sales estimates will decrease by 65% from the values that use an R&D cycle lifespan of 6 years.
Table 6-10 contains estimates of the changes in the cost to sales estimates for the Proposed option with an increase in the R&D cycle lifespan to 10 and 20 years from 6 years. The estimates with the change in R&D cycle lifespan to 10 and 20 years are in parentheses; the other values are those for the Proposed option.

Table 6-10.  Cost-to-Sales Ratio Sensitivity Analysis Results Reflecting Different R&D Cycle Lifespans for the Proposed Option by NAICS Code*

NAICS
Industry Description
All Establishments
Establishments with Fewer than 20 Employees (%)
Establishments with Between 20 and 99 Employees (%)
Establishments with Between 100 and 499 Employees (%)
332510
Hardware manufacturing
3.12 (1.97, 1.09)
19.74 (12.44, 6.91)
3.40 (2.14, 1.19)
0.99 (0.62, 0.35)
333414
Heating equipment manufacturing
1.89 (1.19, 0.66)
20.73 (12.83, 7.26)
2.34(1.47, 0.82)
0.67 (0.42, 0.23)
423720
Plumbing and heating equipment wholesalers
3.50 (2.21, 1.23)
10.15 (6.39, 3.55)
2.71(1.71, 0.95)
2.43 (1.53, 0.85)
         * The first value in parentheses is the cost to sales estimate for a 10 year R&D cycle lifespan; the second value is the cost to sales estimates for a 20 year R&D cycle lifespan.

Initial Regulatory Flexibility Analysis
An IRFA illustrates how EPA considers the proposed rule's small entity effects before a rule is finalized and provides information about how the objectives of the rule were achieved while minimizing significant economic impacts on small entities. We provide a summary of IRFA elements; the preamble for this rule provides additional details.
Reasons Why Action Is Being Considered
These proposals were developed following a Clean Air Act (CAA) section 111(b)(1)(B) periodic review of the existing residential wood heater new source performance standards (NSPS).
Statement of Objectives and Legal Basis of Proposed Rule
The EPA is proposing to amend Standards of Performance for New Residential Wood Heaters, and to add two new subparts: Standards of Performance for New Residential Hydronic Heaters and Forced-Air Furnaces and Standards of Performance for New Residential Masonry Heaters. These proposals are aimed at achieving several objectives, including applying tighter emission limits that reflect today's best systems of emission reduction (BSER); eliminating exemptions over a broad suite of residential wood combustion devices; revising test methods as appropriate; and streamlining the certification process. These proposals do not include any requirements on heaters that are solely fired by gas or oil.
These proposals were developed following a Clean Air Act (CAA) section 111(b)(1)(B) periodic review of the existing residential wood heater new source performance standards (NSPS). We concur with numerous stakeholders that the current body of evidence justifies that the periodic review and revision of the current residential wood heaters NSPS are needed to capture the improvements in performance of such units and to expand the applicability of this NSPS to include additional wood-burning residential heating devices. The changes being proposed with this action are aimed at achieving several objectives, including applying tighter emission limits that reflect today's best systems of emission reduction; eliminating exemptions over a broad suite of residential wood combustion devices; revising test methods as appropriate; and streamlining the certification process.
Description and Estimate of the Number of Small Entities
Small entities that EPA anticipates being affected by the standards would include almost all manufacturers of wood heaters listed in Section 2.2 of this document. EPA estimates that roughly 250 - 300 U.S. companies manufacture residential wood heaters. EPA believes that approximately 90% of these manufacturers meet the SBA small-entity definition of having fewer than 500 employees.
Description and Compliance Costs
A discussion of the methodology used to estimate cost impacts is presented in Section 5 of this RIA.
As required by section 609(b) of the RFA, as amended by SBREFA, EPA has conducted outreach to small entities and convened a SBAR Panel to obtain advice and recommendation of representatives of the small entities potentially subject to the requirements of this rule. On August 4, 2010, EPA's Small Business Advocacy Chairperson convened a Panel under section 609(b) of the RFA. In addition to the Chair, the Panel consisted of representatives of the Director of the Outreach and Information Division within EPA's Office of Air and Radiation, the Chief Counsel for Advocacy of the SBA, and the Administrator of the Office of Information and Regulatory Affairs within the Office of Management and Budget.
Based on consultations with the SBA, and resulting from solicited self-nominations, we prepared a list of 30 potential small entity representatives (SERs), from residential wood heating appliance manufacturers (wood stoves, pellet stoves, hydronic heaters, forced-air furnaces, and masonry heaters), other wood burning appliance manufacturers (fireplaces, cook stoves), equipment suppliers, chimney sweeps, test laboratories, masons, and trade associations. Once the pre-Panel process began and potential SERs were identified, EPA held an outreach meeting with the potential SERs and invited representatives from SBA's Office of Advocacy and the Office of Information and Regulatory Affairs within the Office of Management and Budget on June 29, 2010, to solicit their feedback on the upcoming proposed rulemaking. Representatives from 26 of the 30 companies and organizations that we selected as potential SERs for this SBREFA process participated in the meeting (in person and by phone). At that meeting EPA solicited written comments from the potential SERs, which were later summarized and shared with the Panel as part of the Panel convening document.
After the SBAR Panel was convened, the Panel distributed additional information to the SERs on August 11 and August 19, 2010, for their review and comment and in preparation for another outreach meeting. On August 25, 2010, the Panel met with the SERs to hear their comments on the information distributed via email. The Panel received written comments from the SERs in response to the discussions at this meeting and the outreach materials. The Panel asked the SERs to evaluate how they would be affected and to provide advice and recommendations regarding early ideas to provide flexibility.
Many of the SERs and the Panel had concerns about the breadth of the potential options discussed for this rulemaking and the challenges EPA would face in potentially conducting rulemaking for all of these source categories at one time and the challenges that the small businesses would face in having to potentially comply with standards for all of these source categories at one time. The Panel recommended that EPA should consider focusing efforts first on emissions sources that have the greatest potential to impact public health through the magnitude of emissions and population exposure. The EPA has narrowed the scope of this proposal to focus on the sources with the greatest potential impacts on public health. The Panel was sensitive to the need to carefully develop a rule that will minimize business closures, while still achieving significant emission reductions.
Panel Recommendations for Small Business Flexibilities
The Panel recommended that EPA consider and seek comment on an extensive range of regulatory alternatives to mitigate the impacts of the rulemaking on small businesses, including the options listed below. The following section summarizes the SBAR Panel recommendations.
Consistent with the RFA/SBREFA requirements, the Panel evaluated the assembled materials and comments related to elements of the IRFA. A copy of the Final Panel Report (including all comments received from SERs in response to the Panel's outreach meetings), as well as summaries of both outreach meetings that were held with the SERs, is included in the docket for the proposed rules. The following paragraphs are a subset of the full report.
The Panel encouraged EPA to consider flexibilities that will most directly minimize the small business burdens: Exemptions from the standards based on very low volume production, and delayed compliance dates for low volume production. The delayed compliance approach is predicated on the concept that it will take a number of years for manufacturers to recover the costs of the R&D investment in order to achieve compliance.
The Panel recommended that the EPA Administrator should consider the availability and feasibility of certification, testing labs, testing standards, and other requirements.
The Panel recommended that the EPA Administrator should consider emphasizing that the NSPS will address only new units, and the EPA Administrator should consider clarifying whether exemptions will be considered for historic replica equipment and historic property renovations.
EPA is looking at opportunities for reducing the burden on small entities of potential reporting, record keeping, and compliance requirements. For reporting and record keeping requirements in the revised NSPS, EPA is considering providing flexibilities similar to those in the 1988 NSPS. For example, the Panel recommended that EPA continue allowing manufacturers to keep records and report test results for a representative model appliance rather than testing and reporting results for each individual unit.
Many SERs expressed concern about potential compliance requirements associated with the planned proposed standards. Specifically, SERs anticipated potential logjams at third-party testing facilities as a result of EPA's regulating a broader range of product categories, which the SERs believe will slow down the certification process. In addition, many SERs are concerned about the costs associated with compliance requirements, including research and development, preliminary testing and certification of new products and recertification of products approved under the 1988 NSPS. The Panel recommended that EPA consider ways to streamline compliance certification, in particular, identifying flexible approaches and procedures that will reduce the burden and time for manufacturers to complete the application, testing and approval process for new model lines. For example, the Panel recommended that EPA consider allowing the use of International Standards Organization (ISO)-accredited laboratories and certifying bodies to expand the number of facilities that would be required for testing and certification of the new residential solid biomass combustion appliances. Additionally, the Panel recommended that EPA consider different compliance time frames for different product categories to reduce the potential for logjams at test labs and the overall impact on companies that manufacture multiple categories. Flexible compliance schedules would also help manufacturers of additional new appliances, such as hydronic heaters and forced-air furnaces, which were not subject to the 1988 standards.
Consistent with the RFA/SBREFA requirements, the Panel evaluated the assembled materials and small-entity comments on issues related to elements of the IRFA. A copy of the Panel report is included in the docket for this proposed rule. We invite comments on all aspects of the proposal and its impacts on small entities.

Human Health Benefits of Emissions Reductions
Synopsis
Implementation of emissions limits required by the proposed residential wood heaters NSPS is expected to reduce direct emissions of PM2.5. These reductions result from the imposition of tightened and new PM emissions limits for a number of emissions categories as described in Section 2 of this RIA. In this section, we quantify the monetized benefits for this rule associated with reduced exposure to ambient fine particulate matter (PM2.5) resulting from the reduction of direct emissions of PM2.5. The total PM2.5 reductions are the consequence of the expected design changes to the affected appliances needed in order to meet the limits in the options analyzed in this RIA. We estimate the total monetized benefits for the proposed option to be $1.8 billion to $4.2 billion at a 3% discount rate and $1.7 billion to $3.8 billion at a 7% discount rate on a yearly average between 2014 and 2022. For the alternative option and same time frame, we estimate that the total monetized benefits are $1.9 billion to $4.2 billion at a 3% discount rate and $1.7 billion to $3.8 billion at a 7% discount rate. All estimates are in 2010$. These estimates reflect the monetized human health benefits of reducing cases of morbidity and premature mortality among populations exposed to PM2.5 reduced by this rule.
	Data, resources, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories. Included among the nonmonetized benefits are those associated with reduced exposure to about 3,200 tons of VOCs. VOCs are also precursors to ozone formation and therefore reducing health impact due to ozone exposure. Further, this rule would reduce each year 33,000 tons of CO, black carbon emissions, several HAP emissions such as benzene, formaldehyde, and dioxin. This rule will also reduce ecosystem effects, and visibility impairment due to PM emissions.
PM2.5-Related Human Health Benefits
This rule is expected to reduce direct emissions of PM and emissions of VOCs, which are precursors to formation of ambient PM2.5. Therefore, reducing these emissions would also reduce human exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In this section, we provide an overview of the PM2.5-related benefits. A full description of the underlying data, studies, and assumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a).
In implementing this rule, emission controls may lead to reductions in ambient PM2.5 concentrations below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIA (U.S. EPA, 2012a) also calculated PM benefits, there are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide based on an array of emission reduction strategies for different sources including known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and the estimated costs and benefits are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. However, it is possible that some costs and benefits associated with the required emission controls estimated in this RIA may account for the same air quality improvements as estimated in the illustrative PM NAAQS RIA.
By contrast, the emission reductions for implementation rules such as this rulemaking are generally for specific, well-characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for implementation rules. As such, emission reductions achieved under these and other promulgated implementation rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated with attaining revised future NAAQS. EPA remains forward looking towards the next iteration of the 5-year review cycle for the NAAQS. As a result, EPA does not re-issue NAAQS RIAs that retroactively update the baseline to account for implementation rules promulgated after a NAAQS RIA outside of the NAAQS review process. For more information on the relationship between the NAAQS and rules that are not ambient standards, such as analyzed here, please see Section 1.3 of the PM NAAQS RIA (U.S. EPA, 2012a).
Health Impact Assessment
The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009) identified the human health effects associated with ambient PM2.5, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Table 7-1 provides the quantified and unquantified benefits captured in EPA's benefits estimates for reduced exposure to ambient PM2.5. Although the table below does not include entries for the unquantified health effects such as exposure to ozone and NO2 nor welfare effects such as ecosystem effects and visibility impairment, these effects are itemized in Chapters 5 and 6 of the
Table 7-1.	Human Health Effects of Ambient PM2.5
Category
Specific Effect
Effect Has Been Quantified
Effect Has Been Monetized
More Information in PM NAAQS RIA
Improved Human Health
Reduced incidence of premature mortality from exposure to PM2.5
Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)

Section 5.6

Infant mortality (age <1)

Section 5.6
Reduced incidence of morbidity from exposure to PM2.5
Non-fatal heart attacks (age > 18)

Section 5.6

Hospital admissions -- respiratory (all ages)

Section 5.6

Hospital admissions -- cardiovascular (age >20)

Section 5.6

Emergency room visits for asthma (all ages)

Section 5.6

Acute bronchitis (age 8 - 12)

Section 5.6

Lower respiratory symptoms (age 7 - 14)

Section 5.6

Upper respiratory symptoms (asthmatics age 9 - 11)

Section 5.6

Asthma exacerbation (asthmatics age 6 - 18)

Section 5.6

Lost work days (age 18 - 65)

Section 5.6

Minor restricted-activity days (age 18 - 65)

Section 5.6

Chronic Bronchitis (age >26)
                                     -- [a]
                                     -- [a]
Section 5.6

Emergency room visits for cardiovascular effects (all ages)
                                     -- [a]
                                     -- [a]
Section 5.6

Strokes and cerebrovascular disease (age 50 - 79)
                                     -- [a]
                                     -- [a]
Section 5.6

Other cardiovascular effects (e.g., other ages)
                                       --
                                       --
PM ISA[b]

Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations)
                                       --
                                       --
PM ISA[b]

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.)
                                       --
                                       --
PM ISA[b][,][c]

Cancer, mutagenicity, and genotoxicity effects
                                       --
                                       --
PM ISA[b][,][c]
[a]	We assess these benefits qualitatively due to time and resource limitations for this analysis. In the PM NAAQS RIA, these benefits were quantified in a sensitivity analysis, but not in the core analysis.
[b]	We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
[c]	We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.
PM NAAQS RIA (U.S. EPA, 2012a). It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.
We follow a "damage-function" approach in calculating benefits, which estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoints. Because EPA rarely has the time or resources to perform new research to measure directly either the health outcomes or their values for regulatory analyses, our estimates are based on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under analysis.
The health impact assessment (HIA) quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM2.5 or other air pollutants. We use the environmental Benefits Mapping and Analysis Program (BenMAP) to systematize health impact analyses by applying a database of key input parameters, including population projections, health impact functions, valuation functions (Abt Associates, 2012). For this assessment, the HIA is limited to those health effects that are directly linked to ambient PM2.5 concentrations. There may be other indirect health impacts associated with implementing emissions controls, such as occupational health exposures. Epidemiological studies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 ug/m[3] for PM2.5). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a health effect. We refer the reader to section 5.6 of the PM NAAQS RIA for more information regarding the epidemiology studies and risk coefficients applied in this analysis (U.S. EPA, 2012a), and we briefly elaborate on adult premature mortality below. The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis.
Considering a substantial body of published scientific literature, reflecting thousands of epidemiology, toxicology, and clinical studies, the PM ISA documents the association between elevated PM2.5 concentrations and adverse health effects, including increased premature mortality (U.S. EPA, 2009). The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee of EPA's Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 2009c), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the entire body of scientific evidence. The PM ISA also concluded that the scientific literature consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function.
For mortality, we use the effect coefficients from the most recent epidemiology studies examining two large population cohorts: the American Cancer Society (ACS) cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al., 2012). The PM ISA (U.S. EPA, 2009) concluded that the ACS and Six Cities cohorts provide the strongest evidence of the association between long-term PM2.5 exposure and premature mortality with support from a number of additional cohort studies. The SAB's Health Effects Subcommittee (SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths and weaknesses, we present benefits estimates using relative risk estimates from both these cohorts (Krewski et al., 2009; Lepeule et al., 2012).
As a characterization of uncertainty regarding the PM2.5 -mortality relationship, EPA graphically presents benefits derived from EPA's expert elicitation study (Roman et al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulting from reductions in ambient annual average PM2.5 levels. In that study, twelve experts provided independent opinions of the PM2.5 -mortality concentration-response function. Because the experts relied upon the ACS and Six Cities cohort studies to inform their concentration-response functions, the benefits estimates derived from the expert responses generally fall between results derived from the these studies (see Figure 7-1). We do not combine the expert results in order to preserve the breadth and diversity of opinion on the expert panel. This presentation of the expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA emphasize that "scientific differences existed only with respect to the magnitude of the effect of PM2.5 on mortality, not whether such an effect existed" and that the expert elicitation "supports the conclusion that the benefits of PM2.5 control are very likely to be substantial." Although it is possible that newer scientific literature could revise the experts' quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.
Economic Valuation
After quantifying the change in adverse health impacts, we estimate the economic value of these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. Therefore, the appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. For some health effects, such as hospital admissions, WTP estimates are generally not available, so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect. The unit values applied in this analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).
Avoided premature deaths account for 98% of monetized PM-related benefits. The economics literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and public policy analysis community. Following the advice of the SAB's Environmental Economics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating estimates of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual's willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.
EPA continues work to update its guidance on valuing mortality risk reductions, and the Agency consulted several times with the SAB-EEAC on the issue. Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, 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) while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$).
We then adjust this VSL to account for the currency year used in this RIA and to account for income growth from 1990 to the analysis year. The adjusted value for VSL is $8.0 million ($2010).
The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC's specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper on "Valuing Mortality Risk Reductions in Environmental Policy," (U.S. EPA, 2010c) which recently underwent review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). Draft guidance responding to SAB recommendations will be developed shortly.
In valuing premature mortality, we discount the value of premature mortality occurring in future years using rates of 3% and 7% (OMB, 2003). We assume that there is a "cessation" lag between changes in PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths.
Benefit-per-ton Estimates
Due to analytical limitations, it was not possible to conduct air quality modeling for this rule. Instead, we used a "benefit-per-ton" approach to estimate the benefits of this rulemaking. EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011d, 2012b). These benefit-per-ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of PM2.5 (or PM2.5 precursor such as NOx or SO2) from a specified source. Specifically, in this analysis, we multiplied the estimates from the "Residential Wood Heaters" sector[,] by the corresponding emission reductions. The method used to derive these estimates is described in the Technical Support Document (TSD) on estimating the benefits-per-ton of reducing PM2.5 and its precursors (U.S. EPA, 2013). One limitation of using the benefit-per-ton approach is an inability to provide estimates of the health benefits associated with exposure to HAP, CO, NO2 or ozone.
The benefit-per-ton estimates described in the TSD (U.S. EPA, 2013) were derived using the approach published in Fann et al. (2012), but they have since been updated to reflect the studies and population data in the final PM NAAQS RIA (U.S. EPA, 2012a). The approach in Fann et al. (2012) is similar to the work previously published by Fann et al. (2009), but the newer study includes improvements that EPA believes would provide more reliable estimates of PM2.5-related health benefits for emissions reductions in specific sectors. Specifically, the air quality modeling data reflect sectors that are more narrowly defined. In addition, the updated air quality modeling data reflect more recent emissions data (2005 rather than 2001) and has higher spatial resolution (12km rather than 36 km grid cells).
As noted below in the characterization of uncertainty, all benefit-per-ton estimates have inherent limitations. Specifically, all national-average benefit-per-ton estimates reflect the geographic distribution of the modeled emissions, which may not exactly match the emission reductions in this rulemaking, and they may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location.
Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The sector-specific modeling does not provide estimates of the PM2.5-related benefits associated with reducing VOC emissions, but these unquantified benefits are generally small compared to other PM2.5 precursors (U.S. EPA, 2012a).
PM2.5 Benefits Results
Table 7-2 summarizes the monetized PM-related health benefits by precursor pollutant, including the emission reductions and benefit-per-ton estimates using discount rates of 3% and 7%. Benefits estimates are based on the average of annual emission reductions from proposed rule implementation between 2014 and 2022 (inclusive). Table 7-3 provides a summary of the reductions in health incidences associated with these pollution reductions. Figure 7-1 provides a visual representation of the range of PM2.5-related benefits estimates using concentration-response functions from Krewski et al. (2009) and Lepeule et al. (2012) as well as

Table 7-2.	Summary of Monetized PM2.5-Related Health Benefits Estimates for the Proposed Residential Wood Heaters NSPS in the 2014 - 2022 Time Frame (2010$)[a]
Pollutant
Emissions Reductions (tons)
Benefit per ton (Krewski, 3%)
Benefit per ton (Lepeule, 3%)
Benefit per ton (Krewski, 7%)
Benefit per ton (Lepeule, 7%)
Total Monetized Benefits (millions 2010$ at 3%)
Total Monetized Benefits (millions 2010$ at 7%)
Proposed

 Direct PM2.5
                                     4,825
                                   $373,000
                                   $913,562
                                   $333,435
                                   $820,294
                                    $1,800
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,700
 PM2.5 Precursors

 VOC[b]
                                     3,250
                                       --
                                       --
                                       --
                                       --
                                       --
                                      to
                                       --
                                       --
                                      to
                                       --

                                     Total
                                    $1,800
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,700
Alternative

 Direct PM2.5
                                     4,878
                                   $373,000
                                   $913,562
                                   $333,435
                                   $820,294
                                    $1,900
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,800
 PM2.5 Precursors

 VOC[b]
                                     3,250
                                       --
                                       --
                                       --
                                       --
                                       --
                                      to
                                       --
                                       --
                                      to
                                       --

                                     Total
                                    $1,900
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,800
[a]	All estimates reflect the average of annual emission reductions expected to occur between 2014 and 2022 (inclusive) resulting from proposed rule implementation. All estimates are rounded to two significant figures so numbers may not sum across columns. It is important to note that the monetized benefits do not include reduced health effects from direct exposure to NO2, ozone exposure, ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology.
[b]	Estimates of VOCs health benefits are currently not monetized and will be addressed only qualitatively.

Table 7-3.	Summary of Reductions in Health Incidences from PM2.5-Related Benefits for the Proposed Residential Wood Heaters NSPS in the 2014-2022 Time Frame[a]
Avoided Premature Mortality
Proposal
Alternative
  Krewski et al. (2009) (adult)
209
211
  Lepeule et al. (2012) (adult)
472
477
Avoided Morbidity

  Emergency department visits for asthma (all ages)
106
102
  Acute bronchitis (age 8 - 12)
321
325
  Lower respiratory symptoms (age 7 - 14)
4,108
4,153
  Upper respiratory symptoms (asthmatics age 9 - 11)
5,930
5,995
  Minor restricted-activity days (age 18 - 65)
165,238
167,047
  Lost work days (age 18 - 65)
27,941
28,247
  Asthma exacerbation (age 6 - 18)
14,533
14,692
  Hospital admissions -- respiratory (all ages)
54
54
  Hospital admissions -- cardiovascular (age > 18)
66
66
  Non-Fatal Heart Attacks (age >18)

     Peters et al. (2001)
226
228
     Pooled estimate of 4 studies
25
25
[a]	All estimates are rounded to whole numbers with two significant figures. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology.
the 12 functions supplied by experts. Figure 7-2 provides a breakdown of monetized benefits by Pollutant. In Table 7-4, we provide the benefits using our anchor points of Krewski et al., and Lepeule et al., as well as the results from the 12 experts' elicitation on PM mortality.
Characterization of Uncertainty in the Monetized PM2.5 Benefits
In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits. Therefore, the estimates of annual benefits

Figure 7-1.	Total Monetized PM2.5 Benefits of the Proposed Residential Wood Heaters NSPS in the 2014 - 2022 Time Frame[a]
[a]	This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. study and the Lepeule et al. study, as well as 12 effect coefficients derived from EPA's expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response functions provided in those studies.

Figure 7-2.	Breakdown of Total Monetized PM2.5 Benefits of Proposed Residential Wood Heaters NSPS by Category

Table 7-4.	All PM2.5 Benefits Estimates for the Proposed Residential Wood Heaters NSPS at Discount Rates of 3% and 7% for the 2014 to 2022 Time Frame ($2010 millions)[a]

Proposal
Alternative

3%
7%
3%
7%
Benefit-per-ton Coefficients Derived from Epidemiology Literature

  Krewski et al.
$1,800
$1,700
$1,900
$1,700
  Lepeule et al.
$4,200
$3,700
$4,200
$3,800
Benefit-per-ton Coefficients Derived from Expert Elicitation

  Expert A
$4,800
$4,300
$4,800
$4,300
  Expert B
$3,800
$3,400
$3,900
$3,500
  Expert C
$3,800
$3,400
$3,800
$3,400
  Expert D
$2,700
$2,400
$2,700
$2,500
  Expert E
$6,100
$5,500
$6,200
$5,600
  Expert F
$3,500
$3,200
$3,600
$3,200
  Expert G
$2,200
$2,000
$2,200
$2,000
  Expert H
$2,800
$2,500
$2,800
$2,500
  Expert I
$3,700
$3,400
$2,800
$3,400
  Expert J
$3,000
$2,700
$3,000
$2,800
  Expert K
$440
$400
$450
$400
  Expert L
$2,500
$2,300
$2,500
$2,300
[a]	All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they were derived through the benefit-per-ton technique described above. The benefits estimates from the expert elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated with the concentration-response function. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology.
should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.
This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) because we lack the necessary air quality input and monitoring data to run the benefits model. However, the results of the uncertainty analyses presented in the PM NAAQS RIA can provide some information regarding the uncertainty inherent in the benefits results presented in this analysis. Sensitivity analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions could change the PM2.5-related mortality benefits discounted at 3% by between 10% and  - 27% and that alternate income growth adjustments could change the PM2.5-related mortality benefits by between 33% and −14%.
Unlike the PM NAAQS RIA, we do not have data on the specific location of the air quality changes associated with this rulemaking. As such, it is not feasible to estimate the proportion of benefits occurring in different locations, such as designated nonattainment areas. Instead, we applied benefit-per-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or under-estimate of the actual benefits of controlling PM precursors. Use of these $/ton values to estimate benefits may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton for emission reductions in specific locations may be very different than the estimates presented here. To the extent that the geographic distributions of the emissions reductions for this rule are different than the modeled emissions, the benefits may be underestimated or overestimated. In general, there is inherently more uncertainty for new sources, which may not be included in the emissions inventory, than existing sources. For more information, see the TSD describing the calculation of these benefit-per-ton estimates (U.S. EPA, 2013).
Our estimate of the total benefits is based on EPA's interpretation of the best available scientific literature and methods and supported by the SAB-HES and the NAS (NRC, 2002). Below are key assumptions underlying the estimates for premature mortality, which accounts for 98% of the total monetized PM2.5 benefits:
1.	We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA concluded that "many constituents of PM2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes" (U.S. EPA, 2009).
2.	We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.
3.	We assume that there is a "cessation" lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality benefits at different discount rates.
In general, we are more confident in the magnitude of the risks we estimate from simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML] or one standard deviation below the mean of the air quality data in the study) allow readers to determine the portion of population exposed to annual mean PM2.5 levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 mortality benefits. There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. Rather, the benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associated with the emission reduction strategies and because the current body of scientific literature indicates that a no-threshold model provides the best estimate of PM-related long-term mortality. In other words, although we may have less confidence in the magnitude of the risk at concentrations below these benchmarks, we still have high confidence that PM2.5 is causally associated with risk at those lower air quality concentrations.
For this analysis, policy-specific air quality data is not available due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortality associated with this specific rule's emission reductions at each PM2.5 level. However, we believe that it is still important to characterize the distribution of exposure to baseline air quality levels. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM2.5 level in the baseline of the source apportionment modeling used to calculate the benefit-per-ton estimates for this sector. It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy. The most important aspect, which we are unable to quantify for rules without rule-specific air quality modeling, is the shift in exposure associated with this specific rule. Therefore, caution is warranted when interpreting the LML assessment for this rule because these results are not consistent with results from rules that had air quality modeling.
Table 7-5 provides the percentage of the population exposed above and below two concentration benchmarks (i.e., LML and 1 standard deviation below the mean) in the modeled baseline. Figure 7-3 shows a bar chart of the percentage of the population exposed to various air quality levels in the baseline, and Figure 7-4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.
Unquantified Benefits
The monetized benefits estimated in this RIA only reflect a subset of benefits attributable to the health effect reductions associated with ambient fine particles. Data, time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the benefits from several important benefit categories, including benefits associated with the potential exposure to ozone formation due to VOC emissions as a precursor, VOC emissions as a PM2.5 precursor,
Table 7-5.	Population Exposure in the Baseline Above and Below Various Concentration Benchmarks in the Underlying Epidemiology Studies[a]
Epidemiology Study
Below 1 Std. Dev.
Below AQ Mean
At or Above 1 Std. Dev. Below AQ Mean
Below LML
At or Above LML
Krewski et al. (2009)
                                      89%
                                      11%
7%
                                      93%
Lepeule et al. (2012)
                                      N/A
                                      N/A
23%
                                      67%
[a]	One standard deviation below the mean is equivalent to the middle of the range between the 10[th] and 25[th] percentile. For Krewski, the LML is 5.8 ug/m[3] and one standard deviation below the mean is 11.0 ug/m[3]. For Lepeule et al., the LML is 8 ug/m[3] and we do not have the data for one standard deviation below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for each study, the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5 do not occur.

Among the populations exposed to PM2.5 in the baseline:
      93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study
      67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study
Figure 7-3.	Percentage of Adult Population by Annual Mean PM2.5 Exposure in the Baseline

HAP, CO exposure, as well as ecosystem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analysis. This does not imply that there are no benefits associated with these emission reductions. In this section, we provide a qualitative description of these benefits.
HAP Benefits
Even though emissions of air toxics from all sources in the U.S. declined by approximately 42% since 1990, the 2005 National-Scale Air Toxics Assessment (NATA) predicts that most Americans are exposed to ambient concentrations of air toxics at levels that have the potential to cause adverse health effects (U.S. EPA, 2011c). The levels of air toxics to

Among the populations exposed to PM2.5 in the baseline:
      93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study
      67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study
Figure 7-4.	Cumulative Distribution of Adult Population by Annual Mean PM2.5 Exposure in the Baseline

which people are exposed vary depending on where people live and work and the kinds of activities in which they engage. In order to identify and prioritize air toxics, emission source types and locations that are of greatest potential concern, U.S. EPA conducts the NATA. The most recent NATA was conducted for calendar year 2005 and was released in March 2011. NATA includes four steps:
1.	Compiling a national emissions inventory of air toxics emissions from outdoor sources
2.	Estimating ambient and exposure 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
Based on the 2005 NATA, EPA estimates that about 5% of census tracts nationwide have increased cancer risks greater than 100 in a million. The average national cancer risk is about 50 in a million. Nationwide, the key pollutants that contribute most to the overall cancer risks are formaldehyde and benzene. Secondary formation (e.g., formaldehyde forming from other emitted pollutants) was the largest contributor to cancer risks, while stationary, mobile and background sources contribute almost equal portions of the remaining cancer risk.
Noncancer health effects can result from chronic, subchronic, or acute inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive systems. 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. Results from the 2005 NATA indicate that acrolein is the primary driver for noncancer respiratory risk.
Figures 7-5 and 7-6 depict the estimated census tract-level carcinogenic risk and noncancer respiratory hazard from the assessment. It is important to note that large reductions in HAP emissions may not necessarily translate into significant reductions in health risk because toxicity varies by pollutant, and exposures may or may not exceed levels of concern. For example, acetaldehyde mass emissions are more than double acrolein emissions on a national basis, according to EPA's 2005 National Emissions Inventory (NEI). However, the Integrated Risk Information System (IRIS) reference concentration (RfC) for acrolein is considerably lower

Figure 7-5.	Estimated Census Tract Carcinogenic Risk from HAP Exposure from Outdoor Sources (2005 NATA)

than that for acetaldehyde, suggesting that acrolein could be potentially more toxic than acetaldehyde. Thus, it is important to account for the toxicity and exposure, as well as the mass of the targeted emissions.
Due to methodology limitations, we were unable to estimate the benefits associated with the hazardous air pollutants that would be reduced as a result of these rules. In a few previous analyses of the benefits of reductions in HAP, EPA has quantified the benefits of potential reductions in the incidences of cancer and non-cancer risk (e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk factors (URF) developed through risk assessment procedures.These URFs are designed to be conservative, and as such, are more likely to

Figure 7-6.	Estimated Chronic Census Tract Noncancer (Respiratory) Risk from HAP Exposure from Outdoor Sources (2005 NATA)

represent the high end of the distribution of risk rather than a best or most likely estimate of risk. As the purpose of a benefit analysis is to describe the benefits most likely to occur from a reduction in pollution, use of high-end, conservative risk estimates would overestimate the benefits of the regulation. While we used high-end risk estimates in past analyses, advice from the EPA's Science Advisory Board (SAB) recommended that we avoid using high-end estimates in benefit analyses (U.S. EPA-SAB, 2002). Since this time, EPA has continued to develop better methods for analyzing the benefits of reductions in HAP.
As part of the second prospective analysis of the benefits and costs of the Clean Air Act (U.S. EPA, 2011a), 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 (IEc, 2009). 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" (U.S. EPA-SAB, 2008).
In 2009, EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011).
In summary, monetization of the benefits of reductions in cancer incidences requires several important inputs, including central estimates of cancer risks, estimates of exposure to carcinogenic HAP, and estimates of the value of an avoided case of cancer (fatal and non-fatal). Due to methodology limitations, we did not attempt to monetize the health benefits of reductions in HAP in this analysis. Instead, we provide a qualitative analysis of the health effects associated with the HAP anticipated to be reduced by these rules. EPA remains committed to improving methods for estimating HAP benefits by continuing to explore additional concepts of benefits, including changes in the distribution of risk.
Below we describe the health effects associated with the HAPs that would be reduced by this rulemaking.
Benzene
The EPA's IRIS database lists benzene as a known human carcinogen (causing leukemia) by all routes of exposure, and concludes that exposure is associated with additional health effects, including genetic changes in both humans and animals and increased proliferation of bone marrow cells in mice.[,][,] EPA states in its IRIS database that data indicate a causal relationship between benzene exposure and acute lymphocytic leukemia and suggest a relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic lymphocytic leukemia. The IARC has determined that benzene is a human carcinogen and the DHHS has characterized benzene as a known human carcinogen.[,]
A number of adverse noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also been associated with long-term exposure to benzene.[,]
Dioxins (Chlorinated dibenzodioxins (CDDs)
A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that are at least 10 times higher than background levels. The most obvious health effect in people exposure to relatively large amounts of 2,3,7,8-TCDD is Chloracne. Chloracne is a severe skin disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, sugar, and protein have been reported in people exposed to relatively high concentrations of 2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some people these effects may last for many years. Slight increases in the risk of diabetes and abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result in reproductive or developmental effects in people, but animal studies suggest that this is a potential health concern.
In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a single exposure. Exposure to lower levels can cause a variety of effects in animals, such as weight loss, liver damage, and disruption of the endocrine system. In many species of animals, 2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight bacteria and viruses at relatively low levels (approximately 10 times higher than human background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often had severe birth defects including skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were observed at body burdens 10 times higher than human background levels.
Formaldehyde
Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. Substantial additional research since that time informs current scientific understanding of the health effects associated with exposure to formaldehyde. These include recently published research conducted by the National Cancer Institute (NCI) which found an increased risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde., In an analysis of the lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI confirmed an association between lymphohematopoietic cancer risk and peak formaldehyde exposures. A recent NIOSH study of garment workers also found increased risk of death due to leukemia among workers exposed to formaldehyde. Extended follow-up of a cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or lymphohematopoietic cancers, but a continuing statistically significant excess in lung cancers was reported.
In the past 15 years there has been substantial research on the inhalation dosimetry for formaldehyde in rodents and primates by the Chemical Industry Institute of Toxicology (CIIT, now renamed the Hamner Institutes for Health Sciences), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment.[,][,] CIIT's risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. These data were modeled using a biologically-motivated two-stage clonal growth model for cancer and also a point of departure based on a Benchmark Dose approach. However, it should be noted that recent research published by EPA indicates that when two-stage modeling assumptions are varied, resulting dose-response estimates can vary by several orders of magnitude.[,][,][,] These findings are not supportive of interpreting the CIIT model results as providing a conservative (health protective) estimate of human risk. EPA research also examined the contribution of the two-stage modeling for formaldehyde towards characterizing the relative weights of key events in the mode-of-action of a carcinogen. For example, the model-based inference in the published CIIT study that formaldehyde's direct mutagenic action is not relevant to the compound's tumorigenicity was found not to hold under variations of modeling assumptions.
Based on the developments of the last decade, in 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in experimental animals -- a higher classification than previous IARC evaluations. After reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as "sufficient," based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as "strong."
Formaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also cause airway inflammation -- including eosinophils infiltration into the airways. There are several studies that suggest that formaldehyde may increase the risk of asthma -- particularly in the young.[,]
The above-mentioned rodent and human studies, as well as mechanistic information and their analyses, were evaluated in EPA's recent Draft Toxicological Review of Formaldehyde -- Inhalation Assessment through the Integrated Risk Information System (IRIS) program. This draft IRIS assessment was released in June 2010 for public review and comment and external peer review by the National Research Council (NRC). The NRC released their review report in April 2011 (http://www.nap.edu/catalog.php?record_id=13142). The EPA is currently revising the draft assessment in response to this review.
Polycyclic Organic Matter (POM)
The term polycyclic organic matter (POM) defines a broad class of compounds that includes the polycyclic aromatic hydrocarbon compounds (PAHs). One of these compounds, naphthalene, is discussed separately below. POM compounds are formed primarily from combustion and are present in the atmosphere in gas and particulate form. Cancer is the major concern from exposure to POM. Epidemiologic studies have reported an increase in lung cancer in humans exposed to diesel exhaust, coke oven emissions, roofing tar emissions, and cigarette smoke; all of these mixtures contain POM compounds[,]. Animal studies have reported respiratory tract tumors from inhalation exposure to benzo[a]pyrene and alimentary tract and liver tumors from oral exposure to benzo[a]pyrene. EPA has classified seven PAHs (benzo[a]pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, dibenz[a,h]anthracene, and indeno[1,2,3-cd]pyrene) as Group B2, probable human carcinogens. Recent studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several adverse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development in preschool children (3 years of age).[,] EPA has not yet evaluated these recent studies.
Other Air Toxics
In addition to the compounds described above, other compounds would be affected by this rule. Information regarding the health effects of these compounds can be found in EPA's IRIS database.
Carbon Monoxide Co-Benefits
Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO is "likely to have a causal relationship" with cardiovascular morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects following long-term exposure to CO, and for central nervous system effects linked to short- and long-term exposure to CO.
Black Carbon (BC) Benefits
Incomplete combustion of wood results in emissions of fine and ultrafine particles, including black carbon (BC), brown carbon (BrC), and other nonlight, absorbing organic carbon (OC) particles. BC and BrC are collectively considered light, absorbing carbon (LAC) with BC referring to the most strongly light‐absorbing form of carbon per unit mass. BC impacts the earth's climate because of its high capacity for light absorption. The role of BC in key atmospheric processes links it to a range of climate impacts, including increased temperatures, accelerated ice and snow melt, and disruptions in precipitation patterns. A recent study by the UN Environment Programme (UNEP) concluded that reductions in BC and ozone will slow the rate of climate change within the first half of this century with a small number of targeted BC and ozone precursor emissions mitigation measures providing immediate protection for climate, public health, water and food security, and ecosystems (UNEP, 2011).
While less effective in absorbing solar radiation than BC, BrC may contribute significantly to positive radiative forcing. At present the ability to quantify the climate impacts of BrC is limited. OC from incomplete combustion of wood (exclusive of BrC) is generally considered nonlight‐absorbing carbon. Nonlight absorbing compounds scatter rather than absorb solar radiation and, therefore, provide a net direct cooling effect on climate. Thus, particles generated by residential wood combustion consist of components that are warming to the atmosphere (BC and BrC) and particles that are cooling (OC exclusive of BrC).
Residential wood combustion contributed about 380,000 tons of PM2.5 emissions across the United States in 2005. Of these PM2.5 emissions, approximately 21,000 tons are estimated to
be elemental carbon (EC[)] (EPA NEI, 2005).
The EC/OC ratio is a metric sometimes used to crudely compare the warming potential of emissions from various BC sources with a ratio of less than 1 indicating that cooling potential exceeds warming. Based on the speciated 2005 NEI, the EC/OC for residential wood combustion is estimated to be less than one (~ 0.11), indicating a predominance of OC or light-scattering particles relative to light absorbing ones. Exactly how much of the OC from RWC sources is light absorbing (BrC) is not known currently, and the LAC may vary by fuel type, combustion conditions, and operating environment.
While OC emissions are generally considered to have a cooling effect, OC emissions over areas with snow and ice may be less reflective than OC over dark surfaces and may even have a slight warming effect (Flanner et al., 2007). Significantly, the vast majority of residential wood smoke emissions occur during the winter months; the highest percentage of wood stove use is in the upper Midwest (e.g., Michigan), the Northeast (e.g., Maine), and the mountainous areas of the Pacific Northwest (e.g., Washington), where snow is present a good portion of the winter months. A recent study of the effect of soot-induced snow albedo on snowpack and hydrological cycles in the western United States concludes that radiative forcing induced by soot deposition on snow is an important anthropogenic source affecting the global climate. The study concludes that soot-induced snow albedo perturbations increase the surface net solar radiation flux during late winter to early spring, increase the surface air temperature, reduce the snow accumulation and spring snowmelt, and may alter stream flows with implications for water resources in the western United States (Qian, et al., 2009). Further study is needed to better understand and quantify the impact of PM2.5 emissions and deposition from the RWC sector on climate.
VOCs as a PM2.5 Precursor
This rulemaking is expected to reduce emissions of VOCs, which are a precursor to PM2.5. Most VOCs emitted are oxidized to carbon dioxide (CO2) rather than to PM, but a portion of VOC emission contributes to ambient PM2.5 levels as organic carbon aerosols (U.S. EPA, 2009c). Therefore, reducing these emissions would reduce PM2.5 formation, human exposure to PM2.5, and the incidence of PM2.5-related health effects. However, we have not quantified the PM2.5-related benefits associated with VOC reductions in this analysis. Analysis of organic carbon measurements suggest only a fraction of secondarily formed organic carbon aerosols are of anthropogenic origin. The current state of the science of secondary organic carbon aerosol formation indicates that anthropogenic VOC contribution to secondary organic carbon aerosol is often lower than the biogenic (natural) contribution. Given that a fraction of secondarily formed organic carbon aerosols is from anthropogenic VOC emissions and the extremely small amount of VOC emissions from this sector relative to the entire VOC inventory it is unlikely this sector has a large contribution to ambient secondary organic carbon aerosols. Photochemical models typically estimate secondary organic carbon from anthropogenic VOC emissions to be less than 0.1 ug/m[3].
Due to limited resources, we were unable to perform air quality modeling for this rule. Therefore, given the high degree of variability in the responsiveness of PM2.5 formation to VOC emission reductions, we are unable to estimate the effect that reducing VOCs will have on ambient PM2.5 levels without air quality modeling.
VOCs as an Ozone Precursor
In the presence of sunlight, VOCs can undergo a chemical reaction in the atmosphere to form ozone. Reducing ambient ozone concentrations is associated with significant human health benefits, including mortality and respiratory morbidity (U.S. EPA, 2008a). Epidemiological researchers have associated ozone exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2006). These health effects include respiratory morbidity such as fewer asthma attacks, hospital and ER visits, school loss days, as well as premature mortality.
In addition to health impacts reduction, there are ecological benefits from reducing the formation of ozone and related exposure that leads to reduced net primary productivity and visible foliar injury which are associated with a range of ecosystems services.
Visibility Impairment Co-Benefits
Reducing secondary formation of PM2.5 would improve visibility levels in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people's enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009). Previous analyses (U.S. EPA, 2011a) show that visibility benefits can be a significant welfare benefit category. Without air quality modeling, we are not unable to estimate visibility related benefits, nor are we able to determine whether the emission reductions associated with this rule would be likely to have a significant impact on visibility in urban areas or Class I areas.
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COMPARISON OF MONETIZED BENEFITS AND COSTS
Summary
Because we are unable to monetize the co-benefits associated with reducing other pollutants such as VOCs and CO, all monetized benefits reflect improvements in ambient PM2.5 concentrations. This results in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of this proposed rule to be $1.8 billion to $4.2 billion in the 2014 - 2022 time frame (Table 8-1). Using a 7% discount rate, we estimate the total monetized benefits to be $1.7 billion to $3.8 billion in the 2014 - 2022 time frame. For the Alternative option, using a 3% discount rate, we estimate the total monetized benefits of this proposed rule to be $1.9 billion to $4.2 billion in the 2014 - 2022 time frame (Table 8-1). Using a 7% discount rate, we estimate the total monetized benefits under the Alternative option to be $1.7 billion to $3.8 billion in the 2014 - 2022 time frame.  The annualized social costs are $15.7 million for the Proposed rule and $28.3 million for the Alternative option in the 2014 - 2022 time frame (2010 dollars), and are $14.8 million for the proposed rule and $26.9 million for the Alternative option, respectively, in the same time frame using a 3% interest rate. The net benefits (benefits  -  costs) are therefore $1.8 billion to $4.1 billion at a 3% discount rate for the benefits and $1.7 billion to $3.7 billion at a 7% discount rate for the Proposed option and $1.8 billion to $4.2 Billion at a 3% discount rate and $1.7 billion to $3.8 billion at a 7% discount rate for the Alternative option in the 2014 - 2022 time frame. The net benefits with annualized social costs at a 3% interest rate are essentially identical to those shown above with costs at a 7% interest rate. Annual benefits were equal through all options thereafter. All estimates are in 2010$. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing nearly 3,200 tons of VOC, nearly 33,000 tons of CO, black carbon and several HAPs emissions such as formaldehyde and benzene among others each year.
Figure 8-1 shows the full range of net benefits estimates (i.e., annual benefits minus annualized costs) quantified in terms of PM2.5 benefits reflecting the average annual impact for the 2014 - 2022 time frame of the analysis under the Proposed option, and Figure 8-2 shows the full range of net benefits estimates for the Alternative option. The net benefits reflect a 3% discount rate for the benefits.
Table 8-1.	Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Proposed Residential Wood Heater NSPS in the 2014 - 2022 Time Frame ($2010 millions)[a]
Proposed Option
3% Discount Rate
7% Discount Rate
Total Monetized Benefits[b]
$1,800
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,800
Total Social Costs[c]
                                      $15
                                      $16
Net Benefits
$1,800
                                      to
                                    $4,100
                                    $1,700
                                      To
                                    $3,700
Nonmonetized Benefits
32,700 tons of CO

3,200 tons of VOC

Reduced exposure to HAPs, including formaldehyde, benzene, and polycyclic organic matter

Reduced Climate effects due to reductions in black carbon emissions

Reduced ecosystem effects

Reduced visibility impairment
                              Alternative Option
Total Monetized Benefits[b]
                                    $1,900
                                      to
                                    $4,200
                                    $1,700
                                      To
                                    $3,800
Total Social Costs[c]
                                      $27
                                      $28
Net Benefits
                                    $1,800
                                      to
                                    $4,200
                                    $1,700
                                      to
                                    $3,800
Nonmonetized Benefits
33,000 tons of CO

3,200 tons of VOC

Reduced exposure to HAPs, including formaldehyde, benzene, and polycyclic organic matter

Reduced Climate effects due to reductions in black carbon emissions

Reduced ecosystem effects

Reduced visibility impairment
[a]	All estimates are for the time frame from 2014 to 2022 inclusive and are rounded to two significant figures. These results include units anticipated to come online and the lowest cost disposal assumption. Total annualized social costs are estimated at a 7% interest rate.
[b]	The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of directly emitted PM2.5. It is important to note that the monetized benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range from Krewski et al. (2009) to Lepeule et al. (2012). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. Because these estimates were generated using benefit-per-ton estimates, we do not break down the total monetized benefits into specific components here. See Figure 7-1 in this RIA for an illustration of the breakdown, or the RIA for the final Cross-States Air Pollution Rule (EPA, 2011) for more information.
[c]	The annualized social costs are $14.8 million for the Proposed Option and $26.9 million for the Alternative Option at a 3% interest rate. We assume that annual compliance costs serve as an approximation of the social costs of the proposal.

Figure 8-1.	Net Annual Benefits Range in 2014 - 2022 Time Frame for PM2.5 Reductions for the Proposed Option

Figure 8-2.	Net Annual Benefits Range in 2014 - 2022 Time Frame for PM2.5 Reductions for the Alternative Option

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